What if data really is the fuel that powers engines of insight? Imagine a world exactly like ours in every way except for one key difference. Instead of the modern fossil fuel supply chain (i.e., Big Oil) being created before (or roughly coincident with) the mass production of automobiles (i.e., Ford, Volkswagen, etc.), the technology underlying actual race cars was perfected first. So, in this world, you get the Ford vs. Ferrari battles *before* there’s a gas station on every corner. In this world, you still have the thrilling 24-hour race at Le Mans and so on, but the fuel that powered those beautiful machines would be an artisanal, bespoke affair, roughly like the production of craft beer in Brooklyn or burritos in the Mission District.
That world might be unusual, but it’s perfectly conceivable. Here’s the point of my thought experiment: It’s a perfectly apt analogy for the actual world– our world – with respect to IT, data, and analytics. In this thought experiment by analogy, the “beautiful machines” (i.e., the race cars) are our actual ML- and AI-powered analytics systems and the artisanal supply chain for gasoline is data integration. Data integration in our world is, pardon the pun, relatively crude. We have cutting-edge engines of insight, but they are still powered by the same kludgy data integration systems we first started using in the 1970s. To adopt a shorthand, they are still powered by storage-level physical consolidation of data; they’re still powered by data location.
Surely there’s a better way? Of course there is. Since data really is the fuel that powers our beautiful insight machines, we better start acting like it; that is, we better move data integration from the storage layer to the computation layer, and we better leverage data meaning rather than just data location. My thought experiment describes our status quo and it’s not great. It’s workable but far from ideal. But the very near-term future looks much more troubling.
Read the rest of the article by CEO Kendall Clark on Dataversity here.