Data Mesh with Emily Gorcenski | Speaker interview
Emily Gorcenski is the Head of Data & AI for Thoughtworks Germany and one of the key note speakers at Business Architecture Convention in November. Emily’s background in computational mathematics and research engineering has led her to solve interesting problems across a wide range of industries, from spaceflight to retail and medical devices. Most recently, she has been working on Data Mesh theory and practice and is using her engineering and science background to develop best practices for data product development. A passionate activist, she has also used her skills to contribute to award-winning data journalism.
How would you explain data mesh to someone who is new to the concept?
The simple way of explaining Data Mesh is that it is similar to a microservices architecture, but for analytical data instead of operational systems. In short, Data Mesh emerged because of the drawbacks inherent in centralizing analytical data work into domain-agnostic teams. Not only are these teams increasingly overworked and unable to produce results in time, but they are also not aware of the meaning of the data they are processing. Data Mesh hopes to solve that by connecting people who need data directly to people who have data.
What are the biggest challenges when going from a centralized approach to data, to data mesh and decentralization?
The biggest challenge is a shift in organizational mindset. Data Mesh turns a lot of conventional wisdom on its head and also puts a lot of responsibility (e.g. data quality management) into teams not accustomed to having that responsibility. This means we need to make these processes simpler through automation, well-designed self-service platforms, and also education and upskilling. This is a lot of work up front, and it’s not easy.
“…we are only barely scratching the surface of what we can do with data”
You will participate at Business Architecture Convention in November. What do you wish for the participants to take away from you session?
My main hope with Data Mesh is that people start to truly understand data as a strategic business asset. I hope that the attendees start to understand that we are only barely scratching the surface of what we can do with data, and that a strong data mesh architecture will unlock possibilities for analytics, AI, and experimentation that go way beyond the conventional use cases for data. The main message is that what we do is limited by the architectures we have, and with a new approach, we’ll soon be seizing opportunities we never knew we had.