Building data governance frameworks in financial services: What’s working and what needs to change

Maintaining data governance is one of the biggest challenges faced by organizations across all industries today. But for those in financial services, it’s a uniquely complex issue. 

With an ever-growing range of national and international regulations to comply with, huge numbers of legacy systems to maintain and a wealth of sensitive data to safely operationalize, data governance is an issue every financial services organization is working hard to stay on top of.

Through our ongoing work with financial institutions of all sizes, Thoughtworks has built up a strong understanding of which data strategies are working for today’s organizations — and the aspects of data governance they’re struggling with. In this article, we’ll explore both. 

Challenge #1: Acquiring data and making it visible and available at scale

For many banks, acquiring, combining and visualizing data across their diverse systems remains a major barrier to robust governance and compliance.

During a recent conversation with one of the world’s largest international banks, they told us they have more than 20,000 unique systems that they manage data across. In environments like that, it’s easy to see how silos can form, limiting the accessibility of data, while making governance extremely hard to maintain at scale.

With siloed data, teams and systems that lack transparency, organizations are often powerless to enforce consistent governance. That’s a fundamental challenge that needs to be solved before they can embrace new data platforms and strategies to drive a wider transformation.

Challenge #2: Managing data governance without stifling innovation

With the use of AI increasing sharply across the sector, another issue financial services leaders face today is effectively balancing data governance with the freedom to innovate.

In traditional data strategies and architectures, striking a balance between empowerment and control remains very difficult. Some organizations are making use of advanced access control to help make data more easily available but within defined guardrails. Others, meanwhile, have had to work backward to govern and control new data use cases like AI after their teams have already started utilizing them.

Because it’s so hard to strike that perfect balance, many institutions have had to err on the side of caution and build heavyweight practices that they acknowledge can hinder innovation and make relevant data harder for some domains to access.

Challenge #3: Helping the entire organization evolve at the same pace

Perhaps the biggest challenge of all faced by today’s Financial Services leaders is inconsistency in how governance is applied and upheld. While most leaders recognize the importance of building a consistent data culture across diverse domains, many have found that hard to practically build and uphold.

Every domain has its own unique data priorities and often very different levels of data maturity. The only way to build a consistent culture across them is to bring everyone along with you as you transform your data strategy.

However, due to the siloed nature of many domains and their technology, this remains a huge barrier to effective and consistent data transformation in financial institutions of all sizes.

The good news: Most leaders have a strong idea of how data approaches need to evolve

Faced with these challenges, many leaders appear to be evolving their data strategies in similar ways by actively investing in one or more of three key areas:

• Enabling self-service: With more domains eager to access and operationalize a greater range of data sets, banks are exploring ways to empower domains to help themselves to the data they need. While this creates new governance challenges, it helps tackle centralized data bottlenecks.

• Improving data literacy: A huge part of data governance is ensuring people across an organization understand how to properly access and utilize data. Many banks are investing in widespread training to help advance data literacy and give people the knowledge they need to become responsible custodians of data.

• Increasing data visibility: Across the board, banks are looking for ways to make their data more discoverable and visible to teams across diverse domains. Greater visibility will help end users act on valuable insights faster, while helping data stakeholders apply and combine data in new and innovative ways. 

Banks are moving toward a new data paradigm — Data Mesh is perfectly suited to help.

Looking across those priorities, it’s clear to see the direction that today’s banks want to move in. They’re shifting toward a user and domain-centric data approach, where teams are empowered to harness data in unique ways and apply it to create value at scale. 

But, if their approach to governance doesn’t evolve to support that new paradigm, they’ll not only continue to face the same governance issues they’re trying to tackle today — they’ll add to them. That’s where Data Mesh becomes extremely valuable for today’s financial services organizations. 

In a Data Mesh, domains are empowered to access the data they need, build their own bespoke data products and discover valuable data sets from across the business. But crucially, freedom and flexibility are supported by a new federated and computational approach to governance. Governance is upheld across the mesh by roles, responsibilities and guardrails that are clearly defined. Moreover, they are supported by automation, which can enforce or monitor them at the code and platform level. This provides a real-time view of how governance is being upheld across the organization. 

When Saxo Bank wanted to increase data visibility, accessibility and quality — while improving governance — we helped them adopt their own bespoke Data Mesh. The mesh made it easy to search data assets and offered transparency on their origins, giving users clarity, building trust and improving governance.