Are you often confused about what people mean by when they talk about analytics?
Me too.
Let me state the obvious: analytics is a catchall, and a trendy one at that. There's hardly a standard definition of it. Adding to the confusion in many circles, the term big data analytics has entered the business vernacular over the past five years. Are the big ones different than their smaller or regular counterparts? If so, how?
Nomenclature aside, it's folly to think that everything can be easily and meaningfully quantified, a point that Nate Silver makes:
...the rapid and tangible progress in sports analytics is more the exception than the rule. It’s important to remind sports nerds...of this fair and maybe even obvious point. We still have tremendous trouble predicting how the economy will perform more than a few months in advance, or understanding why a catastrophic earthquake occurs at a particular place and time, or knowing whether a flu outbreak will turn into a bad one.
Good point. Ascertaining baseball pitch counts and basketball field goal percentage by heat map may be relatively easy. Understanding what's driving sales in a retail business, however, might be a bit tricky—especially given privacy considerations.
And then there's the issue of how we interact with analytics, both newfangled and traditional. With rare exceptions like Walmart's infamous inventory system, analytics don't act on themselves. People do—and often we just don't understand what the data is telling us. Take Google Analytics, for example. It sports nearly 30 million users, yet many if not most users find its user interface counterintuitive.
Many organizations continue to rely upon traditional BI tools, ones that weren't built for mobile devices like many of today's contemporary applications. It's a big point in The Visual Organization. As such, they may have difficulty finding the signal in the noise.
Finally, there's the elephant in the room: Analytics are based on both core enterprise data and, increasingly, external data sources (read: social data, linked data, open data, and the like). Enterprises may not be able to fully control third-party data and metadata, but how many of them sufficiently manage what they can control: the valuable information that their employees, partners, customers, and users generate? In my experience, not a terribly high percentage.
Simon Says: Manage the data behind the analytics
Bad data means bad analytics, period. While data quality and data management may not be the sexiest of topics today, make no mistake: they matter. Big time. It's easy for employees to blame the very tools that generate these metrics because those tools can't blame us back.
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