I’ve seen many occasions where a company or organization either didn’t put their internal data to good use, or mishandled it in some way that cost them dearly (I wish I could show you my data on these cases.)

Not only is understanding complex data difficult, but once it’s understood, you have to be darn careful what you do with it. Otherwise you could make the kind of grand mistakes that even major players have been known to make (heard about the time Target knew a woman was pregnant before her father did? Or the time Office Max mailed a flyer that mentioned how the recipient’s daughter had recently died in a car crash?)

Regardless of how human it is to err, the fact is, every mistake you’ve ever made can be traced back to inaccurate, incomplete, or misinterpreted customer-centric data.

Think about it. We mistake “TSP” for “TBSP” (inaccurate data) and make the soup too salty. We call up the wrong “Steve” because we never put his last name in our phone (incomplete data). When Aunt Lily said she arrives at 8:41, we assumed she meant p.m., not a.m. (misinterpreted data).

Considering that even simple situations like these come down to how we deal with the data at hand, imagine how easy it is to get things wrong in complex situations, such as at your company — a company with processes and channels affecting thousands, perhaps millions of customers, each of which represent many consumer variables.

To be blunt, it’s a good bet that blind spots, misinterpretations and perhaps even mistakes within your data are affecting your profit margins.

No matter how successful your company is, you can run into mishaps with customer-centric data that can diminish your company’s sales and brand value, or simply cause you to make internal mistakes which waste time and money.

Our research indicates that you won’t like that.