Mar 18, 2021

How banks can achieve the promise of open banking with a data mesh architecture

Mohammad Syed

Mohammad Syed

How banks can achieve the promise of open banking with a data mesh architecture

The changes that banking is set to undergo over the next decade will be both radical and fundamental. CDOs should use this opportunity to drive greater organisational agility through agile approaches to enterprise data architecture.

Open, agile, and integrated

The banking market is shifting towards modularisation, with many small companies providing specialised services as part of a larger banking network. Within this network, financial service providers are choosing to integrate with other providers’ offerings, rather than having complete control over the supply chain. This shift from the vertically integrated behemoths of the late 20th century towards specialisation affects not only commercial and business models, but the fundamentals of data and IT architecture and how these services are delivered.

Consider a future scenario in which customers are happy for their information to be shared freely between organisations in return for financial benefits and tailored advice. In this scenario, banks would need to be able to continuously imagine, build, and deploy data-driven services to remain relevant and continue providing unique value in this highly connected world. At the same time, their data and IT architecture would need to continuously evolve at pace.

Achieving this outcome requires the organisation to shift in mindset from ‘tactical vs. strategic’ to ‘continuous’. They will also need to consider the adoption of Agile and DevOps methods to continuously reinvent services and capabilities. Given that open banking is now backed by a new regulatory regime in the UK, this change is inevitable. Banks will be required to rapidly integrate new technologies such as APIs, Cloud, and Advanced Analytics into a dynamic, data-driven architecture.

To accomplish this, larger banks might consider strategic partnerships with FinTechs to tackle the threat of ‘BigTech’ inserting itself between them and their customers. Integrating with cutting-edge FinTech platforms that have a relentless focus on experience, convenience, and low operating costs can allow the banks to remain competitive as they struggle to transform. As a result, they will be required to achieve much greater fluidity when augmenting their data and IT architecture.

Read next: How a data 'A-Team' can help overcome systematic changes in banking

Agile data architecture

At a holistic level, banks need to shift towards greater agility across all layers of the enterprise architecture. They need to adopt a ‘test & learn’ mentality towards innovation, backed by Agile and DevOps.

In the world of data, this means transitioning from an enterprise data warehouse or data lake-centred architecture towards a data mesh model. In a data mesh model, the data architecture is decentralised into independent, interoperable, and business-owned data products which operate as microservices. While each data product is built to common standards of quality and interoperability, each product’s architecture - from storage types to data models - is optimised for its domain-specific business use case and can deliver value more precisely than a centralised data architecture.

This continuously improved, modular architecture delivers rapid innovation across the value chain and introduces new delivery models and technical skills into the business. In addition, disciplines such as data governance - which are traditionally seen as blockers - are achieved through product delivery rather than through committees and abstract artefacts.

Some key features of the data mesh include:

• A culture of business accountability: Instead of data being ‘someone else’s problem’, the business users themselves take accountability for the data that they need and use.

• A decentralised data & IT architecture: Monolithic platforms are replaced by an ecosystem of products enabled by self-service technology and a continuous, highly engaged model of data governance.

• A ‘best of breed’ approach to design: Data acquisition and egress is closely aligned to the business domain driving demand, with architectural components highly optimised against the intended business usage.

• A ‘true business ownership’ delivery model: Product design, delivery, deployment, and ongoing ownership is decentralised to business-led teams that collaborate with customers to create innovative solutions.

• A mindset of agile team formation: Multi-disciplinary, agile teams deliver at pace using DevOps and other best practices for product delivery and gradually teach the business to deliver value on its own.

• A model of self- service infrastructure: Self-service approaches are adopted towards Cloud and on-premise infrastructure, as well as the common services to monitor, manage, and secure it.

• A focus on gradual improvement: Enterprise maturity in cloud and data & analytics is achieved through continuous improvement and the gradual introduction of new services, such as Telemetry.

This approach can transform the organisation into a data-driven, digitally-enabled enterprise by enabling rapid and business-led delivery of bespoke data & analytics solutions at scale. To support this, the data mesh paradigm needs to be championed by an impactful data strategy that informs business leaders on how this new approach to data architecture can drive rapid transformation.

Start with strategy

The business landscape is evolving quickly and as a result, it is difficult to maintain rigid three-to-five-year plans. Instead, CDOs need to institute practices that are grounded in Agile, DevOps, and CI/CD.

We recommend that CDOs maintain a coherent data strategy to integrate ideas that excite the business, with a new architectural vision centred on agility and value delivery. The data strategy should be used to establish a coherent vision that generates excitement and gains real buy-in from the business.

CDOs should identify pilot candidates and use ‘hackathons’ and innovation labs to introduce a ‘test and learn’ approach to innovation, including new architectural patterns and delivery practices. In addition, CDOs should use this engagement to explain the nature and value of multi-disciplinary teams and clarify what business accountability for data products truly entails over the long-term.

The data mesh concept should be used by CDOs to test new ways of delivering data & analytics solutions at pace and at scale. At the same time, the concept can help them realise how to provide enterprise-wide services in data management and infrastructure management that enable rapid delivery. This is particularly important given that previous ‘paradigm shifts’ may have failed to deliver on their promises.

In a nutshell

In our view, the data mesh concept will be key to realising the architectural agility required by digitally-enabled banks to provide data-driven services in the banking ecosystems of the future.

CDOs should engage business leaders and generate excitement with a new architectural vision that is centred on product delivery and an increasingly open business model. Using a lightweight but compelling data strategy will help them to gain additional buy-in from business sponsors. If you would like to learn more about the topics discussed in this blog, please get in touch with one of our data experts.


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