Data Quality Rules
The chapters so far have been about understanding how to shape your data quality initiative – who should be consulted, how to win their support, and how to ensure you focus on the right areas.
Having used data discovery techniques in the previous chapter to identify critical data and identify its flaws, it is now time to define data quality rules. This moves the work into a critical phase, as the rules lead to a data quality score that, ultimately, people will judge an organization’s data against.
This chapter will help you write a clearly understandable business definition of a rule, which can then be converted into a programmatic check of data with a data quality tool. We will explore all the different features of a rule, such as rule thresholds, how they are assigned to data quality dimensions, assigning a monetary value to a rule failure, and weighting important rules over others.
In this chapter, we will cover the following topics:
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