The growing trend for data products in financial services

Harnessing their increasing volumes of internal and external data is crucial for banks and insurers as they look to become more agile, competitive, and innovative. Relevant, reliable data needs to be available to employees and AI to deliver value, improve performance and underpin new products and services.

All of this needs to be achieved within strict regulatory and security guidelines, protecting information and closely governing its use. How can financial services players drive value from their data, while ensuring governance and auditability? 

Gartner’s latest report, Top Data & Analytics Trends in Banking and Insurance for 2026, outlines five key areas to focus on in order to scale AI and data usage. Based on this study, this blog focuses on the first of these trends – data products – to explain how they can be successfully deployed and shared through intuitive data product marketplaces.

Understanding and responding to customer needs and changing market conditions has always been critical in financial services. Banks and insurers have to be able to make rapid, well-informed decisions at scale to maximize performance and minimize risk.

This requires seamless access to data and robust analytics to turn raw information into valuable insights. The rise of AI adds another dimension, requiring CIOs and Chief Data and Analytics Officers (CDAOs) to create strong data and technology foundations in order to effectively harness the power of Large Language Models (LLMs) and agentic AI.

These trends are leading to dramatically increased spending on IT, as highlighted in Gartner’s recent Top Data & Analytics Trends in Banking and Insurance for 2026 report. Over three-quarters (76%) of insurance CIOs and 79% of those in banking said they would increase funding for business intelligence/data analytics in 2026, according to the 2026 Gartner CIO and Technology Executive Survey. Around 30% said investments would grow by 25% or more compared to 2025.

The 2025 Gartner Business Outcomes of Technology Survey demonstrates where this money is going. It found that key priorities were to create the infrastructure for scaling AI across the business, especially around data & analytics.

At the same time, financial services organizations face specific challenges when it comes to harnessing data and AI. They must meet growing regulatory needs, be able to scale AI, and ensure they can provide a clear, auditable record of their activities and processes, particularly around security and confidentiality.

To help CIOs and Chief Data and Analytics Officers (CDAOs) invest in the right areas, Gartner outlines five trends they need to address in 2026 if they are to scale data and AI programs. These are:

• Deploying decision‐ready data products that deliver business value and justify IT budgets
• Utilizing knowledge graphs to connect data, definitions, and relationships into a semantic layer to power analytics and AI
• Implementing agentic AI for data & analytics governance to automate processes and policies
• Operationalizing embedded intelligence by embedding analytics and AI directly inside core systems

Embracing behavioral analytics to give near instant decision-making to better manage risk and improve the customer experience

Data products are ready-to-consume, curated and governed data assets that can be used across the business by both humans and AI. They are able to combine internal and external data, and are consumable through controlled, audited access. They are designed to be usable at an operational level by employees without requiring technical skills, underpinning better, faster and more informed decision-making.

Data products solve many of the key challenges around expanding data and AI usage in financial services:

• They put reliable, up-to-date data in the hands of those that need it, in a self-service format that is trusted, understandable and consumable
• They provide full control, monitoring and auditability, with built-in SLAs and data contracts reinforcing governance, lineage and privacy
• They are accessible and consumable by AI and human employees, maximizing reuse  
• They allow the monetization of data, either internally or externally, as well as supporting open finance initiatives
• As data products are built using set templates, they deliver a consistent experience and standardize their ongoing management 

Due to these advantages, Gartner states that “By 2027, 60% of insurers and 70% of banks will operationalize data products with defined owners and reliable SLAs to improve data usability and address decision latency.”

To deliver data products at scale, financial services companies have to involve the entire business, decentralizing responsibility for specific products to data owners within the organization. As these owners best understand their own data, this enables the creation of a wider range of data products that are built on genuine business needs while meeting regulatory requirements. Overall, a decentralized data product strategy maximizes access to trusted data while minimizing risk and standardizing management.

Creating data products is only the first step in making data available and consumable by employees, external partners and AI. Given the range and volume of data products that financial services organizations can generate, it is vital that they can be easily discovered and used by non-technical audiences. Without connecting data products to users, their value will be lost.

Data product marketplaces are therefore essential to scaling data product consumption. They provide an intuitive self-service interface that quickly puts the right data in the hands of those that need it, in a form that they understand and can operationalize. As Gartner states, CIOs should partner with the CDAO to:

“Launch centralized data marketplaces or catalogs to make certified data products discoverable, interoperable, and regularly updated for recency and relevance across business lines. Evaluate and select tools that offer both easy integration and ease of use for nontechnical users.”

Data product marketplaces provide a centralized, one stop shop for data for everyone, from human business users to AI models and agents. They deliver secure access to all data products and assets, at scale, through an intuitive, self-service, AI-powered experience. 

The data product marketplace underpins the creation, consumption, and management of data products in financial services through seven key capabilities:

Through their e-commerce style interface, data marketplaces connect all users with relevant data through AI-powered search and discovery. Complete machine-readable descriptions of data deliver transparency around what data products contain, backed up by standardized metadata to aid search. Authorized users can access data directly through the marketplace, without having to switch tools or locations.

Protecting data from unauthorized access is central to meeting regulatory requirements. Data product marketplaces provide granular access management controls to govern who can view specific data products, based on factors such as their role and seniority. Users can apply to gain access to specific data products, with a full audit trail governing the process.

As they record exactly who has accessed specific data products and what they have been used for, data product marketplaces deliver full lineage of data product consumption. This reinforces governance processes and helps audit which data has been used within AI, meeting regulatory needs and ensuring explainability.

Data product marketplaces provide a collaboration space around data. They bring together data product owners, users, and data teams, allowing them to communicate and work together. This breaks down departmental silos and helps create a data-centric culture across the organization.

Making data products easily available sparks the creation of new products and services, as well as the improvement of existing solutions. Collaboration and access to data delivers innovation, as well as allowing data to be monetized and made available to partners across the financial services ecosystem.

Financial services organizations are often substantial, with thousands of staff and customers, and correspondingly large volumes of data. It is essential that data products are available across the organization, and are able to be simultaneously accessed by large numbers of users in different locations. Cloud-based data product marketplaces have the ability to scale to cope with these enormous loads, in terms of user numbers, data volumes, and connections to a wide range of data sources and systems.

Rather than being a one-off report, data products are designed to meet an ongoing need. Data product marketplaces allow data owners to monitor their usage, collect feedback, make improvements and ensure they evolve to meet business needs. As with an e-commerce sales journey, they provide full metrics around the conversion funnel of discovery, access, and consumption of data products, helping demonstrate where action needs to be taken to reduce abandonment rates.

As Gartner’s report explains, banks and insurers are increasing their investment and focus on data and analytics to support AI deployments and greater data consumption at scale. Data products provide the perfect method of delivering curated, trustworthy and audited data to employees and AI. However, they need to be easily accessible and consumable by all. Data product marketplaces deliver this consumption layer, providing intuitive and secure access to data products, maximizing value and overall business performance.