By Grant Shirk - January 22, 2020
Over the past decade, the idea of a Chief Data Officer (CDO) grew from a loosely-defined, pioneering role to a key partner and peer of the CIO.
Recently Forbes shared a survey of Fortune 1000 companies where almost 70% reported hiring a full-time executive in that role. For most teams, before any strategic work could begin, the first priority was aligning business and technology resources around the collection and consolidation of a highly fragmented data infrastructure.
Fortunately, for most companies these investments have been wildly successful. Another recent market survey found that enterprises were capturing structured data at an incredible rate. And it’s accelerating – enterprise storage of structured data is growing at a CAGR of 12.7% over a seven-year period. Most companies are now sitting on truly “internet-scale” data warehouses. In theory, this should mean that each of these organizations is now in a position to make data-informed decisions on a frequent basis. But while we’re incredibly effective at capturing this data, our ability to turn it into a competitive asset is falling behind. This is the next challenge facing the Chief Data Officer.
If the first phase of a data transformation is defined by the collection and consolidation of data assets, the hallmark of the second phase is descriptive analytics. We’ve democratized access to data through dashboards, reports, and other visualization tools, but thinking effectively about that data and putting it to use requires a completely different skillset. Armed with richer dashboards, business units are now aware of the data that describes what happened, but we still lack the ability to effectively understand why something has changed.