I recently attended the SIM Seattle Technology Leadership Summit to hear from a diverse group of IT leaders on both the development and Ops side of the fence. In particular, the opening keynote from Matt McIlwain, managing director of Madrona Ventures gave me some new market perspective.
Matt’s predictions are pointing to a category of solutions he calls “Dataware” (also, see Mcilwain’s recent Skytap guest blog on the topic here). Basically, with the growing volume of data, and the ever increasing velocity and availability of data, the ability to capture, process and interpret this information intelligently and intuitively becomes the killer application itself.
Dataware is going beyond the enabling infrastructures of big data and cloud, and building upon several layers we can already see in operation today. Matt illustrated this with two examples:
1. Workday – a first-generation example of a company that came out of the gate making correlations between old data and new data. “You take the structured data from HR and capital management functions, then you combine that with an outside data provider like LinkedIn – and as it turns out, they can predict you are a more likely flight risk if you recently updated your profile,” he said.
While I always keep up my LinkedIn profile since social connections are part of my job, I can see it certainly makes sense – if someone’s more likely to be looking, they would be more likely to keep their profile current.
2. The Football Pro. “I have a friend who plays in the NFL I talk to when I can. His team started monitoring and tracking everything he does: when he eats, when he sleeps, when he needs to drink more water, and when he needs to cut down on the reps in his next practice because he’s prone to injury.”
At first, he was resistant to this change, to this constant surveillance and suggestion by his employer. “Now, he says he’s never going back, because this data makes him a better player.”
These examples pointed out an evolutionary process for Matt’s dataware category proposition. Starting with the enabling infrastructure and data, you add data intelligence on top of that – handling the interpretation or correlation we referred to above. Then, you create “Intelligent Apps and Services” atop that material.
“These intelligent apps aren’t static,” said Matt. “They use predictive models to determine actions, and then deliver the resulting outcomes.”
With the amount of attention and funding going to Big Data companies these days, I can picture how this trend could continue over the next 3 years. With so many data feeds available, from economics, to politics, to weather, the value of interpreting data becomes a live, shared market force with more potential value than traditional application vendors. That’s why Matt notes we’ll see big investments from all the big traditional enterprise and consumer players influencing this future space, as well as the kinds of startups his firm is looking at.
Overall a great intro to a great day in Seattle that brought together an exclusive senior IT management audience.
