Getting Data-as-a-Product Right and Other Learnings From Adevinta's Data Mesh Journey - Interview w/ Xavier Gumara Rigol

Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Transcript for this episode (https://docs.google.com/document/d/14kCvtE28g6HROYU_6Lzlemz9NjFhX6QaC9O1H7ryFE0/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova?datameshradio (here) (info gated) Scott interviewed Xavier Gumara Rigol who has been helping lead Adevinta's data mesh implementation as Area Manager for Experimentation and Analytics Enablement. The discussed the data as a product concept and learnings from Adevinta's journey thus far. Xavi has put out some great articles and did a Data Mesh Learning meetup that are linked below. One key aspect to data as a product is to understand the need for data product evolution, both relative to maturity and to what is consumed. This is a common theme in many data mesh conversations as historically, data consumption has resisted evolution and change. Consumers need to really understand that the business is evolving so what they consume will too. If you manage data products well, it won't be a sudden change but if we are trying to share insights into a domain, those insights will change. When thinking about data product maturity, it's totally okay to start by thinking of a data product as a single table or view. Xavi also mentioned some pitfalls to forced data product evolution - e.g. getting it wrong as changes can be quite costly to backfill. Adding new attributes is easy but computing something for 3 to 6 months in hindsight can cost a lot of compute charges. To do do evolution right, versioning and deprecation plans are key. To get data as a product right, Xavi recommends start by prioritizing which data you want to make available; this is a process, not a switch to flip. You should figure out which data is important for each domain and at the broader organization level. Applying data as a product thinking to your data sets is easier said than d