Data Governance in Data Mesh: Address the Micro and the Micro - Interview w/ Mohammad Syed

Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) In this episode, Scott interviewed Mohammad Syed, Lead Strategist - Data at Caruthers and Jackson about how data mesh governance has to be different from what we've done historically. Per Mohammad, data governance in data mesh is very different to doing governance for either a data lake or a data warehouse. The warehouse has a focus on high-level quality and usability but at the expense of context and agility. Data lake is about metadata and lineage but at the severe expense of usability - schema on query is not fun for consumers - and often quality. For most data organizations, governance has been very macro focused - governing the data warehouse or lake as a whole. That is part of why data governance has become a major bottleneck - the focus is on the macro but the individual requests are the micro. In data mesh, governance can shift to being about maximizing the value of the data instead of mostly preventing risk. Of course, there is a balance between local maximization - the value of each data product - and global maximization - the value at the overall data mesh level. A key focus to data mesh data governance is enabling - especially enabling the domains to govern their data products. Mohammad made the point that you need to enable your domains by creating the technical and business definitions of a "good" data product. Then the governance team needs to teach teams about the quality definitions, e.g. data product consumability. There is a need for policies of course but mostly focus on frameworks to enable policy creation and enforcement - decentralize! A key point Mohammad made was: governance only works with informed governors - you must teach domains to govern properly. Transparency is key to make data governance work. Mohammad emphasized the "good" data product definition leads to the separation of data quality and data product quality. A data product might be more valuable