Quality a Key Concern for Data Management Professionals

Jovan King's picture
Data quality is a primary concern for data management professionals.

When it comes to enterprise data management, organizations know it is essential to be able to effectively handle the large volume of information while maintaining the integrity of their data. I recently read an article from EnterpriseAppsToday that discusses how data management best practices help data professionals maintain quality. 

One of the main values of data is that when analyzed, it can give companies insights that otherwise may have been overlooked. However, companies' ability to derive such insights through analysis depends in part on data quality. This point is highlighted by Priya Singh of Information Builders, who suggests that "subpar, inaccurate and inconsistent data" can be costly to businesses.

However, employing best practices will help data management professionals maintain high quality sources of information. To help achieve these goals, Singh recommends keeping a current analysis of the organization's data, including that from legacy systems, in order to locate the source of any bad or inaccurate data. 

Every organization that has a great deal of data faces the challenge of keeping quality consistent. As bad data can "pollute" an enterprise system, data management professionals may want to consider employing tactics like using a firewall or other protective measures. 

In addition to establishing a firewall to help keep bad data out from the start, data management professionals must have a clear view of all the types of data entering the system and where they are coming from. It is recommended to employ methods that pinpoint priority data sets.

As Singh concludes, strategy is the key to successfully managing data quality. With the right strategies in place, data management professionals can achieve these goals. 

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