As I build out Greenplum’s Data Science Services team, I’m in a very privileged position to witness, first-hand, the organizational transformations underway across nearly all sectors, private and public. Based on these observations as well as my own personal experience at Yahoo!, I believe that any enterprise initiative to move towards more pervasive utilization of data science is disruptive on multiple levels. Any CIO, CTO, or even CEO considering this strategic shift must place the transformation front and center at the C-level table, or risk significant erosion of comparative advantage to the first movers who are getting it right.
I am frequently asked by our Analytics Labs customers which levers they should control to drive towards the “good outcome” as they embrace data science. The levers are numerous, and each is integral to the success of the effort. I’ve mentally cultivated my list over an extended period of time, based on my team’s data science work with our customers and prospects, my observations of the travails and successes of various Greenplum customers, and my pre-Greenplum days at Yahoo!, where I ran central Insights Services and led globalized data solutions during the company’s data “glory days”.
In this post, I provide some high-level color on these levers, or “transformation catalysts” as I call them. In subsequent posts I will cycle back to those warranting a deeper dive.