Remember what travel was like before GPS? You could usually manage if you were traveling along familiar roads, but go on a long trip and it could get exciting. Which exit were we looking for? Had we passed the red barn? Did the gas station attendant say to turn after three lights or four? And let's not forget the kids in the back seat asking, "When will we get there?" Okay, that part hasn't changed, although the good news is that they can usually see the GPS screen too and answer their own questions.
The great thing about GPS is that it lets us get from wherever we are to wherever we want to go. Not only that, but it also has access to data that lets it figure out exactly where we are in the first place! Let's face it, if we don't know where we're starting, it's very hard to figure out how to get where we want to go. This, of course, is exactly why in the corporate talent management setting any non-trivial organizational change is so difficult: even if we know where we want to go, that is, what the organization should look like, rarely do we actually know where we're starting. I realize that seems a bit counterintuitive: we can see the business, we can talk to the people. Unfortunately, in the context of our GPS analogy, that's a little like saying that we can look out the window and see trees or a street: that's great, but what does that tell us about where we are? Without a larger context, the information we do have is of limited value. So what to do?
Fortunately, this is an area where big data can be helpful. It's important to remember that however you define "big data," it is not about the data; it's about the insights that data analytics provide in supporting data-driven decision-making. Data analytics can give us a snapshot of what is actually going on: it can tell us where we are starting. Granted, it still takes someone with knowledge of organizational behavior and psychology to turn that data into a road map, but it's still a lot better than guesswork. For example, consider the chart shown in Figure 1. It's a simple age distribution in a manufacturing company, produced using a data analytics engine developed by Macromicro. But what it tells us is that this manufacturer has a leadership vacuum waiting to happen: note the bulge of younger, hence newer, employees, and the second bulge of considerably older employees. Note also how thin it is in between. It doesn't take Mr. Spock from Star Trek to figure out that most of the company leadership and experience is in the older group. At some point, those older employees will be retiring. Who is going to be running the show at that point?
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