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BiPM ENCYCLOPEDIA →
Business Intelligence →
SECTION - Data Quality →
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CHAPTER - Data Quality Overview
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Before one gets onto working on data quality, one has to appreciate
on what is data quality?, why is it so important? and what are the
reasons for data quality failures?
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Topics
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Data quality is not linear and has many dimensions like Accuracy, Completeness, Consistency, Timeliness and Auditability. Having data quality on one dimension is as good as 'no quality'.
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Data quality should not be perfect, but should meet the expectation of the user. Data quality pursuit has to pass the test of business case. Data Tolerances, leniency on non-financial data and historical data are some examples of staying with imperfect but acceptable data quality.
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The reasons for bad data quality include fast changing business dynamics, systems management gaps, un-controlled application proliferation and people issues. Before one looks at how to improve data, its prudent to understand the root cause, or the events, which result in data quality deterioration or brings out the existing data quality issues.
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Data Quality impacts range from a pure transaction level loss up to catastrophic impact for an enterprise.
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All Chapters in "Data Quality." Section
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