Establishing a Data Quality Baseline
“If you cannot measure it, you cannot improve it.” This quote by Lord Kelvin is as relevant today for data quality as it was when it was first written in 1883 regarding sciences in general.
Many companies have no idea about the quality of their data. Most of the time they recognize their issues, but they don't realize the extent of those issues. Furthermore, they continue to make wrong assumptions and incorrect business decisions because they are relying upon incorrect information.
What companies need is an assessment of the quality of their information combined with a strong process for continuous improvement. The first step is to get situated by establishing a data quality baseline. This section will discuss what needs to be considered when creating this baseline, while Chapter 8 will describe a data maintenance model for continuous data quality measurement and improvement.
Many companies fail to scope their data quality projects with an emphasis on business needs. It is not data quality for the sake of data quality. It must serve a purpose, and as such, the business must be driving what needs to be measured—and why—on a traditional top-down approach.
It is easy for business teams to get overwhelmed with their day-to-day activities and lose sight of data quality improvements because they may not see immediate benefits. It is not uncommon for data quality projects to take a while before they start showing results. That's why a culture of emphasis ...