A structured process for commercializing Big Data products

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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.

Establishing the basics

At the organization level, three main sets of processes need to be defined to manage information assets:

  • An enterprise-wide ownership framework for the end-to-end management of data – from the moment of creation through to the point of use.
  • 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.
  • Detailed policies and procedures that govern how data is collected, analyzed, and acted upon.
Managing data assets

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.

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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.

  1. Data acquisition.  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.
  2. Discovery. 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.
  3. Delivery. 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.
  4. Dollars. 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.

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.

The D4 process acts as an overall management approach when developing Big Data products, as outlined in our Productizing Big Data page.

 

Big data monetization – the Jewel in the Crown for mobile operators

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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.

What is Big Data for the operators?

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.

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.

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.

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.

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.

What does Big Data consist of?

The diagram below outlines the diverse nature of the available network data:

 

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Structured data is the accumulation of bulk transaction and profile records amassed by operators on a daily basis:

  • Itemized calling and messaging records – CDRs/xDRs
  • Electronic data records – Web logs, searches
  • Geo-positioning data records – Location coordinates, time, duration
  • Billing profile records – Gender, age, address, spend

Unstructured data reflects the detailed interactions between subscribers within the network represented by the exchange of textual, numeric and graphical content:

  • Social media posts
  • Web browsing
  • Media downloads and streams
  • E-books and newsreader content
  • App usage and interactions

Inferred data comprises the patterns of behaviour derived from observed social media activity and point-to-point movements within the network service area

  • Social graph and influence graph – relationships, personal interests, attitudes, sentiments
  • Location-based activity and context – retail footfall, travel dynamics, social preferences
Data – a new asset class

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.

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.

We identify a number of detailed approaches to monetize Big Data in our Monetizing Big Data page.

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.