Introduction
The financial services industry and technology have a long and storied history. From ancient times, when Mesopotamian temples acted as financial intermediaries, through the early banks and moneylenders of ancient Greece and Rome to the founding of the world’s first bank in Siena in 1472, through the creation of the Bank of England in 1694, onto the mid-18th century’s Industrial Revolution as well as the deregulation of the industry in 80s and 90s, and, finally, to the Fintech boom of the 2010s, technology has always been a big part of the finance industry’s growth. Data governance is just the latest technological innovation the industry has embraced to its enormous benefit.
For hundreds, if not thousands of years, banking was an in-person activity. Lenders knew their customers on a first-name basis. Records were written down on paper. Physical money changed hands. Bank managers approved and oversaw loans. Now, just about everything banking is digital. Few people even bother going into their bank branch to get money now, as ATMs are everywhere. Automated algorithms approve or reject loans. Through micro-financing, the normally unbanked get banked.
The mid-20th century and early 21st century saw a new Fintech revolution. From the ATMs of the 1960s to the electronic banking of the 70s and 80s to the online banking in the 90s, to the mobile banking of the 2000s, to the digital wallets of the 2010s, onto the current fascination with everything crypto, the banking industry has changed how people use, store, and manage their money. All of these changes have required an extraordinary understanding of data integration, financial regulation, and cybersecurity.
Today’s banking is barely recognizable from what it was just a few decades ago. What is constant, however, is data. Whether that data is collected in the double-accounting ledgers of the Medicis, the banking mainframes of Citibank, or the decentralized blockchains of crypto, data is the key that unlocks monetary value. And 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.”
Data governance is the system of decision rights and accountabilities for information-related processes, executed according to defined models describing who can take what actions, when, and with what information under what particular circumstances, and with which methods. In essence, a solid data governance framework is crucial for enterprise organizations because of the complexity and distribution of their data assets. It ensures the quality, security, and accessibility of data, enabling organizations to make informed decisions while maintaining a competitive business edge.
Industry Data Governance
Explore sector-specific strategies and practical approaches to enhance your organization’s data governance.
For financial services companies in particular, data governance ensures the company’s data is accurate, secure, consistent, and compliant with regulations like GDPR, Basel III, and all anti-money-laundering (AML) legislation. This reduces the risk of financial companies facing regulatory fines and penalties, which could lead to severe financial and reputational damage.
High-quality data is essential for accurate reporting and decision-making. Strong data governance helps financial services companies manage risks, enhance customer trust, and drive business growth by ensuring data accuracy, security, and accessibility. Ultimately, better governance drives better decision-making, which improves overall operational efficiency.
The Difference Between Financial Services and Banking
The financial services industry and the banking industry are closely related. They are distinct sectors within the broader financial ecosystem. For a breakdown of data governance in the banking Industry, see my article, Data Governance in Banking: Key Insights and Best Practices .
Primarily focused on deposit-taking and lending activities, the banking industry includes institutions like commercial and savings banks as well as credit unions. Core services include accepting deposits, providing loans, and offering basic financial products, like checking and savings accounts, mortgages, and certificates of deposit. Banks often act as intermediaries between borrowers and savers.
Because of their fiduciary responsibilities, banks are heavily regulated by government agencies, including the Federal Reserve and the FDIC in the U.S. These regulations focus on ensuring liquidity, solvency, and consumer protection. They were a direct response to the bank runs of the 1930s, which highlighted the fragility of the banking system of the time. Today, banks must adhere to strict capital requirements and reporting standards to ensure the health and solvency of the nation’s banking system. Because money drives just about everything in society, the banking system mustn’t grind to a halt in times of crisis. People have to trust banks, and this trust comes at a price — regulation.
Fintech – Innovation and Decentralization
The financial services industry, however, includes companies involved in activities beyond traditional banking. Investment management, wealth management, stock trading, financial planning, insurance, payment processing, and crypto broker all fall under the umbrella term “financial services”. Investment banks, brokerage firms, insurance companies, asset management firms, crypto brokers, and fintech companies all work in this highly competitive industry. These companies focus on helping individuals and organizations manage, grow, and protect their assets. They are still regulated, but usually by different agencies depending on the services they provide: the SEC for securities providers; state regulators for insurance products. These regulations focus on transparency, fraud prevention, and market stability. They have less stringent capital requirements as compared to banks.
The Fintech industry was built on innovation. It leverages cutting-edge technologies like blockchain, AI, machine learning, and cloud computing, while focusing on agility. This allows the industry to rapidly develop and deploy new products and services. For example, crypto companies like Ethereum and Solana continuously evolve their blockchain protocols to improve scalability and security. Companies like Stripe and Square use APIs to seamlessly integrate payment solutions into their clients’ businesses. The user experience is pre-eminent. It is often what differentiates the successful and failing fintech businesses. Companies in this space offer intuitive apps, real-time services, and personalized financial products. They often cater to underserved markets, such as the unbanked or underbanked populations. Crypto wallets like MetaMask and fintech apps like Revolut and Wise provide seamless, borderless financial services.
Historical Context
The banking industry has been heavily reliant on decades-old legacy systems that were built on outdated technologies, like COBOL or mainframe systems. These systems are so deeply embedded in an operation that they are difficult and costly to replace — if replaceable at all. In some cases, these legacy systems require specialized knowledge that is, sadly, dying out, literally, because the programmers who know this technology are retiring or passing away. Young programmers tend to ignore these old programming languages, choosing to focus on Python, R, JavaScript, C#, and that oldie but goodie, SQL.
Modernization will not be easy because many banks still use legacy systems for critical functions like transaction processing, account management, and compliance reporting, which means it will be a struggle to integrate these systems with new, modern technologies, which will probably hinder innovation.
Data Governance Essentials
Discover fundamental frameworks and tools that power effective data governance initiatives.
Bad Data: Not Just an Inconvenience
In my article, Data Governance in Banking: Key Insights and Best Practices , I state, “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.” Key regulations like GDPR, Basel III, CCPA, Brazil’s General Data Protection Law (LGPD), and the UK’s Data Protection Act 2018 (DPA) all have similar goals: to govern the collection, use, and disclosure of personal data. Financial services companies have the same goal as banks do because both institutions are seen as the same in the eyes of the regulator. Many times, in the eyes of the consumer as well.
Having bad data isn’t just an inconvenience, it can be extremely expensive. In its
Financial Data Governance: Strategies, Trends & Best Practices
,
Atlan, the data democratization company, says, “Finance data governance can help in dealing
with losses from poor data management, regulatory oversight and non-compliance, data breaches.”
Gartner claims bad data can
lead to an average company loss of USD $15M annually
.
Since the enactment of laws like GDPR, there has been a proactive shift in companies’ attitudes towards data. Data governance went from a passive to an active activity,
Atlan says .
“Instead of relying on manual, reactive procedures, there’s a drive towards being preemptive with respect to real-time security and compliance. For instance, Citibank now uses predictive analytics to anticipate potential regulatory infringements, allowing them to address concerns before they become violations,” reports Atlan.
Data Quality Impact
$15M
Average Annual Loss from Poor Data Quality
Poor data quality can significantly impact a company’s bottom line, leading to substantial
financial losses and operational inefficiencies.
Good Quality Data Empowers Innovation
In contrast to working with bad data, working with good data has substantial business benefits. “Innovative organizations like Airbnb and Amazon are using good quality data to allow them to know who their customers are, where they are, and what they like,” claims Mei Yang Selvage, research director at Gartner, in Susan Moore’s How to Stop Data Quality Undermining Your Business . “Good quality data empowers business insights and starts new business models in every industry. It allows enterprises to generate revenue by trading data as a valuable asset,” Selvage adds.
Fines: Money to Burn
Financial institutions like Equifax, which faced a significant breach in 2017 and Capital One are just two noteworthy examples of the huge fallout a data failure can cause. Substantial fines and protracted court cases are often the result of data breaches, underscoring the dire consequences of data mismanagement.
In 2016, Tesco Bank, a UK-based financial institution, suffered a cyberattack that resulted in the theft of £2.5 million from customer accounts. Weak cybersecurity defenses failed to detect the attack promptly. As a result, the UK’s Financial Conduct Authority (FCA) fined Tesco Bank £16.4 million . Tesca also had to reimburse all affected customer accounts.
In 2021, the American brokerage film, Robinhood experienced a data breach that exposed the personal information of seven million customers. The breach occurred due to a social engineering attack on an employee, highlighting weaknesses in the company’s employee training and access controls. Robinhood faced regulatory investigations and reputational damage, which was already under siege at the time. In January 2025, the SEC had settled with Robinhood, assessing it a $45 million judgment because of a former data breach as well as the company’s other security violations. Customer and potential customers notice fines of this kind, so these breaches cost lost revenue for customers who turn away from the company because of its collapsing reputation.
Enterprise Data Management
Learn how to implement robust data management practices that drive business value and efficiency.
Data Governance Best Practices
As previously mentioned, data governance is a comprehensive framework designed to effectively manage an organization’s data assets. It encompasses several key components that ensure data integrity, security, and accessibility. These are particularly critical in the financial services sector. Data governance is a structured approach to managing, organizing, and controlling data within an organization, establishing policies, processes, roles, responsibilities, and standards to ensure that the company’s data is accurate, consistent, secure, and used effectively to support business goals. With a well-planned data governance framework, organizations can maximize the value of their data while minimizing risks such as data breaches, compliance violations, and poor decision-making due to low-quality or even bad data.
Develop a Data Governance Framework
A data governance framework is an 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 that a typical data governance framework includes the following key components:
Data governance policies define data management rules and guidelines.
Data governance procedures outline the steps and processes for managing data.
Data governance roles and responsibilities that define the positions, duties, obligations, 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 ripe for improvement.
Overall, 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 corporate-wide compliance. This enhances overall operational efficiency. Data governance frameworks enable effective risk management by identifying and mitigating data-related risks, which allow fintech companies to effectively manage credit, operational, and compliance risks.
A best-practice data-governance model
In its Designing data governance that delivers value , McKinsey claims, “a typical governance structure includes three components:
a central data management office (DMO), typically led by a chief data officer (CDO), with a targeted data strategy and governance leaders who set the overall direction and standards
governance roles organized by data domain, where the day-to-day work occurs
A data council that brings domain leaders and the DMO together to connect the data strategy and priorities to the corporate strategy, approve funding, and address issues.”
This structure works as the foundation for data governance, says McKinsey (see Figure 2). The employees creating and using the data oversee its management while balancing central oversight, proper prioritization, and consistency, they add.
Figure 1: A best-practice data-governance model. Source: McKinsey. Create a Data Management Office and The Data Council
A data management office (DMO) is a centralized function within an organization responsible for overseeing and managing data-related activities. The DMO ensures that data is treated as a strategic asset, aligning data management practices with business objectives, regulatory requirements, and organizational goals. It serves as the backbone for implementing and maintaining effective data governance, data quality, and strategic data initiatives.
A data council is a cross-functional group of senior leaders and stakeholders within an organization responsible for overseeing and guiding data governance initiatives. It serves as the decision-making body for data-related policies, standards, and practices, ensuring that data becomes a strategic asset aligned with the organization’s goals. The council plays a critical role in establishing accountability, resolving conflicts, and driving the adoption of data governance across the organization.
Both the DMO and data council perform critical components of an organization’s data governance framework, but they serve distinct roles and functions. While the DMO handles the day-to-day execution of data management activities, such as data quality, data architecture, and data security, the data council provides strategic direction and oversight for data governance initiatives. The DMO ensures data governance policies and standards apply across an organization. The data council makes high-level decisions about data policies, standards, and priorities. The former handles the technical and operational aspects of data management, while the latter ensures data governance aligns with the organization’s overall business objectives.
Organizational Structure
Establish effective data governance with proper roles, responsibilities, and reporting structures.
For companies choosing to implement a data governance solution, McKinsey believes an enterprise-wide analytics transformation is needed. They present the case of a leading global retailer, whose data governance was handled within IT, and who continuously struggled to capture value from their data year in and year out. The company invested in educating and involving the entire senior-executive leadership team in data and “assigned to each executive leader (CFO, CMO, and so on) several data domains, or business-data subject areas, some of which, such as consumer transactions and employee data, spanned multiple functions or lines of business,” says McKinsey.
Once the leaders understood the value of data governance, they championed it. “Within their domains, they selected representatives to act as data-domain owners and stewards and directly linked data-governance efforts to priority analytics use cases,” claims McKinsey. These representatives quickly identified priority data based on the value they could deliver and then checked in with senior management every few weeks, says McKinsey. These efforts soon paid off, allowing the organization to set up data domains within a few months rather than a few years. The amount of time data scientists spent on data cleanup was reduced as well, and this accelerated analytics use-case delivery, says McKinsey.
“Without quality-assuring governance, companies not only miss out on data-driven opportunities; they waste resources,” contends McKinsey. “Data processing and cleanup can consume more than half of an analytics team’s time, including that of highly paid data scientists, which limits scalability and frustrates employees,”
Stay ahead with practical insights and expert perspectives on modern data governance.
Conclusion
From the ancient temples of Mesopotamia acting as financial intermediaries to today’s
cryptocurrency exchange platforms, the financial services industry has a long and complicated
history with technology, evolving significantly over the past few decades. Without strong
data governance tools to keep track of data lineage, from creation to use to archiving and
onto destruction, there’s no way the industry could have been as successful as it has been.
Data governance is just the latest technology affecting the industry. As financial institutions navigate an increasingly complex landscape, the importance of accurate, secure, and accessible data cannot be overstated. Because financial institutions maintain extensive compliance and risk management records, the challenge of managing and leveraging access controls to this growing pool of data has become increasingly complex. Questionable data quality, data incompleteness, 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.
Enterprise Time Impact
30%
Enterprise Time Spent on Non-Value Tasks
In Gartner’s 2019 Global Data Transformation Survey, companies reported that “an average
of 30 percent of their total enterprise time was spent on non-value-added tasks because
of poor data quality and availability.”
Data governance frameworks ensure that organizations can harness the full potential of their data while adhering to stringent regulatory requirements. While banks grapple with legacy systems and stringent regulations, fintech companies can leverage cutting-edge technologies that drive innovation and cater to underserved markets. Both sectors, however, share a common imperative: the need for robust data governance to ensure data integrity, security, and compliance.
Data governance provides a framework that ensures data is accurate, complete, and consistent across a financial services company. By implementing strong data governance practices, fintech 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 company’s data, which is vital for strategic decision-making and operational efficiency.
155+
Successful Client Partners
25+
Years of Excellence
Transform Your Data Strategy
