Scaling data governance for business value through data product marketplaces – lessons from Gartner

Data governance is becoming increasingly vital as data volumes grow, AI use accelerates, and data stacks become more and more complex. To turn data into value – and to meet regulatory needs – information has to be more than just reliable and compliant. It has to be easily accessible and trusted by all to drive consumption while meeting corporate standards.

However, traditional data governance frameworks were designed to be compliance-first, policing data, rather than looking to make it available more widely. To meet changing organizational needs, Gartner argues that it is time for more agile, adaptive and flexible data governance. The need to transform governance is pressing – while 89% of data leaders surveyed by Gartner agree that effective data governance is essential for enabling business and technology innovation, nearly a third said that ineffective or inadequate processes were preventing their success.

Drawing on recent Gartner data governance research and best practice papers this article looks at how data leaders can embrace adaptive governance – and the role of the data marketplace in underpinning transformation.

Traditional, IT-centric data governance frameworks were created to control and secure data, ensuring compliance with internal standards and regulations such as the GDPR and CCPA. This approach of cataloging and controlling data is no longer enough due to:

• The growing range of decentralized data sources that enterprises now have access to, both inside and outside the organization
• New frameworks and architectures to manage data, such as data mesh, which spread responsibility for data across the organization, rather than in a central IT team
• Fluid, and fast-changing business needs, especially around AI. Business users now need self-service access to trusted, relevant data to ensure performance and better decision-making.

According to Gartner, governance is an area of primary responsibility for over two-thirds (68%) of data and analytics leaders. They understand this need to transform at a strategic and operational level. Nearly three-quarters report either making minor or major changes to their governance approach over the last year to ensure it remains relevant and business-focused.

Adaptive governance is designed to balance risk management with value creation. It achieves this by personalizing how governance is enforced to specific business contexts, tailoring processes to the actual environment and needs. It helps deliver:

• Operational agility, reducing governance delays within data workflows 
• Enhanced data quality, with more effective and automated processes
• Integrated governance within new services or data products, accelerating their creation and deployment

Essentially, rather than applying a “one size fits all” governance model, it adapts governance to specific business needs to balance compliance and innovation. Gartner analyst Guido De Simoni describes this in more detail in the User Guide for an Adaptive Data and Analytics Governance Technology Stack, published in January 2026.

Achieving adaptive governance requires a focus on two areas  – the technology stack and the governance models deployed within key use cases.

The data governance technology stack

The data and analytics governance technology stack provides a multi-layer framework that brings together technology, people and process. It has five layers, supported by transversal capabilities such as active metadata management, DataOps and PlatformOp. In ascending order these are:

• • Infrastructure: Cloud platforms and hardware to handle data processing and storage
  • Platform: A unified environment for data ingestion, transformation, storage, and model training/deployment

• Al-Ready data layer: data curation, qualification and monitoring to ensure data underpins specific AI use cases 

• Products/marketplaces: creating standardized, reusable data products, packaged with quality metrics to promote business reuse
• Analytics, Bl, Data Science, and Al: delivering actionable insights through business intelligence and AI models

Governance models

Adaptive governance enables businesses to be flexible about the type of governance they deploy in specific use cases. Essentially, these styles have a different balance of risk management and business access, designed around the scenario and context.  

• Control: This is the tightest, most centralized form of governance, suited to high-risk use cases or where regulatory requirements are strict, such as around financial data.
• Outcomes: A more balanced approach that provides more flexible governance, for more dynamic business processes or areas such as customer experience.
• Agility: The ability to grant access to data is more distributed, enabling faster, localized decision-making. This enables self-service data sharing and DataOps.
• Autonomous: Governance and access decisions are made and monitored in real-time by systems themselves, with minimal human involvement. Use cases include AI models deployed in real-time operations or fraud detection.

The technology governance stack has to be able to support all four of these styles, if necessary simultaneously around different use cases. This requires a flexible, scalable and closely-integrated architecture that adapts to specific business needs.

Achieving adaptive governance, and data governance more generally, is a journey. It evolves as business needs change, particularly around data maturity and AI deployments. As set out in Gartner’s A Journey Guide to Successful Data and Analytics Governance It is a multi-step, end-to-end process. 

Step 1: Gain foresight and get grounded

Understand where you are now and where improving data governance will deliver measurable business improvements. Look at key organizational objectives (such as increasing efficiency or reducing risk), and understand which data governance elements will support these. Create backing by introducing and discussing the concept of data governance early with senior stakeholders, focusing on the specific benefits it will deliver to them. 

Step 2: Build the value case and gain buy-in

Move beyond initial conversations with stakeholders by communicating the purpose and value of data governance in language that will gain their buy-in. Be clear on the business outcomes it supports, elevating it from being seen as a technical project. Create an initial plan, including structures, roles and success metrics, and then socialize this through detailed conversations with stakeholders. Take on board feedback and adapt to gain sign-off.

Step 3: Execute 

Formalize the structure of your data governance program and how it will operate on an ongoing basis. Make the program visible and understandable through business-focused materials, such as a data governance charter that explains the benefits and demonstrates senior stakeholder buy-in. Select specific adaptive governance types (control, outcomes, agility, autonomous) for different use cases, based on business context.

Step 4: Scale and sustain

Ensure that you are continually monitoring progress against agreed KPIs. Highlight successes and use learnings to drive improvements. Once initial KPIs are met, look to expand and scale the program at a manageable pace in line with business needs to ensure continued engagement.

The data product marketplace is a key layer in Gartner’s data governance stack. It enables controlled self-service data sharing that balances compliance with maximizing data value. 

Through their e-commerce style interface, data product marketplaces make it easy and intuitive for business users and AI to discover, understand, access and consume data products and other data assets. 

They enforce governance standards, integrating with the wider tech stack to deliver an overall architecture that drives greater data consumption while ensuring data is reliable, trustworthy and protected. 

Key data marketplace governance capabilities include:

• Granular access management, including role-based access rights, linked to corporate directories
• Full security, including MFA and SSO to protect data
• Ability for users to request access to specific data, with audited workflows to ensure compliance   
• Support for data contracts and data product standards
• Built-in quality and metadata standards
• Full audit trail for data lineage, usage and access
• Information security compliance with frameworks such as ISO 27001, ensuring audit readiness and risk management
• Governance and regulatory support to align with internal policies, industry standards, and external regulations

Thanks to these features, data product marketplaces not only underpin adaptive data governance, they also turn data into business value, supporting governance business cases and enabling programs to scale effectively over time.