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Remediate your data by preventing any and all defects

Paul Newman Archive

As companies have obtained more robust technological solutions for gathering large volumes of data and analyzing them, they've made a conscious choice to invest more time and money in the process. Many executives in marketing and HR are now armed with vast banks of information on their customers and employees, and they're using it to sharpen their strategies both internally and externally.

One word of caution, though - the larger that companies' data clusters get, the more concerned they should become about data quality. With growing banks of information, the potential for technical malfunction and human error only increases, and companies need to be able to find any imperfections in their databases and eliminate them before they balloon into bigger problems.

According to Information Management, an important quality of data technicians is the ability to remediate data and fix mistakes. Data quality expert Jim Harris advises that companies must have data governance frameworks in place if they want to be successful.

"Resolving data quality issues requires a combination of data cleansing and defect prevention," Harris stated. "Data cleansing is reactive and its common - and deserved - criticism is that it essentially treats the symptoms without curing the disease. Defect prevention is proactive and through root cause analysis and process improvements, it essentially is the cure for the quality ills that ail your data."

Sitting in the dentist's chair
A simple way to explain data remediation is that it's like taking care of your teeth. When you go to the dentist's office, there are generally two different types of procedures you can undergo - routine measures like cleaning your teeth and flossing, and more complex procedures like root canals and other surgeries.

Teeth cleaning is a relatively painless experience, as trips to the dentist go. By keeping your pearly whites pearly, you can keep from needing any difficult, invasive procedures later. Maintaining data quality is the same way - if you watch your data as it comes in, keeping an eye out for any mistakes that need to be fixed right away, you can prevent major problems from popping up, further down the road.

If you need to make major alterations to a data set six months from now, that's a far worse problem - the equivalent of a root canal. Going too far with inaccurate data can be devastating, as it leads businesses to make poor decisions that harm them financially and legally. The best approach to data quality, just like dentistry, is a proactive one.