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Investigating different strategies for ensuring data quality

Rachel Wheeler Archive

All organizations, whether large or small, public or private, have an interest in ensuring data quality. When collecting hefty volumes of information about consumers, it's important to maintain accuracy, as proceeding with sloppy data can lead executives to make rash decisions with harmful repercussions. It's clear that data quality is key.

There's disagreement, however, about how best to achieve this quality. According to IT Business Edge, there are generally accepted ideas about data quality best practices, including involving the business users and establishing data governance, but these theoretical concepts often fall flat in the real world.

Many companies are lacking when it comes to developing specific strategies for data quality. Lyndsay Wise, president and founder of research and analysis BI firm WiseAnalytics, clearer procedures are necessary.

"Many operational systems were developed years or even decades ago without processes for correcting inaccurate and inconsistent data entries," Wise stated, according to IT Business Edge. "A majority of companies have yet to implement master data management programs and systems that use master reference data to help identify and fix quality issues as data is entered into systems."

Two basic strategies can ensure data quality. One is to do it proactively by working quality assurance into the data integration process - when a company loads its information into a database, it can scan it beforehand to look for any errors.

The other, perhaps more complicated, strategy is to apply data quality tools after the information is moved into a centralized database, but before it enters the business intelligence layer. This can be logistically challenging, but it can be done.

Either way, businesses and nonprofits alike should be willing to choose a strategy and stick with it. Staying noncommital will only hurt organizations' chances of working with accurate data in the future.