A Relationship Business
Banking and technology have a long and storied history, and data governance is just the latest technology the industry has embraced. The synergistic relationship between banking and technology isn’t that surprising since the instrument of money is what many consider the tool that makes the world go round. This isn’t literally true, of course, but one could argue that the rise and fall of nations has been over resources, and these resources have always been a tradeable commodity that runs on money.
According to the Swiss digital payments company, Netcetera , the roots of banking started “in Assyria and India in around 2000 BC, when merchants gave out loans to buy grain. In ancient Greece and Rome, temples began offering loans, accepting deposits, and even exchanging currency. By the 14th century, banking became more recognisable by today’s standards, when Italian cities like Florence and Venice established the first modern banks.” For thousands of years, banking was done in person; it was a relationship business. Records were written on paper, physical money changed hands, and bank managers knew their customers on a first-name basis, says Netcetera.
The mid-20th century and early 21st century saw a Fintech revolution. From the ATMs of the 1960s to electronic banking of the 70s and 80s to online banking of the 90s to mobile banking of the 2000s to the digital wallets of the 2010s, onto the current fascination with everything AI, the banking industry has completely revolutionized how people manage their money. Even the notoriously unbanked are now banked. All of these changes require an extraordinary understanding of data, regulation, and security . This is where data governance comes in.
What is Data Governance?
In my article, Foundations of Enterprise Data Governance , I state, “ Data governance is the planning, oversight, and control over management of data and the use of data and data-related resources, and the development and implementation of policies and decision rights over the use of data. It is the foundational component of an enterprise data management or enterprise information management program.”
For banks in particular, data governance ensures data is accurate, secure, consistent, and compliant with regulations like GDPR, Basel III, and anti-money-laundering (AML) legislation. This reduces the risk of banks facing regulatory fines and penalties, which could lead to reputational damage.
High-quality data is essential for accurate reporting and decision-making. Strong data governance helps banks manage risks , enhance customer trust, and drive business growth by ensuring data accuracy, security, and accessibility. Better governance and data quality tools drive better decision-making, which improves overall operational efficiency.
Historical Context
In the banking industry, data governance has evolved significantly over the past few years. However, various historical and technological factors have hampered progress. Many older financial institutions continue to rely on legacy systems developed decades ago, long before interoperability became an important part of data warehousing. This has resulted in isolated data repositories, which complicates data integration and hinders compliance. Over time, historical data silos have appeared, leading to a fragmented data landscape that impedes an organization’s ability to comprehensively analyze a company’s decision-making processes.
Legacy Systems Impact
80%
Banks Still Using Legacy Systems
80% of NA bank ops leaders say legacy system modernization is critical for survival, as outdated systems limit data governance and innovation.
Source: Accenture Newsroom
According to the Accenture news release, Four in Five North American Bank Operations Leaders Believe Their Bank’s Survival Depends on Updating Legacy Systems to Innovate Faster
Historical organizational structures have also played a significant role in data fragmentation. In the past, different company departments have operated semi-autonomously, each employing its own data management solution. This independence accelerates data fragmentation, making it challenging for organizations to consolidate and streamline data effectively. As these independent departments evolve, they have contributed to a broader data ecosystem that lacks cohesion, leading to inefficiencies and difficulties in regulatory compliance.
The Role of Data Governance in Banking
The primary goal of data governance in banking is to ensure the integrity, quality, security, and compliance of data across an organization. Regulatory compliance is a big part of this.
Data governance in banking is not just about compliance – it’s about creating a foundation of trust, security, and operational excellence that enables financial institutions to serve their customers better while protecting their interests.
Data governance in banking is not just about compliance – it’s about creating a foundation of trust, security, and operational excellence that enables financial institutions to serve their customers better while protecting their interests.
Key regulations like the General Data Protection Regulation (GDPR), Basel III, the California Consumer Privacy Act (CCPA), Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA), Brazil’s General Data Protection Law (LGPD), and UK’s Data Protection Act 2018 (DPA) all have similar goals — to govern the collection, use, and disclosure of personal data.
In general, these regulations require organizations to obtain consent for data collection while providing individuals with the right to access and correct their data. These regulations establish rights for data subjects as well as impose obligations on data controllers and processors, including the greatest motivating tactic of all, large financial fines discouraging bad or illegal behavior. Without strong data governance, financial institutions would find it very difficult to remain compliant with these regulations.
As national and international regulations evolve, financial institutions must continually adapt to them by utilizing new tools, new hardware, and new software. However, if these new systems are not fully integrated with existing frameworks, they can magnify data fragmentation. Moreover, banks operating in multiple jurisdictions face inconsistent international standards, requiring these companies to implement different systems or tools tailored to specific regulatory requirements. This scenario often results in disparate data management practices across regions and countries, leading to serious potential compliance issues.
Banks are a Cybercrime Target
The rising escalation of cyberattacks on businesses the world over is a trend that is not about to abate anytime soon. With everything from malware to ransomware to phishing to malvertising to distributed denial-of-service attacks as well as a new breed of attacks originating just about every month, banks need to keep a tight rein on their data. Strengthening business endpoints, reinforcing network security, protecting email systems, filtering out spam, defending against malware, and implementing data protection policies are all good steps in fighting fraud, but strong data governance should be a foundational step in the process. Organizations can combat cybercrimes by utilizing technology, including AI, to counter cyberthreats, but strong data governance needs to be a big part of every bank’s cybersecurity defense.
Data Breach Statistics
$10.5T
Projected Annual Global Cybercrime Cost by 2025
In 2022, 43% of financial institutions reported experiencing a data breach, highlighting significant vulnerabilities in the sector. These incidents were often attributed to inadequate data governance and security practices, emphasizing the need for enhanced security measures.
Source: Accenture
Strong data governance is the foundation of strong cybersecurity.
Best Practices
Data governance is a all-inclusive framework designed to effectively manage an organization’s data assets. It encompasses several key components that ensure data integrity, security, and accessibility, which are particularly critical in the banking sector.
Manage Data Quality
Data quality management involves systematic practices designed to maintain high standards of data quality . This includes processes for data validation, data cleansing, and data enrichment. Effective data quality management is essential for mitigating errors from poor data quality while also ensuring that data remains a reliable asset for decision-making.
Detailed data policies serve as a guideline that governs the use of data across an organization. These policies define acceptable practices for data handling, ensuring consistency and compliance with regulatory requirements. Well-defined data policies help reduce data discrepancies and promote a unified approach to data governance.
Detailed data policies serve as a guideline that governs the use of data across an organization. These policies define the acceptable practices for data handling, ensuring consistency and compliance with regulatory requirements. Well-defined data policies help reduce data discrepancies and promote a unified approach to data governance.
Compliance monitoring is critical to ensure that all data governance practices match up with legal and regulatory requirements. Continuous oversight helps to identify any potential risks associated with data misuse, thereby safeguarding the organization from negative legal repercussions while enhancing data security measures.
Effective data governance is crucial for managing the complexities of modern banking and addressing challenges that come with siloed data and cybersecurity threats . Clear data ownership fosters accountability for quality, security, and compliance, which are vital for maintaining customer trust.
The heavy regulation of the financial sector is understandable. Any business handling customer funds in a fiduciary way should have to follow strict data privacy, data security, and data reporting requirements. The bank runs of the past showed governments that trust in the banking system is as important as trust in the government.
Develop a Data Governance Framework
A data governance framework is a organized approach outlining an company’s management of its data assets. It establishes the policies, procedures, roles, and responsibilities necessary for effective data management, ensuring data quality, integrity, security, and compliance. In my article Foundations of Enterprise Data Governance , I state a typical data governance framework includes the following key components:
Data governance policies that define the rules and guidelines for data management.
Data governance procedures outlining the steps and processes for managing data.
Data governance roles and responsibilities that define the roles and responsibilities of individuals and teams involved in data governance.
Data governance metrics and monitoring tools that help organizations track their progress as well as identify areas for improvement.
Data Lineage in Banking
69%
Banks Planning Data Lineage Implementation
A significant majority of banks are planning to implement comprehensive data lineage tracking from the front office through to the reporting layer, indicating a strong industry-wide move toward enhanced data governance and transparency.
Source: Deloitte BCBS 239 Benchmark Survey 2024
Modern Data Governance
In his article, The Path to Modern Data Governance
Figure 1: Data governance framework. Source: Eckerson For Wells, the framework breaks down as follows:
Goals: Why the data is governed.
Methods: How the data is governed.
People: Who governs the data.
Processes: The series of actions taken to achieve specific results.
Technology: The features and functions that fill many roles in data management.
Culture: The environment where data governance, agile projects, and self-service data analysis and reporting all work together in harmony.
Data governance involves monitoring and measuring to identify improvements. It ensures that only the right people have the right access to the right — and often highly sensitive — data. Implementing a robust and reliable data governance framework allows finance companies to conduct regular audits and monitor compliance. This enhances overall operational efficiency . Data governance frameworks enable effective risk management by identifying and mitigating data-related risks, which allow banks to effectively manage credit, operational, and compliance risks.
Creating a successful data governance framework is crucial for financial institutions seeking to ensure data integrity, security, and usability. Key components of an effective framework include data stewardship, data quality management, data validation processes, and compliance monitoring. These frameworks also define clear roles and responsibilities, establishing accountability for overall data quality and compliance.
Hire Data Stewards
Data stewardship is a fundamental aspect of a data governance program. It involves the assignment of individuals responsible for overseeing data assets. These stewards ensure that data governance policies are implemented and adhered to throughout an organization. They play a crucial role in maintaining data integrity and addressing any data issues that might arise.
Data stewardship is a fundamental aspect of a data governance program. It involves the assignment of individuals responsible for overseeing data assets. These stewards ensure that data governance policies are implemented and adhered to throughout an organization. They play a crucial role in maintaining data integrity and addressing any data issues that might arise.
Data stewards monitor and maintain the accuracy, completeness, and consistency of financial data. They implement data quality metrics and standards to assess data integrity, establish and enforce data governance policies and procedures, and collaborate with other stakeholders to define data ownership and accountability. They maintain a data catalog to provide visibility into data sources and usage. This catalog classifies data based on sensitivity, regulatory requirements, and the organization’s needs.
On the regulatory front, data stewards ensure financial data management practices comply with all relevant regulations, including anti-money laundering requirements as well as GDPR, Basel III, CCPA, PIPEDA, LGPD, and DPA. They work closely with compliance teams to implement necessary data protection measures.
Every byte of data has a lifecycle, and data stewards oversee that entire lifecycle, from creation and storage to modeling to archiving and deletion. They ensure proper retention policies are in place to meet legal and operational requirements. They act as liaisons between data users, IT, and other departments, facilitating data sharing and data usage . When issues arise, they collaborate with IT and data engineering teams to resolve technical data problems. They identify and address data-related issues, such as data discrepancies or inconsistencies. Finally, they share the knowledge, providing training and support to staff on data management best practices. All-in-all, data stewards advocate that data is a strategic asset to an organization.
Creating a Data Council
A Data Governance Council is a formal group within an organization that provides leadership and strategic direction for the company’s data governance initiatives. Overseeing the management, quality, and security of data assets, the council plays an important role in ensuring all data governance policies and practices are effectively implemented and globally followed. The council also ensures all data management practices maintain high-quality data that meets the business’s needs. Another key responsibility is ensuring the company is fully aligned with regulatory requirements as well as industry best practices.
The council helps define the roles and responsibilities of all data stewards and stakeholders involved in the company’s data management. They help facilitate collaboration among data stewards throughout the company. The data stewards help monitor data quality metrics and initiate improvement efforts as needed to protect sensitive data. They also identify risks related to data management and ensure compliance with all data protection regulations. The council also establishes metrics to assess the effectiveness of all data governance initiatives.
Another important aspect of data governance is communication and training. A data council promotes awareness of all policies related to data governance. It offers training and resources to employees who want and need to skill up on data governance practices. When it comes to data governance, everyone should be on the same page. Training helps establish this. It also provides performance metrics for each employee involved.
Finally, the data council commissions regularly scheduled reviews and reports on the progress of all data governance initiatives to senior management.
Data Governance Council Representatives
A data governance council includes representatives from various business units, including:
IT : Data management and technology experts.Compliance/Risk Management : Professionals ensuring adherence to regulations.Business Units : Representatives from departments that generate or use data (e.g., finance, marketing, operations).Data Stewards : Individuals responsible for managing specific data domains.
Foster a Strong Data Culture
Organizations should foster a data-driven culture company-wide. However, they should not be naive. Although change is constant, so is resistance to change. Employees get comfortable in their roles. They often see change as a threat rather than what it should be — a motivator to improve their work experience.
Cultural challenges can also get in the way of data governance adoption, so businesses should be aware of the unique challenges offices in different locations and countries face.
Clarity of purpose is also important. Clear objectives make reaching goals much easier. The data governance goals should, of course, align with all of the business’s objectives. Stakeholder engagement during the entire data governance process is imperative.
Embrace Technology
Incorporating technology solutions, such as data management tools, can significantly strengthen a financial organization’s data governance practices. These tools assist organizations in assessing and monitoring data quality, establishing data lineage, and improving collaboration across data governance teams.
By leveraging technology, financial institutions can increase their risk management capabilities and ensure regulatory compliance. By understanding and implementing these key components of a robust data governance strategy, financial institutions can create a robust framework that supports accurate decision-making, effective risk management, and regulatory adherence.
The integration of technological solutions into data governance practices can yield significant benefits. Automating repetitive data tasks helps free up resources for more strategic initiatives. Moreover, organizations can focus on developing user-friendly data rules that support employee workflows without introducing unnecessary complexity into data processes. Additionally, establishing comprehensive policies and procedures ensures consistency and adherence to data governance principles and standards.
Effective metadata management involves capturing and managing descriptive information about data assets, which aids stakeholders in understanding the context, structure, and meaning of various data elements. This practice enhances data comprehension and facilitates better data integration and sharing across the organization.
Utilize AI
Organizations should also recognize the role AI and machine learning play in data governance. Although AI has been touted as a panacea for just about everything ailing your IT tech stack, data governance is an area where AI’s promise might come to fruition.
Organizations should also recognize the role AI and machine learning play in data governance. Although AI has been touted as a panacea for just about everything ailing your IT tech stack, data governance is an area where AI’s promise might actually come to fruition.
It’s not just hype and vaporware as it might be in other technical areas. AI can ensure that the data banks and financial institutions use is accurate and reliable. This helps minimize errors and inconsistencies through standardized data entry, storage, and management processes.
AI-driven platforms enable real-time data analysis, allowing banks to identify trends, anomalies, and potential threats that may go unnoticed by human analysts. Furthermore, machine learning models can be employed to predict customer behaviors and market trends, enhancing operational efficiency and customer engagement.
Case Study: Poor Data Governance is Costly
JPMorgan Chase
In his article, Data Governance Is Vital for Personalized Customer Experiences — JPMC Executive Director, Head of Data for ML and Intelligent Ops
Data is at the heart of everything in the current financial services industry, says Dimple Thakkar, Executive Director and Head of Data for Machine Learning and Intelligent Operations at JPMC. Thakkar adds that data roles are created “to keep data at the heart of customer experience across channels, which is a key aspect of financial banking and services.”
Consumer Trust in Digital Banking
81%
Consumers Would Abandon Brands After Data Breach
The high percentage of consumers willing to abandon brands after a data breach highlights the critical importance of maintaining robust data governance and security measures in the banking sector.
Source: Consumer Trust Report 2024
Thakkar’s team works closely with the engineering, architectural, design, and product groups, helping them understand complicated data sources and the governance aspects of data storage, says CDO Magazine . They add, “According to Thakkar, customer 360 has been the biggest hurdle for financial services, which is why JPMC understands the unparalleled need for data. She adds that the organization has laid down four rules in this context:
Determining the data movement.
Data governance.
Delving deep into newly generated systems and how the experience is aligned with data.
Leveraging AI and ML on top of experiences and as enablers.”
The Customer Experience
When balancing customer experiences and data security when rolling out agents, Thakkar recommended looking at it from two different lenses. “From the perspective of financial services, as the guardian of data, balancing becomes necessary,” says Thakkar, but from a data perspective, the product and design teams should “be able to curate personalized experiences for customers while withholding crucial information such as card numbers.”
From a customer perspective, nothing less than seeing all their connected experiences is acceptable, says Thakkar. So, while implementing agents or AI/ML for customer experiences, businesses must be cautious and careful about the data shared, claims Thakkar. “JPMC does not share any data across browsers, except for the usage of its application or Chase.com,” she contends.
Future Trends
The landscape of data governance in banking is rapidly evolving, driven by advancements in technology and the increasing importance of compliance and customer experience. As banks strive to stay competitive and responsive to regulatory demands, several key trends are shaping the future of data governance in the sector. Generative AI will soon play an outsized role in the industry.
“Financial institutions are missing a valuable opportunity if they don’t harness the benefits that artificial intelligence (AI) can bring to data management,” says JP Morgan Chase legal chief data officer Jennifer Ippoliti in the A-Teaminsight article Generative AI Will Play Role in Data Management says JP Morgan Chase Executive
Generative AI Industry Adoption
28%
Highest AI Adoption Expected in Marketing
JPM’s 2023 ML Conference showed Gen AI adoption forecasts: Marketing (28%), legal/insurance (21%), media (20%), analytics (18%), consumer tech (13%).
Source: J.P. Morgan Global Machine Learning Conference 2023
Ippoliti identifies two broad categories of AI : traditional automation forms and generative AI, which is built on large language models (LLMs). LLMs are having their moment in the sun right now, largely because of the fawning coverage of companies like OpenAI and tools like ChatGPT, Perplexity, and Claude in the global media. Ippoliti believes generative AI has the potential to radically improve the data management process. “LLMs are coming,” she says, adding, “There may be issues now with hallucinations and not citing sources, but I am confident those things will be addressed.”
Large Language Models
However, LLMs and generative AI do have a veracity problem right now. “While it can demonstrably put together succinct and convincing content, it has been criticized for generating incorrect information known as hallucinations,” Ippoliti adds. LLMs often generate incorrect, misleading, or nonsensical information that does not align with reality or factual data. This phenomenon most likely occurs due to the model’s reliance on patterns and training data, rather than a true understanding of the content. These hallucinations include everything from fabricated facts, inconsistent information, overgeneralizations, and ambiguity, so caution needs to be applied when using LLMs.
Large Language Models are a subset of AI Oftentimes, the LLM model creates data and/or facts that sound plausible but are entirely made up. In addition, responses might contradict established knowledge or previous statements. The model might also generalize based on limited context, leading to inaccurate conclusions. The model might also interpret ambiguous prompts in unintended ways, resulting in irrelevant or confusing outputs.
“These technologies will become a lot more commonplace, and we should be thinking now about how we’re going to use them so that data management functions don’t get left behind when the rest of the company starts investing in them,” Ippoliti concludes.
Conclusion
Calling our time the “Golden age of data governance” is probably hyperbolic, but maybe it’s not that far off. The data governance tools we have now are truly impressive. From ancient Mesopotamia through Medieval and Renaissance Europe to the banking dynasties of the Rothschilds and Barings, through the expansion of banking into the New World, onto the founding of the Bank of New York in 1784, the creation of state-chartered banks, and the establishment of the Federal Reserve System in 1913, banking has always flourished when tech flourished too.
As financial institutions maintain extensive compliance and risk management records, the challenge of managing and leveraging access controls to this growing data pool has become increasingly complex. Questionable data quality, data incompleteness, and data inaccuracies, to say nothing of data security, can undermine trust in a corporation’s data-driven decision-making process. Effective and robust data governance practices are essential for organizations to efficiently and effectively manage their data assets.
Data governance provides a framework that ensures data is accurate, complete, and consistent across an organization. By implementing strong data governance practices, organizations can ensure that their data is secure and compliant with relevant regulations and corporate standards. This not only helps mitigate data risks but also enhances the overall trustworthiness of the data, which is vital for strategic decision-making and operational efficiency.
In this day and age, when companies often have to make million-dollar decisions based on the data flowing through their systems — sometimes in real-time — it is paramount for all corporate decision-makers to have the best possible data to work from. Without strong data governance, this just isn’t going to be possible.
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