Data Quality Dimensions and measures
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This document outlines several dimensions and measures for evaluating data quality, including accuracy, completeness, consistency, timeliness, usability, validity, and availability. It discusses calculation accuracy, relevance, confidence levels, validation methods, ranges, frequencies, sources, and perceptions of value. Structural dimensions include vertical and horizontal integrity, synchronization, and being up-to-date. Contextual measures include row counts, checksums, nullability, fill rates, duplication, update rates, and access response times.
Data Quality Dimensions and measures
- 1. Mind Mapping – DQ dimension & measures Data Quality Principals Accuracy Completeness Consistency Timely Usable + Trust Validity Calculation accuracy Relevance Confidence level of source Measures Validation(if else, pattern) Lookup Range Max/min Frequency distribution Source of reference Perception value to biz Source of data(manual entry?/ drop down ) Structural Vertical Structural Integrity(synchornized) Up to date Availability Speed Ease of use Horizontal Contextual Dimensions Row count Row checksum Nullability / missing info Full context of desc. Fill rate % for row No duplication Availability to read / update Rates of update – data decay rate Access respond time Data Presentation quality Contextual Zero duplication(structurally) column, row, dataset Referential integrity Definition consistency Naming convention Contextually Equivalence Data type, length Definition Data coverage (transact-ability + confidence) Prepared by Alex CK Yap 2010