What is data governance in banking?

Data governance in banking is a framework of policies and procedures that help ensure data security and compliance specific to banking industry regulations. A banking institution implements data governance in order to secure their data, monitor their data, and ensure that it complies with industry regulations like BCBS 239, GDPR, and more. 

Data governance for financial services is not a single software or internal rule, but layers of safeguards put in place to ensure that the sensitive data managed by financial institutions is taken care of in a way to mitigate risk and protect user information.

In using automated data governance, banking institutions can improve their security, minimize their risk, and better serve customers, in addition to optimizing their data to drive decision-making and unlock cross-sell opportunities for growth. 

Why data governance is critical for financial institutions

Having a data governance framework for banks is critical for financial institutions because of the volume and nature of the sensitive information that these businesses own. It also helps to manage the quality of the data and create opportunities for innovation by de-siloing information across the organization. 

Regulatory compliance & reporting (BCBS 239, GDPR)

Banking may be one of the most highly-regulated industries in the modern world — and for good reason. Banks collect the most sensitive data of all from people — their names, addresses, social security numbers, and more — and tie them to account numbers that represent their financial worlds. A small misstep in handling that data can create massive shockwaves in the life of an individual or group of customers. 

A thorough data governance and compliance plan for financial institutions helps banks meet regulatory compliance requirements and assist with automating reporting tasks for the institution. Banks must comply with a host of rules and regulations, such as the General Data Protection Regulation (GDPR). GDPR is the European Union (EU) law that applies globally to any organization that holds or processes the data of EU residents. This strict law gives consumers more control over their information, leveling fines for institutions found in non-compliance. 

In addition to meeting regulations, unified data governance helps banking institutions strive toward best practices, such as the BCBS 239 principles. These are 14 guidelines laid out by the Basel Committee that outline effective data management. With automated data quality management, financial institutions can meet these important regulations and work toward best practices with less manual and more accurate methods. 

By harnessing the power of a data governance platform and AI-powered automation, banking institutions can be ready to meet regulatory compliance rules and automate reporting for efficient compliance. 

Risk management and fraud detection

When we think of risk management in data, we automatically think of hackers and data leaks and attacks. That’s all a very real and very present danger in banking, but so too are the losses that banks face when making decisions with incomplete information. 

With a unified data platform like Ataccama ONE, your data sets start working for you rather than against you. With AI-powered insights, banks can depend on their data to limit losses incurred by lending to unqualified borrowers, ensure identity verification before releasing funds, and more. 

A robust data governance platform designed with banking in mind is absolutely going to help combat fraud and cyberattacks, as well as avoid data breaches using real-time anomaly detection. But it’s also going to help the banker sitting across from the customer make real-time decisions based on the whole story that the data tells, rather than just a piece of it. 

Breaking down data silos

In a world where banks merge and consolidate all the time, it’s not just possible but likely that a customer at an institution has a fragmented data profile across branches and business segments. With AI-powered data governance, your data teams can break down data silos and ensure that every decision-maker has access to the information that they need when they need it. 

Proactive data quality management is the key to breaking down those data silos and unleashing the ability to cross-sell, promote new products, and serve your customer with a 360-degree view of their current portfolio and needs. In auditing your data and assessing who uses what data in which part of an organization, you can identify areas for improvement and consolidation, which creates efficiencies and keeps data from existing in a vacuum. 

Building a data governance framework for banks

Data governance best practices for banking including defining the roles that interface with data, as well as creating data quality standards and leveraging data lineage and traceability. 

In building a data governance framework for banks, Ataccama ONE has kept the idea of a unified data landscape front-and-center so that institutions can empower each role in the organization to steward data well and use it to inform their decisions moving forward. 

Key roles (The Data Steward, The Chief Data Officer) in data governance

Data governance isn’t the purview of a single individual in any organization. All employees must be empowered to maintain data quality and be trained on the best practices of how to do so. From the Chief Data Officer to frontline employees, each plays a role in maintaining the data for an organization. Clearly defining the roles and tasks of each person is essential to creating a quality data repository that can be relied upon to be accurate, trustworthy, and actionable. 

Some key data-related roles: 

• Chief Data Officer (CDO) is responsible for creating data governance rules and ensuring that all staff are trained in the rules and regulatory compliance actions necessary for their role. 
• Data Owners are responsible for their specific data domains, such as the data that belongs to sales, purchasing, or personnel files. 
• Data Stewards are the day-to-day managers of data quality, ensuring the accuracy of the data and correcting broken data and broken policies when necessary. 
• Data Custodians are the IT teams responsible for managing the infrastructure of the data, but not necessarily the data itself. 

All employees must hold data quality in high regard in order to participate in creating accurate and timely data for the banking organization to rely on. Inaccurate, inconsistent, or low-value data is untrustworthy data, and all employees should be equipped and trained to ensure data quality from beginning to end of the data catalog. 

Data lineage & traceability

In order to rely on the data that you do have, access to data lineage is a must. With data lineage, users can see where the data came from, spot errors in the chain of information, and identify areas of missing data that could be cleansed or consolidated to make it more useful. 

By following data quality standards, a banking organization can ensure that they are using their data in a way that not only keeps it secure, but allows the organization to grow and innovate. Data lineage is also essential in banking to comply with regulatory reporting, helping them trace where data is coming from and when it needs to be secured or archived. 

Data quality standards

CDOs and Data Stewards must set rigorous data quality standards and enforce them across the banking organization. These data quality standards include: 

• Accuracy. Data is correct at all times. 
• Completeness. Data sets are complete and contain useful information. 
• Timeliness. Data is up-to-date, not stale. 
• Validity. Data is able to be verified as true. 
• Uniqueness. Data isn’t duplicated across the organization. 
• Accessibility. Data is able to be used by the people who need it. 

In creating data quality standards, data analysts and data compliance officers move their organization closer to AI-powered growth. 

Once data quality standards are implemented, financial institutions can use their data to create better customer experiences, increase their regulatory compliance rates, and create efficiencies in their business.

Top data governance best practices in banking

Data governance best practices in banking include identifying those key roles and how the data will move through the organization. Some of the essential rules for data governance include: 

• Clear ownership of data
• Thorough data-handling policies 
• Strict compliance with regulation
• Monitoring data and maintaining quality 
• Using automation to increase security and efficiency
• Data lifecycle management

None of these best practices will matter, however, if there isn’t buy-in across the whole organization, from the top down. From CEOs to front-line employees, each person must commit to creating and maintaining quality data across the entire data pipeline. 

Shift from reactive to proactive governance

One best practice to highlight in more depth is to shift from reactive to proactive governance across your financial institution. Instead of waiting for error reports to give you a picture of what’s missing or damaged in your data catalog, create policies that manage your data quality from creation forward. 

Routine data validation and cleansing will create continually trustworthy data, and having a data management system that monitors data from creation to storage to deletion will ensure accuracy — and position your data as a trusted resource, not just digital-dust collecting information. 

Automate policy enforcement 

Automating policy enforcement is key to implementing data quality across an organization, but automation can’t happen if your data management platform isn’t integrated from top to bottom. Ataccama ONE can automate your policy enforcement because it’s a unified data management platform that reduces complexity from the ground up.

Ataccama ONE AI Agent links centrally-defined business rules with the technical controls to automate policy enforcement. This ensures that data quality policies are consistently enforced across the organization everywhere that data is processed and used — automatically, without room for human error. 

Treat data as an asset

If you aren’t treating data as your most important asset, you should be. Usable data is the key to growing your business, and owning that data also poses an enormous risk to your organization. 

Just as you would lock up valuable physical assets like servers and cash, your data needs to be secure at all times. Automated policy enforcement ensures that only authorized employees have access to data that they need, and automated alerts can keep your organization in the know about security breaches or data leaks. 

And just like you would inventory physical products, data needs to have an inventory taken once in a while, too. What all is there? Who is using it? Do we need to continue to maintain it, or could this specific data be deleted or archived? These questions and more can help maintain the data you do own, and dispose of data you no longer need to lower your risk. 

Common challenges in the banking industry

The banking industry faces many common challenges when dealing with data: 

• Data siloes across departments or legacy systems keep data fragmented and limit communication between systems. 
• Modern data volumes can overwhelm legacy systems and cause further fragmentation. 
• Data quality issues arise when data hasn’t been properly maintained in the past.
  • Human error, duplicate entries across systems, inconsistent data, and broken data lineage can all hinder reporting and using data to drive clear-eyed decision making. 
• Regulatory compliance and reporting can be time-consuming and costly to manage. 
• Banks are often targeted in cybersecurity attacks, and maintaining data privacy across disparate systems can be hard. 
• Unclear data governance policies and procedures lead to confusion and unsafe practices. 

These common data governance challenges stand in the way of efficiency and limit an organization’s ability to harness AI for future growth. 

Data governance helps banking innovate

It may not sound as attention-grabbing or innovative as self-driving cars or robotic food delivery, but robust data governance is the best way for banks to innovate in their industry and unlock the future of their company. 

A clear, concise data governance policy and an integrated data quality management platform can help a financial institution get a grip on its data and harness that data to make game-time decisions and drive growth. Without a unified picture of an organization’s data, a bank is leaving innovative opportunities on the table. 

Conclusion: the future of financial data governance

Banking institutions are managing an incalculable amount of data and transactions each and every day. With all the complexity comes a great deal of responsibility, too. 

The future of financial data governance is already here: it’s the need to be able to see an end-to-end picture of your data, trace and correct errors, and use real-time data to make up-to-the-moment decisions. Anything less than a unified data management platform is going to leave banking institutions falling behind in their industry. 

FAQs

• What does data governance mean to a bank?
  • Data governance is made up of the policies and procedures that a bank creates to not only keep its data safe and secure, but to use that data to accurately and reliably meet regulations and compliance duties. 
• What is the difference between data management and data governance in banking?
  • Data management is continuously monitoring and cleansing data for accuracy, timeliness, and trustworthiness. Data governance includes data management, but also addresses data security, cross-selling opportunities, and compliance and regulatory reporting. 
• Why is a data governance framework important for banks?
  • Data governance is important for banks because without data governance, they are missing out on the opportunity to see an end-to-end picture of their data and then use that data to promote efficiency, product placement, and information security.