Recently, we attended a financial data industry event and listened to one company after another share their experiences in their data governance and management journey. That day, we learned that only a few had moved forward with a data mesh save for leaders in the industry. Those leaders shared their implementation stories, and we will share this question with you. What came to our minds at the time was whether Data Mesh was a unicorn in the investment industry. Is the self-service data platform a myth or a reality? With that question in mind, we spoke to several firms at the gathering and interviewed others afterward to learn about the data mesh ‘state of the union’ in the investment industry.


We gathered information from multiple sources: the industry event, interviews with clients, our professional networks, and publications. Our research generated insights into the adoption patterns and data strategy goals for different market segments.

Small firms (Hedge Funds, PE Firms, OCIOs, and Small Asset Managers Under $50B)

After speaking with over twenty small firms, we found that none had implemented a data mesh architecture or business governance strategy. Many of these firms had strong data management and governance, controls, and IT teams that provided the required data to the investment teams. However, we found these firms were focused on migrating to the Cloud, leveraging data lakes and data warehouses, and modernizing their data pipelines. The majority of firms lacked maturity in either (or both) data management and architecture to even consider a move to a data mesh.

Medium Firms – Where data mesh begins to emerge (Firms with up to $300B in Assets)

We also spoke to several medium-sized firms during the gathering and in follow-up research. Results were slim when searching for data mesh. While one contact who was the head of a data governance program in a mid-sized investment asset manager admitted he needed a refresher on what a data mesh was, a few leaders in this group had implemented (or were implementing) a data mesh framework. However, a significant margin appeared between them and the rest of the companies in this group. Most of the firms had an element of data management and governance established in data quality, accountability, and observability. In contrast, others had little to no formal data governance, management, or observability.

Large Firms – Of which, a few caught a unicorn (Firms with >$300B in Assets)

We interviewed and researched four large firms and found two were moving into or had partially implemented a data mesh program. Our research showed that larger firms with diverse, hybrid data ecosystems typically include a mix of legacy and contemporary data architectures. A mesh architecture provides a path forward to service the needs of the business while tending to the modernization required to transform the data foundation. Mesh implementations have been mainly targeted in the front office, enabling research, portfolio management, and trading efficiencies, or organized and aligned to servicing investment operations, performance measurement, reporting, and business development functions. The other two firms are prioritizing upgrades to data pipelines, modernizing tech stacks, and bringing down the cost of their tech stacks.


While mesh is an important, enabling architectural and conceptual framework, you must recognize that you own the layer and all the code and kit. Thus, it requires proper IT governance and support as a critical component of your data ecosystem. It’s not free.

We believe there are important prerequisites to successfully implementing a data mesh architecture:

  • Having complete buy-in and support from top to bottom
  • A legacy data management structure that is at the end of its usefulness
  • Having general alignment and business understanding of data ownership
  • A mature and effective governance and quality framework.

Our study demonstrated that using data mesh architecture in the investment industry requires a strong commitment to mesh principles, a concrete and durable data strategy, and a higher level of sophistication and maturity in data management, governance, and architecture. Data mesh is certainly not a unicorn in the investment industry. It exists in the firms at the higher end of the data management and architecture maturity model.

Regardless of where you are in your data journey, benchmarking yourself to the industry can provide insight and direction in setting the data strategy for your future. Contact the Meradia Data Practice to arrange a data strategy review and/or a data maturity assessment.

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Andrew Jacob, CFA

Andrew Jacob brings a well-rounded data management perspective to Meradia’s global portfolio of clients. Throughout his career, Andrew has supported constituents across the investment management industry, including retail investors, advisors, wholesale teams and institutional investors. He is a seasoned investment professional with diverse experience in business and entrepreneurship, possessing strong acumen of front office operations, data governance and management, and retail client journeys. Andrew’s passion for helping people and driving outcomes aligns with Meradia’s principles of Perspective, Passion and Impact. 

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