Data Quality Challenges and Priorities

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ADDRESSING TODAY'S TOP DATA QUALITY ISSUES

Maintaining data quality has always been a top issue for enterprises, but with changing data needs and business environments—including big data, unstructured data, and data governance—it’s never been more challenging. We look at the top issues that enterprises are asking about data quality with Anne Buff, business solutions manager and thought leader for SAS Best Practices.

TDWI: How are industry leaders using data quality to advance business strategy?

Anne Buff: Organizations that design their data management strategy within the context of overarching corporate initiatives are leading their industries, often with large gaps. While there are many great data quality best practices we can learn from these companies, they often share three common elements in their approach:

Designed process. Data quality does not have a one-size-fits-all template—not even within an organization. Designing data quality rules, policies, and procedures around the needs and culture of the business is essential for buy-in and long-term support from the organization.

Business metrics. Metrics-based measurement is an understood management success factor. When it comes to successful data management, though, it is imperative that metrics are business based, not technology based. Data management metrics should have specific, measurable business outcomes and articulate value in at least one of the following areas: increased productivity/efficiency, regulatory compliance, reduced cost/complexity, and decreased risk. Simply put, executives listen when programs make money, save money, or keep them out of jail.

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Date: 20 June 2016, 8:16 am   |   Provider: SAS Institute GmbH   |   Size: 1.06 MB   |   Language: English
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