A good analytics vendor should offer solutions and be able to tell you the cost of those solutions and whether your current programs and benefit designs are worth their price. (Image: Shutterstock)

Without a full picture of your company's population health, making decisions on programs that improve health and reduce rising benefit costs would be like throwing darts while blindfolded. Having current and correct analytics based on all your company's health care data empowers you to make accurate decisions and implement programs and benefit design changes based on real information.

The good news is that a growing number of employers are relying on analytics to improve population health. However, there is also bad news: most employers I come across routinely make at least one if not more of the following four common mistakes when it comes to how they create and use their population health analytics. Here is how you can avoid some common pitfalls and get all the benefits that analytics can offer.

1. Don't trade speed for accuracy.

Many of us as benefits professionals have been in the situation where our boss wants something and they “needed it yesterday.” Far too often I meet employers who have literally rushed to upload their population health data to begin a new analytics program, only to cringe when they see the results. When it comes to anything in data, there is an old saying that is true: “junk in, junk out.” To get reliable reports based on data, the initial data we put into the system needs to be fully accurate and “clean,” free of errors or confusion.