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Data, owned by the teams who use it.

Retail banking Data architecture

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01 / Retail Bank: Data Mesh

A large retail bank modernised its data on Google Cloud and adopted a data mesh-inspired approach, so that departments could reach and analyse the data they need with far more autonomy.

01 / The Situation

A bank modernising how it holds data.

The bank had already been using cloud technology to modernise its data infrastructure, and had begun to explore strategies along data mesh lines.

The aim, as the study describes it, was a more integrated approach to managing data across the whole organisation.

The starting point

  1. 01

    Cloud already under way

    Modernisation of the data infrastructure had begun on cloud technology.

  2. 02

    Data mesh in view

    The bank was exploring data mesh-like strategies, in which data is owned by the domains that produce and use it.

  3. 03

    One integrated approach

    The goal was a more integrated approach to managing data across the organisation.

02 / The Work

Google Cloud, BigQuery and a mesh mindset.

  1. 01

    Partner with Google Cloud

    The bank partnered with Google Cloud to migrate and manage its data.

  2. 02

    BigQuery as the warehouse

    BigQuery became the cloud data warehouse, optimising how data is stored and processed.

  3. 03

    Data within reach

    The data mesh-inspired architecture made it easier for teams to access and analyse the datasets they need.

Fig. 1Two ways to hold a bank's data

A comparison of a centralised model, where every department's question goes through one central team, and a data mesh, where each domain owns its data products on a shared platform.

Illustrative diagram of the data mesh principle by Saizen Acuity. The domains are examples, not the bank's structure.

03 / The Outcome

Faster decisions, closer to the customer.

No figures were published for this case. The study reports qualitative outcomes, so that is what is shown here. We do not estimate numbers the client did not publish.

  • Data democratisation

    Qualitative

    Departments work more autonomously with their data, supporting innovation and faster decision-making.

  • Enhanced customer insight

    Qualitative

    More integrated analysis improved the bank's understanding of customer behaviour, enabling more personalised services.

How we grade evidence Result · Projection · Target · Qualitative · Scope

04 / What It Shows

What other leaders can take from it.

  1. 01

    Data mesh is an operating model as much as an architecture: ownership moves to the teams closest to the data.

  2. 02

    A managed cloud warehouse takes infrastructure work off the table, so the effort goes into making data usable.

  3. 03

    The test of the model is autonomy: whether a department can answer its own questions.

· / Source Record

Where this comes from.

Every statement on this page is taken from the original case study. Where the study is silent, this page says so rather than filling the gap.

Last reviewed
20 October 2026
Figures
None published.
Figure 1
Illustrative unless its caption says otherwise.