TLDR: In the data-intense financial sector, a lack of trust in data stalls decision-making and hampers innovation. This guide to data governance in financial services explains how to move beyond simple compliance to create a secure, accessible, and accurate data foundation. By implementing a robust governance framework, financial institutions can streamline KYC and AML processes, safely adopt AI technologies, and deliver the personalised experiences customers demand, turning data from a liability into a high-value asset.
Author Bio
Agile is a leading data consultancy firm, founded by industry experts Steve Whiting and Owen Lewis, dedicated to helping organisations modernise, manage, and monetise their data. With a mission to unlock the power of data, their team of highly skilled, creative, and collaborative problem-solvers works with a wide range of clients, from FTSE250 to S&P500 companies, delivering tangible, value-for-money data solutions. Our unique Agile Information Management Framework (AIM) ensures projects are executed with integrity and a focus on measurable business outcomes, making them a trusted partner for organisations looking to transform their data landscape.
Navigating data governance in financial services
72% of business leaders say data volume and their distrust of data has stopped them from making a decision. That may be a very familiar scenario to leaders in financial services, where data is particularly plentiful, and decisions are highly consequential. In order to make data volume a benefit and not a hindrance, and to make the data itself an asset and not a liability, you need robust Data Governance in financial services. In an Agile Solutions survey of Financial Services data leaders, 50% said lack of Data Governance was the biggest barrier to transformation projects in their business.
The challenge of fragmented data in financial services complicates the ability to create a unified view, which is essential for informed decision-making. To overcome this, organizations must prioritise establishing clear data governance frameworks that facilitate the integration and management of disparate data sources. By doing so, they can leverage the wealth of data available to drive innovation and enhance customer experiences.
So what is Data Governance, how do you apply it, and what is its impact in Financial Services?
What is Data Governance?
In short, Data Governance ensures that data is secure, accurate, available and usable, wherever it is stored and however it is used. It starts by posing a series of questions including:
- How do we source, structure and store our data?
- How accessible is our data and who should access it?
- Is our data clean, and if not, how do we improve and maintain the cleanliness?
Then, it applies a framework that keeps data usable, accessible, and protected, with rules for data storage, data responsibility, and data stewardship.
Data Governance is a principle, a habit, and an ongoing practice — not a project.
Data Governance improves Know Your Customer (KYC) processes
Since KYC depends on accurately and up to date customer information, Data Governance supports a reliable process by protecting the consistency of data. It also improves the efficiency of KYC by enabling more detail in the data and storing and presenting it consistently and accessibly. That accessibility is of course a regulatory requirement as well as an operational advantage.
From the customer’s perspective KYC will then improve too. Accurate and accessible data makes the processes quicker, and less demanding or frustrating for the client. That means better customer satisfaction, and lower chances of losing opportunities to competitors because of an intolerably long KYC process.
Data Governance improves customer experience in Financial Services
If a bank improves its customer experience score by just 10%, it can expect 27.5% higher growth, and Data Governance will enhance any initiative or process that improves customer experience.
- Marketing and communication: better customer data means messaging that is timelier and more relevant, boosting customer acquisition, retention, and spend
- Customer service teams have access to more and better data, which means they have vital context for customer interaction, meaning they have to ask fewer questions, resolutions are quicker, and customers are less frustrated. Governance also supports AI and automated customer service (e.g. chatbots) by providing the AI models with that same data
- Richer and more reliable data powers personalisation, so an experience can be more tailored and satisfactory, with better recommendations and customer journeys
Data Governance is the foundation for safe, effective, compliant AI in Financial Services
In 2023, Financial Services collectively spent around $35 billion on AI, yet in our survey of Financial Services data leaders, 90% described their relationship with AI as ‘early-stage’.
One respondent confirmed the appetite for AI, mingled with caution: ‘We’re all treading carefully and keep asking ourselves, “When can we take the stabilisers off?”’ Another express the anxiety associated with unsafe AI: ‘We want to be absolutely sure [that AI is safe], because one minor slip-up can have big ramifications. Who wants to be a trailblazer?’ Another acknowledged that “It all comes down to the foundations, and making sure you've got the right Data Governance in place. You have to structure data in the right way and manage it properly.”
Training Data is one of the foundations of AI, and Data Governance ensures that the data you feed your AI model is rich, accurate, and comprehensive enough to create desirable, commercial, and compliant artificial intelligence outputs.
How to introduce better Data Governance in your financial organisation
Financial businesses cannot improve their Data Governance simply by buying technology or upgrading their data platforms. Rather, those tools empower the business’s vision and strategy as part of emerging or strengthening data culture. This means that organizations must prioritise cultivating a deeper understanding of their data assets and fostering collaboration among teams. Building a data management strategy that aligns with the overall business objectives is essential for driving accountability and transparency. By focusing on these foundational elements, firms can leverage their technology investments more effectively and ensure sustainable data governance practices.
This approach not only enhances operational efficiency but also mitigates risks associated with data mismanagement. By actively engaging stakeholders across the organisation, firms can ensure that building a data management strategy is a collective effort, thus fostering a sense of ownership and commitment. Ultimately, this holistic commitment to data governance will enable businesses to adapt to evolving regulatory requirements and market conditions.
Agile Solutions can help you at every stage of the Data Governance journey — from establishing your Data Strategy, to the selection of the right solutions, to the implementation of your chosen technology.
Our approach covers the four pillars of any digital project: people, process, data, and technology.
With a constant eye on the commercial objectives, we analyse your business processes to identify where governance is a priority. We then outline the roles and responsibilities of every person accountable for it — considering data literacy, current capabilities and knowledge, future vision, processes, and technologies to implement the correct, scalable support that can elevate confidence across the board.
Our enterprise data strategy framework (see how we help organisations modernise here) ensures your governance efforts are grounded in both mission and commercial goals.
To start your path to transformation, get in touch today.
Frequently asked questions about data governance in financial services
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Further reading
Downloadable guide to Data Governance
A one-page explanation of Data Governance Principles and Processes
How a bank used Data Governance and Master Data Management to streamline compliance
For guidance on shaping a robust data strategy and governance framework, visit our Data Strategy service page.