3.6 Summary
■ Data quality is defined in terms of accuracy, completeness, consistency, timeliness, believability, and interpretabilty. These qualities are assessed based on the intended use of the data.
■ Data cleaning routines attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data. Data cleaning is usually performed as an iterative two-step process consisting of discrepancy detection and data transformation.
■ Data integration combines data from multiple sources to form a coherent data store. The resolution of semantic heterogeneity, metadata, correlation analysis, tuple duplication detection, and data conflict detection contribute to smooth data integration.
■ Data reduction
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