(2968,5,'2013-09-14 06:13:39','2013-09-14 06:13:39','<h4 style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2855\" alt=\"Structured process for commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Structured-process-for-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></h4>\r\n<h4 style=\"text-align: justify;\"><b>Mobile operators have unique access to the personal and business interactions that their customers generate via their use of phones, tablets, laptops and other wireless devices. Unlocking the potential of these information assets demands that MNOs have a coherent and integrated approach to develop new Big Data products.<br /> </b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Establishing the basics<br /> </b></h5>\r\n<p style=\"text-align: justify;\">At the organization level, three main sets of processes need to be defined to manage information assets:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>An enterprise-wide ownership framework for the end-to-end management of data - from the moment of creation through to the point of use.</li>\r\n<li>Clear goals for how the data will be used - whether for optimizing the service delivery platform, developing closer relationships with customers and anticipating or responding to market changes.</li>\r\n<li>Detailed policies and procedures that govern how data is collected, analyzed, and acted upon.</li>\r\n</ul>\r\n<h5 style=\"text-align: justify;\"><b>Managing data assets<br /> </b></h5>\r\n<p>The ‘D4’ Process Model is a standard framework being adopted widely within the global network operator community. It characterizes the four primary development phases of data asset management in complex service delivery environments.</p>\r\n<p align=\"center\"> <img class=\"size-full wp-image-2589 aligncenter\" alt=\"Post - big data monetisation chart 1\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-big-data-monetisation-chart-1.jpg\" width=\"429\" height=\"300\" /></p>\r\n<p style=\"text-align: justify;\">Each of the four phases has its own inherent complexity, and rigorous discipline is required in order to maintain the integrity of the end-to-end data management process.</p>\r\n<ol style=\"text-align: justify;\">\r\n<li><b>Data acquisition</b>.  Quality-tested, contextually-relevant and easily interpreted data is a required to drive effective analysis and action. This requires significant workflow planning and investment in systems and processes - even machine-generated and curated data is error-prone.</li>\r\n<li><b>Discovery. </b>This is the phase of the process where most management attention is currently being focused.  The fierce competition for data sciences talent is a direct consequence of the need for companies to create first mover in-house analytics capabilities.</li>\r\n<li><b>Delivery. </b>Big Data products are created by pre-packaging data or providing customized access to data sets, dependent on customer needs. Securing the trust and confidence of customers is the “acid test” of any Big Data product, and requires data accuracy and timeliness together with raw data and visualization interfaces.</li>\r\n<li><b>Dollars. </b>A Big Data product also requires an associated business model, which defines the target market, the basis for charging, and the associated processing and delivery costs. Ultimately, product profitability is the measure of success.</li>\r\n</ol>\r\n<p style=\"text-align: justify;\">The D4 Process Model thus provides a mechanism for embedding structure into the planning and implementation of early stage Big Data initiatives, and helps teams break down a complex task into a simpler set of intermediate steps.</p>\r\n<p style=\"text-align: justify;\">The D4 process acts as an overall management approach when developing Big Data products, as outlined in our <a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\"><span style=\"text-decoration: underline;\">Productizing Big Data</span></a> page.</p>\r\n<p style=\"text-align: justify;\"> </p>','A structured process for commercializing Big Data products','Operators are increasingly focusing on the strategic and operational implications of aligning their organizations to address the Big Data opportunity.','inherit','closed','open','','2587-revision-v1','','','2013-09-14 06:13:39','2013-09-14 06:13:39','',2587,'http://redwingconsulting.com/uncategorized/2587-revision-v1/',0,'revision','',0),(2969,5,'2013-09-14 06:14:55','2013-09-14 06:14:55','<p><img class=\"alignnone size-full wp-image-2549\" alt=\"Post - big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-big-data-graphic.jpg\" width=\"700\" height=\"250\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>In the face of shrinking revenues and declining operating margins from their core communications services, mobile operators are retooling their business models in pursuit of new sources of top and bottom line growth. One of most exciting areas is Big Data monetization - mobile operators have access to unparalleled amounts of customer and network data, and yet they are only just beginning to unlock the value of this information. </b></h4>\r\n<h5>What is Big Data for the operators?</h5>\r\n<p style=\"text-align: justify;\">Historically, operators have assumed the roles of enablers for information flows, with surprisingly little visibility into the context and content of their captive data assets.</p>\r\n<p style=\"text-align: justify;\">Utilized intelligently and creatively, this data holds the key to an intimate understanding of customer needs and preferences, and in a broader sense, the successful evolution of the role of operators at the center of the mobile communications eco-system.</p>\r\n<p style=\"text-align: justify;\">Mobile operators are the ‘natural’ Big Data companies, given they have a unique view into the behaviour and preferences of millions of customers by virtue of the network data that they process on a daily basis.</p>\r\n<p style=\"text-align: justify;\">Any activity which touches the wireless infrastructure - voice calls, data transmissions and app downloads - creates a digital footprint which can be analyzed and synthesized into valuable insights.</p>\r\n<p style=\"text-align: justify;\">And with the average smartphone now being active on the network for twenty hours per day (as measured by global operator Teléfonica), it is clear that mobile devices are becoming deeply embedded into the lives of the majority of users.</p>\r\n<h5 style=\"text-align: justify;\">What does Big Data consist of?</h5>\r\n<p style=\"text-align: justify;\">The diagram below outlines the diverse nature of the available network data:</p>\r\n<p>&nbsp;</p>\r\n<p align=\"center\"><img class=\"size-full wp-image-2546 aligncenter\" alt=\"Post - big data chart 1\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-big-data-chart-1.jpg\" width=\"443\" height=\"297\" /></p>\r\n<p style=\"text-align: justify;\"><strong>Structured data</strong> is the accumulation of bulk transaction and profile records amassed by operators on a daily basis:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>Itemized calling and messaging records - CDRs/xDRs</li>\r\n<li>Electronic data records - Web logs, searches</li>\r\n<li>Geo-positioning data records - Location coordinates, time, duration</li>\r\n<li>Billing profile records - Gender, age, address, spend</li>\r\n</ul>\r\n<p style=\"text-align: justify;\"><strong>Unstructured data</strong> reflects the detailed interactions between subscribers within the network represented by the exchange of textual, numeric and graphical content:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>Social media posts</li>\r\n<li>Web browsing</li>\r\n<li>Media downloads and streams</li>\r\n<li>E-books and newsreader content</li>\r\n<li>App usage and interactions</li>\r\n</ul>\r\n<p style=\"text-align: justify;\"><strong>Inferred data</strong> comprises the patterns of behaviour derived from observed social media activity and point-to-point movements within the network service area</p>\r\n<ul style=\"text-align: justify;\">\r\n<li style=\"text-align: justify;\">Social graph and influence graph - relationships, personal interests, attitudes, sentiments</li>\r\n<li style=\"text-align: justify;\">Location-based activity and context - retail footfall, travel dynamics, social preferences</li>\r\n</ul>\r\n<h5 style=\"text-align: justify;\">Data - a new asset class</h5>\r\n<p style=\"text-align: justify;\">With this wealth of data becoming accessible to operators, their attention is turning to the use of advanced data analytics in order to drive internal initiatives - such as customer loyalty programmes and service personalisation - as well as monetizing the latent value of this information with the development of new offerings for B2B markets.</p>\r\n<p style=\"text-align: justify;\">The decreasing cost of data storage coupled with the availability of high performance computing applications now enables operators to create information-based offerings - not only for their own subscriber base, but also for third party organisations seeking a better understanding of their mobile users.</p>\r\n<p style=\"text-align: justify;\">We identify a number of detailed approaches to monetize Big Data in our <a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a> page.</p>\r\n<p style=\"text-align: justify;\">Over time subscriber data with this degree of specificity and richness has the potential to become a new asset class - with a corresponding balance sheet valuation - for network operators.</p>\r\n<p style=\"text-align: justify;\"> </p>','Big data monetization - the Jewel in the Crown for mobile operators','Mobile operators are seeking new sources of growth. One of most exciting areas is Big Data monetization - operators are only just beginning to unlock the value of the information their networks carry.','inherit','closed','open','','2544-revision-v1','','','2013-09-14 06:14:55','2013-09-14 06:14:55','',2544,'http://redwingconsulting.com/uncategorized/2544-revision-v1/',0,'revision','',0),(2970,5,'2013-09-14 06:16:28','2013-09-14 06:16:28','<p><img class=\"alignnone size-full wp-image-2874\" alt=\"Post - how are operators exploiting Big Data opportunity graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-how-are-operators-exploiting-Big-Data-opportunity-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\">Mobile operators are leveraging their Big Data assets. Initially this has an internal focus, with the aim of better understanding their own customers. Over time, it becaomes clear that these data services also create an external opportunity to sell to third party customers.</h4>\r\n<h5 style=\"text-align: justify;\"><b>Internal Big Data opportunities: towards a 720-degree view<br /> </b></h5>\r\n<p style=\"text-align: justify;\">The mobile operators with ‘smart’ capabilities such as dynamic network policy management, high performance computing and in-memory analytics are able to utilize these data assets in order to manage the customer experience in an increasingly intimate manner.</p>\r\n<p style=\"text-align: justify;\">The “who, what, when and how” dimensions of marketing campaign design and management take on new levels of specificity and precision with the so-called ‘720-degree’ view of customers uniquely available to operators:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>An internal, 360-degree view of their customers i.e. what are their product and service needs? how and when and are they using the services? what stimulates greater engagement levels?</li>\r\n<li>An external, 360-degree view of how they utilize the available services in their daily lives i.e. where do they live, work and play? who are they interacting with? how and why are their services preferences shifting?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">This adds a powerful dimension to the market segmentation process, as well as informing the customer experience and campaign design programs that flow from it.</p>\r\n<p style=\"text-align: justify;\">By knowing more about their subscriber base, operators can devise precise marketing messages that reach the right target customers, at the right time and most importantly in the right context.</p>\r\n<h5 style=\"text-align: justify;\">More precise segmentation</h5>\r\n<p style=\"text-align: justify;\">The fusion of subscriber, network and social media data now will allow operators to perform their ‘business-as-usual’ processes more efficiently and effectively - defining new market segments, interacting dynamically with both customers and channel partners and proactively addressing their specific demands as individual stakeholders.</p>\r\n<p align=\"center\"><img alt=\"Post - big data chart 2\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-big-data-chart-2.jpg\" width=\"350\" height=\"245\" /></p>\r\n<p style=\"text-align: justify;\">As an example, a number of operators have been successful in refining their customer segmentation models through the analysis of subscriber-generated information sourced from custom built self-care portals.</p>\r\n<p style=\"text-align: justify;\">This data analysis has enabled them to stratify their customer bases into multiple precisely-defined micro segments, each with discrete behavioral attributes and actionable characteristics. These operators have used this information to generate a continuous stream of segment-specific direct marketing promotions in order to stimulate customer adoption and usage.</p>\r\n<h5 style=\"text-align: justify;\">The external Big Data opportunity</h5>\r\n<p style=\"text-align: justify;\">Mobile operators are pursuing new lines of business that have the potential both to monetize the value of their big data assets and to entrench the operators as a pivotal player in their markets.</p>\r\n<p><img class=\"size-full wp-image-2548 aligncenter\" alt=\"Post - big data chart 3\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-big-data-chart-3.jpg\" width=\"503\" height=\"233\" /></p>\r\n<p style=\"text-align: justify;\">We examine these business opportunities in more detail in our <a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a> page.</p>','How are operators exploiting the Big Data opportunity?','Mobile operators are leveraging their Big Data assets to better understand their own users and to create opportunities to sell to third party customers.','inherit','closed','open','','2870-revision-v1','','','2013-09-14 06:16:28','2013-09-14 06:16:28','',2870,'http://redwingconsulting.com/uncategorized/2870-revision-v1/',0,'revision','',0),(2971,5,'2013-09-14 06:18:24','2013-09-14 06:18:24','<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/general-posts/welcome-to-redwingconsultings-website/attachment/post-welcome-to-our-new-site-graphic/\" rel=\"attachment wp-att-2632\"><img class=\"alignnone size-full wp-image-2632\" alt=\"Post - welcome to our new site graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-welcome-to-our-new-site-graphic.png\" width=\"700\" height=\"180\" /></a></p>\r\n<p style=\"text-align: justify;\"><strong><span style=\"color: #993300;\">Welcome to the new Redwing Consulting website!</span></strong></p>\r\n<p style=\"text-align: justify;\">This website aims to address the commercial, organizational and marketing issues that mobile operators and telcos face as they monetize their Big Data assets.</p>\r\n<p style=\"text-align: justify;\">The telecoms industry is entering a critical phase as the seismic shift from voice and sms towards data services continues apace. This shift will require a dramatic transformation of telecoms organizations in all markets around the world.</p>\r\n<p style=\"text-align: justify;\">A critical part of that transformation will be turning the incredibly rich data assets owned by every telecoms operator into profitable revenue streams.</p>\r\n<p style=\"text-align: justify;\">Building on over twenty years experience of helping telecoms operators to transform their businesses, Redwing brings a powerful market-driven context to the challenge of Big Data monetization, combining end-user analysis with deep insights into the value chains, partnerships and customer-facing processes that will be required to turn Big Data into a major future revenue stream.</p>\r\n<p style=\"text-align: justify;\">We believe telecoms operators are the natural Big Data companies, and have the potential to combine data analytics with innovative business models in order to compete with - and even exceed - companies such as Google, Facebook and Amazon in this critical future business opportunity.</p>\r\n<p style=\"text-align: justify;\">You can see our approach to commercializing Big Data assets in the following three areas of the site:</p>\r\n<p style=\"text-align: justify;\"><a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a></p>\r\n<p style=\"text-align: justify;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\"><span style=\"text-decoration: underline;\">Productizing Big Data</span></a></p>\r\n<p style=\"text-align: justify;\"><a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\"><span style=\"text-decoration: underline;\">Organizing for Big Data</span></a></p>\r\n<p style=\"text-align: justify;\">And also view our regular analysis and insight pieces on our <a title=\"Insights and Blog\" href=\"http://redwingconsulting.com/insights-blog/\"><span style=\"text-decoration: underline;\">Insights and Blog</span></a> page.</p>\r\n<p style=\"text-align: justify;\"><strong> <span style=\"color: #993300;\">Yours,</span></strong></p>\r\n<p style=\"text-align: justify;\"><strong><span style=\"color: #993300;\">The Redwing Team</span></strong></p>','Welcome to Redwing Consulting\'s Big Data website!','Redwing\'s website aims to address the commercial, organizational and marketing issues that mobile operators and telcos face as they monetize their Big Data assets.','inherit','closed','open','','1891-revision-v1','','','2013-09-14 06:18:24','2013-09-14 06:18:24','',1891,'http://redwingconsulting.com/uncategorized/1891-revision-v1/',0,'revision','',0),(2973,5,'2013-09-14 06:26:14','2013-09-14 06:26:14','','Monetizing Big Data graphic','','inherit','closed','open','','monetizing-big-data-graphic','','','2013-09-14 06:26:14','2013-09-14 06:26:14','',2725,'http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-graphic.jpg',0,'attachment','image/jpeg',0),(2974,5,'2013-09-14 06:28:21','2013-09-14 06:28:21','','Monetizing Big Data chart','','inherit','closed','open','','monetizing-big-data-chart','','','2013-09-14 06:28:21','2013-09-14 06:28:21','',2725,'http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg',0,'attachment','image/jpeg',0),(2975,5,'2013-09-14 06:29:54','2013-09-14 06:29:54','<p style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2973\" alt=\"Monetizing Big Data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-graphic.jpg\" width=\"760\" height=\"246\" /></p>\r\n<h4 style=\"text-align: justify;\">The potential for mobile operators to generate significant amounts of revenue from Big Data is undeniable, solely based on the volume, richness and breadth of customer data that flows across a mobile network. So clearly the key question in monetizing Big Data is <strong><em>how</em></strong> to turn raw data into products and services which corporate customers and individuals will want to buy.</h4>\r\n<h5>Monetizing Big Data needs to start with the customer</h5>\r\n<p style=\"text-align: justify;\">Over the past two decades Redwing has worked with mobile operators in over 30 countries to help them get from business concepts to revenue-generating service launches.</p>\r\n<p style=\"text-align: justify;\">We\'ve learned that many operators have a tendency to first launch a product, and then see which customers buy it - the classic \'build it and they will come\' mentality.</p>\r\n<p style=\"text-align: justify;\">But a far more effective approach is to use a set of disciplined processes that drive product and service definitions from a fundamental evaluation of customer needs:</p>\r\n<p><img class=\"size-full wp-image-2837 aligncenter\" alt=\"Monetizing Big Data text box 10\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-text-box-10.png\" width=\"572\" height=\"156\" /></p>\r\n<p style=\"text-align: justify;\">On the surface these look like simple questions to answer.</p>\r\n<p style=\"text-align: justify;\">But in reality, these are <strong>extremely non-trivial</strong> questions, and require significant amounts of new thinking on the part of the Big Data team.</p>\r\n<h5 style=\"text-align: justify;\">Who are your customers?</h5>\r\n<p style=\"text-align: justify;\">Are the customers for Big Data your existing corporate accounts? Or do you see potential within the small and medium business sectors? Or is Big Data something that can be sold to individuals?</p>\r\n<p style=\"text-align: justify;\">Should you sell Big Data to the customers\' marketing departments? Or through their existing ad agencies? Or to their IT groups?</p>\r\n<p style=\"text-align: justify;\">Or is the market defined by product and application: are we selling to buyers of customer profile data? Or buyers of identity services?</p>\r\n<p style=\"text-align: justify;\">The potential answer to all these questions is: \'yes\'. Because the buyers of Big Data services form a complex multi-dimensional market which is defined by:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>The industry vertical you are selling to (fmcg, utilities, retail, etc)</li>\r\n<li>The role of the person you are selling to (media planner, digital media executive, strategic planner, etc)</li>\r\n<li>The type of Big Data product you are selling (customer profile data, location data, real-time data, etc)</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">And understanding the interaction between these customer types is key to identifying real, growable markets for your Big Data products.</p>\r\n<h5 style=\"text-align: justify;\">What do your customers want?</h5>\r\n<table style=\"width: 780px;\" border=\"0\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 300px;\" align=\"left\" valign=\"top\">\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The good news</span></p>\r\n<p style=\"text-align: justify;\">There are many Big Data applications that customers are clamoring for that are well-defined and can be readily identified.</p>\r\n<p style=\"text-align: justify;\">Redwing conducts regular in-depth research efforts to identify potential Big Data products for mobile operators, and a subset of our database is shown in the graphic to the right.</p>\r\n<p style=\"text-align: justify;\"><em>(click on the graphic to view a larger version)</em></p>\r\n</td>\r\n<td style=\"width: 200px;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\"><img class=\"size-full wp-image-2974 aligncenter\" alt=\"Monetizing Big Data chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\" width=\"340\" height=\"212\" /></a></td>\r\n</tr>\r\n</tbody>\r\n</table>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The bad news</span></p>\r\n<p style=\"text-align: justify;\">Customers themselves often don\'t know what they want from Big Data services. Obtaining Big Data products from mobile operators is new to them too, and they often don\'t know what is available or what could be available.</p>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The solution</span></p>\r\n<p style=\"text-align: justify;\">There are three basic approaches to help customers tell you what their Big Data needs are:</p>\r\n<ol>\r\n<li style=\"text-align: justify;\">Set up mechanisms to poll your potential customers, such as market research programs or customer feedback panels.</li>\r\n<li style=\"text-align: justify;\">Build relationships with exemplar customers in the key verticals you wish to target, and learn from them.</li>\r\n<li style=\"text-align: justify;\">Look for opportunities to integrate your high-value data into their existing systems and programs - evolution is always easier than revolution for customers.</li>\r\n</ol>\r\n<h5>How do customers want your Big Data products to be packaged?</h5>\r\n<p>There are two issues to consider here:</p>\r\n<ol>\r\n<li>What types of Big Data products do customers want?</li>\r\n<li>How do they want those products to be packaged and delivered?</li>\r\n</ol>\r\n<p>Redwing has identified five product families where mobile operators can provide high-value data to customers:</p>\r\n<p><img class=\"size-full wp-image-2938 aligncenter\" alt=\"Big Data product categories\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<p>We examine each of the product families - and the ways that products can be delivered to customers - in more detail in our <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> page.</p>\r\n<h5>What are the elements of a great customer experience?</h5>\r\n<p style=\"text-align: justify;\">Rolling out Big Data products is not only about delivering high quality data analytics, it requires attention to all aspects of the customer experience.</p>\r\n<p style=\"text-align: justify;\">In helping operators with product and service design, we focus on 5 main areas of the customer experience:</p>\r\n<p><img class=\"size-full wp-image-2934 aligncenter\" alt=\"Customer experience elements 2\" src=\"http://redwingconsulting.com/wp-content/uploads/Customer-experience-elements-2.png\" width=\"694\" height=\"340\" /></p>\r\n<p style=\"text-align: justify;\">This customer experience critically depends on how your Big Data team is structures, and we examine organizational issues in our <span style=\"text-decoration: underline;\"><a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\">Organizing for Big Data</a></span> page.</p>\r\n<p style=\"text-align: justify;\">Providing an integrated end-to-end experience benefits operators, both in the obvious sense of customer satisfaction, but also in building closer customer relationships which provide valuable insights into new Big Data products and services.</p>\r\n<p>&nbsp;</p>\r\n<p>&nbsp;</p>\r\n<p>&nbsp;</p>','Monetizing Big Data','','inherit','closed','open','','2725-revision-v1','','','2013-09-14 06:29:54','2013-09-14 06:29:54','',2725,'http://redwingconsulting.com/uncategorized/2725-revision-v1/',0,'revision','',0),(2977,5,'2013-09-14 06:32:49','2013-09-14 06:32:49','<h4 style=\"text-align: justify;\"> </h4>\r\n<p><img class=\"alignnone size-full wp-image-2976\" alt=\"Organizing for Big Data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-graphic.jpg\" width=\"760\" height=\"246\" /></p>\r\n<h4 style=\"text-align: justify;\">In order to fulfill the potential of Big Data, mobile operators need to commercialize Big Data services at scale. This means that Big Data teams which have previously been internally focused need to transform into <strong>commercially-oriented</strong> and <strong>externally-focused</strong> business units.</h4>\r\n<h5 style=\"text-align: justify;\">Development Phases</h5>\r\n<p style=\"text-align: justify;\">In our experience of working with mobile operators in over 30 different countries around the world, organizations follow a common set of steps in getting to the point of launching an externally-focused Big Data organization:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2765 aligncenter\" title=\"Redwing Consulting - Development phases for Mobile Operators to develop a Big Data organization\" alt=\"Redwing Consulting - Development phases for Mobile Operators to develop a Big Data organization\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizaing-for-Big-Data-flowchart-5.png\" width=\"662\" height=\"599\" /></p>\r\n<h5 style=\"text-align: justify;\">Organizational impacts</h5>\r\n<p style=\"text-align: justify;\">The <strong>Basic Tooling</strong> and <strong>Data Lab</strong> phases usually occur within the IT department, with minimal impacts on an operator\'s organization and culture.</p>\r\n<p style=\"text-align: justify;\">The <strong>Internal Testing</strong> phase is more challenging, and starts to reveal a key issue: who should control the Big Data budget - Marketing or IT? Presently, Big Data teams tend to sit within IT, but Big Data\'s profound impact on a mobile operator\'s customer-facing activities mean that the formation of cross-functional teams is essential for success, and that the budget must be driven from commercial imperatives.</p>\r\n<p style=\"text-align: justify;\">The <strong>Commercial Prototypes</strong> phase requires new sets of skills in order to design new Big Data products and deliver those to a range of corporate and individual customers. Some of these skills do not exist inside most operators, necessitating a new wave of recruitment and the formation of partnerships. At this stage, many operators consider spinning out the new Big Data division into a separate organization (although it has been seen that this is not necessarily the optimal structure).</p>\r\n<p style=\"text-align: justify;\">We examine the issues of creating a commercial business from Big Data assets in our <span style=\"text-decoration: underline;\"><a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\">Monetizing Big Data</a></span> and <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> pages.</p>\r\n<p style=\"text-align: justify;\">The <strong>Data Factory</strong> phase (which - in our opinion - no operator has yet reached) will require additional skills in terms of scalable systems, integration with other data providers and deep partnerships with marketing and CRM providers, as well as strong collaboration with various web players and OTT service providers.</p>\r\n<h5 style=\"text-align: justify;\">Benchmarking: how does your organization compare to others?</h5>\r\n<p style=\"text-align: justify;\">The chart below shows the detailed characteristics of each Big Data development phase, along with Redwing\'s estimation of the proportion of the world\'s operators in each phase.</p>\r\n<p style=\"text-align: justify;\">At this point in time, we do not believe any operator has yet implemented a Data Factory, and we do not expect to see this happen until 2015.</p>\r\n<p style=\"text-align: justify;\">(<em>Click on chart for larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\"><img class=\"alignnone size-full wp-image-2766\" title=\"Redwing Consulting - Development of Big Data Organitions within the world\'s Mobile Operators\" alt=\"Redwing Consulting - Development of Big Data Organitions within the world\'s Mobile Operators\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\" width=\"994\" height=\"626\" /></a></p>\r\n<p style=\"text-align: justify;\"> </p>','Organizing for Big Data','','inherit','closed','open','','2730-revision-v1','','','2013-09-14 06:32:49','2013-09-14 06:32:49','',2730,'http://redwingconsulting.com/uncategorized/2730-revision-v1/',0,'revision','',0),(2978,5,'2013-09-14 06:33:41','2013-09-14 06:33:41','','Productizing big data graphic','','inherit','closed','open','','productizing-big-data-graphic','','','2013-09-14 06:33:41','2013-09-14 06:33:41','',2826,'http://redwingconsulting.com/wp-content/uploads/Productizing-big-data-graphic.jpg',0,'attachment','image/jpeg',0),(2979,5,'2013-09-14 06:34:13','2013-09-14 06:34:13','<h4 style=\"text-align: justify;\"> </h4>\r\n<p><img class=\"alignnone size-full wp-image-2978\" alt=\"Productizing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Productizing-big-data-graphic.jpg\" width=\"760\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\">Productizing Big Data assets requires an operator\'s Big Data team to combine marketing, product design, analytics and IT implementation skills. By thinking about the market in a structured manner, and driving product design from clearly-identified customer needs, operators can launch Big Data products more quickly and with a significantly improved chance of market success.</h4>\r\n<h5 style=\"text-align: justify;\">The Big Data product categories</h5>\r\n<p style=\"text-align: justify;\">Based on an extensive analysis of potential Big Data customers in numerous markets around the world, Redwing has identified five product categories where mobile operators can provide high-value data to customers:</p>\r\n<p><img class=\"size-full wp-image-2938 aligncenter\" alt=\"Big Data product categories\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<h5 style=\"text-align: justify;\">Launch focus: the quick wins</h5>\r\n<p style=\"text-align: justify;\">The mobile industry is still in the early days of Big Data. It is critical that early product launches are successful, both to maintain support from the C-suite and also to give momentum to the Big Data team as it expands its skill set.</p>\r\n<p style=\"text-align: justify;\">For this reason, Redwing always seeks to identify <strong>early quick wins</strong> for Big Data products, while putting these within the context of a longer-term platform-based Big Data commercialization strategy.</p>\r\n<p style=\"text-align: justify;\">To identify quick wins, the best approach is to overlay product types with market segments to create a product launch market map, as illustrated below.</p>\r\n<p style=\"text-align: justify;\">(<em>click on graphic to view a larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\"><img class=\"alignnone size-full wp-image-2942\" alt=\"Big Data product launch market map 3\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\" width=\"1319\" height=\"571\" /></a></p>\r\n<p>We look at the process of identifying customer segments and customer needs in more detail in our <a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a> page.</p>\r\n<h5>Delivering Big Data products and services</h5>\r\n<p style=\"text-align: justify;\">A further critical element of product and service design is the means of delivery. Customers vary widely in their level of IT sophistication and their desire for ease of use and customization.</p>\r\n<p style=\"text-align: justify;\">A smart Big Data commercialization strategy stratifies the market based on customer type, giving customers the option of:</p>\r\n<ul>\r\n<li style=\"text-align: justify;\">Pre-packaged Big Data services: simple and standardized data packages.</li>\r\n<li style=\"text-align: justify;\">Selectable Big Data services: access to various aspects of the raw data, customizable by the customer.</li>\r\n<li style=\"text-align: justify;\">Managed Big Data services: hosted systems and data environments, taking the technology burden off the customer.</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">See our <a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\"><span style=\"text-decoration: underline;\">Organizing for Big Data</span></a> page for a detailed examination of the issues around creating an effective organization structure to deliver successful Big Data products and services.</p>','Productizing Big Data','','inherit','closed','open','','2826-revision-v1','','','2013-09-14 06:34:13','2013-09-14 06:34:13','',2826,'http://redwingconsulting.com/uncategorized/2826-revision-v1/',0,'revision','',0),(2980,5,'2013-09-14 07:04:12','2013-09-14 07:04:12','<h4 style=\"text-align: justify;\"> </h4>\r\n<p><img class=\"alignnone size-full wp-image-2978\" alt=\"Productizing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Productizing-big-data-graphic.jpg\" width=\"760\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\">Productizing Big Data assets requires an operator\'s Big Data team to combine marketing, product design, analytics and IT implementation skills. By thinking about the market in a structured manner, and driving product design from clearly-identified customer needs, operators can launch Big Data products more quickly and with a significantly improved chance of market success.</h4>\r\n<h5 style=\"text-align: justify;\">The Big Data product categories</h5>\r\n<p style=\"text-align: justify;\">Based on an extensive analysis of potential Big Data customers in numerous markets around the world, Redwing has identified five product categories where mobile operators can provide high-value data to customers:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2938 aligncenter\" title=\"Redwing Consulting\'s five categories of Big Data products\" alt=\"Redwing Consulting\'s five categories of Big Data products\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<h5 style=\"text-align: justify;\">Launch focus: the quick wins</h5>\r\n<p style=\"text-align: justify;\">The mobile industry is still in the early days of Big Data. It is critical that early product launches are successful, both to maintain support from the C-suite and also to give momentum to the Big Data team as it expands its skill set.</p>\r\n<p style=\"text-align: justify;\">For this reason, Redwing always seeks to identify <strong>early quick wins</strong> for Big Data products, while putting these within the context of a longer-term platform-based Big Data commercialization strategy.</p>\r\n<p style=\"text-align: justify;\">To identify quick wins, the best approach is to overlay product types with market segments to create a product launch market map, as illustrated below.</p>\r\n<p style=\"text-align: justify;\">(<em>click on graphic to view a larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\"><img class=\"alignnone size-full wp-image-2942\" title=\"Redwing Consulting - example of a Big Data product launch market map\" alt=\"Redwing Consulting - example of a Big Data product launch market map\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\" width=\"1319\" height=\"571\" /></a></p>\r\n<p>We look at the process of identifying customer segments and customer needs in more detail in our <a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a> page.</p>\r\n<h5>Delivering Big Data products and services</h5>\r\n<p style=\"text-align: justify;\">A further critical element of product and service design is the means of delivery. Customers vary widely in their level of IT sophistication and their desire for ease of use and customization.</p>\r\n<p style=\"text-align: justify;\">A smart Big Data commercialization strategy stratifies the market based on customer type, giving customers the option of:</p>\r\n<ul>\r\n<li style=\"text-align: justify;\">Pre-packaged Big Data services: simple and standardized data packages.</li>\r\n<li style=\"text-align: justify;\">Selectable Big Data services: access to various aspects of the raw data, customizable by the customer.</li>\r\n<li style=\"text-align: justify;\">Managed Big Data services: hosted systems and data environments, taking the technology burden off the customer.</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">See our <a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\"><span style=\"text-decoration: underline;\">Organizing for Big Data</span></a> page for a detailed examination of the issues around creating an effective organization structure to deliver successful Big Data products and services.</p>','Productizing Big Data','','inherit','closed','open','','2826-revision-v1','','','2013-09-14 07:04:12','2013-09-14 07:04:12','',2826,'http://redwingconsulting.com/uncategorized/2826-revision-v1/',0,'revision','',0),(2981,5,'2013-09-14 07:09:12','2013-09-14 07:09:12','<h4 style=\"text-align: justify;\"> </h4>\r\n<p><img class=\"alignnone size-full wp-image-2978\" title=\"Productizing Big Data page - Redwing Consulting\" alt=\"Productizing Big Data page - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Productizing-big-data-graphic.jpg\" width=\"760\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\">Productizing Big Data assets requires an operator\'s Big Data team to combine marketing, product design, analytics and IT implementation skills. By thinking about the market in a structured manner, and driving product design from clearly-identified customer needs, operators can launch Big Data products more quickly and with a significantly improved chance of market success.</h4>\r\n<h5 style=\"text-align: justify;\">The Big Data product categories</h5>\r\n<p style=\"text-align: justify;\">Based on an extensive analysis of potential Big Data customers in numerous markets around the world, Redwing has identified five product categories where mobile operators can provide high-value data to customers:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2938 aligncenter\" title=\"Redwing Consulting\'s five categories of Big Data products\" alt=\"Redwing Consulting\'s five categories of Big Data products\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<h5 style=\"text-align: justify;\">Launch focus: the quick wins</h5>\r\n<p style=\"text-align: justify;\">The mobile industry is still in the early days of Big Data. It is critical that early product launches are successful, both to maintain support from the C-suite and also to give momentum to the Big Data team as it expands its skill set.</p>\r\n<p style=\"text-align: justify;\">For this reason, Redwing always seeks to identify <strong>early quick wins</strong> for Big Data products, while putting these within the context of a longer-term platform-based Big Data commercialization strategy.</p>\r\n<p style=\"text-align: justify;\">To identify quick wins, the best approach is to overlay product types with market segments to create a product launch market map, as illustrated below.</p>\r\n<p style=\"text-align: justify;\">(<em>click on graphic to view a larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\"><img class=\"alignnone size-full wp-image-2942\" title=\"Example of a Big Data product launch market map - Redwing Consulting\" alt=\"Example of a Big Data product launch market map - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-launch-market-map-3.png\" width=\"1319\" height=\"571\" /></a></p>\r\n<p>We look at the process of identifying customer segments and customer needs in more detail in our <a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\"><span style=\"text-decoration: underline;\">Monetizing Big Data</span></a> page.</p>\r\n<h5>Delivering Big Data products and services</h5>\r\n<p style=\"text-align: justify;\">A further critical element of product and service design is the means of delivery. Customers vary widely in their level of IT sophistication and their desire for ease of use and customization.</p>\r\n<p style=\"text-align: justify;\">A smart Big Data commercialization strategy stratifies the market based on customer type, giving customers the option of:</p>\r\n<ul>\r\n<li style=\"text-align: justify;\">Pre-packaged Big Data services: simple and standardized data packages.</li>\r\n<li style=\"text-align: justify;\">Selectable Big Data services: access to various aspects of the raw data, customizable by the customer.</li>\r\n<li style=\"text-align: justify;\">Managed Big Data services: hosted systems and data environments, taking the technology burden off the customer.</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">See our <a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\"><span style=\"text-decoration: underline;\">Organizing for Big Data</span></a> page for a detailed examination of the issues around creating an effective organization structure to deliver successful Big Data products and services.</p>','Productizing Big Data','','inherit','closed','open','','2826-revision-v1','','','2013-09-14 07:09:12','2013-09-14 07:09:12','',2826,'http://redwingconsulting.com/uncategorized/2826-revision-v1/',0,'revision','',0),(2982,5,'2013-09-14 07:10:41','2013-09-14 07:10:41','<p style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2973\" title=\"Monetizing Big Data page - Redwing Consulting\" alt=\"Monetizing Big Data page - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-graphic.jpg\" width=\"760\" height=\"246\" /></p>\r\n<h4 style=\"text-align: justify;\">The potential for mobile operators to generate significant amounts of revenue from Big Data is undeniable, solely based on the volume, richness and breadth of customer data that flows across a mobile network. So clearly the key question in monetizing Big Data is <strong><em>how</em></strong> to turn raw data into products and services which corporate customers and individuals will want to buy.</h4>\r\n<h5>Monetizing Big Data needs to start with the customer</h5>\r\n<p style=\"text-align: justify;\">Over the past two decades Redwing has worked with mobile operators in over 30 countries to help them get from business concepts to revenue-generating service launches.</p>\r\n<p style=\"text-align: justify;\">We\'ve learned that many operators have a tendency to first launch a product, and then see which customers buy it - the classic \'build it and they will come\' mentality.</p>\r\n<p style=\"text-align: justify;\">But a far more effective approach is to use a set of disciplined processes that drive product and service definitions from a fundamental evaluation of customer needs:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2837 aligncenter\" title=\"Key questions for developing Big Data into a new line of business - Redwing Consulting\" alt=\"Key questions for developing Big Data into a new line of business - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-text-box-10.png\" width=\"572\" height=\"156\" /></p>\r\n<p style=\"text-align: justify;\">On the surface these look like simple questions to answer.</p>\r\n<p style=\"text-align: justify;\">But in reality, these are <strong>extremely non-trivial</strong> questions, and require significant amounts of new thinking on the part of the Big Data team.</p>\r\n<h5 style=\"text-align: justify;\">Who are your customers?</h5>\r\n<p style=\"text-align: justify;\">Are the customers for Big Data your existing corporate accounts? Or do you see potential within the small and medium business sectors? Or is Big Data something that can be sold to individuals?</p>\r\n<p style=\"text-align: justify;\">Should you sell Big Data to the customers\' marketing departments? Or through their existing ad agencies? Or to their IT groups?</p>\r\n<p style=\"text-align: justify;\">Or is the market defined by product and application: are we selling to buyers of customer profile data? Or buyers of identity services?</p>\r\n<p style=\"text-align: justify;\">The potential answer to all these questions is: \'yes\'. Because the buyers of Big Data services form a complex multi-dimensional market which is defined by:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>The industry vertical you are selling to (fmcg, utilities, retail, etc)</li>\r\n<li>The role of the person you are selling to (media planner, digital media executive, strategic planner, etc)</li>\r\n<li>The type of Big Data product you are selling (customer profile data, location data, real-time data, etc)</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">And understanding the interaction between these customer types is key to identifying real, growable markets for your Big Data products.</p>\r\n<h5 style=\"text-align: justify;\">What do your customers want?</h5>\r\n<table style=\"width: 780px;\" border=\"0\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 300px;\" align=\"left\" valign=\"top\">\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The good news</span></p>\r\n<p style=\"text-align: justify;\">There are many Big Data applications that customers are clamoring for that are well-defined and can be readily identified.</p>\r\n<p style=\"text-align: justify;\">Redwing conducts regular in-depth research efforts to identify potential Big Data products for mobile operators, and a subset of our database is shown in the graphic to the right.</p>\r\n<p style=\"text-align: justify;\"><em>(click on the graphic to view a larger version)</em></p>\r\n</td>\r\n<td style=\"width: 200px;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\"><img class=\"size-full wp-image-2974 aligncenter\" title=\"Potential Big Data product types for mobile operators - Redwing Consulting\" alt=\"Potential Big Data product types for mobile operators - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\" width=\"340\" height=\"212\" /></a></td>\r\n</tr>\r\n</tbody>\r\n</table>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The bad news</span></p>\r\n<p style=\"text-align: justify;\">Customers themselves often don\'t know what they want from Big Data services. Obtaining Big Data products from mobile operators is new to them too, and they often don\'t know what is available or what could be available.</p>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The solution</span></p>\r\n<p style=\"text-align: justify;\">There are three basic approaches to help customers tell you what their Big Data needs are:</p>\r\n<ol>\r\n<li style=\"text-align: justify;\">Set up mechanisms to poll your potential customers, such as market research programs or customer feedback panels.</li>\r\n<li style=\"text-align: justify;\">Build relationships with exemplar customers in the key verticals you wish to target, and learn from them.</li>\r\n<li style=\"text-align: justify;\">Look for opportunities to integrate your high-value data into their existing systems and programs - evolution is always easier than revolution for customers.</li>\r\n</ol>\r\n<h5>How do customers want your Big Data products to be packaged?</h5>\r\n<p>There are two issues to consider here:</p>\r\n<ol>\r\n<li>What types of Big Data products do customers want?</li>\r\n<li>How do they want those products to be packaged and delivered?</li>\r\n</ol>\r\n<p>Redwing has identified five product families where mobile operators can provide high-value data to customers:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2938 aligncenter\" title=\"Five categories of Big Data products - Redwing Consulting\" alt=\"Five categories of Big Data products - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<p>We examine each of the product families - and the ways that products can be delivered to customers - in more detail in our <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> page.</p>\r\n<h5>What are the elements of a great customer experience?</h5>\r\n<p style=\"text-align: justify;\">Rolling out Big Data products is not only about delivering high quality data analytics, it requires attention to all aspects of the customer experience.</p>\r\n<p style=\"text-align: justify;\">In helping operators with product and service design, we focus on 5 main areas of the customer experience:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2934 aligncenter\" title=\"Key dimensions of the customer experience for Big Data products - Redwing Consulting\" alt=\"Key dimensions of the customer experience for Big Data products - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Customer-experience-elements-2.png\" width=\"694\" height=\"340\" /></p>\r\n<p style=\"text-align: justify;\">This customer experience critically depends on how your Big Data team is structures, and we examine organizational issues in our <span style=\"text-decoration: underline;\"><a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\">Organizing for Big Data</a></span> page.</p>\r\n<p style=\"text-align: justify;\">Providing an integrated end-to-end experience benefits operators, both in the obvious sense of customer satisfaction, but also in building closer customer relationships which provide valuable insights into new Big Data products and services.</p>\r\n<p>&nbsp;</p>\r\n<p>&nbsp;</p>\r\n<p>&nbsp;</p>','Monetizing Big Data','','inherit','closed','open','','2725-revision-v1','','','2013-09-14 07:10:41','2013-09-14 07:10:41','',2725,'http://redwingconsulting.com/uncategorized/2725-revision-v1/',0,'revision','',0),(2983,5,'2013-09-14 07:12:02','2013-09-14 07:12:02','<h4 style=\"text-align: justify;\"> </h4>\r\n<p><img class=\"alignnone size-full wp-image-2976\" title=\"Organizing for Big Data page - Redwing Consulting\" alt=\"Organizing for Big Data page - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-graphic.jpg\" width=\"760\" height=\"246\" /></p>\r\n<h4 style=\"text-align: justify;\">In order to fulfill the potential of Big Data, mobile operators need to commercialize Big Data services at scale. This means that Big Data teams which have previously been internally focused need to transform into <strong>commercially-oriented</strong> and <strong>externally-focused</strong> business units.</h4>\r\n<h5 style=\"text-align: justify;\">Development Phases</h5>\r\n<p style=\"text-align: justify;\">In our experience of working with mobile operators in over 30 different countries around the world, organizations follow a common set of steps in getting to the point of launching an externally-focused Big Data organization:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2765 aligncenter\" title=\"Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" alt=\"Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizaing-for-Big-Data-flowchart-5.png\" width=\"662\" height=\"599\" /></p>\r\n<h5 style=\"text-align: justify;\">Organizational impacts</h5>\r\n<p style=\"text-align: justify;\">The <strong>Basic Tooling</strong> and <strong>Data Lab</strong> phases usually occur within the IT department, with minimal impacts on an operator\'s organization and culture.</p>\r\n<p style=\"text-align: justify;\">The <strong>Internal Testing</strong> phase is more challenging, and starts to reveal a key issue: who should control the Big Data budget - Marketing or IT? Presently, Big Data teams tend to sit within IT, but Big Data\'s profound impact on a mobile operator\'s customer-facing activities mean that the formation of cross-functional teams is essential for success, and that the budget must be driven from commercial imperatives.</p>\r\n<p style=\"text-align: justify;\">The <strong>Commercial Prototypes</strong> phase requires new sets of skills in order to design new Big Data products and deliver those to a range of corporate and individual customers. Some of these skills do not exist inside most operators, necessitating a new wave of recruitment and the formation of partnerships. At this stage, many operators consider spinning out the new Big Data division into a separate organization (although it has been seen that this is not necessarily the optimal structure).</p>\r\n<p style=\"text-align: justify;\">We examine the issues of creating a commercial business from Big Data assets in our <span style=\"text-decoration: underline;\"><a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\">Monetizing Big Data</a></span> and <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> pages.</p>\r\n<p style=\"text-align: justify;\">The <strong>Data Factory</strong> phase (which - in our opinion - no operator has yet reached) will require additional skills in terms of scalable systems, integration with other data providers and deep partnerships with marketing and CRM providers, as well as strong collaboration with various web players and OTT service providers.</p>\r\n<h5 style=\"text-align: justify;\">Benchmarking: how does your organization compare to others?</h5>\r\n<p style=\"text-align: justify;\">The chart below shows the detailed characteristics of each Big Data development phase, along with Redwing\'s estimation of the proportion of the world\'s operators in each phase.</p>\r\n<p style=\"text-align: justify;\">At this point in time, we do not believe any operator has yet implemented a Data Factory, and we do not expect to see this happen until 2015.</p>\r\n<p style=\"text-align: justify;\">(<em>Click on chart for larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\"><img class=\"alignnone size-full wp-image-2766\" title=\"Development phases of Big Data Organizations within the world\'s Mobile Operators - Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" alt=\"Development phases of Big Data Organizations within the world\'s Mobile Operators - Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\" width=\"994\" height=\"626\" /></a></p>\r\n<p style=\"text-align: justify;\"> </p>','Organizing for Big Data','','inherit','closed','open','','2730-revision-v1','','','2013-09-14 07:12:02','2013-09-14 07:12:02','',2730,'http://redwingconsulting.com/uncategorized/2730-revision-v1/',0,'revision','',0),(2984,5,'2013-09-14 07:18:44','2013-09-14 07:18:44','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telfonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help navigate into an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>Organization models for commercializing Big Data</h4>\r\n<p>&nbsp;</p>\r\n<h5><p>Mobile operators must define the right organization model for managing large Big Data projects.</p>\r\n<p>The model needs to effectively align the demands of the business with the technology requirements needed to support those demands.</p>\r\n<p>Such initiatives necessarily involve an element of cultural change within the operator, as they require collaboration across conventional functional and business unit silos...</p></h5>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">Organization models for commercializing Big Data</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-14 07:18:44','2013-09-14 07:18:44','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(2985,5,'2013-09-16 13:19:59','2013-09-16 13:19:59','','Home page background 4 980x300 redline','','inherit','closed','open','','home-page-background-4-980x300-redline','','','2013-09-16 13:19:59','2013-09-16 13:19:59','',2656,'',1,'attachment','image/jpeg',0),(2986,5,'2013-09-16 13:24:17','2013-09-16 13:24:17','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>Organization models for commercializing Big Data</h4>\r\n<p>&nbsp;</p>\r\n<h5>\r\n<p>Mobile operators must define the right organization model for managing large Big Data projects.</p>\r\n<p>The model needs to effectively align the demands of the business with the technology requirements needed to support those demands.</p>\r\n<p>Such initiatives necessarily involve an element of cultural change within the operator, as they require collaboration across conventional functional and business unit silos...</p>\r\n</h5>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">Organization models for commercializing Big Data</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-16 13:24:17','2013-09-16 13:24:17','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(2988,5,'2013-09-16 13:42:35','2013-09-16 13:42:35','','Monetizing Big Data text box 11','','inherit','closed','open','','monetizing-big-data-text-box-11','','','2013-09-16 13:42:35','2013-09-16 13:42:35','',2725,'http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-text-box-11.png',0,'attachment','image/png',0),(2989,5,'2013-09-16 13:44:07','2013-09-16 13:44:07','','Customer experience elements 3','','inherit','closed','open','','customer-experience-elements-3','','','2013-09-16 13:44:07','2013-09-16 13:44:07','',2725,'http://redwingconsulting.com/wp-content/uploads/Customer-experience-elements-3.png',0,'attachment','image/png',0),(2990,5,'2013-09-16 13:45:47','2013-09-16 13:45:47','<p style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2973\" title=\"Monetizing Big Data page - Redwing Consulting\" alt=\"Monetizing Big Data page - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-graphic.jpg\" width=\"760\" height=\"246\" /></p>\r\n<h4 style=\"text-align: justify;\">The potential for mobile operators to generate significant amounts of revenue from Big Data is undeniable, solely based on the volume, richness and breadth of customer data that flows across a mobile network. So clearly the key question in monetizing Big Data is <strong><em>how</em></strong> to turn raw data into products and services which corporate customers and individuals will want to buy.</h4>\r\n<h5>Monetizing Big Data needs to start with the customer</h5>\r\n<p style=\"text-align: justify;\">Over the past two decades Redwing has worked with mobile operators in over 30 countries to help them get from business concepts to revenue-generating service launches.</p>\r\n<p style=\"text-align: justify;\">We\'ve learned that many operators have a tendency to first launch a product, and then see which customers buy it - the classic \'build it and they will come\' mentality.</p>\r\n<p style=\"text-align: justify;\">But a far more effective approach is to use a set of disciplined processes that drive product and service definitions from a fundamental evaluation of customer needs:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2988 aligncenter\" title=\"Key questions for developing Big Data into a new line of business - Redwing Consulting\" alt=\"Key questions for developing Big Data into a new line of business - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-text-box-11.png\" width=\"572\" height=\"156\" /></p>\r\n<p style=\"text-align: justify;\">On the surface these look like simple questions to answer.</p>\r\n<p style=\"text-align: justify;\">But in reality, these are <strong>extremely non-trivial</strong> questions, and require significant amounts of new thinking on the part of the Big Data team.</p>\r\n<h5 style=\"text-align: justify;\">Who are your customers?</h5>\r\n<p style=\"text-align: justify;\">Are the customers for Big Data your existing corporate accounts? Or do you see potential within the small and medium business sectors? Or is Big Data something that can be sold to individuals?</p>\r\n<p style=\"text-align: justify;\">Should you sell Big Data to the customers\' marketing departments? Or through their existing ad agencies? Or to their IT groups?</p>\r\n<p style=\"text-align: justify;\">Or is the market defined by product and application: are we selling to buyers of customer profile data? Or buyers of identity services?</p>\r\n<p style=\"text-align: justify;\">The potential answer to all these questions is: \'yes\'. Because the buyers of Big Data services form a complex multi-dimensional market which is defined by:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li>The industry vertical you are selling to (fmcg, utilities, retail, etc)</li>\r\n<li>The role of the person you are selling to (media planner, digital media executive, strategic planner, etc)</li>\r\n<li>The type of Big Data product you are selling (customer profile data, location data, real-time data, etc)</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">And understanding the interaction between these customer types is key to identifying real, growable markets for your Big Data products.</p>\r\n<h5 style=\"text-align: justify;\">What do your customers want?</h5>\r\n<table style=\"width: 780px;\" border=\"0\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 300px;\" align=\"left\" valign=\"top\">\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The good news</span></p>\r\n<p style=\"text-align: justify;\">There are many Big Data applications that customers are clamoring for that are well-defined and can be readily identified.</p>\r\n<p style=\"text-align: justify;\">Redwing conducts regular in-depth research efforts to identify potential Big Data products for mobile operators, and a subset of our database is shown in the graphic to the right.</p>\r\n<p style=\"text-align: justify;\"><em>(click on the graphic to view a larger version)</em></p>\r\n</td>\r\n<td style=\"width: 200px;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\"><img class=\"size-full wp-image-2974 aligncenter\" title=\"Potential Big Data product types for mobile operators - Redwing Consulting\" alt=\"Potential Big Data product types for mobile operators - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Monetizing-Big-Data-chart.jpg\" width=\"340\" height=\"212\" /></a></td>\r\n</tr>\r\n</tbody>\r\n</table>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The bad news</span></p>\r\n<p style=\"text-align: justify;\">Customers themselves often don\'t know what they want from Big Data services. Obtaining Big Data products from mobile operators is new to them too, and they often don\'t know what is available or what could be available.</p>\r\n<p style=\"text-align: justify;\"><span style=\"text-decoration: underline;\">The solution</span></p>\r\n<p style=\"text-align: justify;\">There are three basic approaches to help customers tell you what their Big Data needs are:</p>\r\n<ol>\r\n<li style=\"text-align: justify;\">Set up mechanisms to poll your potential customers, such as social media analysis, crowd sourcing tools, market research programs or customer feedback panels.</li>\r\n<li style=\"text-align: justify;\">Build relationships with exemplar customers in the key verticals you wish to target, and learn from them.</li>\r\n<li style=\"text-align: justify;\">Look for opportunities to integrate your high-value data into their existing systems and programs - evolution is always easier than revolution for customers.</li>\r\n</ol>\r\n<h5>How do customers want your Big Data products to be packaged?</h5>\r\n<p>There are two issues to consider here:</p>\r\n<ol>\r\n<li>What types of Big Data products do customers want?</li>\r\n<li>How do they want those products to be packaged and delivered?</li>\r\n</ol>\r\n<p>Redwing has identified five product families where mobile operators can provide high-value data to customers:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2938 aligncenter\" title=\"Five categories of Big Data products - Redwing Consulting\" alt=\"Five categories of Big Data products - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Big-Data-product-categories.png\" width=\"387\" height=\"373\" /></p>\r\n<p>We examine each of the product families - and the ways that products can be delivered to customers - in more detail in our <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> page.</p>\r\n<h5>What are the elements of service delivery excellence?</h5>\r\n<p style=\"text-align: justify;\">Rolling out Big Data products is not only about delivering high quality data analytics, it requires attention to all aspects of the service delivery.</p>\r\n<p style=\"text-align: justify;\">In helping operators with product and service design, we focus on 5 main areas of service delivery:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2989 aligncenter\" title=\"Key dimensions of the customer experience for Big Data products - Redwing Consulting\" alt=\"Key dimensions of the customer experience for Big Data products - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Customer-experience-elements-3.png\" width=\"694\" height=\"340\" /></p>\r\n<p style=\"text-align: justify;\">Service delivery critically depends on how your Big Data team is structured, and we examine organizational issues in our <span style=\"text-decoration: underline;\"><a title=\"Organizing for Big Data\" href=\"http://redwingconsulting.com/organizing-for-big-data/\">Organizing for Big Data</a></span> page.</p>\r\n<p style=\"text-align: justify;\">Providing integrated end-to-end service delivery benefits operators, both in the obvious sense of customer satisfaction, but also in building closer customer relationships which provide valuable insights into new Big Data products and services.</p>','Monetizing Big Data','','inherit','closed','open','','2725-revision-v1','','','2013-09-16 13:45:47','2013-09-16 13:45:47','',2725,'http://redwingconsulting.com/uncategorized/2725-revision-v1/',0,'revision','',0),(2992,5,'2013-09-16 14:12:08','2013-09-16 14:12:08','','Organizing for Big Data graphic 2','','inherit','closed','open','','organizing-for-big-data-graphic-2','','','2013-09-16 14:12:08','2013-09-16 14:12:08','',2730,'http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-graphic-2.jpg',0,'attachment','image/jpeg',0),(2993,5,'2013-09-16 14:18:37','2013-09-16 14:18:37','<h4 style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2992\" title=\"Organizing for Big Data page - Redwing Consulting\" alt=\"Organizing for Big Data page - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-graphic-2.jpg\" width=\"760\" height=\"246\" /> </h4>\r\n<h4 style=\"text-align: justify;\">In order to fulfill the potential of Big Data, mobile operators need to commercialize Big Data services at scale. This means that Big Data teams which have previously been internally focused need to transform into <strong>commercially-oriented</strong> and <strong>externally-focused</strong> business units.</h4>\r\n<h5 style=\"text-align: justify;\">Development Phases</h5>\r\n<p style=\"text-align: justify;\">In our experience of working with mobile operators in over 30 different countries around the world, organizations follow a common set of steps in getting to the point of launching an externally-focused Big Data organization:</p>\r\n<p style=\"text-align: center;\"><img class=\"size-full wp-image-2765 aligncenter\" title=\"Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" alt=\"Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizaing-for-Big-Data-flowchart-5.png\" width=\"662\" height=\"599\" /></p>\r\n<h5 style=\"text-align: justify;\">Organizational impacts</h5>\r\n<p style=\"text-align: justify;\">The <strong>Basic Tooling</strong> and <strong>Data Lab</strong> phases usually occur within the IT department, with minimal impacts on an operator\'s organization and culture.</p>\r\n<p style=\"text-align: justify;\">The <strong>Internal Testing</strong> phase is more challenging, and starts to reveal a key issue: who should control the Big Data budget - Marketing or IT? Presently, Big Data teams tend to sit within IT, but Big Data\'s profound impact on a mobile operator\'s customer-facing activities mean that the formation of cross-functional teams is essential for success, and that the budget must be driven from commercial imperatives.</p>\r\n<p style=\"text-align: justify;\">The <strong>Commercial Prototypes</strong> phase requires new sets of skills in order to design new Big Data products and deliver those to a range of corporate and individual customers. Some of these skills do not exist inside most operators, necessitating a new wave of recruitment and the formation of partnerships. At this stage, many operators consider spinning out the new Big Data division into a separate organization (although it has been seen that this is not necessarily the optimal structure).</p>\r\n<p style=\"text-align: justify;\">We examine the issues of creating a commercial business from Big Data assets in our <span style=\"text-decoration: underline;\"><a title=\"Monetizing Big Data\" href=\"http://redwingconsulting.com/monetizing-big-data/\">Monetizing Big Data</a></span> and <span style=\"text-decoration: underline;\"><a title=\"Productizing Big Data\" href=\"http://redwingconsulting.com/productizing-big-data/\">Productizing Big Data</a></span> pages.</p>\r\n<p style=\"text-align: justify;\">The <strong>Data Factory</strong> phase (which - in our opinion - no operator has yet reached) will require additional skills in terms of scalable systems, integration with other data providers and deep partnerships with marketing and CRM providers, as well as strong collaboration with various web players and OTT service providers.</p>\r\n<h5 style=\"text-align: justify;\">Benchmarking: how does your organization compare to others?</h5>\r\n<p style=\"text-align: justify;\">The chart below shows the detailed characteristics of each Big Data development phase, along with Redwing\'s estimation of the proportion of the world\'s operators in each phase.</p>\r\n<p style=\"text-align: justify;\">At this point in time, we do not believe any operator has yet implemented a Data Factory, and we do not expect to see this happen until 2015.</p>\r\n<p style=\"text-align: justify;\">(<em>Click on chart for larger version</em>)</p>\r\n<p style=\"text-align: justify;\"><a href=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\"><img class=\"alignnone size-full wp-image-2766\" title=\"Development phases of Big Data Organizations within the world\'s Mobile Operators - Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" alt=\"Development phases of Big Data Organizations within the world\'s Mobile Operators - Development phases for Mobile Operators to develop a Big Data organization - Redwing Consulting\" src=\"http://redwingconsulting.com/wp-content/uploads/Organizing-for-Big-Data-chart-1.png\" width=\"994\" height=\"626\" /></a></p>\r\n<p style=\"text-align: justify;\"> </p>','Organizing for Big Data','','inherit','closed','open','','2730-revision-v1','','','2013-09-16 14:18:37','2013-09-16 14:18:37','',2730,'http://redwingconsulting.com/uncategorized/2730-revision-v1/',0,'revision','',0),(2994,5,'2013-09-16 14:19:36','2013-09-16 14:19:36','','Redwing main areas of activity 2','','inherit','closed','open','','redwing-main-areas-of-activity-2','','','2013-09-16 14:19:36','2013-09-16 14:19:36','',32,'http://redwingconsulting.com/wp-content/uploads/Redwing-main-areas-of-activity-2.png',0,'attachment','image/png',0),(2996,5,'2013-09-16 14:28:40','2013-09-16 14:28:40','<h5 style=\"text-align: justify;\"><img class=\"alignnone size-full wp-image-2945\" alt=\"Page - About Us\" src=\"http://redwingconsulting.com/wp-content/uploads/Page-About-Us.jpg\" width=\"700\" height=\"240\" /></h5>\n<h5 style=\"text-align: justify;\">Who we are</h5>\n<p style=\"text-align: justify;\">Redwing Consulting is a highly specialized advisory firm that works with mobile operators to help them commercialize their Big Data services.</p>\n<p style=\"text-align: justify;\">The Redwing team consists of strategists, market plannners, product designers, business model experts, economists and computer scientists, who bring a multi-disciplinary approach to designing and launching Big Data services.</p>\n<p style=\"text-align: justify;\">Everyone in the Redwing team has previously held senior management positions in mobile operators, and we bring deep industry expertise to our work.</p>\n<h5>What we do</h5>\n<p style=\"text-align: justify;\">We help operators launch commercial Big Data services.</p>\n<p style=\"text-align: justify;\">We do this by bringing an obsessive focus on the customer, and utilize a number of proprietary tools which ensure all the product design, technical implementation and marketing packaging are closely aligned with clearly identified customer needs.</p>\n<p style=\"text-align: justify;\">Typically, our main areas of activity range from Big Data market analysis and product design, through launch management and growth planning to culture change:</p>\n<p><img class=\"size-full wp-image-2994 aligncenter\" title=\"Redwing main areas of activity - Big Data planning, implementation and growth\" alt=\"Redwing main areas of activity - Big Data planning, implementation and growth\" src=\"http://redwingconsulting.com/wp-content/uploads/Redwing-main-areas-of-activity-2.png\" width=\"591\" height=\"370\" /></p>\n<h5>Our track record</h5>\n<p style=\"text-align: justify;\">Redwing has undertaken projects in over 30 countries, and our client list includes most of the world\'s largest mobile operators and technology companies:</p>\n<p><img class=\"size-full wp-image-2846 aligncenter\" alt=\"Redwing client logos 2\" src=\"http://redwingconsulting.com/wp-content/uploads/Redwing-client-logos-2.png\" width=\"400\" height=\"275\" /></p>\n<p>&nbsp;</p>\n<p style=\"text-align: justify;\">Our projects have ranged from creating launch plans for single products up to the management of the commercial launch of whole new businesses.</p>\n<p style=\"text-align: justify;\"> </p>','About Us','','inherit','closed','open','','32-revision-v1','','','2013-09-16 14:28:40','2013-09-16 14:28:40','',32,'http://redwingconsulting.com/uncategorized/32-revision-v1/',0,'revision','',0),(2997,5,'2013-09-16 15:06:53','2013-09-16 15:06:53','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>Big data monetization - the Jewel in the Crown for mobile operators</h4>\r\n<p>&nbsp;</p>\r\n<p>In the face of shrinking revenues and declining operating margins from their core communications services, mobile operators are retooling their business models in pursuit of new sources of top and bottom line growth.</p>\r\n<p> One of most exciting areas is Big Data monetization – mobile operators have access to unparalleled amounts of customer and network data, and yet they are only just beginning to unlock the value of this information. ...</p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-16 15:06:53','2013-09-16 15:06:53','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(2998,5,'2013-09-17 09:08:40','2013-09-17 09:08:40','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>A structured process for commercializing Big Data products</h4>\r\n<p>&nbsp;</p>\r\n<p>Mobile operators have unique access to the personal and business interactions that their customers generate via their use of phones, tablets, laptops and other wireless devices.</p>\r\n<p> Unlocking the potential of these information assets demands that MNOs have a coherent and integrated approach to develop new Big Data products...</p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-17 09:08:40','2013-09-17 09:08:40','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(2999,5,'2013-09-17 09:09:23','2013-09-17 09:09:23','','A structured process for commercializing Big Data products','','publish','closed','open','','a-structured-process-for-commercializing-big-data-products','','','2013-10-14 05:57:11','2013-10-14 05:57:11','',0,'http://redwingconsulting.com/?p=2999',5,'nav_menu_item','',0),(3000,5,'2013-09-18 21:13:32','2013-09-18 21:13:32','','Where are the opportunities to monetize Big Data?','','publish','closed','open','','where-are-the-opportunities-to-monetize-big-data','','','2013-10-14 05:57:11','2013-10-14 05:57:11','',0,'http://redwingconsulting.com/?p=3000',4,'nav_menu_item','',0),(3001,5,'2013-09-18 21:17:41','2013-09-18 21:17:41','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>Where are the opportunities to monetize Big Data?</h4>\r\n<p>&nbsp;</p>\r\n<p>Falling profit margins from voice, messaging and data services are driving mobile operators to find ways to monetize Big Data, thereby turning their existing information assets into new sources of revenue.</p>\r\n<p>In order to assert their position in this new competitive world, mobile operators are pursuing new lines of business that have the potential both to monetize the value of their big data assets and to entrench the operators as a pivotal player in their markets.</p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-18 21:17:41','2013-09-18 21:17:41','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(3003,5,'2013-09-19 11:21:11','2013-09-19 11:21:11','','Organization models for commercializing Big Data','','publish','closed','open','','organization-models-for-commercializing-big-data','','','2013-10-14 05:57:11','2013-10-14 05:57:11','',0,'http://redwingconsulting.com/?p=3003',3,'nav_menu_item','',0),(3004,5,'2013-09-19 11:25:23','2013-09-19 11:25:23','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>Organization models for commercializing Big Data</h4>\r\n<p>&nbsp;</p>\r\n<p>Mobile operators must define the right organization model for managing large Big Data projects. The model needs to effectively align the demands of the business with the technology requirements needed to support those demands.</p>\r\n<p>Such initiatives necessarily involve an element of cultural change within the operator, as they require collaboration across conventional functional and business unit silos.</p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">Organization models for commercializing Big Data</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-19 11:25:23','2013-09-19 11:25:23','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0);
INSERT INTO `redwingc_posts` VALUES (3006,5,'2013-09-28 18:43:30','2013-09-28 18:43:30','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" title=\"Bottom-up phases to commercialize Big Data products and services\" alt=\"Bottom-up phases to commercialize Big Data products and services\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','Once a Big Data team has been formed inside a mobile operator, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.','publish','closed','open','','bottom-up-approach-to-commercialize-big-data-products','','','2013-09-28 18:53:19','2013-09-28 18:53:19','',0,'http://redwingconsulting.com/?p=3006',0,'post','',0),(3007,5,'2013-09-28 18:32:42','2013-09-28 18:32:42','','Post - bottom-up approach to commercializing big data graphic','','inherit','closed','open','','post-bottom-up-approach-to-commercializing-big-data-graphic','','','2013-09-28 18:32:42','2013-09-28 18:32:42','',3006,'http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg',0,'attachment','image/jpeg',0),(3009,5,'2013-09-28 18:35:43','2013-09-28 18:35:43','<p><img class=\"alignnone size-full wp-image-3007\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to launch Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"size-full wp-image-3008 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart.png\" width=\"624\" height=\"445\" /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<p style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></p>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<p style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></p>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercializing Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:35:43','2013-09-28 18:35:43','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3010,5,'2013-09-28 18:40:00','2013-09-28 18:40:00','<p><img class=\"alignnone size-full wp-image-3007\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to launch Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"size-full wp-image-3008 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart.png\" width=\"624\" height=\"445\" /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercializing Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:40:00','2013-09-28 18:40:00','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3011,5,'2013-09-28 18:40:24','2013-09-28 18:40:24','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to launch Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"size-full wp-image-3008 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart.png\" width=\"624\" height=\"445\" /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercializing Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:40:24','2013-09-28 18:40:24','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3012,5,'2013-09-28 18:42:35','2013-09-28 18:42:35','','Post - bottom-up approach to commercializing big data chart 2','','inherit','closed','open','','post-bottom-up-approach-to-commercializing-big-data-chart-2','','','2013-09-28 18:42:35','2013-09-28 18:42:35','',3006,'http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png',0,'attachment','image/png',0),(3013,5,'2013-09-28 18:42:52','2013-09-28 18:42:52','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to launch Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" alt=\"Post - bottom-up approach to commercializing big data chart 2\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercializing Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:42:52','2013-09-28 18:42:52','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3014,5,'2013-09-28 18:46:37','2013-09-28 18:46:37','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" title=\"Bottom-up phases to commercialize Big Data products and services\" alt=\"Bottom-up phases to commercialize Big Data products and services\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','','inherit','closed','open','','3006-autosave-v1','','','2013-09-28 18:46:37','2013-09-28 18:46:37','',3006,'http://redwingconsulting.com/uncategorized/3006-autosave-v1/',0,'revision','',0),(3015,5,'2013-09-28 18:45:32','2013-09-28 18:45:32','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" alt=\"Post - bottom-up approach to commercializing big data chart 2\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:45:32','2013-09-28 18:45:32','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3016,5,'2013-09-28 18:46:47','2013-09-28 18:46:47','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" title=\"Bottom-up phases to commercialize Big Data products and services\" alt=\"Bottom-up phases to commercialize Big Data products and services\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:46:47','2013-09-28 18:46:47','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3017,5,'2013-09-28 18:52:22','2013-09-28 18:52:22','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" title=\"Bottom-up phases to commercialize Big Data products and services\" alt=\"Bottom-up phases to commercialize Big Data products and services\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:52:22','2013-09-28 18:52:22','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3018,5,'2013-09-28 18:53:19','2013-09-28 18:53:19','<p><img class=\"size-full wp-image-3007 aligncenter\" alt=\"Post - bottom-up approach to commercializing big data graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-graphic.jpg\" width=\"700\" height=\"240\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>The six realizations of a data analytics team</b></h5>\r\n<p style=\"text-align: justify;\">We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.</p>\r\n<p style=\"text-align: justify;\">Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:</p>\r\n<p align=\"center\"><img class=\"alignnone size-full wp-image-3012\" title=\"Bottom-up phases to commercialize Big Data products and services\" alt=\"Bottom-up phases to commercialize Big Data products and services\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-bottom-up-approach-to-commercializing-big-data-chart-2.png\" width=\"624\" height=\"445\" /><br /> </p>\r\n<p style=\"text-align: justify;\">Let\'s examine what typically happens in each of these phases:</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 1: delivering internal value</b></h5>\r\n<p style=\"text-align: justify;\">The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 2: an initial opportunity is identified to trial a Big Data service</b></h5>\r\n<p style=\"text-align: justify;\">Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.</p>\r\n<p style=\"text-align: justify;\">Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 3: the trial turns into a commercial service</b></h5>\r\n<p style=\"text-align: justify;\">Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.</p>\r\n<p style=\"text-align: justify;\">Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 4: additional product and service opportunities</b></h5>\r\n<p style=\"text-align: justify;\">Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 5: the development of cross-functional teams</b></h5>\r\n<p style=\"text-align: justify;\">As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.</p>\r\n<p style=\"text-align: justify;\">It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.</p>\r\n<p style=\"text-align: justify;\">The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.</p>\r\n<h5 style=\"text-align: justify;\"><b>Phase 6: creation of a new Line of Business</b></h5>\r\n<p style=\"text-align: justify;\">By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.</p>\r\n<p style=\"text-align: justify;\">This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.</p>\r\n<h5 style=\"text-align: justify;\"><b>External Big Data services will be an industry-wide trend</b></h5>\r\n<p style=\"text-align: justify;\">Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.</p>\r\n<p style=\"text-align: justify;\">Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.</p>','The bottom-up approach to commercialize Big Data products','Once a Big Data team has been formed inside a mobile operator, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.','inherit','closed','open','','3006-revision-v1','','','2013-09-28 18:53:19','2013-09-28 18:53:19','',3006,'http://redwingconsulting.com/uncategorized/3006-revision-v1/',0,'revision','',0),(3019,5,'2013-09-28 18:54:03','2013-09-28 18:54:03','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>The bottom-up approach to commercialize Big Data products</h4>\r\n<p>&nbsp;</p>\r\n<p>Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling.</p>\r\n<p>Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics.</p>\r\n<p>This bottom-up approach is a pragmatic and cost-effective path for operators to launch Big Data services.</p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/bottom-up-approach-to-commercialize-big-data-products/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/bottom-up-approach-to-commercialize-big-data-products/\">The bottom-up approach to commercialize Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">Organization models for commercializing Big Data</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/operators-exploiting-big-data-opportunity/\">How are operators exploiting the Big Data opportunity?</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-09-28 18:54:03','2013-09-28 18:54:03','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(3020,5,'2013-09-28 18:54:59','2013-09-28 18:54:59','','The bottom-up approach to commercialize Big Data products','','publish','closed','open','','the-bottom-up-approach-to-commercialize-big-data-products','','','2013-10-14 05:57:11','2013-10-14 05:57:11','',0,'http://redwingconsulting.com/?p=3020',2,'nav_menu_item','',0),(3022,5,'2013-10-14 05:44:22','2013-10-14 05:44:22','<p><img class=\"size-full wp-image-3024 aligncenter\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\r\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\r\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\r\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\r\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\r\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\r\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\r\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\r\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\r\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\r\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\r\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\r\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\r\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\r\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\r\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\r\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\r\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\r\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\r\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\r\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\r\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\r\n<p style=\"text-align: justify;\"><strong> </strong></p>\r\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data monetization','6 key issues for mobile operators in turning their internal analytics capabilities into external Big Data monetization opportunities.','publish','closed','open','','from-internal-analytics-to-external-big-data-monetization','','','2013-10-14 05:47:55','2013-10-14 05:47:55','',0,'http://redwingconsulting.com/?p=3022',0,'post','',0),(3023,5,'2013-10-14 05:39:23','2013-10-14 05:39:23','','Post - internal to external chart','','inherit','closed','open','','post-internal-to-external-chart','','','2013-10-14 05:39:23','2013-10-14 05:39:23','',3022,'http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png',0,'attachment','image/png',0),(3024,5,'2013-10-14 05:39:34','2013-10-14 05:39:34','','Post - internal to external graphic','','inherit','closed','open','','post-internal-to-external-graphic','','','2013-10-14 05:39:34','2013-10-14 05:39:34','',3022,'http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png',0,'attachment','image/png',0),(3025,5,'2013-10-14 05:41:55','2013-10-14 05:41:55','<p><img class=\"alignnone size-full wp-image-3024\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\r\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\r\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\r\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\r\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\r\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\r\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\r\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\r\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\r\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\r\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\r\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\r\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\r\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\r\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\r\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\r\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\r\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\r\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\r\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\r\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\r\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\r\n<p style=\"text-align: justify;\"><strong> </strong></p>\r\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data commercialization ','','inherit','closed','open','','3022-revision-v1','','','2013-10-14 05:41:55','2013-10-14 05:41:55','',3022,'http://redwingconsulting.com/uncategorized/3022-revision-v1/',0,'revision','',0),(3026,5,'2013-10-14 05:43:15','2013-10-14 05:43:15','<p><img class=\"alignnone size-full wp-image-3024\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\r\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\r\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\r\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\r\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\r\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\r\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\r\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\r\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\r\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\r\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\r\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\r\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\r\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\r\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\r\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\r\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\r\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\r\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\r\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\r\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\r\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\r\n<p style=\"text-align: justify;\"><strong> </strong></p>\r\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data monetization','','inherit','closed','open','','3022-revision-v1','','','2013-10-14 05:43:15','2013-10-14 05:43:15','',3022,'http://redwingconsulting.com/uncategorized/3022-revision-v1/',0,'revision','',0),(3027,5,'2013-10-14 05:43:48','2013-10-14 05:43:48','<p><img class=\"size-full wp-image-3024 aligncenter\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\r\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\r\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\r\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\r\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\r\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\r\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\r\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\r\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\r\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\r\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\r\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\r\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\r\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\r\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\r\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\r\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\r\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\r\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\r\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\r\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\r\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\r\n<p style=\"text-align: justify;\"><strong> </strong></p>\r\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data monetization','','inherit','closed','open','','3022-revision-v1','','','2013-10-14 05:43:48','2013-10-14 05:43:48','',3022,'http://redwingconsulting.com/uncategorized/3022-revision-v1/',0,'revision','',0),(3028,5,'2013-10-14 05:49:05','2013-10-14 05:49:05','<p><img class=\"size-full wp-image-3024 aligncenter\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\n<ul style=\"text-align: justify;\">\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\n</ul>\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\n<p style=\"text-align: justify;\"><strong> </strong></p>\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data monetization','6 key issues for mobile operators in turning their internal analytics capabilities into external Big Data monetization opportunities.','inherit','closed','open','','3022-autosave-v1','','','2013-10-14 05:49:05','2013-10-14 05:49:05','',3022,'http://redwingconsulting.com/uncategorized/3022-autosave-v1/',0,'revision','',0),(3029,5,'2013-10-14 05:47:55','2013-10-14 05:47:55','<p><img class=\"size-full wp-image-3024 aligncenter\" alt=\"Post - internal to external graphic\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-graphic.png\" width=\"700\" height=\"200\" /></p>\r\n<h4 style=\"text-align: justify;\"><b>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</b></h4>\r\n<h5 style=\"text-align: justify;\"><b>Externalizing Big Data is a change management task</b></h5>\r\n<p style=\"text-align: justify;\">We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:</p>\r\n<p style=\"text-align: center;\"><b> <img class=\"alignnone size-full wp-image-3023\" alt=\"Post - internal to external chart\" src=\"http://redwingconsulting.com/wp-content/uploads/Post-internal-to-external-chart.png\" width=\"727\" height=\"409\" /></b></p>\r\n<p style=\"text-align: justify;\"><b>Issue #1: Leveraging external datasets</b></p>\r\n<p style=\"text-align: justify;\">The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.</p>\r\n<p style=\"text-align: justify;\">However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. </p>\r\n<p style=\"text-align: justify;\">Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #2: Establishing requirements specifications</b></p>\r\n<p style=\"text-align: justify;\">In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.</p>\r\n<p style=\"text-align: justify;\">In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #3: Generating customer demand</b></p>\r\n<p style=\"text-align: justify;\">Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.</p>\r\n<p style=\"text-align: justify;\">However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.</p>\r\n<p style=\"text-align: justify;\">Hence the Marketing and Sales functions of the operator have to take the lead in this process.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #4: Managing service delivery quality</b></p>\r\n<p style=\"text-align: justify;\">The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.</p>\r\n<p style=\"text-align: justify;\">However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support - all seamlessly integrated for the client - become of paramount importance to the success of the Big Data service delivery platform.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #5: Developing a partner eco-system</b></p>\r\n<p style=\"text-align: justify;\">Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.</p>\r\n<p style=\"text-align: justify;\">The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.</p>\r\n<p style=\"text-align: justify;\"><b>Issue #6: Securing subscriber and client trust</b></p>\r\n<p style=\"text-align: justify;\">Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.</p>\r\n<p style=\"text-align: justify;\">The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.</p>\r\n<p style=\"text-align: justify;\">The ‘top-of-mind’ questions for these stakeholders at this early stage are:</p>\r\n<ul style=\"text-align: justify;\">\r\n<li><b>For subscribers</b>: am I confident that the operator will not violate my privacy in the external use of my personal data?</li>\r\n<li><b>For clients</b>: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?</li>\r\n</ul>\r\n<p style=\"text-align: justify;\">However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.</p>\r\n<p style=\"text-align: justify;\">This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.</p>\r\n<p style=\"text-align: justify;\">Data privacy and security - which in most jurisdictions is governed both by industry-wide standards and by government regulation - is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.</p>\r\n<p style=\"text-align: justify;\"><strong> </strong></p>\r\n<p style=\"text-align: justify;\"> </p>','From internal analytics to external Big Data monetization','6 key issues for mobile operators in turning their internal analytics capabilities into external Big Data monetization opportunities.','inherit','closed','open','','3022-revision-v1','','','2013-10-14 05:47:55','2013-10-14 05:47:55','',3022,'http://redwingconsulting.com/uncategorized/3022-revision-v1/',0,'revision','',0),(3030,5,'2013-10-14 05:56:09','2013-10-14 05:56:09','<div class=\"ez-only\">[nivoslider slug=\"redwing-home-page-slider\"]</div>\r\n<div class=\"ez-two-fifths ez-first\">You\'re building a Big Data analytics capability...<br /><sp><br />...we help you build a business around that.<br />\r\n</sp></div>\r\n<div class=\"ez-three-fifths\">\r\n<p>Redwing specializes in helping mobile operators commercialize their Big Data assets by transforming their data analytics teams into externally-oriented new lines of business.</p>\r\n<p>We help operators to create new revenue streams by identifying key target customers, creating Big Data products and services, developing pricing and revenue models and identifying partnership requirements.</p>\r\n<p>With over twenty years experience of working around the globe with companies such as Verizon, Telefonica and Vodafone, we know the challenges of transforming large telecoms companies. We can help your Big Data team create an exciting and profitable new future.</p>\r\n</div>\r\n<div class=\"ez-row-wrap-border\"> </div>\r\n<div class=\"ez-two-sixths ez-first\">\r\n<h3>LATEST POST</h3>\r\n<p><img title=\"How mobile operators are tooling up for Big Data\" alt=\"How mobile operators are tooling up for Big Data\" src=\"http://redwingconsulting.com/wp-content/uploads/Latest-post.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>MOST POPULAR POSTS</h3>\r\n<p><img title=\"Redwing Consulting most popular posts\" alt=\"Redwing Consulting most popular posts\" src=\"http://redwingconsulting.com/wp-content/uploads/Ranking.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-two-sixths\">\r\n<h3>BIG DATA ARTICLES</h3>\r\n<p><img title=\"Redwing Consulting useful links\" alt=\"Redwing Consulting useful links\" src=\"http://redwingconsulting.com/wp-content/uploads/Useful-links.jpg\" width=\"260\" height=\"74\" /></p>\r\n</div>\r\n<div class=\"ez-one-third ez-first\">\r\n<h4>From internal analytics to external Big Data monetization</h4>\r\n<p>&nbsp;</p>\r\n<p>Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience.</p>\r\n<p>However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients.</p>\r\n<p>We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.</p>\r\n<p><a href=\"http://redwingconsulting.com/operations-posts/from-internal-analytics-to-external-big-data-monetization/\">[Read full post]</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://redwingconsulting.com/operations-posts/from-internal-analytics-to-external-big-data-monetization/\">From internal analytics to external Big Data monetization</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/bottom-up-approach-to-commercialize-big-data-products/\">The bottom-up approach to commercialize Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/organization-models-commercializing-big-data/\">Organization models for commercializing Big Data</a></p>\r\n<p><a href=\"http://redwingconsulting.com/big-data-products/opportunities-monetize-big-data/\">Where are the opportunities to monetize Big Data?</a></p>\r\n<p><a href=\"http://redwingconsulting.com/organization-posts/structured-process-commercializing-big-data-products/\">A structured process for commercializing Big Data products</a></p>\r\n<p><a href=\"http://redwingconsulting.com/analysis-posts/big-data-monetization-jewel-in-the-crown/\">Big data monetization - the Jewel in the Crown for mobile operators</a></p>\r\n</div>\r\n<div class=\"ez-one-third\">\r\n<p><a href=\"http://www.totaltele.com/view.aspx?ID=477094\" target=\"_blank\">Telcos Can Survive On Big Data Alone - Sprint</a></p>\r\n<p><a href=\"http://www.technologyreview.com/news/513016/how-wireless-carriers-are-monetizing-your-movements/\" target=\"_blank\">How Wireless Carriers Are Monetizing Your Movements</a></p>\r\n<p><a href=\"http://ovum.com/2012/10/08/asian-mobile-operators-claim-that-user-data-analytics-gives-them-an-edge/\" target=\"_blank\">Asian Mobile Operators Claim That User Data Analytics Gives Them An Edge</a></p>\r\n<p><a href=\"http://www.eurocomms.com/features/opinion/8860-opinion-theres-no-turning-back-for-telcos-in-2013-reinvention-is-a-must\" target=\"_blank\">There\'s No Turning Back For Telcos in 2013 - Reinvention Is A Must</a></p>\r\n<p><a href=\"http://www.hottopicsevents.com/2013/04/24/telcos-transactions-and-trust/\" target=\"_blank\">Telcos, Transactions and Trust</a></p>\r\n</div>\r\n','Home','','inherit','closed','open','','2797-revision-v1','','','2013-10-14 05:56:09','2013-10-14 05:56:09','',2797,'http://redwingconsulting.com/uncategorized/2797-revision-v1/',0,'revision','',0),(3031,5,'2013-10-14 07:11:47','2013-10-14 07:11:47','<p><img class=\"alignnone size-full wp-image-2086\" alt=\"Page - Contact Us - graphic 3\" src=\"http://redwingconsulting.com/wp-content/uploads/Page-Contact-Us-graphic-3.jpg\" width=\"700\" height=\"200\" /></p>\r\n<p><img class=\"size-full wp-image-1993 alignleft\" alt=\"Icon - email\" src=\"http://redwingconsulting.com/wp-content/uploads/Icon-email.png\" width=\"100\" height=\"100\" /></p>\r\n<p>&nbsp;</p>\r\n<p><strong>Please send any enquiries, comments or feedback to us using the following form:</strong></p>\r\n<p><strong> </strong></p>\r\n<p>&nbsp;</p>\r\n<p style=\"text-align: left;\">[contact-form-7 id=\"12\" title=\"Contact form 1\"]</p>\r\n<p>&nbsp;</p>\r\n<p><strong>To contact Redwing at our UK or Asia offices:</strong></p>\r\n<p>&nbsp;</p>\r\n<table style=\"width: 700px;\" border=\"0\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 120px;\"><img alt=\"Icon - address\" src=\"http://redwingconsulting.com/wp-content/uploads/Icon-address.png\" width=\"100\" height=\"100\" /></td>\r\n<td style=\"width: 230px;\"><strong>Redwing Consulting (UK)<br /> </strong>Arena Business Centre\r\n<p>Holyrood Close</p>\r\n<p>Poole BH17 7BA, England</p>\r\n</td>\r\n<td style=\"width: 120px;\"><img alt=\"Icon - phone\" src=\"http://redwingconsulting.com/wp-content/uploads/Icon-phone.png\" width=\"100\" height=\"100\" /></td>\r\n<td style=\"width: 230px;\"><strong>Tel: +44 1202 606080<br /> </strong></td>\r\n</tr>\r\n</tbody>\r\n</table>\r\n<p>&nbsp;</p>\r\n<table style=\"width: 700px;\" border=\"0\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 120px;\"><img alt=\"Icon - address\" src=\"http://redwingconsulting.com/wp-content/uploads/Icon-address.png\" width=\"100\" height=\"100\" /></td>\r\n<td style=\"width: 230px;\"><strong>Redwing Consulting (Asia)<br /> </strong>Level 24\r\n<p>One Raffles Place</p>\r\n<p>Singapore</p>\r\n</td>\r\n<td style=\"width: 120px;\"><img alt=\"Icon - phone\" src=\"http://redwingconsulting.com/wp-content/uploads/Icon-phone.png\" width=\"100\" height=\"100\" /></td>\r\n<td style=\"width: 230px;\"><strong>Tel: +65 6408 0657<br /> </strong></td>\r\n</tr>\r\n</tbody>\r\n</table>','Contact Us','','inherit','closed','open','','34-revision-v1','','','2013-10-14 07:11:47','2013-10-14 07:11:47','',34,'http://redwingconsulting.com/uncategorized/34-revision-v1/',0,'revision','',0);

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YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1155,'2013-10-19 20:17:04','/index_files/image452.png','Mozilla/5.0 (compatible; YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1156,'2013-10-19 20:28:04','/index_files/image511.png','Mozilla/5.0 (compatible; YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1157,'2013-10-19 20:32:28','/index_files/image684.png','Mozilla/5.0 (compatible; YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1158,'2013-10-19 20:36:57','/index_files/image1366.jpg','Mozilla/5.0 (compatible; YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1159,'2013-10-19 20:42:14','/index_files/image620.png','Mozilla/5.0 (compatible; YandexImages/3.0; +http://yandex.com/bots)',NULL,3340067688),(1160,'2013-10-19 20:43:48','/client-services/','Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)',NULL,2637635685),(1161,'2013-10-19 20:51:30','/index_files/image959.png','Mozilla/5.0 (compatible; YandexImages/3.0; 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