Data Mesh is a paradigm shift in big analytical data management that addresses some of the limitations of the past paradigms, data warehousing and data lake. Data Mesh is founded in four principles: "domain-driven ownership of data", "data as a product", "self-serve data platform" and a "federated computational governance".
In our webinar series we will have a closer look into each of the four principles, highlighting their benefits but also challenges that you might encounter while implementing Data Mesh in your company. Register today and hear more about Data Mesh and its four core principles.
In this talk Zhamak Dehghani explores the principle of "data as a product" and describes how this simple change in perspective has a deep and profound impact on how we collect, serve and manage data; how we treat the data consumers as customers and how we provide experiences that delight.
Data as a Product is one of the foundational pillars to move toward growing an innovation culture where data is readily and safely available for experimentation
In this talk Danilo and Zhamak explore the principle of "domain-driven ownership of data" and why it is a foundational pillar for data mesh. They will share approaches to tackle some of the common hard problems around identifying domain boundaries, and how to contrast data mesh with a more traditional centralized approach to master data management. Finally, they will discuss ways of starting on the transformation journey towards a domain-driven approach to data ownership.
In this webinar, Zhamak Dehghani will explain how Data Mesh addresses governance needs through “federated computational governance”. We will then have a panel discussion with Chris Ford (Head of Technology for Thoughtworks Spain), Jason Hare (Data Governance and Information Assurance expert) and Zhamak where we will relate the Data Mesh approach to data management and governance as practiced in the software industry at large.
Access to data typically requires multiple conversations, tickets and approvals. Data mesh seeks to eliminate the friction to deliver quality data for producers and enable consumers to discover, understand and use the data at rapid speed. This approach ensures compliance and security and can significantly lower the lead time to get insights from data. How do we empower individual domain teams to deliver data products, while lowering their cognitive load? How do we build a platform that enables data generalists and reduces the need for specialization?