A Brief History of Data Quality

摘要： 摘要

The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact checking and Data Governance, and, as a consequence, make decisions that give them an advantage. The purpose of ensuring accurate data is to support good decision-making in both the short-term (real-time customer responses) and the long-term (business intelligence). Data is considered to be of high quality when it correctly represents reality.

With this in mind, executives and decision-makers must consider the quality of their data, and that potential inconsistencies may result in unreliable business intelligence insights. For example, when working with predictive analytics, projections should be based on accurate and complete data. When data is not accurate and complete, projections will have only limited value, and false assumptions may seriously damage an organization. Issues to consider in Data Quality include:

1.Accessibility 2.Completeness 3.Objectivity 4.Readability 5.Timeliness 6.Uniqueness 7.Usefulness 8.Accuracy

Some organizations perform significant research and establishing good Data Quality may include developing specific protocols for research methods. These behaviors would be part of a good Data Governance program.

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