<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>
<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.
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<h5 style="text-align: justify;"><b>Establishing the basics
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<p style="text-align: justify;">At the organization level, three main sets of processes need to be defined to manage information assets:</p>

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	<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>
	<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>
	<li>Detailed policies and procedures that govern how data is collected, analyzed, and acted upon.</li>
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<h5 style="text-align: justify;"><b>Managing data assets
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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 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>
<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>

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	<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>
	<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>
	<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>
	<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>
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<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>
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