Are you responsible for Data content or quality? If so, perhaps you could help brainstorm how data producers/consumers can reduce Data Regression…I suggest 4 ways below, but I'd like to hear from the community so please share your thoughts below if you have creative ideas to share!
Data Regression is the unintended loss in one or more of the 4C's of Data Quality (Completeness, Correctness, Coverage and Currency) usually as a side effect of a data quality improvement or data update activity. Flaws in a data production/update process can cause Data Regression, and you might incur serious losses in data quality without even knowing it's happening. Whether you are a data producer or a data consumer simply updating data you acquired, you are at risk and should take actions to detect or prevent it!
How does Data regression occur? A common occurrence could be one of your project teams losing sight of their impact on the existing data – and data that already possessed good quality becomes compromised. Typically, existing data that might be outside the scope of the project (for example, other features that are spatially related to the project scope or joined to the project scope by means of an ID) are damaged. For example, a project team responsible for improving water polygon completeness can successfully introduce large amounts of spatially accurate water, but neglect to check the impact on less spatially inaccurate roads, points of interest or buildings. As a result, the data quality overall is decreased because after improving the water polygons, roads, points of interests, and/or buildings now display inside the water polygons. Damage like this can happen to virtually all data types.
How can you reduce data regression? Here are 4 ideas:
Please share your thoughts below on Data Regression and how data producers/consumers can reduce it.