Currently I'm on a data quality project at my company. So far I have been told what to do. Been doing data profiling, completeness checks and automating business rules tests with SQL.

I would like to know what things I can do (asides what I'm already being told to do) that would be a great value-add for a data quality project.

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Data quality. Your rule based business tests in SQL, any of that can be forecasted? How about predicting where it will be and preventing that from happening, before it happens?

To make that happen, you’ll do a predictive model. Perhaps timeseries. Start with a linear regression and go deep

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Thanks for your suggestion. Just saw an article on collibra that elaborates on this idea and it seems to be a great idea. Will play around with it hands-on and see what I come up with.

If you’re a consultant and the client didn’t pay for it why would you do it?

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I would do it for personal interest. Package it and offer it up to the client with a price menu. If they like it, charge of it and get a raise.

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I would see what the impact is of failures. Do stakeholders need to be notified? Could you do something about that (powerapps notification)

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That depends on what your project encompasses. But anything that you are doing now, any processes you are building can always be standardized and automated. Make it so that they dont have yo hire another consultant to do this again

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Completeness, Correctness (wrt schema, master data etc), Timeliness, Drift (if the data is consumed by ML). You can go one level up and slice-and-dice the errors by categories and show their distribution along different axes. If your tool allows it, show Lineage changes as well.

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Timeliness could mean freshness and in some scenarios - downstream applications having the right data when they need it. E.g. weather data is needed every hour but the table shows it was last updated 5 hours ago. For lineage, you can check out Databricks Delta live tables and Unity Catalog which work well with lakehouse. There should be other tools as well.

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