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Analysis:
Analysis

Managing data risks, unlocking trust and innovation

Key trends and challenges in data risk management

Managing data risk is crucial for financial services organizations due to regulatory scrutiny, customer expectations, and competitive pressures. Data risk affects the security, privacy, quality, accuracy, and availability of information essential for business processes and decisions. This article explores the key trends and challenges in data risk management and how organizations can use leading practices and tools to address them.

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Bytes, barriers, and breakthroughs

Effective data risk management requires identifying and prioritizing the sources and drivers of data risk across an organization. However, many companies encounter significant barriers that prevent them from developing a comprehensive and consistent view of data risk and its impact on their strategic objectives and operational performance. These challenges include a lack of accountability within the enterprise risk framework, unclear ownership of data risks, limited understanding of data risks within the first line of defense, and low visibility into the adverse impact of data risks across the enterprise, among others. Addressing these challenges is crucial for organizations to manage data risk successfully.

Financial services organizations are increasingly influenced by trends in data risk management due to evolving regulatory requirements and the adoption of data-driven decision-making and emerging technologies like artificial intelligence (AI) and machine learning (ML). Key trends include:

  • Regulatory scrutiny: Regulators have become more knowledgeable about data, demanding better organized, granular, traceable, and frequently collected data. Noncompliance can lead to significant penalties.
  • Increased confidence in data: Leadership and consumers expect high-quality, ready-to-use data. Organizations must identify and mitigate emerging data risks to maintain confidence in their data.
  • Formalized data risk management: Due to regulatory requirements, diverse product offerings, and complex operations, financial services organizations must prioritize data risk within their enterprise risk framework. This shift is moving data risk management from sporadic efforts to a more structured approach.
  • Board and management visibility: Enhanced reporting and visibility into data risks are crucial for accountability and better decision-making. This includes executive views for high-level insights, business views for specific insights and accountability, operational views for monitoring effectiveness, and custom views for tailored information analysis.

These trends highlight the growing importance of data risk management in ensuring regulatory compliance, maintaining data quality, and supporting informed decision-making in financial services.

Key tenets of managing data

Executing a data management program is a continuous process that requires coordination and alignment across the organization. Organizations focused on managing their data assets typically execute a data management program that addresses the following tenets:

This includes defining applicable data risk sub-types and developing the data risk taxonomy that the organization can follow. The definition of data risk may be tweaked and tailored to meet the business needs of an individual organization. Still, Deloitte often defines data risk as the risk that enterprise frameworks, policies, standards, target operating models, and scope of coverage are insufficient to holistically manage the organization’s data and metadata across its life cycle. It includes the risk of failure in the collection, storage, retention, use, transfer, management, protection, and/or disposal of data captured, created, or acquired by an organization.

A DRM framework may enable organizations to establish a consistent and effective approach to identifying and mitigating data risks in alignment with enterprise data strategy. The framework addresses risks associated with valuable data assets through systematic identification, assessment, mitigation, and ongoing monitoring. Several key needs, including regulatory scrutiny, drive the DRM framework. A combination of policies/procedures, technology, and key personnel typically enables it.

The framework comprises six key components: policy and accountability, risk identification and mitigation, data testing and validation, issue management and remediation, monitoring and reporting, and training and change management.

Assign clear roles and responsibilities across the organization’s three lines of defense.

Deloitte assets to accelerate DRM

Deloitte leverages its experience and assets to help clients prioritize and accelerate data risk management initiatives, whether establishing a new program or enhancing an existing one. Deloitte emphasizes the importance of establishing baseline DRM capabilities by understanding the current maturity level of the capabilities; updating and defining data risk standards (including data life cycle phases, risk inventory, and minimum expectations); prioritizing data risk types, operating units, domains, and use cases based on current maturity levels; and creating a DRM roadmap and implementation process to mature data risk management capabilities.

Deloitte offers extensive experience and various assets to help clients efficiently achieve their data risk objectives. These assets include the Data Risk Taxonomy, Data Risk Management Operating Model, Issue Management Framework, and Data Risk Metrics Library.

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Satish Iyengar

Satish Iyengar