The Evolution of Analytics
Those of us who have spent years studying âDATA SMARTâ companies believe weâve already lived through two eras in the use of analytics. We might call them 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. 2.0 releases donât 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 âBig Dataâ that was surely the case.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a companyâs operations but also to its offerings to embed data smartness into the products and services
customers buy. Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change those with a sneak peek at Analytics 3.0 will be best positioned to drive that change.Â
My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could. Today it isnât just online and information firms that can create products and services from analyses of data. Itâs every firm in every industry which can do this.
