The goal of a Data Quality assessment is not only to identify incorrect data but to also estimate the damage done to the business’s processes and to implement corrective actions. Many large businesses struggle to maintain the quality of their data.
It is important to remember data is not always in storage and static but gets used periodically. After being created, data becomes downloaded, adjusted, reformatted, exchanged, and even destroyed.
If done incorrectly, each action comes with the threat of having a negative impact on the data’s quality. In turn, poor Data Quality may result in bottlenecks and often negatively affects the decisions an organization makes.