Finance Data Governance: Importance, Current State, Trends, and Success Stories

Updated August 25th, 2023

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Navigating the intricate landscape of finance data governance is pivotal in today’s digitized ecosystem. As the financial sector continues to evolve, so does the importance of ensuring effective data governance within its realms.

In this article, we look into the intricacies of finance data governance. We’ll review the fundamentals, explore the current state of the industry, and observe imminent trends that are poised to shape its future.

What is finance data governance?
The need for finance data governance
The state of data governance in the financial industry
Trends in finance data governance
Success stories with data governance in finance
Implementation and best practices
Conclusion
Related reads

What is finance data governance?

Finance data governance encompasses the comprehensive oversight and management of all data stored and maintained by financial organizations. This sphere of governance doesn’t limit itself just to banks. It pervades various financial entities, including insurance companies, real estate brokers, and real estate investment trusts (REITs), among others.

Its significance extends beyond mere compliance.

Adopting robust data governance helps financial institutions adhere more closely to regulatory standards, thus potentially reducing regulatory fines. Furthermore, finance data governance cultivates deeper consumer trust by ensuring the sanctity and accuracy of their financial data and is essential for making accurate decisions.

According to Brianna Vandre, who leads data governance activities at GitLab:

Financial data plays a vital role in decision-making, risk management, and compliance. Given the sensitive nature of financial data, it is essential to have strong finance data governance in place.

The need for finance data governance

Finance data governance can help in dealing with:

Regulatory oversight and non-compliance
Data breaches
Losses from poor data management

Let’s see how.

Dealing with regulatory oversight and non-compliance

Financial organizations, given their handling of customer data, inevitably fall under regulations such as the General Data Protection Regulation (GDPR). The consequences of non-compliance aren’t merely reputational but can also be financially burdensome.

For instance, regulators fined Danske Bank in Denmark €1.3M after it admitted it couldn’t verify the completion of its procedures for deleting customer data that was no longer relevant.

Beyond GDPR, the industry is governed by numerous laws across the globe.

In the US, these include acts such as the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), and the cybersecurity regulations of 23 NYCRR 500.

Alongside these regulations, there exist industry standards such as PCI DSS, SOC 1, and more.

Data breaches and their ramifications

Poor data management extends beyond mere compliance oversights or monetary penalties. It opens the floodgates to data breaches which can inflict severe damage on consumers.

Financial institutions like Equifax, which faced a significant breach in 2017, or Capital One in 2019, are just two noteworthy examples of fallout from data failures. Substantial fines and protracted court battles are often the result, underscoring the dire consequences of data mismanagement.

The high cost of poor data management

Bad data isn’t merely an inconvenience. Poor data quality can lead to an average loss of USD $15M annually, according to Gartner.

For the finance industry, with its high stakes and tight margins, such losses can be catastrophic. Such was the case for the Commonwealth Bank in Australia which paid out more than half a billion dollars in fines related to money laundering and terrorism.

Senior management can be affected as well. The Desjardins Group, the largest federation of credit unions in North America, replaced two senior leaders in the wake of their 2019 data leak in an effort to restore trust and mitigate disruption.

The state of data governance in the financial industry

The financial industry, with its vast reserves of consumer data and intricate regulatory web, stands at a critical juncture in the realm of data governance.

Both challenges and innovations define its current state, such as:

Data protection officers
Proactive data governance
Investments in governance tools
Security adequacy

Let’s see how.

The rise of the data protection officer (DPO)

Historically seen as an additional role, hiring a Data Protection Officer (DPO) has gone from option to mandate. Especially for sizable organizations, the GDPR has made the appointment of a DPO not just advisable but obligatory.

Notably, this isn’t limited to European companies. Global financial powerhouses, such as JPMorgan Chase, HSBC, and Goldman Sachs, have also integrated DPOs into their European operations.

A proactive approach to data governance takes center stage

The industry’s stance has seen a marked shift from passive to active data governance. 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.

This forward-thinking approach, of showcasing compliance ahead of time instead of trying to justify it after issues arise, is a proactive shift significantly influenced by the standards set by GDPR.

Investments in governance tools surge

From threat detection to GDPR compliance monitors, the technology stack is expanding toward specialized data governance and metadata management tools.

A notable inclusion is the modern data catalog, crucial for classifying data, tracing its lineage, and enforcing data governance policies.

Persistent challenges from security risks, uncertainties, and industry complexities

The financial sector is undergoing significant change, embracing the latest tech trends and innovations. Yet, the journey isn’t smooth. Several specific challenges remain, especially concerning security risks, evolving tech uncertainties, and the complexities of the industry itself.

Cyber threats are particularly concerning, with Check Point reporting 50% YoY increase in attacks in 2022. These attacks bring to the fore the vulnerabilities of financial institutions in safeguarding data.

A study by KPMG in 2021 highlighted a concerning insight: a significant 43% of banking executives confessed that their organizations weren’t prepared enough to ensure data privacy.

Further complicating matters is the task of data management across large-scale financial institutions. For instance, operations as seemingly simple as data retention and deletion can morph into significant legal complications.

Deloitte’s James Fitzgerald and Rich Vestuto state in their report on legal considerations for data deletion that a “relaxed attitude towards a key component of information governance ignores the very real collateral costs of hoarding data.”

Trends in finance data governance

The financial sector is undergoing a dynamic transformation that’s reshaping the very pillars of data governance. Here are some noteworthy trends shaping finance data governance:

Proliferation of regulatory frameworks
A surge in compliance spending
A push for unification of third-party solutions
The digital leap since 2020

Let’s explore each trend further.

Proliferation of regulatory frameworks

There’s a clear and identifiable move towards more stringent regulatory frameworks, inspired by the likes of GDPR.

As reported by OECD, even countries beyond the European Union are keenly observing its success and have already initiated or are in the process of adopting GDPR-like models, both in the US and globally.

A surge in compliance spending

Recognizing the criticality of data governance, financial institutions are putting their money where their data is.

VMWare surveyed 130 financial sector CISOs and security leaders from around the world in early 2022. 41% of the financial institutions were headquartered in North America, 29% were in Europe, 16% were in Asia-Pacific, 12% were in Central and South America, and 2% were in Africa.

These institutions were set to increase their spending on compliance by 20% to 30% from 2021 to 2022.

A push for unification of third-party solutions

The disparate nature of third-party communications has long been a bane for financial institutions. A 2022 report from Kiteworks shows that 22.5% of financial firms surveyed were keen on unifying management, tracking, and reporting mechanisms related to third-party communications.

The digital leap since 2020

Banking isn’t just about brick-and-mortar entities anymore. The shift towards digitizing assets and processes has seen many banking functions moving online.

JPMorgan Chase continued investments in digital banking platforms and tools in 2020. They expanded features in their mobile banking app, allowing customers to trade stocks and make investments right from their smartphones.

Bank of America reported a significant growth in its digital channels well into 2023, with 83% of households going digital.

HSBC undertook various digital initiatives in 2020 and 2021, including launching digital wallet offerings and expanding its mobile banking services.

Success stories with data governance in finance

Let’s look at three case studies highlighting the enormous benefits of proper data governance in terms of compliance, operational efficiency, and market responsiveness.

1. CSE Insurance

CSE Insurance is a subsidiary of Covéa, a $20 billion global insurer in the United States. The insurance firm was modernizing its data stack with AWS, Amazon Aurora, Amazon Redshift, and Tableau.

They had to tackle issues with siloed data, ambiguous data definitions and metrics, and manual migration efforts. These issues could be resolved using a metadata management and data governance tool as it would help them set up a single source of truth for all users.

With Atlan, CSE Insurance was able to set this up within 6 weeks (instead of taking 3-4 months). Their data governance efforts became seamless as migrations to Atlan also included the original tags, descriptions, permissions, etc.

Moreover, finding data assets, getting complete context, and tracing their transformations via data lineage was just a click away — bringing down the data discovery time from a couple of hours to mere minutes.

2. Austin Capital Bank

Another example is Austin Capital Bank. They faced challenges querying across disconnected AWS and PostgreSQL databases, prompting the need to optimize query mechanisms for their new digital products model.

By integrating Atlan’s data catalog, the bank revolutionized its data query mechanisms. What once was a tedious, time-consuming process is now expedited, with customer data queries streamlined to mere hours.

This newfound efficiency wasn’t restricted to just data queries. It also enabled the bank to launch new financial products at a much faster pace. The FreeKick product launch is an example.

Austin Capital Bank aimed to launch FreeKick, a deviation from their usual products, before the crucial graduation season. However, their internal CRM wasn’t ready due to new infrastructure and transactional databases, which would have required Austin’s team to be constantly on-call to support client data queries.

Using Atlan’s “Insights” allowed for rapid and efficient client service support, without the need to understand SQL. This saved a lot of time and helped in rolling out the product on time.

3. Octane

Octane Lending needed to democratize its data to enhance business intelligence and ensure a unified understanding of its data assets among different teams.

They faced challenges with siloed teams and tools, where different business segments had their own analytics approaches, leading to inconsistent interpretations and uses of shared data. The absence of centralized documentation further made it challenging to establish a common language and methodology for data, especially as remote work became prevalent during the pandemic.

By ensuring data availability to a wider audience through Atlan’s centralized, easily navigable platform, Octane did more than empower its analysts. It also ensured that the data insights they gleaned were holistic and comprehensive.

The impact on data governance outcomes was significant. For instance, the volume of questions in their internal Slack channel for data support dropped by 40% in just three months, translating to a savings of 200 hours per month.

Implementation and best practices for finance data governance

Proper implementation of data governance in the financial sector is not just about adhering to regulatory compliance. It’s about laying the foundation for enhanced decision-making, risk management, and operational efficiency.

Here’s a tailored approach for financial institutions:

Set precise objectives: Start by outlining your primary goals, which can range from maintaining compliance and elevating customer relations to optimizing operations. A tangible target could be a bank’s intent to diminish data discrepancies by x% within a specific timeframe.
Assess your data: Understand the breadth of your data - its location, users, and purpose. An initial step might involve a thorough review of all active databases and their respective operational roles.
Delineate data domains: Organize your data into clear domains like customer or transaction data. Assign respective domain chiefs and pinpoint domain beneficiaries.
Assign governance roles: Establish roles, from the overarching Chief Data Officer overseeing the strategy to data stewards ensuring quality and adherence within their domains.
Formulate a governance framework: Design a structured approach detailing data handling processes, assignment of responsibilities, and protective measures for data.
Incorporate relevant tools: Leverage essential technologies like data catalogs, quality tools, and protection software to amplify your governance plan’s potency.
Establish performance metrics: Introduce clear metrics to gauge the traction and effectiveness of your governance approach, ranging from quality checks to business results rooted in data-driven endeavors.
Promote continuous learning: Understand that data governance is evolutionary. Foster a culture of ongoing training and adoption.
Track and refine: Consistently review and adjust your metrics, conducting recurrent checks to spotlight areas for refinement and optimization.
Nurture a data-centric ethos: Cultivate an environment where data’s value is recognized universally, empowering all to base decisions on insights derived from this invaluable asset.

Conclusion

The finance industry, already a labyrinth of complexities, faces its share of challenges, as discussed earlier. We also looked at the current state of data governance in finance and the trends shaping the industry.

The numerous success stories underscore the transformative potential of robust data governance. Meanwhile, the step-by-step approach detailed above is a great starting point to ensure successful implementation.

With institutions increasingly investing in this domain, the path forward is about embracing an active approach to data governance.

Are you part of a progressive financial institution? Let’s embark on a journey together, optimizing your results through active data governance.

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