In my last three posts, we walked through a thought experiment about the decision-making process, with the conclusion that a method for ensuring the quality of report data used to make decisions will highlight the value of those individuals whose instincts and experience allow them to generally make good decisions. That being said, what is necessary to ensure the quality of reported data?

A comprehensive answer to that question is beyond the scope of what can be conveyed in a single blog entry. However, let me suggest some key criteria:

1)    Knowledge of data lineage – the quality of reported data is critically dependent on the quality of the data used to source the data warehouse and generate the report. That requires managing data lineage and understanding the quality characteristics of the original sources.

2)    Qualifying consumer data expectations – data usability is cast in the context of how that data is to be consumed. Soliciting input from the consumers as to how the data is used to make decisions will help you identify any business rules for data compliance that must be validated as part of the data provisioning workflow.

3)    Validation scoring – if you know the quality requirements, there is a corresponding requirement to institute measures for assessing the degree to which the data sets comply with those requirements. Presenting the quality scores along with the report establishes the report’s usability.

Does improving the quality of data really lead to improved decisions? That specific question may be hard to answer. However, ensuring the quality of reported data certainly eliminates data quality as an excuse for not making decisions.