Many big companies jumped on the data mesh bandwagon, hoping it would fix the problems of traditional data lakes. Initially, it looked promising. Companies like Netflix and Intuit adopted this approach, expecting smoother data workflows and better quality. But just a few years later, the enthusiasm faded. Some now say data mesh was just a passing trend, or worse, it’s dead.

So, what went wrong? To understand that, it helps to look back at why data mesh became popular. In the late 2010s, companies heavily relied on data lakes. These centralized storage spots were seen as the ideal solution for collecting all company data in one place for analysis. But by the early 2020s, cracks started to show. The main issue was that data lakes were managed by teams that didn’t have deep knowledge of the actual data sources. This led to multiple copies of the same data, incomplete or inaccurate information, and delays when fixing errors. When source teams updated data or added new columns, everyone else had to adjust their workflows, causing bottlenecks and sometimes data loss.

This frustration pushed companies to look for better ways, and that’s where data mesh came in. It was designed to flip the traditional model on its head. Instead of a central team handling all data, each source team owned their data and shared it directly with others. This meant fewer steps in the process, less copying, and faster updates. The idea was that teams who knew the data best could keep it accurate. The hope was that this would improve data quality and reduce the time it took to get insights.

But the promise of data mesh didn’t fully materialize. Many companies hit roadblocks. One big problem was that adopting data mesh wasn’t a quick fix; it required ongoing effort and a change in mindset. Each team had to create a schema—a kind of blueprint—that downstream systems could read easily. Unfortunately, many teams lacked proper training or leadership support, leading to poorly designed schemas. This caused multiple teams to do the same work on the same data, creating duplication and higher costs. Without clear coordination, tables in the data mesh became incomplete or disconnected. Missing columns or incompatible tables meant teams still had to build their own versions or do extra transformations, pushing them back to square one.

So, was data mesh just a fad? Not exactly. It’s not a magic solution that fixes all data problems. But, when done right, it can significantly cut down on management overhead and boost data quality. Essentially, data mesh shifts how teams see and handle data. Instead of viewing data as a byproduct, teams treat it as a product that they own and maintain. This means designing datasets to meet everyone’s needs upfront, with all necessary columns and clear schemas. For example, a manufacturing dataset shared via data mesh should already include all columns needed by finance, marketing, and other departments—no extra copying or transformations needed.

That said, data mesh isn’t suitable for every organization. Smaller teams with fewer datasets might find a centralized data lake simpler and more cost-effective. For large enterprises with many teams constantly updating the same data, decentralization can be a game changer. It allows source teams to build complete, ready-to-use datasets, reducing errors and saving resources.

Companies that have implemented data mesh properly report excellent results. One major bank, for instance, saw a 45% reduction in the time needed to complete operational tasks after switching to this architecture. If your organization has the right scale and mindset, adopting a data mesh can make data more accessible and higher quality, helping your analytics teams deliver better insights with less hassle.

Inspired by

• https://www.infoworld.com/article/4080370/was-data-mesh-just-a-fad.html

Sources

• gmpg.org
• netflixtechblog.com
• medium.com
• tcs.com
• podigee.com