Skip to main content

Three ways to make 'OK' data quality better

Rachel Wheeler Archive

When people first begin to learn a new skill, like playing the piano or learning to type, they must cognitively think about every single finger stroke and hand movement, Jim Harris writes in his latest blog post for Information Management. After they get a feel for the movements, they find they can perform the actions automatically without having to think their actions through head of time. This is where the OK plateau sets in for many.

At some point, people go into autopilot, which allows their brains to think about other things, such as the words they are going to write or the notes they are going to play, Harris explains. They are OK at the skill, but they don't necessarily improve no matter how often they perform the same action, because they aren't actively thinking about it. The same might become the case for data quality, he writes. While companies are discovering the consequences that come with foregoing address management systems and other quality control tools, they may not improve unless they make it a priority. 

To take data quality from passable to stellar, businesses may need to employ these three tips: 

1. Hire qualified individuals (The Data Round Table Blog cautions HR teams to fully understand the breadth of their data quality needs before making an offer to any old 'data scientist.' The field is hot right now and many people are finding creative ways to repackage themselves so they can cash in.)

2. Invest in the right tools (It might be more money up front, but having the necessary platforms in place will save resources and embarrassment in the long run.) 

3. Be willing to see the whole picture (Many companies have poor data quality because they're not willing to look in that storage room to see how big the mess is. Admitting there is a problem is the first step and can put the gears in  motion for major improvements.)