Twitter: @lshannon45
Earlier this year, I wrote a blog about the value of customer data and that operators should make sure in partnerships not to lose visibility of the actions that their customers are taking online. My point was that in the coming world of Machine Learning and Artificial Intelligence, systems training data will have even more value to the companies who can access it than customer usage data already has today.
One of the tricks, though, is to access and leverage this data without violating end user privacy, and that’s not easy to do. So it’s with interest that I see a few early attempts by operators to reward end users who allow their data to be anonymized and mined. There are only a few of these programs out there so far, and they take different forms, but one structure is to ask for permission to access usage data “for greater personalization” in exchange for membership in a generous rewards program. How successful these early programs are will indicate whether or not end users are willing to exchange their usage data for other objects of value – and begin to gauge what end users perceive the value of their data to be. Will end users decide that the opportunity to score front row Lady Gaga tickets is worth letting their operator know how often they play Candy Crush, or will the desire for privacy keep people away? These early data usage reward programs are experiments that we can all learn from, whichever way they lead.
A similar dynamic is at work on the verticals side as well. Both IBM and Google have built Open Source Machine Learning libraries that companies can access for free online. Why build cutting-edge software and then just give it away? For a start, Machine Learning benefits from being run on training data, and exposure to different kinds of large data sets for different purposes can only help IBM and Google sharpen their algorithms and speed up developments and discoveries in Artificial Intelligence. But the real signal to pay attention to here is that by open-sourcing their Machine Learning programs, both companies are indicating that an enterprise’s proprietary data is slowly replacing its proprietary software as a key source of differentiation and value. That’s a big industry shift, and one that might be easy to miss if you’re not looking in the right direction.
Whether it’s the data of consumer customers or your own enterprise data, its value is starting to rise. Keep this in mind as you plan your next set of strategic activities and partnerships, and make sure that you’re doing everything you can to access and leverage the data goldmine that you and your customers produce every single day.
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Source: Nokia Networks
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