Organizations need data and AI catalogs to successfully manage, govern, and deliver data and AI insights to their teams. Implementing a data and AI catalog provides a solid foundation but is challenging for most organizations. Data and AI professionals should use this template to understand the most critical steps to prepare for and implement a data and AI catalog. In addition, this template will give you a place to determine and track who is responsible for each step — a data steward, business subject matter expert (SME), data engineer, or AI steward.

The Forrester data and AI governance model connects data and AI governance as complementary and interdependent disciplines that must work together to augment business outcomes of assurance, control, enablement, intelligence, and trust. Data, technology, risk, and AI leaders must build trusted data foundations and governed AI capabilities and synergy to shift governance from a limiting force to one that unleashes innovation by supporting trust, assurance, control, enablement, and intelligence across the enterprise.

Data governance plays a critical role in enabling organizations to use data safely and effectively across analytics and AI initiatives. Successful data governance requires coordination across a cross‑functional group of stakeholders, including business, data, technology, and risk leaders, who collectively make decisions about data ownership, quality, access, and reuse. To help organizations assemble and guide this governance team, Forrester has created the Data Governance RACI Matrix. This tool helps organizations understand which leaders should be responsible, accountable, consulted, and informed for core data governance activities across the data governance responsibility areas.

Governance leaders have a narrow window to prove that governance fuels progress, not bureaucracy. Whether you’re building from zero or evolving a partial setup, the first 90 days are crucial to define a mandate, activate ownership, and deliver visible wins. This report provides a sequenced plan for data and AI governance leaders to stand up lightweight governance services, embed guardrails in delivery, and measure impact. It draws on Forrester’s governance model to help you productize governance, activate metadata, and align to outcomes like privacy, compliance, and self-service.

Increasingly, AI is transitioning business applications and digital experiences to a distributed landscape that runs on connected intelligence. It requires a complete reset in data strategy and technology to develop and run AI and ML models and ensembles in the cloud and on devices. Technology architecture leaders can use the Forrester Connected Intelligence Framework to help master the transience of data in AI applications and meet the new demands of distributed data.

To scale AI, deliver real-time insights, and keep data synchronized across distributed ecosystems, enterprises should adopt a connected intelligence paradigm. The Forrester Connected Intelligence Framework provides a proven model for orchestrating data, AI, and applications across edge, cloud, and core systems. By leveraging strong partnerships, agile practices, and modern platforms, the framework enables continuous intelligence and resilient operations. This report introduces the framework and outlines eight essential activities to operationalize connected intelligence for trusted, scalable outcomes.

The SAP-Reltio Deal Is About Who Controls Your Future Architecture We have seen a lot of M&A activity trying to capitalize on AI hype. SAP’s acquisition of Reltio isn’t that. Instead, the acquisition is a strategic, pragmatic shot by SAP to own the master data layer and make the future enterprise architecture more interoperable, particularly […]

What Modern Enterprises Need From Data Quality Platforms In the age of AI, data quality has become one of the most critical capabilities to promote trust in data and AI for modern enterprises. As organizations scale generative AI (genAI) and agentic AI, data quality solutions now sit at the forefront of enterprise success in the […]

AI took center stage at Reltio DataDriven 2026, where global data and AI leaders aligned on one urgent priority: turning trusted, real-time, contextual data into scalable AI and measurable business impact. Reltio made a clear strategic bet: The next enterprise AI bottleneck isn’t model choice or orchestration but shared context — rebranding its platform around […]

In our evaluation of data quality solutions vendors, we identified the most significant ones and researched, analyzed, and scored them. This report shows how each vendor measures up and helps you select the right one for your needs.