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The volume of data passing through financial systems has been rising at an accelerating rate for more than forty years. This phenomenal growth has meant that data governance and management has played a not particularly effective game of catch up, wherein increasing data volumes lead to ever greater data governance and management challenges.

As a consequence, banks are constantly engaged in data cleansing exercises, report-focussed thinking and updating siloed spreadsheets. Despite its centrality to all business processes, this has prevented decision-makers from seeing data as the critical – and valuable – asset it is.

By GreySpark’s Ben Sallows, Consultant, and Rachel Lindstrom, Senior Manager

In this article, the first in a series of articles that explore the role of data governance and management within banks, GreySpark aims to highlight the importance of approaching data as an asset that can drive value-adding improvements in banks.

Increasing Regulatory Scrutiny

Even where banks are not seeking to use data to gain a competitive edge, per se, the importance of data management cannot be underestimated as global regulators push firms toward an enhanced data governance programme that streamlines data management and data reporting to facilitate better management of risk. In 2022, US regulators, the SEC and CFTC, issued combined penalties of more than USD 1.8bn to 15 major financial institutions for violating electronic communication and recordkeeping regulatory provisions. In addition, regulators fined several firms for failing to prevent customer identify theft, which they attributed to a lack of reasonable data governance on the firms’ behalf.

As well as facing large penalties for data management failures, banks face increasing risk from the growth in real-time processes in automated trading, operations and client interactions, evidenced by a corresponding rise in fines by regulators. The UK’s Financial Conduct Authority (FCA), for instance, issued GBP 313mn in fines, marking a significant increase of 65% year-on-year.

Data Governance Considerations

There is no denying that banks are increasingly allocating more time and resources towards the management of their data. However, many firms lack the strategy and framework to effectively utilise it and, consequently, expend significant effort on the location and preparation of data in order to be able to use it. Figure 1 illustrates the different ratio of in time spent prepping data vs locating data vs using data by organisations with a data strategy and those organisations without one.

Figure 1: The Proportion of Time to Locate and Prepare Data vs the Time it is Used for Banks without and with a Data Strategy
Source: GreySpark analysis

Data as an Asset

Unlike a financial asset, which can be accounted for on a balance sheet, data is intangible. As banks are increasingly realising greater volumes of data as part of their operations and business processes, the value of their data is multiplying. The challenge for banks is how to distinguish valuable data from non-valuable data in order to direct their data management resources toward data with the greatest potential for driving profitability and avoiding penalties.
Certain data types are valuable across the bank, while other types are valuable only to specific functions with the firm. In order to understand where and why each data set or data type is valuable, consistent data valuation rules must be applied across the firm.

Data Quality

Ensuring that the data used by a bank is of a high quality is key to good data management. Poor quality of data is costly to firms, in one way or another, and while some costs are easily understood – such as reduced revenues or increased regulatory penalties – the cost to banks in terms of inefficiencies, low productivity, increased compliance effort and the inability to innovate are less so and, therefore, are not easy to measure or forecast. Yet, these often-unmeasured costs can be critical to determining an organisation’s success and profitability.

Enhancing the quality of data is a continuous exercise that requires a robust data strategy supported by senior management, which ensures a sustained focus on enhancing systems and processes to produce and build quality data.

Data Strategy

Ensuring that the right data is available to support business strategy objectives can only be achieved when a bank’s data strategy is aligned with its business strategy. Mapping business processes to the data strategy creates an understanding of the data techniques, tools and processes required to maximise existing business processes and services. Many banks use an enterprise data model, such as the model in Figure 2, to understand the data entities and data attributes specific to each business process. Not only is this key to enriching existing processes, but it also ensures firms retain effective oversight of their data across the enterprise, remain compliant with regulation and manage the cost of third-party data.

Figure 2: A High-level Enterprise Data Model
Source: GreySpark analysis

Data Roles and Responsibilities

Each bank’s data strategy should also align with its operating model. Articulating the existing and required roles and responsibilities ensures that suitable resources are available for every aspect of the data change management roadmap.

Traditionally, banks took a centralised approach to data strategy, wherein data stewards / owners reported to an enterprise-wide committee responsible for data, typically known as the Data Management Organisation (DMO), as depicted in Figure 3. An alternative to the traditional approach used more recently by some banks is to apply the data strategy across the whole organisation, but allow it to manifest in a way that was suitable to the operational shape of each area of the bank.

Over the last few years, a more applicable model has begun to appear, wherein the DMO retains a centralised role, but is able to adapt how the data strategy is applied to facilitate innovation and the development of new digital solutions and services (see Figure 3).

Where organisations adopt this hybrid model, firms can maintain the overall data strategy via the DMO, which can delegate responsibilities to business unit teams to address specific challenges and maintain a greater focus on specific business priorities.

Figure 3: Traditional and Hybrid Approach to Data Management
Source: DAMA-DMBOK, GreySpark analysis

Transitioning from Data Problems to Data Opportunities

With increasing pressure from regulators to streamline data management, reporting and risk management, banks are effectively being forced to treat data as an asset and focus on the degree to which it will be able to meet the present and future demands of an ever-evolving financial technology landscape. Banks must align their data strategy with their business strategy and operating model to release the value from their data.

For the data-driven bank to become a reality, it is critical that firms meet their data management and data governance challenges by implementing an enterprise-wide strategy that is fit-for-purpose, enriches existing operations and business processes, and remains futureproof. Then a new era for the governance of data will truly have begun.

GreySpark’s Data Management and Analytics practice is designed to assist clients in the governance and management of their data as well as the implementation of analytical solutions. We work closely with our clients to solve both strategic and analytical data problems.

Ben is a Analyst Consultant with experience working on advisory projects looking at key controls and systems for managing risk, and has delivered data protection and governance assurance, advising organisations on data and regulatory compliance.