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So you think big data is the opportunity of the decade? You’re right, but at the same time it’s possible that you are overlooking a major issue that thwarts your company’s big data ambitions. For many service providers, even those that have invested heavily in big data infrastructure, the challenge is “no data,” or more succinctly, wrong or insufficiently formulated data.
When the grand moment arrives and they push the button on advanced analytics solutions to reveal key insights from their big data, they find a massive void. The data they need doesn’t exist, or if it does, is too aggregated to be of any value to driving their business objectives.
Do you find that hard to believe? Then consider the following real-world scenarios:
–A global mobile operator decided to forecast the volume of customer calls by destination country, but came up blank.
–A machine-to-machine company discovered that it can’t analyze revenue and margins per specific type of device.
–A tier-one service provider pumped tens of millions into a big data infrastructure that today sits idle.
All three companies are stuck in neutral on their big data initiatives. What’s going on here?
Adaptive analytics: History with a future
The untold story of big data monetization is how carriers fail to take essential preliminary steps to ensure a return on investment from analytic solutions. To do meaningful predictive analysis and forecasting, analytics apps typically rely on access to months or years of detailed historical data.
All too often, however, carriers don’t look ahead to the types of questions they’ll need answers to in the future. Even when they do, the ingrained practice of summarizing or tossing that data every few weeks can moot the point and value of analytics. With the useful data gone and off the books, there’s nothing left to analyze.
A new approach called adaptive analytics puts carriers on the right track to leveraging their big data. Adaptive Analytics is a fresh solution based on three principles:
1. Having data formulated at the entity levels – customer, product, service, location, etc. – to answer new questions that may surface in the future.
2. Keeping relevant data for two to three years in order to have sufficient historical information to see and analyze trends.
3. Understanding the level at which you’ll want to ask questions, take action, and measure results.
Carriers’ inability to leverage big data and their investment in analytics due to lack of properly formulated data is an all too common problem. Service providers need to “adapt” quickly so that they have the right data, correctly formulated, for questions that may arise in the months and years ahead.
Knowing which data to retain
Notice that I say “right” data, not all data.
Keeping years of raw data may not be feasible, especially when it comes to data as voluminous as call detail records and IP detail records. However, having the data at the proper levels of analysis is extremely important – and that means thinking ahead.
Here are two simple examples that show how planning is a vital step in making the long-term investment in big data a positive experience.
If a carrier wants to perform analysis at a device level, yet only has data aggregated to a subscriber level, then device-level analysis becomes impossible or, at best, distorted. They need device-level data before the analysis they seek can begin, and the data must be aggregated to a proper time dimension.
Similarly, for a prepaid mobile operator that needs to forecast “adds and deletes” at a daily level, having monthly aggregates is of little use. The data must apply to daily adds and deletes.
Adapt or … else
More than ever, the communications ecosystem is evolving at breakneck speeds. New technologies, devices, services, pricing options, and most importantly consumer demands, are popping up with greater and greater frequency.
What does this mean for operators? Since it is impossible to predict all events, carriers must be nimble. Adaptive analytics provides this flexibility, helping carriers plan ahead on the issues and questions they will face in the future, and make certain they have stored the most relevant data in the most useable format to get the right answers.
Agility is fast becoming the major differentiator in the communications space. Customers can change their minds, interests and preferences in a heartbeat. How quickly you respond to this change will hinge less on big data and more on the right data that lets you anticipate and adapt to shifts in your market.
