From internal analytics to external Big Data monetization

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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.

Externalizing Big Data is a change management task

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:

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Issue #1: Leveraging external datasets

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.

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. 

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.

Issue #2: Establishing requirements specifications

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.

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.

Issue #3: Generating customer demand

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.

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.

Hence the Marketing and Sales functions of the operator have to take the lead in this process.

Issue #4: Managing service delivery quality

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.

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.

Issue #5: Developing a partner eco-system

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.

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.

Issue #6: Securing subscriber and client trust

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.

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.

The ‘top-of-mind’ questions for these stakeholders at this early stage are:

  • For subscribers: am I confident that the operator will not violate my privacy in the external use of my personal data?
  • For clients: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?

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.

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.

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.