Discover how the Data Mesh concept has evolved over the last 5 years and how it may fit your organization
- September 16th at 4 pm CEST
- Live - Ask questions at any time!
Five years ago, Zhamak Dehgani introduced the four principles of data mesh, offering organizations a framework to overcome various recognizable data challenges.
These principles emphasize the importance of decentralizing the data practice by shifting data ownership to domains, providing self-service data infrastructure, and introducing product thinking into data management. According to Dehgani, less reliance on a centralized team improves scalability, drives innovation speed, enforces trust in data, and ultimately results in a more efficient and data-driven organization.
However, this all started with the concept. Since its introduction, many organizations have experimented with the data mesh setup and made minor adjustments.
If you want to dive into the current state of data mesh, register for a webinar and learn:
- Leanings of early adopters
- What versions of data mesh we see in practice
- When it is relevant to implement a data mesh structure in your organization
- Do’s and don’t’s to implement data mesh successfully
Our Speakers
Niels Zeilemaker, CTO Xebia Data
Niels is the CTO of Xebia Data. At Xebia Data, Niels is responsible for defining the tech stack used at clients, amongst other tech stacks supporting the data mesh structure. Niels has worked for many companies, ranging from small startups to big (global) corporates.
Steven Nooijen, Head of Data Strategy
Steven is the Head of Data & AI Strategy at Xebia Data. He has over 10 years of consulting experience with the challenges and growth that organizations face in their data journey and together with his clients he has condensed these learnings into a framework called "The AI Maturity Journey".
Kiki Boonen, Data Strategist
Kiki is a Data Strategist at Xebia Data. She specializes in advising companies on how to position and organize data to leverage it efficiently. Her expertise includes assessing whether data should be centralized or decentralized and understanding the broader implications for the organization and its operations.