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This lesson describes the motivations and mechanics of creating a data quality scorecard.
The classification of the different areas of impact of poor data quality resulted in four different impact areas. Within each impact area several different parameters of impact can then be determined to generate what is called a data quality scorecard which contains complex data metrics. These can then be analyzed using three different view points.
This post differentiates between data quality inspection and data validation.
The concept of data governance has come about because traditional methods of ensuring data quality are not living up to the expectations.
Organizing the rules of data quality into dimensions not only improves the specification and measurement of the data quality, it also provides the framework under which quality can be measured and reported.
The data input and editing process is one of the most time consuming and interactive processes that can be prone to many data errors.
Reserve requirements are the amount of funds that a bank must hold in reserve against specified deposit liabilities.
