Data Mesh Grand Challenges - Closing the gap between concept, implementation, and business impact

“A new world is not built by discarding the old, but by reshaping its enduring truths into novel forms." — Anonymous

The concept of data mesh was introduced over 5 years ago, but in the age of GenAI and ML, it is even more relevant than ever. Since then, data mesh and its more narrow derivatives, such as data product management, have become hundreds of companies' “next new” data strategy globally. We have learned that despite the global adoption of data mesh vernacular, large-scale implementations remain rare, and transformations are complex and costly. The underlying reason is that data mesh introduced a radical change in how companies and teams have managed data for decades, fundamentally changing technology, processes, and organizational structure. Making it even harder, adopters lacked the right tools to experience the new concepts that data mesh defines and depends on, e.g., data products and computational policies. In this session, we examine the challenges many teams face in transitioning to data mesh. Then, we surface the hacks, workarounds, and antipatterns that we have seen in the adoption of data mesh. Lastly, we discuss a new holistic solution to help adopters of decentralized data strategies like data mesh reach escape velocity—overcoming the inertia of their legacy data stack, processes, and culture. Data mesh has been hard. But it doesn’t have to be, and this talk will illustrate how data mesh can simplify many challenges of data management –including recent GenAI.