The importance of holistic data risk management

Why data risk management matters

Data risk is the potential for harm or loss arising from inaccurate, incomplete, insecure, or poorly governed data that underpins critical business processes and regulatory compliance.

Holistic data risk management is essential in a digital first world where data provides the foundation for nearly every business process, strategic decision, and customer interaction. By adopting an integrated view of data risks across all business units, systems, and data types, organisations are better positioned to identify gaps, prevent costly mistakes, and maintain trust with stakeholders.

Organisations often struggle with planning for and identifying risk origination sources and subsequent impacts. Good data risk management provides several benefits such as:

• Support regulatory returns and alleviate potential concerns, such as Section 166 notices
• Providing assurance and confidence over internal control environments in M&A scenarios
• Clear understanding of upstream and downstream impacts as part of transformation and change programmes (e.g. system migrations and decommissioning)

Effective data risk management reduces the likelihood of regulatory breaches, financial losses, and reputational harm, and provides a foundation for innovation and competitive advantage.

Data governance related risks: Key areas of focus

Strong data governance is the cornerstone of sound data risk management.  While large institutions often follow principle-based directives such as BCBS 239, financial institutions not formally required to comply should still prioritise strong risk aggregation and reporting practices. Management of risks related to the following areas is especially critical:

• Data quality: Poor data quality undermines business decisions, disrupts operations, and increases regulatory penalties. Organisations must implement data quality frameworks, defining clear ownership, validation rules, and monitoring processes, to ensure accuracy, consistency, and reliability.
• Manual processes and end user computing (EUC): EUC applications such as spreadsheets and databases, often developed outside formal IT controls, present significant risks including errors, version control issues, and insufficient documentation. EUC risk should be addressed through inventory, risk assessment, access controls, and governance policies.
• Data privacy: With increasing regulations like GDPR and the Data Protection Act, data privacy is under intense scrutiny. Risks include unauthorised access, inappropriate sharing, and non-compliance with consent requirements. A privacy-by-design approach, ongoing training, and regular audits are essential components of mitigation.
• Data lineage: Lack of visibility into data lineage, the ability to trace the origin, movement, and transformation of data across systems, can lead to significant risks. Without clear lineage, organizations may struggle to identify the root causes of data quality issues, comply with regulatory requirements, or demonstrate data integrity during audits. Incomplete or undocumented data lineage can also hinder incident response and increase the likelihood of propagating errors into downstream systems, causing cascading data quality issues, regulatory breaches, and operational disruptions.
• Reporting: Data related risks can significantly impact the accuracy, completeness, and timeliness of risk reporting measures. Inaccurate data or gaps in data lineage may result in misleading reports, affecting management’s ability to make informed decisions and comply with regulatory requirements. Additionally, fragmented data sources and lack of standardised processes can lead to inconsistent reporting, increasing operational and reputational risks.

Industry trends and regulatory perspective

The regulatory and industry landscape for data risk is rapidly evolving. The Prudential Regulation Authority (PRA) has increased its focus on operational resilience, data integrity, and the governance of critical data assets.

The PRA’s “Dear CEO” letter to deposit takers highlights the increasing regulatory scrutiny of data risk. Poor data quality is a key driver of risk within firms. Organisations must improve data aggregation to enable effective risk management, informed board decisions, and accurate regulatory reporting. The growing use of AI increases the need for high-quality, reliable data.

Although not all firms fall under BCBS 239 standards, these principles offer a strong framework for managing data risk. The PRA plans to rely more on data tools for supervision and expects firms to submit complete, timely, and accurate regulatory returns. Data accuracy will remain a supervisory focus throughout 2025

In terms of industry trends, organisations are increasingly leveraging automation, artificial intelligence, and advanced analytics to monitor and manage data risks. There is also a greater focus on cross-functional collaboration, bringing together risk, IT, compliance, and business teams to address data risk holistically. Regulators expect firms to demonstrate not just technical controls, but also a culture of data risk awareness and proactive management.

Embedding data risk management throughout the organisation

To build a resilient and data driven organisation, data risk management must be woven into the fabric of business operations. Practical solutions include:

• Define data risk taxonomy and enterprise governance framework: Implement organisation-wide policies and standards for data ownership, stewardship, and lifecycle management to establish minimum requirements.
• Maturity assessment: Understand current maturity of data management activities.
• Define data strategy: By aligning data strategy with risk management goals, organisations can proactively address potential vulnerabilities, ensure that data assets support regulatory compliance, and embed risk considerations into every stage of the data lifecycle. This strategic alignment helps prioritise investment in data quality, privacy, and security initiatives, ultimately reducing the likelihood of data related incidents and fostering trust among stakeholders.
• Data risk controls assessment: Regularly assess data risk across all levels, prioritise based on sensitivity and criticality, and apply controls proportionate to risk levels.
• Training and awareness: Foster a risk aware culture through ongoing education programmes on data privacy, security, and governance for all employees.
• Technology enablement: Utilise tools for data lineage, quality monitoring, and access management; automate where possible to increase efficiency and reduce human error.
• EUC risk management: Create an inventory of EUC applications, assess their risks, enforce version control, and migrate critical EUCs to governed platforms as appropriate.
• Integration with operational risk: Align data risk management with broader operational risk frameworks to ensure consistency and holistic oversight.
• Regular testing and incident response: Test data management controls, run simulations, and ensure robust plans are in place for incident detection, response, and recovery.
• Continuous improvement: Review and update data risk management strategies in response to new threats, regulatory updates, and business changes.

How 4most can help

Complementary data risk assessment survey for organisations starting their data risk management journey.

Light touch assessment survey to help organisations assess and benchmark their data risk maturity. All respondents will receive an insights report on their organisation’s data risk maturity level across specific areas with SME inputs on best practices.

Comprehensive maturity assessment using the DCAM framework for organisations looking to enhance their data strategy and governance.

Authorised partner to offer deep dive assessment based on the DCAM (Data Management Capability Assessment Model) framework, which is a globally recognised standard for evaluating and enhancing data management practices within organizations. It provides a structured approach to assess data governance, data quality, architecture, privacy, and operational processes. DCAM enables organisations to identify gaps, benchmark their data management maturity, and develop targeted improvement strategies.

Conclusion

Holistic data risk management is no longer optional but a necessity for organisations of any size that wish to thrive in an increasingly complex, regulated, and data reliant environment. By addressing governance risks, keeping pace with regulatory expectations, and embedding robust practices at every level, organisations can not only mitigate threats but also unlock the true potential of their data assets.