Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Analytics 3.0
Reprint: R1312C
Those who study “data smart” companies believe that we’ve already lived through two eras in the use of analytics—we might think of them as “before big data” and “after big data”—and are entering a third. It is characterized by a far-reaching resolve to apply powerful data gathering and analysis not just to a company’s operations but also to its services and products.
This strategic change in focus means a new role for analytics. Companies will need to recognize a host of related challenges and respond with new capabilities, positions, and priorities. Requirements will include:
- multiple types of data, often combined
- a new set of management options
- faster technologies and methods of analysis
- embedded analytics
- data discovery
- cross-disciplinary data teams
- chief analytics officers
- prescriptive analytics
- analytics on an industrial scale
- new ways of deciding and managing
These new capabilities can’t be developed using old models for how analytics support business. The big data model was a huge step forward, but it will not provide advantage for much longer. Companies must once again fundamentally rethink how the analysis of data can create value for them and their customers.