With the very initiation of a data science-powered transformation, the endeavor and whoever is driving it are acknowledging that the status quo for analytics utilization does not deliver against the believed potential value for the given business (however defined). As a result, any individual (wherever they are on the totem pole), technology, and even organization overly associated with legacy or the status quo will find themselves exposed to some degree of uncertainty and possibly even vulnerability. What transpires when an enterprise kicks off such an initiative ranges within two extremes. On one side – the “bad outcome” – the effort yields a hot mess of organizational wrangling over concepts like “data ownership” and “where analytics should live”, shortsighted technology investments or the digging-in-of-heels around legacy platforms, and analytical project work-to-nowhere – all with the enterprise’s competitive advantage wavering on a precipice.
On the other end of the spectrum – the “good outcome” – the effort can yield a complete rebirth or transformation of the company built upon data-derived innovation, resulting in data science-generated intellectual property and competitive advantage. On this end of the spectrum, I often see an executive alignment on prioritization for data initiatives, a thriving data science culture, a drumbeat focus on smart data instrumentation and data quality processes, and modeling efforts with clearly defined paths to operationalization for top- or bottom-line impacting actions.
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”.
For more on the Predictive Enterprise, read my full post on Greenplum’s Datastream Blog.