Data quality management best practices | CCWIS

It's becoming clear to numerous business leaders across the world that it's important to have high standards for data quality. For any enterprise that cares about knowing its customers - either for improving their marketing or sales strategies, or for offering better personalized service - it's vital to have good information about consumers. If companies are collecting data, but it's inaccurate or outdated, they're only going to get themselves into trouble.

Data quality is crucial, but not everyone has a clear idea of how to turn that ideal into a reality. The bigger the company, the harder this becomes - large corporations often have massive stockpiles of information regarding their many customers, and it seems like a near-impossible task to sift through it all, find the inaccuracies and fix them.

It's important that all business leaders - the data technicians, marketing and sales executives and the leaders in the C-suite - work together to attack their data quality concerns. A common problem is these departments are often siloed, making it difficult for them to align their goals and collaborate, but they must fight through this impediment if they want to make the most of their data.

According to Enterprise Apps Today, companies today often fail to make the most of their resources in the data management process. They have a great deal of money and manpower devoted to data, but they often allocate it inefficiently and squander opportunities to improve their data. Priya Singh is director of product marketing for Information Builders, where she works to develop data integration solutions. Singh believes that more companies need to recognize their data quality problems and work together to overcome them.

"All companies struggle to manage the cyclical data quality process," she stated. "A majority of organizations use only a fraction of their enterprise information to gain the kind of actionable insight needed to facilitate superior business performance. Additionally, they fail to realize the substantial cost associated with the presence of subpar, inaccurate and inconsistent data."

Along those lines, here are three examples of best practices that can help companies of all sizes make significant improvements.

Regular data assessment
In order to diagnose the problems with their data, companies need to conduct regular assessments of the information they have in house, periodically checking up on it to ensure accuracy. It's not enough merely to screen pieces of data as they're taken in - companies need to be thorough and follow up regularly.

Data is prone to change. For example, every time someone moves or switches their email address, their contact information needs to be updated. For this reason, it's vital that companies continually stay vigilant.

Unifying data and BI
Business leaders today tend to invest heavily in business intelligence for analyzing their operations and finding ways to improve. It's only logical that customer data should play a role in this process - it can reveal important insights about key demographics.

Companies' analytical thinkers need to maintain a two-way street between external data - on customers - and internal data that's used for BI. Each of the two can bolster the other, as BI insights are equally likely to provide key tidbits about customer interactions as the other way around.

Putting employees in charge
Finally, it's imperative that corporate personnel stop passing the buck and instead accept accountability for their own data-driven pursuits. Employees should take responsibility for matters of data quality - as Singh puts it, they should become "data stewards."

If companies are really feeling ambitious, they could consider putting in place "data governance boards" to oversee all of their information and proactively eliminate any issues.

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