The onset of the digital age at the end of the 20th century led to a proliferation of data that revolutionized how organizations gather, analyze and leverage information. In fact, in 2017, The Economist claimed that “the world’s most valuable resource is no longer oil, but data.”¹ Fast forward to today, and technologies such as AI, social media, e-commerce and cloud computing have created an abundance of data that has profoundly changed the business world. Companies now have access to vast data sets to identify trends, predict future outcomes and improve operations. This information guides their decision-making, enhancing customer understanding and operational efficiency, and fostering innovation. In short, data is the key to increased agility and a competitive edge.

Perhaps no industry has been impacted by this data boom more than banking. Every swipe, transfer, deposit, investment, purchase, sale and loan generates a new data point. With billions of accounts worldwide, the prevalence of mobile/online banking and the growth of fintech and digital assets, banks need help navigating the staggering amount of real-time data that is available to them. With that arise both challenges and opportunities:

Challenges

• Siloed, fragmented data.
• Poor quality and inconsistent data.
• High level of regulatory compliance and privacy expectations.
• Cyber risks.
• Outdated technology to handle modern data volumes and analytics demands.
• Effective governance of emerging technologies.

Opportunities

• Operational efficiency.
• Personalized customer experiences.
• Innovation and product development.
• Use of AI and analytics to simplify regulatory compliance and growth.

Effectively managing data is a complicated endeavor, but absolutely crucial for navigating the future. To position banks for success, we have highlighted areas in which banks can unlock the full value of their data while maintaining trust, enabling transformation and driving growth. These areas include:

1. Governance and Compliance: Stakeholders are placing greater importance on both deep data knowledge and robust governance, emphasizing the ability to manage data with a high degree of transparency and accountability. A critical element across financial institutions, enterprise data offices or programs is the strengthening of an accountability model to address data management risks. A data accountability framework helps ensure that roles are well defined, data quality is maintained and compliance risks are managed, laying the foundation for responsible data use across the enterprise.
2. Modernization: Modernizing the data landscape is critical to enhancing efficiencies, improving decision-making and unlocking innovation. This involves adopting modern technologies and practices to handle data more effectively, including upgrading legacy systems to scalable, cloud-based platforms, establishing robust data governance frameworks and leveraging advanced analytics and AI.
3. AI and Advanced Analytics: Effective data management, including assessment, preparation and readiness of data, is the basis of responsible AI, helping to ensure quality, traceability, privacy and fairness throughout the entire AI lifecycle. Without these safeguards, organizations face consequences such as degraded performance, unintended bias and regulatory noncompliance. Prioritizing data management, coupled with the limitless potential of AI, empowers the entire organization to leverage AI-driven tools and models effectively and confidently, unlocking their full potential while maintaining trust.
4. Operational Efficiency: Large organizations often deal with operational inefficiency due to fragmented data strategies and inconsistent legacy processes, which can negatively impact support functions like finance and accounting operations, as well as business lines. Building an effective data strategy not only creates better practices for governance, security and modernization, it allows for meaningful changes in process for data users. Teams will find significant benefits in automation, on-demand and predictive reporting, and many other functions that would have been challenging, if not completely cost-ineffective, with poor data foundations.
5. Data Privacy: With attorneys general across the U.S. actively pursuing violations, and more than 20 states now governed by privacy laws, privacy compliance is more vital than ever. Organizations must proactively reassess their data privacy programs. Staying compliant requires implementing transparent data practices, securing consent and upholding individual rights. In addition, clearly explaining how sensitive data is managed, especially in AI environments, is essential for building trust and achieving compliance.
6. Cybersecurity: Ransomware remains a persistent and growing threat, and although it is not new, it continues to be a leading source of cyberattacks. To safeguard data, financial services organizations must adopt a risk-based approach that combines technical resilience with robust governance and policy frameworks, as well as control what data is inputted into AI tools to prevent unintended exposure.
7. Tax Transformation: An effective data strategy with integrated AI functionality can automate data management, tax calculations and reporting, which can significantly reduce errors, save time and increase the quality of data used in a complex, ever-changing, global tax environment. A modern data and AI strategy that supports real-time data, analytics and proactive tax capabilities can advance organizations in their strategic insights to stay competitive in the market.