It’s too easy these days to be tired of big data, with all the defining and redefining, marketing, and Hadooping. I can’t help but think to myself, “Just shut up and do it!” Of course, some organizations have gone and done it. Now a new report from TDWI Research describes the common stages they go through on the way to making big data a permanent part of their toolset.
An insurance executive I quoted a few weeks ago in this space spoke heresy: Not only do most data analysts he’s known lack the polish for presentations to execs, even worse was too narrow a point of view for leading an organization. It’s the Analytics Gap.
To that, the prolific tweeter and data scientist @data_nerd, Carla Gentry, reacted for many others in the decision support industry: “Huh?”
“I think a logical mind is perfect for business,” she tweeted to me when I inquired. “You must see the whole picture to run a successful business. Having an analytical mind helps.”
All true. Even the insurance executive and two others of that level — none of whom agreed to be named — would agree. The difference comes in the contrasting views of the “whole picture.”
Carla seems to assume that the whole picture can show itself in the data. The execs argue that it takes experience and judgement to see the real thing. “The conceit of BI,” emails a retired veteran of multiple technology startups and longtime acquaintance, “is that it has intrinsic value in its own right.”
Data analysts are narrowly focused, and rightly so. They’re looking for the certainty — or at least the illusion of certainty — that numbers provide. Execs — at least the good ones — know they are dealing with a messy and uncertain world. Human beings don’t behave as reliably as numbers. And executive decisions must deal with programs that stretch over many quarters, or even longer. In that time all manner of unplanned events may interfere and require solid executive leadership.
I still think that data analysts will someday find their profession to be a good route to the top. Sales and finance are good paths now, and each tends to breed limited candidates. But until data analysis evolves, it looks like the door to the executive suite may not open as readily as I’d thought it would.
A remarkable thing happened in Big Data last week. One of Big Data’s best friends poked fun at one of its icons: the Three V’s.
The well-networked and alert observer Shawn Rogers, vice president of research at Enterprise Management Associates, tweeted his eight V’s: “…Vast, Volumes of Vigorously, Verified, Vexingly Variable Verbose yet Valuable Visualized high Velocity Data.”
He was quick to explain to me that this is no comment on Gartner analyst Doug Laney’s three-V definition. Shawn’s just tired of people getting stuck on V’s.
How strange to be stuck on a definition, but we get stuck all the time trying to define Big Data. Other terms are easier. We’ve always known what visualization is. We seem to agree on “self service BI.” We also know what relational databases are, what ETL is, and all kinds of other established technology. We don’t agree on “business intelligence” or “decision support,” but somehow we don’t dwell on it. We don’t even quibble too heartily with “easy to use,” even though I could argue that we should.