5 Best Practices for Data Quality Management
Updated · Sep 25, 2015
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.
Sean Michael is a writer who focuses on innovation and how science and technology intersect with industry, technology Wordpress, VMware Salesforce, And Application tech. TechCrunch Europas shortlisted her for the best tech journalist award. She enjoys finding stories that open people's eyes. She graduated from the University of California.