A scalable data product operating model for a large financial institution

How esynergy helped a global financial institution establish a scalable data product operating model

A global financial institution recognised the need to move towards a data product operating model as part of its wider data transformation strategy and adoption of data mesh. The organization wanted to improve how data was owned, governed, managed and reused across the business to increase consistency, quality and reuse.

The organization had made a strategic decision to adopt a data product operating model, but turning that ambition into practical reality presented a significant challenge.

While data product principles were gaining traction across the organization, there was no consistent framework for ownership, governance, prioritization or delivery. Different teams had different interpretations of what a data product was, who should be responsible for it and how it should be managed throughout its lifecycle. As a result, there was a risk that adoption would become fragmented, making it difficult to scale successfully across the enterprise.

To address this, our client's enterprise data management function engaged esynergy to define and validate a data product operating model through a series of live pilot implementations. Rather than developing a framework in theory, the objective was to test the model through real delivery, generating practical insights and recommendations that could support wider adoption across the organization.

esynergy worked alongside business stakeholders, technology teams and data leaders to establish the foundations needed to operate data products effectively.

The engagement centred on five pilot data products spanning multiple business domains. Through a combination of workshops, coaching and hands on delivery support, we helped pilot teams align on objectives, clarify ownership responsibilities and establish governance structures that could support long term success.

Alongside the delivery teams, we defined operating model principles, decision making processes and accountability frameworks that provided greater clarity around how data products should be managed. We also supported data product managers and product owners in building roadmaps and backlogs, helping teams adopt product management practices that could be sustained beyond the pilots themselves.

As the pilots progressed, we worked closely with senior data leadership to identify recurring organizational challenges, capability gaps and scaling considerations. This ensured that lessons learned from individual initiatives could be translated into recommendations for enterprise-wide adoption.

By combining strategic operating model design with practical implementation, the organization was able to test and refine its approach in a real-world environment, creating confidence that the model would work at scale.

Through the pilots, ownership and accountability structures were defined more clearly, governance practices were established and product management approaches were embedded within delivery teams. Common themes, risks and opportunities emerged across the different domains, creating valuable insight into the organizational changes needed to support broader adoption.

The programme also resulted in a set of repeatable frameworks and operating model components that can be applied consistently to future data products, supporting the organization's wider move towards data mesh. Most importantly, the organization gained a practical roadmap for scaling the model beyond the initial pilot phase, supported by evidence gathered through real delivery rather than theoretical assumptions.

The engagement delivered far more than the successful implementation of five pilot data products. It provided a practical blueprint for scaling data product practices across the enterprise.

By establishing clearer ownership, stronger governance and more consistent ways of working, the organization is better positioned to accelerate the delivery of reusable data assets, improve collaboration between business and technology teams and reduce duplication of effort across domains.

Most importantly, the organization now has proven approaches, practical lessons and real-world evidence that can be used to accelerate its broader data mesh and data product strategy. With a validated operating model in place, it can move forward with greater confidence as it scales data product practices across the business.