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.If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

The podcast discusses the importance of measuring data quality and setting up implementation metrics in Data Mesh. It explores the concept of data quality, measuring trust in data quality, and measuring data quality at the implementation level. It emphasizes the significance of trust and the need for data to be both trustworthy and useful for decision-making. The podcast provides insights on metrics such as time to detect and fix issues, compliance with quality SLAs, incident detection and resolution, and the impact of trust on implementation success.

Lauren Maffeo, author of Designing Data Governance from the Ground Up, discusses the challenges and importance of data governance. She emphasizes the need to automate standards and create a data-driven culture. Integrating data governance and automation is crucial for success.

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.If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

Jean-Georges Perrin, partner in weekly data mesh roundtables, discusses the importance of having an early thesis, building momentum, and applying software engineering practices to data. The podcast explores topics such as data governance, data quantum in data products, building analytical APIs, local vs global maximization of value, and the challenges of implementing data mesh.

In this podcast episode, Abe Gong, co-creator of Great Expectations and CEO of Superconductive, discusses the importance of implementing data contracts. Topics include preventive changes in data, effortlessly propagating data system changes, creating and managing data contracts, monitoring data quality, and the challenges in achieving fluidity between data consumers and producers.

Scott Hawkins, Principal Data Architect at ITV, discusses organizational challenges in implementing data mesh and strategies for gaining buy-in from domain teams. Topics include implementing data mesh techniques, bringing together domain owners, delivering products efficiently, challenges and integration, overcoming obstacles, and the importance of persistence in data mesh.

The podcast discusses strategies for driving buy-in and aligning incentives for implementing Data Mesh, explores the concept of data contracts and setting expectations, emphasizes the importance of collaboration and expectations in data products, and explores the concept of minimum viable platform and data products in Data Mesh.

Carlos Saona, Chief Architect at eDreams ODIGEO, shares unique implementation and key learnings from eDreams Odigio's approach to Data Mesh. Topics include iterative development, division of responsibilities between data consumers and producers, implementing DataMesh, defining single entity for customers, benefits of small data products, feedback loops and iteration, and use of streaming and batch processing in data analysis.

Data expert Ammara Gafoor discusses driving buy-in and data product discovery in data mesh implementation, emphasizing collaboration with business people, addressing challenges faced by IT and business stakeholders, and incentivizing adoption. The importance of metrics and prioritization, building foundational source data products, and understanding manufacturing KPIs are also explored. Challenges of existing 360 solutions, the need for tailored information, and the importance of getting started with data projects are discussed.