Put Your Eggs in One (Loosely Affiliated) Basket & Win Big: How Data Meshes Transform Banking

As digital banking attracts a growing customer base, banks are reassessing how traditional data management approaches may impede their scalability.

Tech Innovation Meets Strategy

Banks are now increasingly leveraging tech like AI and the cloud – but are they adeptly handling the integration of such innovations with the vast amounts of data they have? And, more to the point, are all the functions in the company empowered or even prepared to autonomously manage their data?

The data mesh architecture presents a transformative opportunity for the banking sector, revolutionizing data management, but it also necessitates a cultural shift within organizations.

However tricky this may seem, according to McKinsey, it’s certainly worth it, for a well-executed data mesh can “speed time to market for data-driven applications and give rise to more powerful and scalable data products.”

What is Data Mesh & How Is It Used in Banking?

Picture this: Your bank operates across diverse regions and offers a wide range of financial services. As your clientele grows ever larger, you begin to face challenges in effectively leveraging your many new data assets. While your bank’s data architecture was once efficient, today’s complex data and technological advancements have created a bottleneck in your system.

To keep pace with dynamic modern data, revolutionizing data management is a must.

Data mesh is a data architecture that simplifies your approach to data by making it easier to locate, access, and leverage. By implementing this architecture, banks can address the scalability and productivity challenges posed by traditional centralized data architectures.

The data mesh concept — first developed by technology pro and author Zhamak Dehghani — embraces a decentralized system that organizes data into smaller data assets managed by specific teams or departments within an organization.

The data mesh architecture is designed around four main principles:

If the data mesh architecture is a new concept to your bank, wrapping your head around the intricacies of this architecture can take time. To help demystify how data mesh functions in a banking environment, let’s re-examine the data mesh principles in the context of banking.

7 Benefits of Data Mesh for Banks

When implemented effectively, data mesh can transform how financial institutions approach and manage their data. A successful data mesh implementation can lead to many benefits for banks, like boosted agility, heightened scalability, and greater overall resilience.

Consider the following seven benefits of data mesh for banks and other financial institutions:

1. Improved Agility & Responsiveness

Data mesh promotes domain-oriented decentralized data ownership, allowing different business units within a bank to take ownership of their data.

This decentralized approach enhances agility as individual units can respond quickly to changing business requirements. Each domain has the autonomy to make decisions related to its data, enabling faster innovation, decision-making, and adaptation to market dynamics.

In a rapidly evolving financial landscape, this agility is crucial for staying competitive and meeting customer demands.

2. Highly Scalable Data Management

Traditional centralized data architectures often become bottlenecks as an organization’s data volume and complexity grow.

Data mesh addresses scalability challenges by treating data as a product and providing self-serve data infrastructure for each domain to manage data according to their exact needs.

In turn, each banking unit is enabled to independently scale their data infrastructure based on their specific security and data quality requirements present in a given scenario. As the overall data ecosystem grows, the federated and decentralized nature of data mesh ensures that scalability without creating centralized constraints.

3. Enhanced Data Quality

The data as a product and federated governance principles of data mesh ensure the architecture is governed by clear contracts and self-service APIs.

By explicitly defining data products, data mesh fosters a culture of data quality and consistency at banking institutions. Each domain team is responsible for the quality of its data, promoting accountability across individual departments.

Improved data quality ensures banks have access to accurate and consistent financial data. By treating data as a product with well-defined standards, data mesh ensures a higher level of data quality across the organization.

4. Collaborative Interoperability

Data mesh ultimately encourages a more communicative and aligned business environment.

To build better organizational connections, data mesh leverages a collaborative approach by treating data as a product that can be accessed via well-defined interfaces. Different business teams within a bank can consume and contribute to data sets through clear tools and APIs.

As a result, data mesh fosters interoperability across an organization, allowing different units to share and integrate data seamlessly. In banking, where various departments need to collaborate to make informed decisions, data mesh eliminates data siloes to facilitate a more cohesive and integrated data ecosystem.

5. Autonomous Data Infrastructure Management

The self-serve data infrastructure offered by data mesh breathes new independence into banking domains. Each domain team has the freedom to manage its data infrastructure autonomously, heightening each team’s sense of ownership and accountability over their data.

This ownership often translates to closer attention paid to domain-specific tasks, such as data quality and compliance. By decentralizing these responsibilities, banking units can tailor their data infrastructure to their specific requirements, reducing dependency on a centralized data team and accelerating decision-making processes.

6. Empowered Business Teams

Data mesh empowers individual teams within a bank to take control of their data. This empowerment extends to decision-making, governance, and innovation related to data.

Compared to a centralized data team, teams within a specific domain are better positioned to understand the nuances of their data requirements and proactively address challenges. Data mesh enhances their sense of ownership and responsibility, fostering a culture of continuous improvement, innovation, and adaptation.

In the banking sector — where different departments have diverse data needs — empowering domain teams is crucial for achieving efficient data management.

7. Federated Computational Governance

The federated governance principle of data mesh distributes decision-making authority across an organization while maintaining a set of common standards and practices to follow. With regulatory compliance becoming more and more complex for banks, the federated approach to governance ensures each department can manage data according to the highly specific regulations and compliance requirements imposed upon them.

Obstacles to Implementing Data Mesh for Banks

Although the data mesh architecture presents compelling benefits for banks, the implementation process is not without challenges.

Financial institutions face several obstacles when adopting this decentralized approach to data management. Understanding and addressing these obstacles is crucial for not only successful implementation but for building a more adaptive and collaborative business culture.

Let’s take a look at the major data mesh implementation challenges for banks:

Navigating a Data Mesh Implementation Successfully as a Bank

Implementing data mesh as a bank is no small task — but it can reap mighty rewards.

Data mesh in banking represents a paradigm shift in how financial institutions approach and manage data. With the right mix of technology and talent, data mesh can provide benefits like improved agility, scalability, data quality, collaboration, and autonomy.

By embracing the principles of data mesh, banks can navigate the complexities of the modern banking landscape with greater efficiency and innovation. The decentralized nature of data mesh aligns well with the dynamic and diverse requirements of the banking sector, making it a promising approach for transforming data management practices in the industry.