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

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Chad's contact info:LinkedIn: https://www.linkedin.com/in/chad-sanderson/csanderson.data at gmail.comIn 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 createdDivergence: 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 realityDowntime: periods of time where the data is missing, wrong, late, etc.; traditionally what most people think of regarding data quality issuesData Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereAll music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman):