In his 2015 post, Palmer argued that the democratization of analytics and the implementation of “built-for-purpose” database engines created the need for DataOps. In addition to the two dynamics Palmer identified, a third has emerged: the need for analysis at the “speed of need”, which, depending on the use, can be real-time, near-real-time or with some acceptable latency. Data must be made available broadly, via a more diverse set of data stores and analytic methods, and as quickly as required by the consuming user or application.
What’s driving these three dynamics is the strategic imperative that enterprises wield their data as a competitive weapon by making it available and consumable across numerous points of use, in short, that their data enables pervasive intelligence. The centralized discipline of SQL-driven business intelligence has been subsumed into a decentralized world of advanced analytics and machine learning. Pervasive intelligence lets “a thousand flowers bloom” in order to maximize business benefits from a company’s data, whether it be speeding product innovation, lowering costs through operational excellence or reducing corporate risk.