CES is always a data scientist’s nightmare, and this year is no different. Why? It’s simple. The hardware vision we’re being served (24/7 connection with everything) immediately triggers one critical question: Where will all this data go? How will this comically fragmented data be integrated in a way that creates value for your lives, our families, our organizations? The central conundrum of wearables and IoT, which we see nowhere here, is that the firehose of data created by these devices can only create value if merged together in a way that’s (a) central, (b) safe, and (c) relevant to our lives. Emphasis on (c), of course.

Right now that data is being outputted in a completely fragmented ecosystem of apps and devices, and it’s hard to see anybody with either the skills or the time to put it all together to create meaningful value through solving real health or lifestyle problems.

Why? It’s simple. What we are seeing at CES isn’t the future of technology, it’s all the bets that VCs have taken in the past 5 years, most of which will fail.

In other words, the technology landscape we see here at CES has been defined not by people’s needs, or product-market fit, but by a combination of a vague vision of what’s technologically possible and investors’ hopes of who is going to be bought by Apple, Google, Microsoft, and Baidu, to mention just the usual suspects. And there’s only so many companies that these cash-rich giants will buy (and the competition will drive prices down, unless you have a good AI-machine learning team).

In the end, it’s likely that all the data will be merged into the Amazon Echos and Google Homes of the world (for consumer-facing platforms) and in the IBM Watsons and whatever-the-heck-Microsoft-is-going-to-hit-us-with (for enterprise-facing applications).

So in the wearables and IoT game, my money is on the big guys. The real opportunity, frankly, is with whoever develops the AI-driven “intelligent data lake” where all this data can be safely and intelligently consolidated around our lives and our needs. And of course in using that data to infer what our needs and wants are.

More on that later…