Data Quality Dimensions Can Raise Practical Concerns

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Quality, Data Quality Assessment | , , , ,

By Elliot King

As everybody knows, data quality is usually measured along seven dimensions–the four Cs of completeness, coverage, consistency, and conformity plus timeliness, accuracy and duplication. And the general method to judge data quality is to establish a standard for each of these dimensions and measure how much of the data meets these standards.

For example, how many records are complete; that is, how many of your records contain all of the essential information that the standard you established requires them to hold?… Read More

More About Data Quality Assessment

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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.
… Read More

You Can’t Identify the Problem Without a Scorecard

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By Elliot King

If you can’t measure a process, you can’t improve upon it. That’s one of the ironclad quality initiatives. Edward Deming’s revolutionary insight was that if you can measure a process or outcome continually, you have created the opportunity to improve it continually.

 If you can’t measure a process, you can’t improve upon it. That’s one of the
ironclad quality initiatives.… Read More