Big Data, Small Results? 3 Ways To Make Your Data Deliver More Value
Big Data – For a good number of years, your company has been investing in capturing data descriptive of systems health, operation and performance. Expectations have been high that predictive maintenance, operation and cost reductions will quickly follow. Alas, expectations and reality have failed to meet. Mountains of data have been collected, but few insights have been gleaned. No meaningful actions have been taken based on the collected data, and no compelling business cases have emerged.
Data is king—we all know that. So, what has gone wrong? Why has so little been done with so much data?
This quandary reminds me of my onetime hobby of stamp collecting. It used to go something like this: The postman would arrive in his truck and deposit the bundle of envelopes into our mailbox and we would rush to examine the goods. To keep from getting caught, we would smuggle the envelopes under our coats and carefully steam them to remove the stamps in one piece. The full color ones were the rarest and most sought after, but we had no control over what we collected. It was all up to some relative who had the impulse to update one of our parents on family matters. It’s certainly possible that the initials “EH” written on the back of a Cuban stamp I once found may have been scribbled by Ernst Hemingway himself, but the chances are weak. In the end, the value was not in the stamps, but in the collecting.
For many companies, data collection is an all too similar affair. The data they collect is random and there’s no control on what can be captured. And, no clear idea what could be done with that data, by who, how, or why. A lot of effort in the collection phase, but very little value afterward.
So, what’s going on? Why are we not getting better value out of the data we capture? There are three reasons behind this massive missed opportunity:
- The Data We Have Is Not Necessarily The Data We Need
Take this scenario: A young man steps out of a bar at night and sees an old man crouched on the ground under a streetlight. “What’s going on?” the young man asks.”I’ve lost my keys and can’t find them,” the old man replies.
So, the young man joins him on the ground to help him search for his keys. After 10 minutes of searching, the young man stops and asks the old man where he dropped the keys. The old man points to the far dark side of the street. “Then why are we looking for the keys here?” the young man asks. “Because here there is light,” says the old man.
All too often, the data companies are using is the data their assets happen to produce. They search for the keys where there is light—not where the keys may actually be located. This relates to how the asset was designed by the manufacturer, probably years before digital optimization, utilization or predictive maintenance were thought of. Garbage in, garbage out. Have a long hard look at the data you capture, is it the right data, or is it just the data that happens to be available?
This article originally appeared on Forbes.com To read the full article, click here.
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