Data: Handle with care

We got the first one on a Monday and thought nothing of it. It was just your typical piece of junk mail from an insurance company trying to entice us to change carriers. The next day, we got six pieces of similar mail representing most of the usual suspects among the insurance companies you see advertised on TV, trying to convince us to drop our current insurer and go with them instead.

One of the pieces of mail had a picture of our house on the outside of the envelope. Seeing our home like that was actually kind of creepy. It was a “they know where we live” moment. I half expected to see a ransom note inside until I realized it was a very old photo, probably retrieved from Google Street View. The picture showed our 1992 Plymouth minivan in the driveway—a vehicle we haven’t owned for many years.

Other insurance offers followed over the next few days—far above the normal amount. It got me wondering, why the sudden interest in protecting what is mine? I don’t recall searching for insurance products online or responding to any surveys about my insurance needs. Somehow, someone identified me as a good prospect. The problem is, the data they supplied to these companies was wrong. I am not looking for new insurance.

Such inaccuracies in data may be why there are growing privacy concerns about the acquisition of data and how it is applied. Apple’s announcement in April of the launch of its App Tracking Transparency feature is another example of requiring a new set of permissions to track users’ activity across the internet. When asked, most users say NO to granting such permissions.

Apple’s is the latest salvo in the fight for data privacy. As CEO Tim Cook explained in announcing the plan, “If a business is built on misleading users, on data exploitation, on choices that are no choices at all, then it does not deserve our praise. It deserves reform.”

Which brings me to ask, how reliable is the data we’re collecting and acting upon for our supply chain operations? There’s a wealth of information available today, but how can we tell which of it is reliable and should be acted upon? The recent pandemic proved we can’t rely solely on historical data any longer.

The good news is that there are supply chain tools that can cut through the data clutter to improve data quality, such as predictive analytics and demand planning software. Without good tools, random data collected from random sources will be as useless as those insurance offers were to me, and will probably endure the same fate—being consigned to the trashcan.