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5 Best Practices for Data Quality Management

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Posted September 25, 2015 By Staff     Feedback
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    5 Best Practices for Data Quality Management

    Here are five best practices for data governance and quality management that are being leveraged by companies that have successfully achieved -- and benefited from -- peak data quality in their enterprise.

All companies struggle to manage the cyclical data quality process. 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.

The significant amount of revenue that is lost to bad information compels a shift in data quality strategies from occasional data cleansing to an ongoing cycle of data quality created by incorporating governance plans. Data governance is a continuous quality improvement process, embraced at all levels of the organization, to filter bad information by defining and enforcing policies and approval procedures for achieving and maintaining data quality.

Data governance is everybody’s business, which is why several best practices involve getting business users involved with data quality initiatives.

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