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Quality Management in Data Governance

About 80% of companies suffer income loss due to poor data quality, with annual losses ranging from $10 to $14 million.

Data quality management (DQM) is essential to address this issue, involving setting measurements, identifying error source and implementing error management strategies.

Key measurements for good data quality include completeness, uniqueness, up-to-date status, correctness, reality and consistency.

Managing reference data is crucial for reducing errors. Reference data whether internal or external should be reliable and up to date with clear responsibilities for maintaining it. Problems can arise at any stage of data handling from acquisition to processing and operations. 

An effective data error management strategy involves dividing responsibilities, defining bug reporting procedures, selecting appropriate tools, classifying errors and prioritizing urgent issues. Failure to manage data quality can lead to errors, customer complaints, regulatory risks and long-term damage to the company's reputation.

Conversely, a well-implemented DQM strategy enhances data reliability, boosts employee efficiency and improves customer satisfaction. Although maintaining data quality has costs, they are far outweighed by the benefits such as informed decision-making and operational savings. This perspective shift underscores the business advantages of high-quality data over the detriments of inaction.  

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