Designing a Data Management Program That Can Handle Disruption

It is no secret that financial institutions rely on data. In the retail banking and insurance industries, the ability to efficiently ingest, process, and leverage information is especially fundamental for data driven decision-making and digital transformation. However, increasingly we are noticing the ways evolving statutory legislation, regulatory guidance, and economic priorities can disrupt an institution’s data management practices. Financial institutions should be cognizant of and proactively address the challenges that arise when these events push organizations to meet new data demands.

As our economic landscape evolves and is disrupted in ways that require expedient processing of massive volumes of data, how can financial institutions best prepare their data management teams to be able to shift priorities and perform with agility and protection?


It is a significant challenge to establish an effective and adaptive data management program. The following are examples of challenges associated with data management:

data management challenges


Our experience in helping financial institutions establish and optimize their data management program has shown that the following initiatives streamline firms’ data operations and advance their data management capabilities:

  • Establish Enforceable Policies & Standards that set tangible objectives for compliance/audit review and are aspirational enough to push the firm to close gaps between its current state and its target maturity state.
  • Drive Consistency in Data Architecture by assessing the disparities in data architecture across the firm’s key functions, and consolidating processes as well as technologies to resolve inconsistencies and capitalize on potential synergies.
  • Develop Outcome Metrics and Reporting to empower preventive and detective data controls and enable risk-informed decision-making for both frontline stakeholders and senior executives.
  • Transform the Operating Model by adjusting organizational structures and energizing business partners (e.g., IT and business) to decentralize data management and increase cost efficiency.
  • Utilize Advanced Solutions such as artificial intelligence or machine learning to automate manual or hybrid data management tasks, including metadata management, data lineage mapping, and incident management.

Financial institutions face unprecedented challenges and a volatile economy. These challenges not only created new data use cases and higher demand for data quality, but also demonstrated that data is an asset and fundamental to risk management, customer experience, and business resiliency. The rapidly changing environment and increasing regulatory scrutiny on data governance are further evidenced by the recent New York State Department of Financial Services cybersecurity enforcement action and European Union privacy shield invalidation ruling. A holistic review of data management strategy and governance model, in combination with continuous enhancement of metrics and reporting, will be crucial to support an organization’s financial health and business success.

Special thanks to contributing author Savannah Xiao.

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