How data management in financial services can create lasting value

Banks that treat data as an asset and shift to value-driven data management will gain a competitive advantage. 

In brief

• A value-driven data management approach allows banks to treat data as a strategic asset, accelerating insights and improving growth potential.
• The integration of AI and cloud technologies has transformed data management, allowing banks to enhance operational efficiency and drive innovation.
• Chief data officers (CDOs) play a central role in reshaping bank data management practices.

Over the past decade, US banks have invested billions in data management, primarily to address legacy issues rather than drive business transformation. This defensive approach increased confidence in the accuracy and completeness used in key reports, but it did not modernize and simplify the fragmented data environments that impact operational efficiency.

Focusing on controls and regulatory reporting was essential in the post-2008 era, but banks must now treat data as a strategic asset rather than a risk. Shifting from risk-based to value-based data management means moving beyond risk and regulatory data requirements – and instead working to facilitate teams’ ability to quickly find and use data to deliver business insights and value. The urgency for this shift is amplified by the potential of generative and agentic artificial intelligence (AI), making high-quality, accessible data vital for competitive advantage.

Cloud computing has further transformed how banks manage and utilize data. With unlimited computing power and elastic architectures, the traditional model of moving data across systems is no longer economically viable. Instead, banks can bring processes directly to the data, creating a more efficient architecture that fundamentally alters the economics of managing data at scale.

How technological advances have transformed data management in banking

Cloud capabilities have emerged as essential for banks. For decades, they operated under a “pre-Copernican” model where processes dictated data movement, leading to duplication, latency and operational drag. The advent of cloud computing has flipped this model on its head. With cloud computing, elastic storage and modern data architectures, institutions no longer need to shuttle data across systems to generate insight or enhance customer experiences. Instead, they can bring processes to the data, executing analytics, AI and applications directly where data resides.

It is a “post-Copernican” moment for financial services: The data is now the center of gravity, and the processes and associated applications, analytics and experiences orbit around it. This shift not only collapses complexity and cost but also redefines the very economics of data — unlocking speed, scalability and innovation at enterprise scale.

The rapid growth of technology and the quick adoption of AI have made AI enablement a top priority for executives. With advancements in cloud computing, big data analytics and sophisticated algorithms, it is now faster, easier and more cost-effective to collect, store and analyze large amounts of data. An MIT analysis highlights that improvements in computing allow AI models to process more information and handle complex tasks more efficiently.¹ For successful AI-driven transformation, organizations must have essential elements in place, including accessible and reliable data.

Leading the shift to value-driven data management in banking

As chief data officers (CDOs) tackle ongoing data governance challenges, the rise of generative and agentic AI presents a chance to change how data is managed and leveraged across the enterprise. Instead of just improving old systems, CDOs can rethink their data capabilities to intentionally support a value-driven approach. 

However, a 2025 EY-Parthenon survey found that banks face significant obstacles, including regulatory compliance challenges (26%), data privacy concerns (22%) and limited access to high-quality data (21%). To overcome these challenges, CDOs should prioritize improving data governance, which 79% of survey respondents identified as essential, and encourage earlier, deeper engagement with stakeholders, noted by 71%.²

CDOs should concentrate on four interrelated areas: 

Value-driven data management reorients data strategy around growth, speed and innovation, linking data investment to customer experience and business outcomes. CDOs are central to this evolution, transforming legacy data environments into engines of efficiency and profitability. By viewing data as a form of capital, banks can deliver tangible results with measurable ROI, compounding value over time.