Data Governance for Banking: Core Challenges, Business Benefits, and Essential Capabilities in 2024

Updated October 16th, 2024

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Effective data governance in banking ensures the availability, accuracy, cleanliness, and high quality of data, supporting a bank’s operations and offerings.
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This article explores how banks can benefit from proper data governance, covering key regulations, business outcomes, and the essential capabilities needed for proper implementation.

Table of contents #

  1. What is data governance for banking?
  2. Data governance for banking: What’s driving the need?
  3. The business benefits of data governance for banking
  4. Key capabilities that can drive effective data governance for banking
  5. Data governance for banking: Vital for growth, compliance, and data security at scale
  6. Related reads

What is data governance for banking? #

“Data governance helps banks to manage their data assets and provide a structure for data governance policies that govern data access, data quality, and data security.”

Fintech News explains the role of data governance for banking

Effective data governance for banking institutions helps in establishing rules, policies, processes, principles, and obligations around the use of data. According to the World Bank, the role of data governance is two-fold:

  1. Control risks by ensuring the security, integrity, and protection of data and systems
  2. Capture value by establishing rules and technical standards to facilitate efficient data transfer, combination, and exchange

Data governance for banking: What’s driving the need? #

“Often firms do not know how their data is used, and by whom within the firm.”

EY on the need for data governance in banking

As banks handle massive amounts of sensitive data, governance frameworks become essential. Without them, banks face significant risks, including compliance failures, security breaches, and operational inefficiencies.

Key drivers for data governance in banking include:

  • Siloed, inconsistent data
  • Compliance risks
  • Data security, privacy, and integrity issues
  • Difficulties in data sharing and interoperability
  • Lost business opportunities from poor governance

1. Siloed, inconsistent data #

Banks rely on multiple systems—from core banking software to CRM platforms—often leading to data silos. These isolated systems create fragmentation, making it hard to get a unified view of customer and operational data.

2. Compliance risks #

The banking industry is heavily regulated, with laws like GDPR, Basel III, and AML governing data usage. Non-compliance can result in severe penalties and reputational damage.

For instance, UK banks incurred fines totaling $222.16 million (£178 million) between June 2022 and June 2023, according to Finbold. The UK’s banking industry has been facing increased scrutiny, leading to unprecedented regulatory fines.

“Fines on financial institutions are projected to grow in the coming years, as the U.S. and other countries reform existing regulations…Overall, new and complex regulations are proving to be a challenge for the compliance departments.”

Oliver Scott, chief editor at Finbold

3. Data security and privacy issues #

“Financial services businesses will often hold huge amounts of data they collect as part of their client onboarding process such as debit and credit card numbers, passports, address information, and other ID documents. This data is highly valuable and is regularly traded on the dark web.”

Ben Marsh, class underwriter at insurance company Chaucer

As custodians of sensitive financial data, banks are frequent targets of cyberattacks. These attacks can undermine trust in banking institutions, disrupt critical services, and even lead to market sell offs or runs on banks.

4. Difficulties in data sharing and interoperability #

Banks frequently collaborate with third parties, such as payment processors, credit agencies, and technology vendors. However, different systems and data formats can lead to inefficiencies when data needs to be shared or integrated. This misalignment can delay operations and create errors in transaction processing.

5. Lost business opportunities from poor governance #

Data-driven innovation is key to staying competitive in banking, but poor data management can slow down innovation. Banks seeking to offer personalized services like AI-based financial advice or advanced risk assessments may struggle if they can’t efficiently access or unify their data.

Moreover, the pressure to drive business growth using data can also have adverse consequences, as pointed out by this EY report on data governance for banking and financial institutions.

“There is an increasing trend of firms trying to explore new ways to monetize their data, adding extra pressure to innovate and sell the insights from the data they hold. This increases the risk of unintentional data abuse.”

The business benefits of data governance for banking #

Data governance offers banks several significant benefits, such as:

  • Regulatory compliance: With data governance, banks can automate compliance reporting, maintain audit trails, and ensure adherence to regulations across regions.
  • Better data privacy and security: Data governance helps banks protect customer data by implementing policies for data masking, encryption, and role-based access control (RBAC). By tracking data lineage, banks can monitor how data flows through systems, identify vulnerabilities, and mitigate risks.
  • Operational efficiency: Effective data governance ensures that data is consistent, accessible, and discoverable across departments, reducing the time spent searching for information and resolving data-related issues.
  • Business growth and innovation: Data governance ensures that data is reliable and accessible, while also deterring poor data sharing and use. So, banks can launch new products, enhance customer experiences, and leverage AI for advanced analytics, without breaking any data-specific regulations.

These benefits might sound generic and vague, so we’re presenting two case studies of banks that reaped business benefits as a result of solid data governance – Austin Capital Bank and Porto.

1. How Austin Capital Bank enabled effective data governance with Atlan #

As mentioned earlier, banks hold a large amount of sensitive customer data, including addresses, names, birth dates, social security numbers, and account numbers. This makes them vulnerable to cyberattacks.

The first step to ensuring better data security, privacy, and use is to “make sure that only the right people within our organization have access to it,” according to Ian Bass, Head of Data & Analytics. The solution was to create masking policies that ensure only authorized personnel got access to sensitive data.

Austin Capital Bank used Atlan as an interface on top of Snowflake to get visibility on who has access to what.

“Atlan’s how we control access in an easily repeatable fashion. And then the fact that we have data lineage on top of it, and the ability to organize all of our information and classifications, and add glossary terms linked to that data? That’s all icing on the cake for us.”

Ian Bass, Head of Data & Analytics

2. How Porto automated the governance of over 1 million data assets, saving 40% of their time #

Brazil’s Porto, a leading bank and insurance company, needed a solution to manage and make sense of their vast troves of data spread across dozens of business domains.

Porto has roughly 14,000 employees, and has been in operation since 1945, leading to siloed infrastructure and knowledge. Obtaining context on data meant scouring the organization, attempting to find the subject matter expert that could answer their questions.

The team was searching for a user-friendly, collaborative data catalog that acted as “a single pane of glass to discover, understand, and apply Porto’s data, in less time than ever before.” The catalog should also cater to the emerging requirements from switching to a federated data governance model, and so the team chose Atlan.

Using Atlan’s rule-based automation, Porto’s team could reduce the manual effort they once spent defining asset owners, enriching data assets, and securing sensitive data. They could:

  • Automatically assign the business domain or team responsible as the owner of each data asset
  • Automatically classify and document context (using metadata) for over 1 million data assets
  • Automate compliance by auto-tagging PII data (per the Brazilian LGPD laws)
  • Build a complete lineage graph for upstream and downstream data assets, driving true end-to-end lineage

“If we consider everything we’re doing now with Atlan compared to before we had Atlan, we are saving 40% in efficiency, in terms of time and expensive operational tasks for everything related to governance. This is a 40% reduction of five people’s time.”

Danrlei Alves, Senior Data Governance Analyst

Key capabilities that can drive effective data governance for banking #

To successfully implement data governance, banks should adopt technologies and processes that offer key governance capabilities, such as:

  • Automated compliance management – audit trails, versioning, risk assessments, regulatory reporting
  • Data contracts that establish clear agreements between data producers and consumers, outlining the expectations, responsibilities, and quality standards for data usage
  • Effective metadata management that captures, describes, and manages all types of metadata, and automates metadata ingestion, classification, and sync for data tracking at scale
  • End-to-end data lineage from source to destination (across systems, column, tables, transformations), helping banks understand where data originates and how it flows through their systems
  • Granular access control, encryption, and personalized data masking and anonymization policies to proper data stewardship
  • AI-assisted policy creation that helps analyze existing data, suggest appropriate policies, and automate policy updates
  • Automated data asset documentation, tagging, and classification to drive data governance at scale
  • Real-time incident alerts notify relevant stakeholders about policy incidents and breaches as they happen (and not years later)
  • Easy integration with your data stack using out-of-the-box connectors for data products in your data stack (data sources, data movement tools, BI tools, etc.)
  • Collaboration capabilities (threads, comments, alerts, mentions, user tagging)
  • Easy deployment (across on-premise, public cloud, SaaS, multi-cloud, etc.), adoption, and quick time-to-value

Data governance for banking: Vital for growth, compliance, and data security at scale #

Effective data governance is crucial for managing the complexities of modern banking.

By addressing challenges like siloed data, compliance risks, and cybersecurity threats, governance frameworks help banks operate more efficiently, innovate responsibly, and protect customer data.

As banking continues to evolve, data governance will act as a cornerstone of sustainable growth and regulatory compliance.

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