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Getting analytical about analytics

Thornton May | March 1, 2016
Too many of us are making ad hoc decisions around things like analytic strategy, architecture and tool sets.

Our industry has to get much more analytical in how we make decisions about the analytic tool sets and techniques we deploy.  

I recently pinged 2,500 senior decision-makers to ask how their organizations made decisions about analytics investments. I was surprised that for more than 60% of the respondents, decision-making around analytic strategy, architecture, tool sets, base platforms, techniques and capability development is basically ad hoc — they’ve been winging it. I needn’t point out the deep irony in that. 

The transcendent importance of analytics has been clear for some time. In my book The New Know: Innovation Powered by Analytics (Wiley, 2009), I argued that analytics was emerging as an affordable and accessible source of competitive advantage. In the seven years since then, almost a thousand books and tens of thousands of blog posts, articles and webinars have piled on the proposition that analytics is a good thing. Washington insiders take it as fact that investment in analytics — or lack of investment — was the difference maker in the 2008 and 2012 presidential elections. Enough already. I don’t think we need any more surveys documenting “analytics, good; no analytics, bad.” It is time we added a little more nuance to the discussion.

Let’s start with the basics, though. Analytics is a big sandbox that encompasses the entire decision spectrum — from operational decisions to tactical decisions to strategic decisions (those with huge impact but low frequency). 

But analytics is a heavily modified term. “Descriptive analytics” is for understanding what happened in the past. “Diagnostic analytics” is for unearthing why something happened. “Predictive analytics” follows linear extrapolations to forecast what will happen in the future. And “prescriptive analytics” considers what we should do next. To all of this I throw in big data and data science as part of the analytics superset. 

Big data, of course, is a big part of why analytics has become essential. The existence of big data is not something we have a choice about. It simply is. Our choice is between ordering and exploiting it, on the one hand, and being overwhelmed by it, on the other. In the next four years, something in the neighborhood of 60 zettabytes of new digital information will be created. That is a number so big, it might require explication, even for the readers of Computerworld. The prefix zetta indicates multiplication by the seventh power of 1,000 [1021]. For perspective, consider that half a zettabyte is thought to approximate the entire World Wide Web in 2009. 

When information is being created at a rate of 15 zettabytes per year, being overwhelmed can seem like the only option, especially given the fact that the digital storage industry manufactures about half a zettabyte of storage capacity a year. What do we do when we are creating more digital data than we have places to store it?  


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