Figure ES-1. Structure of Data Quality Assessment Framework
Step 1. Know your customers: Enumerate the consumers of your data and the type of the data they are using i.e., original source, archived, or traveler information type data).
Step 2. Select measures: Ensure that each of the 6 data quality measures are meaningful and relevant for each data consumer and the version of data they are using. Develop a matrix that shows which data consumers use which version of data.
Step 3. Set data quality targets: Set targets for each measure based on the data consumers' needs and applications.
Step 4. Calculate data quality for unique data: Calculate the data quality measures using the procedures shown for each unique version of the data in the matrix. That is, data quality changes for different version of data, so one must calculate data quality for each significantly unique data version.
Step 5. Identify data quality deficiencies: Compare the data quality results to targets and identify deficiencies in data quality. Identify and program resources to improve data quality or lower targets to be financially constrained.
Step 6. Assign responsibility and automate reporting: Automate data quality reporting and include it with metadata. Assign data quality responsibilities to data steward(s) who ensure that data problems get fixed at the root cause and not simply "scrap and rework". Provide performance incentives based on data quality levels.
Step 7. Complete the feedback cycle: Periodically reassess your data consumers, how they use your data, and the quality targets for their applications. (Arrow returns to Step 1)
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