More About Data Quality Assessment

Melissa Team | Address Quality, Analyzing Data, Analyzing Data Quality, Data Management, Data Profiling, Data Quality, Data Quality Assessment | , , , , , ,

By David Loshin

In our last series of blog entries, I shared some thoughts about data quality assessment and the use of data profiling techniques for analyzing how column value distribution and population corresponded to expectations for data quality. Reviewing the frequency distribution allowed an analyst to draw conclusions about column value completeness, the validity of data values, and compliance with defined constraints on a column-by-column basis.
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Data Quality Assessment: Column Value Analysis

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Profiling, Data Quality, Data Quality Assessment | , , , , ,

By David Loshin

In recent blog series, I have shared some thoughts about methods used for data quality and data correction/cleansing. This month, I’d like to share some thoughts about data quality assessment, and the techniques that analysts use to review potential anomalies that present themselves.
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The Format of Nothing

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Governance, Data Management, Data Profiling, Data Quality | , , , , ,

By David Loshin

The first question I always wonder about missing data is about the format of the missing data, especially in systems that predate the concept of the “system null” value. For example, early systems maintained files storing tables with fixed-width columns.
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Achieving “Proactivity?”

Blog Administrator | Analyzing Data Quality, Data Enrichment, Data Management, Data Profiling, Data Quality | , , , ,

By David Loshin

Standardizing the approaches and methods used for reviewing data errors, performing root cause analysis, and designing and applying corrective or remedial measures all help ratchet an organization’s data quality maturity up a notch or two. This is particularly effective when fixing the processes that allow data errors to be introduced in the first place totally eliminates the errors altogether.
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Four Pillars of Data Quality Improvement

Blog Administrator | Analyzing Data Quality, Data Integration, Data Management, Data Profiling, Data Quality | , , , ,

By Elliot King

Almost all data quality management programs have four key elements that serve as the foundations for success–data profiling, data improvement, integration and data augmentation. Put in other words, data quality programs must determine what is broken; fix what can be fixed; consolidate what can be consolidated and enhance what needs to be enhanced.
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