#16 Data Quality’s 4 Horsemen: Omission, Waste, Divergence, and Downtime – Interview w/ Chad Sanderson

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Chad’s contact info:

LinkedIn: https://www.linkedin.com/in/chad-sanderson/

csanderson.data at gmail.com

In this episode, Scott interviews Chad Sanderson, Head of Product: Data Platform at Convoy. This episode is part of our continuing series on data contracts and related topics. Chad covers a lot of the challenges relative to data quality, both in maintaining quality and in the challenges poor quality data can cause a company that is heavily reliant on data.

Chad also shares his tale of trying to implement data mesh at Convoy via a large-scale inverse Conway Maneuver.

Chad covered 4 categories of data quality pain, which he calls the “4 Horseman of Data Quality” in this post:

Omission – metadata is missing; no tool out today that solves the omission problem, so users have to bounce between too many tools to try to figure out data specifics like where it came from, the specific meaning, what it’s trying to convey, etc.

Waste: growth of unused, unmaintained, or duplicated data; waste happens when the cost of creating new data is less than using something already created

Divergence: the growing divide between what’s going on in “the real world” and what’s happening in your data warehouse; your business logic, unless it is constantly maintained and updated, starts to diverge from what is happening to your business so what you show on dashboards and reports no longer matches business reality

Downtime: periods of time where the data is missing, wrong, late, etc.; traditionally what most people think of regarding data quality issues

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