For businesses seeking to stay ahead of data compliance requirements, while also taking full advantage of new technological opportunities in areas like AI, now is the time to overhaul financial data governance.
When it comes to data governance, the standards that businesses in the financial services industry must meet are especially high due to the strict regulatory requirements that impact financial data management.
At the same time, the ability of financial services companies to meet these challenges can be lower than many would wish. Factors like fragmented data assets and a reliance on legacy systems often complicate their ability to implement highly efficient and effective data governance procedures.
But that doesn’t mean financial services companies have to settle for subpar data governance. With the right data management strategy and tools, it’s possible to rise to the unique challenges of governing financial data effectively – and indeed, doing so will become increasingly crucial in future years as financial services businesses become even more dependent on secure, high-quality data.
The challenges of data governance in financial services
To be sure, effective data governance – meaning the process of managing data assets to ensure consistency, quality, security, and privacy – is never simple. But for businesses in the financial services industry, it tends to be especially vexing due to several factors that are uniquely prevalent in this sector:
- Siloed data: Financial services businesses often divide their operations between multiple product lines, such as retail banking, commercial lending, and insurance underwriting. Each of these lines of business tends to store and manage data using separate systems and processes. This leads to data fragmentation and siloes, making it challenging to consolidate data governance through consistent, centralized policies.
- Legacy technology: The technology stacks of finance companies sometimes include legacy platforms, such as on-prem systems built using bespoke operating systems and, in some cases, even decades-old software running on mainframes. These legacy platforms don’t always directly support modern data access control or security tooling, a limitation that complicates data governance.
- Complex requirements: The finance industry is subject to complex and overlapping regulations, such as Basel III, Solvency II, Sarbanes-Oxley, and the GDPR. Each framework imposes distinct requirements on how businesses must govern data, and in some cases, the rules vary between countries or regions. All of the above translates to substantial complexity in determining which data governance requirements a business even needs to meet, let alone how best to address them.
See also: Data Governance Concerns in the Age of AI
The growing need for effective financial data governance
Failing to address these challenges by managing financial data effectively presents three major risks.
The first and most obvious involves regulations. The inability to demonstrate excellent data hygiene to auditors and regulators could result in fines, reputational damage, and or even operational restrictions in some cases. For example, an audit that reveals inconsistent credit scoring logic or insufficient data retention policies could lead to sanctions or the revocation of licenses.
Second, poor data governance can hamper the ability of financial services companies to operate efficiently and scalably. They are likely to struggle to make data-driven decisions quickly and accurately if they can’t effectively integrate data that originates from disparate sources or if their data is of low quality.
It’s worth noting, too, that the stakes of poor data governance for the financial services industry are likely only to grow in the coming years. The amount of data that businesses in this sector need to manage is constantly increasing, making it all the more important for them to implement efficient, scalable data governance procedures. In addition, the ability to take advantage of novel technological solutions, such as those spawned by the generative AI boom, hinges in significant part on effective data governance. You can’t do things like build custom AI models if you lack high-quality data to feed those models.
Data governance best practices for financial services
There’s no silver bullet that can magically solve the special data governance challenges faced by financial services companies. But there are several discrete steps organizations can take to maximize their ability to govern data effectively, even in the face of increasingly voluminous and complex data assets.
1. Establish federated data governance
First, businesses should establish a federated data governance model. This approach means that data is stored and managed centrally, but at the same time, it’s available to each department or line of business as needed. A federated data governance model enables consistent enforcement of data governance policies without compromising the ability of diverse stakeholders to access the data assets they need.
In this way, a federated approach helps to mitigate the security and consistency problems that can arise from data silos while still enabling domain-level ownership of data by stakeholders within the business.
To implement federated data governance, businesses must make their various data assets centrally accessible.Data mesh principles, which promote domain-oriented ownership and decentralized data product responsibility while operating within a standardized governance framework, can help support this model in large and complex organizations. In addition, organizations should appoint data stewards or product owners from each business line (such as lending, insurance, and compliance) who will take ownership of data quality and access for their parts of the business. At the same time, a centralized governance team should provide tooling, policy guidance, and oversight for the business’s data resources as a whole.
2. Update data tooling
Also critical is investing in modern tooling that enables scalable and automated governance. Storage and analytics platforms like Databricks and Snowflake, transformation tools such as dbt, and orchestration frameworks like Dagster can help enforce access controls and manage data quality at the platform level. This reduces the need for manual audits and controls.
Many organizations are adopting a Modern Data Stack (MDS) approach, which integrates best-of-breed, cloud-native components to create flexible, scalable, and modular data ecosystems. An MDS approach allows businesses to optimize data storage, processing, transformation, and governance with a focus on interoperability and automation.
Modernizing data tooling with solutions like these doesn’t mean that financial services businesses must migrate entirely away from legacy platforms — a prospect that is simply not feasible in the near term in many cases. Instead, they can connect data assets hosted on legacy systems to modern tools and MDS components, providing a way to enhance data governance without overhauling their entire data infrastructure.
3. Automate data governance
Modernized data tooling goes hand-in-hand with automated data governance procedures. Wherever possible, automated tools should replace manual processes in areas like detecting data quality problems or mitigating insecure data access controls.
Data governance automation is valuable not just because it saves time and effort on the part of data governance teams but also because it breeds efficiency and consistency. Unlike humans, data governance tools always make the same decisions in response to the same conditions.
4. Train and upskill staff
Implementing modern data governance tools and procedures is only effective if staff are prepared to take full advantage of them. To that end, it’s important to provide training programs that improve data literacy and clarify data management responsibilities across the various parts of the business. Data management training is important in any business context. But it’s especially vital in financial services due to the complexity of data regulations and infrastructure.
Training also provides an opportunity to convey data governance as a way to increase efficiency and build trust, helping employees to view novel governance procedures and tools as a benefit rather than just another thing they have to learn.
Conclusion: A modern approach to financial data governance
Data governance in the financial services sector has never been easy. But it’s only set to grow more challenging as the amount of data businesses need to manage and the complexity of that data continue to increase. For businesses seeking to stay ahead of data compliance requirements while also taking full advantage of new technological opportunities in areas like AI, now is the time to overhaul financial data governance.
