If there was one adult responsibility that I dread the most it would be going to the doctor. I don’t think it’s hardly a secret why – you have to make it fit into your work schedule, the long wait to see the specialist, and then the anticipation to hear the “results.”

We long for that clean bill of health. We don’t want the doctor to beckon us back through the same appointment carousel so that he can show us on charts just exactly what is wrong.

The problem is that for more stubborn people like me, we don’t like hearing something is wrong. That signifies weakness. Sometimes we’re so stubborn that we refuse to go into the doctor at all. After all, it is MY body and I would know when something is wrong – right?

The truth is that we can’t diagnose ourselves. We need a specialist for that.

To a lesser extent, the same kind of health check is needed for the data that supports your business. I’ve heard it from customers before. They are the gatekeepers for their data and they know their system like the back of their hand. They are SURE they would be the first one to notice when something is off.

I want to illustrate this idea by applying it to a real world scenario. With a previous client we noticed that the ordering and customer database had become very bloated. The quantity of customers seemed to not align with the purchase orders entities on file.  Look at the following chart:

123 E. Island  Road 123 E. Island  Road 123 E. lsland  Road
123 E. Island  Road 123 E. Island  Road 123 E. Island  Road
123 E. lsland  Road 123 E. Island  Road 123 E. Island  Road

 

How quickly can you spot the error in this chart? If you look closely the top right corner and the bottom left corner aren’t spelled correctly. The “I” in island is actually a lower case “L”. This is just a 3×3 square. What happens if this block were repeated for 1,000 clients?  Very quickly you can see how these small errors both can hide themselves to the naked eye but also can significantly change key data calculations.

What was the prognosis for the client? It involved a lot of time consuming work using a business intelligence tool to help us visualize the errors. We were able to spot the values the abnormal values and from there the customer was able to make the corrections in their system. They also were able to start to implement a quality control check for data entry as a preventative measure in the future.

If you think you have a data problem or just want to learn more about preventative data care – contact the friendly staff at Bardess.