What Is Data Gravity, and What Do Financial Institutions Need to Know About It in 2026?

BIZTECH: What is data gravity, and why is it such a hot topic now, more than 15 years after the term was first coined?

FISHMAN: Data doesn’t remain static. As it grows in volume, value and interconnectedness, it generates “gravity,” pulling applications, analytics and AI toward where the data lives. This phenomenon increases the complexity and cost of moving data, and makes it harder and more expensive to move. That’s data gravity.

While the concept of data gravity isn’t new, it’s especially relevant again because data is more distributed than ever, across on-premises, multiple clouds, Software as a Service platforms, edge locations and sovereign environments. That sprawl often creates silos, inconsistent governance and duplicated copies, which slows down innovation and increases risk and cost.

The increasing demand for agentic AI raises the stakes. To deliver timely, accurate outcomes, AI systems need secure, governed access to up-to-date data with predictable performance. In many cases, it’s more effective to bring compute to the data, whether on-prem or in the cloud, than to move large data sets around, particularly when privacy, residency, compliance requirements and regulatory frameworks apply.

As AI becomes an operational necessity, financial institutions are re-examining their data estates and the infrastructure that supports them. Feeding AI with high-quality, well-governed data at scale changes both the amount of data being retained and the performance, security and resiliency requirements of the platforms that store and serve it — all of which increase the effects of data gravity.

DISCOVER: Connect on-premises and cloud systems into a unified, high-performing platform.

BIZTECH: Why should financial institutions be concerned about data gravity?

FISHMAN: Financial institutions experience the effects of data gravity more intensely due to their unique position. They operate at the intersection of massive and rapidly generated data volumes, strict regulations and always-on customer expectations. The cost and effort required to move data make it entirely impractical for them to do anything but leverage their data “in place.” When critical data is fragmented across platforms and locations, it can drive up cost, increase operational complexity and slow modernization — especially for new initiatives like AI.

At the same time, financial institutions have had to build security and governance into their data operations due to the sensitivity of the data in their environments. Trying to maintain those controls over multiple, fragmented environments is cumbersome and increases the risk of a serious error. Instead, by accounting for data gravity and centralizing security and governance, they can extend those same controls to new applications as their data is used in place. Institutions that can make data securely accessible — with consistent governance, protection and performance across on-prem and cloud — are better positioned to use AI for fraud detection, risk modeling, customer personalization and operational efficiency. Building a unified, hybrid data foundation that supports innovation without compromising security or compliance can help financial institutions accelerate the adoption of AI.

BIZTECH: How can financial institutions address this issue?

FISHMAN: Financial institutions can address data gravity by reducing friction between where data is created, where it must be governed and where it needs to be consumed. That starts with eliminating silos and standardizing data services such as storage, replication, protection, governance, security posture and lifecycle management so teams can access and manage data consistently across environments.

It is also important for financial institutions to invest in data visibility and governance: knowing what data you have, where it lives, who can access it and which policies apply. With that foundation, enterprises can securely bring analytics and AI to the right data rather than creating more copies and complexity while maintaining resilience and compliance.

BIZTECH: What role does a hybrid infrastructure play in addressing data gravity?

FISHMAN: Hybrid infrastructure plays a critical role because it lets financial institutions place data and workloads based on risk, latency and regulatory requirements while still getting cloud agility. Being able to securely extend their environments to Platform and Software as a Service — seamlessly, as part of end-to-end AI workflows — makes it easier for companies to quickly adopt basic AI services, such as customer service agents, to add business value while maintaining existing data protection and governance.

When hybrid infrastructure is built on a foundation of intelligent data infrastructure, it improves workload portability and data mobility, simplifies day-to-day operations and helps institutions apply the same governance, protection and performance expectations everywhere. That consistency is especially important as firms expand into distributed architectures for real-time analytics and AI.

BIZTECH: How can financial institutions ensure cybersecurity related to data gravity?

FISHMAN: Cybersecurity and data gravity are inherently connected: As data proliferates across copies, pipelines and endpoints, the attack surface expands. Financial institutions should use a zero-trust mindset and, critically, maintain consistent access controls and policies across the entire data estate so protections follow the data across hybrid environments. Applying consistent security principles such as anti-ransomware and anti-exfiltration protections becomes increasingly difficult across heterogenous data environments.

Resilience is equally critical. Capabilities such as tamper-proof backups and compliant data-retention policies need to be built into storage environments to help enterprises withstand ransomware and restore quickly. Pair that with strong data governance capabilities, including classification, auditability and policy enforcement for residency and retention, and financial institutions have a solid foundation to keep security aligned with regulatory expectations as data and workloads expand.

READ MORE: Turn data into insights and accelerate AI initiatives.

BIZTECH: How could data gravity impact future AI implementations at financial institutions if left unaddressed?

FISHMAN: If left unaddressed, data gravity can become a practical constraint on AI adoption. Fragmented data estates result in incomplete insights, slower experimentation and increased costs due to duplicated data sets and unnecessary data movement. Teams end up spending more time engineering pipelines than delivering AI outcomes. With the AI and surrounding tech landscape evolving so rapidly and unpredictably, that time lost has a real impact on business outcomes. To create the agility needed to adapt to new ways of delivering customer or shareholder value, institutions need a data environment that is ready for whatever the future brings.

In financial services, AI and its underlying data must also meet strict requirements for privacy, residency and auditability. If data is stuck in silos — or spread across environments without consistent controls — it becomes harder to scale AI responsibly for use cases such as fraud detection and real-time risk decisions.

Companies that struggle to manage risk across their data environments need to move more slowly, which translates to less real-time innovation. An AI-ready approach built on intelligent data infrastructure ensures companies always have governed, high-performance access to data wherever it resides or however it is used, enabling institutions to bring AI to the data while maintaining control over risk and compliance. Successful IT leaders at financial organizations are building their infrastructure to be ready for anything so they can meet the rapid pace of innovation in the AI era.

Click the banner below to sign up for the BizTech newsletter for weekly updates.

BIZTECH: Is there anything else readers need to know about this topic?

FISHMAN: Data gravity isn’t inherently negative. It’s a sign that data is becoming more valuable and more widely used. The challenge is that gravity compounds over time: more data, more users, more copies and more places it has to be governed. The most successful institutions plan for that by standardizing how data is managed and protected across environments.

As AI moves from pilots to production, data storage and management become strategic differentiators because they enable rapid experimentation and agility. Institutions that invest in intelligent data infrastructure with a unified view of their data and built-in cyber resilience and governance will be able to innovate faster with AI while maintaining performance, security and compliance.