Forrester Wave - Data Governance Solutions Q3 - 2025

 

The Forrester Wave™: Data Governance Solutions, Q3 2025

 

 Leading Providers Bet On Agentic AI With A Vision Of Self-Driving Governance

Data governance has outgrown its compliance roots: In today’s AI-fueled and data-saturated enterprise, it’s the control plane for trust, agility, and scale. The next frontier isn’t just about managing metadata — it’s about activating it. Agentic AI is setting the pace for the market, promising self-driving governance systems that automate classification, policy enforcement, and remediation while keeping humans in the loop. Recently announced acquisitions in the industry reflect the growing importance of this shift — leading technology providers are prioritizing data governance as a cornerstone for enabling AI readiness and advancing agentic AI capabilities. Looking forward, as organizations shift from managing data to monetizing it, the most advanced solutions are enabling governed, reusable data products to flow through dynamic marketplaces — fueling AI models, accelerating insights, and unlocking new value streams across the business.

Data governance solutions customers using this evaluation to inform a purchase decision should consider providers that:

1. Infuse governance workflows with automation and AI. The most advanced platforms embed AI across the stack — from classification and policy detection to rule generation and remediation. Agentic AI is emerging as a key differentiator, enabling systems to act autonomously while encoding human intent. While many capabilities remain nascent, the direction is clear: Governance is becoming self-steering. Buyers should assess how vendors are operationalizing AI — not just as a feature, but as a foundation for scale, consistency, and speed.
2. Empower business users and stewards with intuitive, intelligent experiences. Governance must scale beyond specialists. Business users, data stewards, and domain experts need no-code access to trusted data, along with intelligent guidance to use it responsibly and effectively. The strongest platforms combine role-based personalization, embedded collaboration, and AI-driven recommendations to support data literacy, accelerate insight, and reduce dependency on central analytics teams.
3. Align governance capabilities with business value creation. Governance is evolving from a defensive posture to a strategic enabler. The most mature platforms support governed data sharing, AI model readiness, and the creation of reusable and insight-ready data assets. Buyers should look for solutions that align governance with business outcomes — enabling faster decision-making, reducing risk, and laying the groundwork for intelligent data products and dynamic data marketplaces.

Evaluation Summary

The Forrester Wave™ evaluation highlights Leaders, Strong Performers, and Contenders (see Figures 1 and 2). We intend this evaluation to be a starting point only and encourage clients to view product evaluations and adapt the findings based on their priorities using Forrester’s interactive provider comparison experience.

Figure 1 - Forrester Wave™: Data Governance Solutions, Q3 2025Figure 2 - Forrester Wave™: Data Governance Solutions Scorecard, Q3 2025

Leaders

Atlan

Atlan entered the market in 2018 with a goal of modernizing metadata management. Its solution has evolved into a data governance platform, with an architecture centered on a metadata lakehouse and active metadata automation.

1. Strategy. Atlan’s strategy focuses on activating metadata as a dynamic layer for governance, collaboration, and AI enablement. The company’s roadmap mirrors its vision to set AI-native governance, proactive policy execution, and role-based personalization at the core of an intuitive and intelligent governance engine. With an integration-first approach, the solution enables seamless alignment with existing data environments, supporting fast deployments and embedding governance directly into daily workflows. Its superior adoption across customer organizations reflects its ease of use and commitment to data literacy, equipping users to better understand, trust, and translate data into insights.
2. Capabilities. Atlan offers features that are among the best in class for policy management, stewardship, and collaborative governance. Its knowledge graph and AI-powered automation support clear data ownership, surfacing policy-relevant context and automating governance workflows. Built-in collaboration tools and personalized experiences drive adoption across technical and business users. To better support federated teams, Atlan should improve access to granular reporting metrics and executive dashboards.
3. Customer feedback. Users consistently praise Atlan’s ease of use, flexibility, and responsiveness. They highlight its intuitive UI, strong lineage and smart search capabilities, and seamless integration across the data stack. Atlan is a Customer Favorite in this evaluation.
4. Forrester’s take. Atlan is a top choice for organizations seeking a modern, AI-native governance platform that blends intelligent automation with deep integration and broad user accessibility.
   
   View Atlan’s detailed scorecard.

Alation

Founded in 2012, Alation looks at data governance through an agentic lens that prioritizes automation, embedded intelligence, and business alignment. The platform has evolved from a traditional catalog into a modular end-to-end governance solution.

1. Strategy. Alation’s differentiated strategy centers on shifting governance from passive documentation to intelligent, agentic workflows. Its bold vision is to organically embed governance into daily workflows and align metadata with business outcomes. To execute its vision, the company has strategically optimized its product organization to accelerate delivery. It has also refocused its roadmap and investments on AI-native capabilities: agent SDKs for automation, data product builders for reuse, embedded data trust controls, and a semantic marketplace to surface context-rich data products tailored to business needs.
2. Capabilities. Alation delivers excellent capabilities in cataloging and collaborative metadata curation, with AI-powered features for data enrichment and policy enforcement. A key differentiator is its data marketplace and product builder modules, designed to operationalize governance and accelerate data-to-value workflows. In recent years, Alation has listened closely to its customer-led community and laid a solid foundation for AI governance, with early investments in intelligent policy suggestions, automated stewardship triggers, and context-aware recommendations to support scalable and low-friction governance. Data observability is a key area for improvement.
3. Customer feedback. Customers highlight Alation’s intuitive UX, flexible integration, and superior collaboration features as standout strengths that help drive broad adoption across business and technical teams. Native data quality and observability are high on customers’ wish lists.
4. Forrester’s take. Alation is ideal for enterprises embracing federated governance and seeking an agentic, AI-augmented platform to embed intelligence into everyday workflows.
   
   View Alation’s detailed scorecard.

Informatica

Founded in 1993, Informatica delivers enterprise-grade data governance through its Intelligent Data Management Cloud (IDMC). The platform unifies cataloging, quality, privacy, and governance into a single, cloud-native architecture, powered by its CLAIRE AI engine.

1. Strategy. Informatica’s strategy focuses on enabling trusted, AI-ready data through metadata-driven governance. Backed by over $250 million in annual R&D spend and recent acquisitions in privacy and access management, it’s expanding its AI capabilities. Its roadmap emphasizes agentic automation, AI governance, and intelligent policy enforcement, led by CLAIRE Copilot and CLAIRE GPT, listening closely to the clear feedback of its extensive customer community. While ideal for enterprise initiatives, the platform’s scale, complexity, and pricing models can challenge adoption by smaller teams without stronger support for platform usage.
2. Capabilities. IDMC offers superior metadata management and glossary creation as well as strong automated lineage, with a generous range of connectors and deep field-level scanning. It excels at data enrichment and has best-in-class quality and impact analysis, with CLAIRE powering classification, policy detection, and rule generation. Unified remediation workflows with intelligent automation are key differentiators, while advanced functionalities, such as group-based approvals and parallel processing, are among its customers’ enhancements requests.
3. Customer feedback. Customers value Informatica’s comprehensive cataloging, classification, and integration capabilities; they frequently cite CLAIRE’s automation and the platform’s scalability as strengths. They note clearer roadmap communication, simplified adoption paths, and composable pricing models as areas for improvement.
4. Forrester’s take. Informatica is a strong choice for enterprises that manage complex data estates and want a scalable, AI-powered platform with a clear agentic vision and unified governance across metadata, policy, and quality.
   
   View Informatica’s detailed scorecard.

Collibra

Collibra was founded in 2008 in Belgium. It offers a mature, configurable governance platform with a metadata management core. The vendor’s customers include large enterprises with highly regulated governance needs and legacy data estates.

1. Strategy. Collibra’s strategy is grounded in delivering a flexible, enterprise-grade solution. Its knowledge graph and customizable operating model remain core differentiators, supporting its vision to unify and enable access to data across platforms and roles. Collibra’s roadmap reflects its excellent innovation efforts and strong community involvement, showcasing AI-assisted policy enforcement, semantic modeling, and deeper integration with popular collaboration tools. However, Collibra’s pace of delivering anticipated enhancements has been uneven.
2. Capabilities. The platform excels in classification, data observability, and policy modeling, supporting complex regulatory frameworks. Its lineage and impact analysis features suit audit and compliance use cases, and its workflow engine supports granular role-based responsibilities. Collibra allows for complex configurability, but this can backfire: Policy enforcement customization can require manual efforts, posing challenges for organizations seeking faster time to value or speedy companywide rollout. Collaboration features have improved significantly in recent years but remain an area for improvement.
3. Customer feedback. Customers recognize the depth and flexibility of a solution crafted into one seamless platform but cite a steep learning curve among nontechnical users. They also note the slower rollout of sought-after features like AI governance and data productization as challenges.
4. Forrester’s take. Collibra is best suited for enterprises with complex governance requirements and that require deep configurability.
   
   View Collibra’s detailed scorecard.

Strong Performers

data.world

Founded in 2016 and recognized as a B Corp-certified company, data.world offers a knowledge-graph-based governance platform. Its lightweight, cloud-native architecture targets intuitive UX and fast time to value, particularly in hybrid and modern data lake environments.

1. Strategy. Powered by its semantic knowledge graph, data.world’s strategy centers on metadata-driven collaboration. Positioned as a modern alternative to legacy platforms, data.world prioritizes usability and extensibility over deep vertical specialization. It’s differentiated by its pricing model and transparency. Its roadmap follows market developments, focusing on AI-driven enrichment, workflow automation, and tighter integration with cloud-native ecosystems; however, better long-term communication to manage customer expectations would strengthen the vendor’s codevelopment model.
2. Capabilities. The data.world platform stands out for its semantics, issue remediation, and intuitive stewardship support capabilities. Its collectors simplify metadata ingestion across diverse sources, and its evolving lineage capabilities cater for clear, interactive visualizations. Role-based personalization and discussion features further support superior collaborative governance. However, it currently lacks native capabilities for observability, data masking, and anonymization. While its AI governance efforts are solid, it’s less suitable for enterprises that require sophisticated automation workflows, integrated policy orchestration, or AI governance at scale.
3. Customer feedback. Customers consistently highlight ease of use, strong modern data stack integration, and reliable customer success teams. Areas for improvement include workflow maturity, more strategic onboarding, and more versatile AI functionalities.
4. Forrester’s take. The data.world platform is a strong fit for organizations seeking fast deployment, intuitive collaboration, and flexible architecture.
   
   View data.world’s detailed scorecard.

Ataccama

Ataccama delivers a unified platform that combines cataloging, data quality, observability, and governance in a single, modular architecture. Its automation-first approach and hybrid deployment model support efforts to consolidate tooling and reduce integration overhead.

1. Strategy. Ataccama’s strategy centers on convergence — offering a single platform to manage profiling, cleansing, lineage, and policy enforcement. Its roadmap emphasizes genAI, agent-based automation, and AI-assisted remediation. While its unified vision is appealing, broad coverage can limit depth in areas like AI governance and advanced policy modeling. To strengthen community engagement, Ataccama should better leverage its platform and growing initiatives to enable effective peer-to-peer knowledge sharing, particularly within industry verticals.
2. Capabilities. The platform scores highly in data quality, observability, and profiling, with robust anomaly detection and remediation workflows. Its lineage engine supports accurate cross-system tracing and impact analysis, while glossary and policy features are tightly integrated with classification and access control. Ataccama’s AI agent automates rule creation and profiling, although broader AI governance and workflow reusability are still evolving — making it less suited to those prioritizing deep AI governance or advanced policy orchestration. The UI is clean and functional, although some advanced features require custom configuration or scripting.
3. Customer feedback. Customers highlight Ataccama’s strengths in data quality, lineage, and automation, along with its transparent pricing. Areas for improvement include workflow reusability, AI governance maturity, and a dedicated marketplace for data products.
4. Forrester’s take. Ataccama is a strong fit for enterprises seeking a unified, automation-driven platform to manage data quality and governance at scale.
   
   View Ataccama’s detailed scorecard.

OvalEdge

OvalEdge was founded in 2013 in Alpharetta, Georgia. It offers a data governance platform with a modular design, no-frills approach, and flexible deployment capabilities that targets a broad range of use cases and fast time to value.

1. Strategy. OvalEdge’s strategy focuses on democratizing governance through simplicity and affordability. Delivering on its vision to eliminate fragmented approaches and enable faster time to value, OvalEdge is investing in market-aligned functionalities like a native AI assistant, off-the-shelf templates, and advanced lineage and visualization tools; however, its innovation strategy is more conservative than its peers. It has a customer-driven roadmap, with frequent releases and strong support from a responsive team. The platform’s practical, value-focused approach resonates with cost-conscious buyers.
2. Capabilities. OvalEdge scores well in stewardship workflows as well as security and compliance capabilities. It offers solid metadata crawling and advanced features like the AskEdgi assistant and governance recipes to support the stewardship persona, while source-code-based lineage enables streamlined workflows for a wide range of roles. AI governance and advanced policy modeling are also solid. However, its advanced data quality, scalable glossary automation, and complex marketplace features are still evolving.
3. Customer feedback. Customers highlight OvalEdge’s affordability, responsive support, and ease of use. Areas for improvement include UI polish, advanced data quality capabilities, and enhanced onboarding assistance.
4. Forrester’s take. OvalEdge is a strong choice for midsize enterprises and those in regulated industries seeking a comprehensive, budget-friendly platform to scale governance with speed and flexibility.
   
   View OvalEdge’s detailed scorecard.

Precisely

Precisely acquired Infogix in 2021 to enhance its data governance and analytics capabilities. Its Data Integrity Suite is a modular, end-to-end platform that integrates governance, quality, enrichment, and observability to drive measurable business outcomes.

1. Strategy. Precisely’s vision is to unify data governance with quality, enrichment, and spatial analytics into a single, accessible platform. The Data Integrity Suite’s common foundation streamlines metadata management, security, and AI-powered intelligence to reduce inefficiencies and accelerate adoption; however, its innovation strategy is somewhat pragmatic and incremental. It has a customer-informed and well-structured roadmap, but some customers report delays in feature delivery and inconsistent communication. The vendor’s approach — underpinned by a robust partner ecosystem and supported by consulting expertise — enables effective adoption and measurable outcomes.
2. Capabilities. Precisely delivers solid capabilities across observability and policy enforcement. Its governance metrics reporting capabilities are noteworthy differentiators, while it offers strong semantic detection, enrichment datasets spanning various domains, and data valuation methodologies. However, its lineage management capabilities are limited: They often require manual effort and lack AI-driven inference. AI governance and data masking features are also underdeveloped.
3. Customer feedback. Customers appreciate Precisely’s intuitive UI, strong classification engine, and responsive support, praising its ease of use and competitive pricing, particularly for smaller enterprises. Key areas for improvement include lineage automation, workflow agility, and enhanced masking capabilities.
4. Forrester’s take. Precisely is a strong fit for organizations seeking a modular, business-friendly platform that balances governance, quality, and enrichment — especially if ease of use and pricing flexibility are priorities.
   
   View Precisely’s detailed scorecard.

BigID

Founded in 2016, BigID has strong security and privacy DNA, from which its current governance platform has evolved. Its modular architecture and deep ecosystem integrations provide the foundation for a solution that prioritizes security, compliance, and AI governance.

1. Strategy. BigID’s strategy is built around agentic AI and security-first governance, redefining how organizations identify, manage, and protect structured and specifically unstructured data; enforce policies; and adapt to evolving regulatory requirements. It continues to advance AI copilots, regulatory-aware agents, and autonomous remediation through its customer-informed roadmap; however, innovation remains centered on security and compliance, with governance enhancements steadily gaining traction. While pricing transparency is an area for improvement, BigID is a trusted and resourceful partner within the ecosystem, collaborating with a wide range of technology solutions providers to deliver customer value.
2. Capabilities. BigID scores highly for its security and privacy features; it provides native controls for masking, anonymization, and automated risk detection to ensure Zero Trust access and proactive threat mitigation. Lineage acquisition and visualization is an area for improvement, with limited depth in contextual mapping and actionable insights for cross-system dependencies and transformations. Its solid business glossary is enriched with metadata and AI-driven suggestions, but BigID needs to add the sophistication seen in competitors for scaling data literacy and managing enterprisewide terminology.
3. Customer feedback. Customers praise BigID’s scanning, classification, and privacy capabilities, along with its professional services. Common asks include improved lineage visualization, scalable glossary automation, and more clarity and versatility in pricing models.
4. Forrester’s take. BigID is ideal for enterprises seeking a modular, scalable, AI-native platform to unify governance, privacy, and security — especially in regulated, data-rich environments.
   
   View BigID’s detailed scorecard.

Quest Software

Originally developed by erwin, erwin Data Intelligence by Quest became part of Quest Software via its acquisition in 2021. It combines cataloging, governance, quality, and marketplace capabilities into one platform, with integration into erwin Data Modeler and a focus on AI readiness.

1. Strategy. Quest Software’s strategy centers on delivering a unified platform that bridges technical and business users via a flexible governance framework and a model-to-marketplace maturity model. Its roadmap emphasizes AI data readiness, marketplace enrichment, and expanded data quality capabilities. Quest Software continues to deliver steady, customer-driven innovation. While its vision is shifting to a stronger AI focus, it lacks clarity and has yet to be effectively communicated. The suite’s integration with Quest Software’s broader portfolio and its focus on practical implementation position it well for incremental growth, particularly in highly regulated industries.
2. Capabilities. Quest Software delivers superior capabilities in data observability and internal marketplace functionality, with standout features like automated data value scoring and AI model certification. The platform supports structured data well and is expanding into unstructured sources. While its glossary, enrichment, and collaboration tools are solid, they lack the depth and automation of leading competitors; its AI governance capabilities are still maturing. Quest Software’s flexible metamodel, mind map visualizations, and integrated data quality engine are key for organizations seeking an end-to-end, reliable governance engine.
3. Customer feedback. Customers praise Quest Software’s ease of use, integration with erwin Data Modeler, and responsive customer support. They would like to see a simpler UI, external marketplace provisioning, and more thorough documentation.
4. Forrester’s take. Quest Software is a strong fit for organizations seeking a practical, integrated platform to scale governance from data modeling to AI readiness.
   
   View Quest Software’s detailed scorecard.

Contenders

OneTrust

Founded in 2016, OneTrust has grown from a privacy management provider into a unified platform that brings together data governance, privacy, and AI governance. The platform aims to help organizations navigate regulatory requirements with integrated policy automation and regulatory intelligence.

1. Strategy. OneTrust’s strategy focuses on compliance-first governance, integrating privacy, consent, and AI risk management within core processes. However, its vision isn’t distinctly articulated and is underwhelming given the speed and expectations of the market. Its roadmap emphasizes AI governance, policy orchestration, and data readiness, but its prioritization and communication with customers on deployments reflect a reactive approach. Its rich partner ecosystem is a key strength, though adoption beyond core compliance and privacy teams is still gaining traction.
2. Capabilities. OneTrust delivers superior policy enforcement and privacy automation and strong ethical AI governance; it earns high marks for its ability to support compliance workflows and regulatory intelligence. Its classification capabilities are strong, using AI to identify and categorize sensitive data across diverse environments. However, its cataloging and lineage acquisition capabilities lag, offering limited automation and depth for metadata management and visualizing data flows. Enrichment capabilities could also benefit from modernization, as they reduce the platform’s ability to enhance metadata dynamically or support advanced data quality monitoring.
3. Customer feedback. Customers value OneTrust’s privacy and data AI readiness strengths, integration flexibility, and platform support. They seek improvements in pricing model flexibility, lineage visualization, and role-based UI personalization.
4. Forrester’s take. OneTrust is best suited for organizations in highly regulated environments prioritizing regulatory compliance, privacy, and AI readiness.
   
   View OneTrust’s detailed scorecard.

IBM

IBM offers data governance capabilities through its Cloud Pak for Data platform, anchored by Watson Knowledge Catalog and integrated with tools like Manta for lineage and watsonx for AI governance.

1. Strategy. IBM’s strategy focuses on delivering enterprise-scale governance via a modular platform that supports cataloging, glossary, and stewardship workflows. Its vision emphasizes AI readiness and self-service access, and while IBM is investing in watsonx to modernize its governance stack and is advancing its AI governance capabilities, its execution lags its peers: Its roadmap lacks clarity and urgency in addressing usability, collaboration, and AI governance automation. Its partner ecosystem and regulatory alignment are strengths, but adoption support and roadmap transparency remain weak points.
2. Capabilities. IBM delivers solid cataloging, glossary, and stewardship features as well as on-par performance in data discovery, semantics, and observability. However, it scores below par in impact analysis and policy compliance due to limited automation and actionable insights. It also needs to improve collaboration, where it faces high demand for customizable UI features and collaboration tool integrations. IBM’s strengths lie in its enterprise-grade catalog and glossary, but it lacks the intuitive UX and automation depth of competitors, making it less suitable for those wanting a modern UX, collaboration, and AI governance automation.
3. Customer feedback. Customers appreciate IBM’s cataloging and glossary capabilities as well as its implementation support. They cite challenges with usability, collaboration, and the speed of innovation, particularly in lineage, policy management, and data productization. IBM didn’t provide reference customers for this evaluation.
4. Forrester’s take. IBM is a good fit for enterprises seeking a scalable, catalog-centric governance platform with strong glossary and stewardship support.
   
   View IBM’s detailed scorecard.

SAS

Founded in 1976 in Cary, N.C., SAS is a pioneer in analytics software and solutions. The vendor offers data governance capabilities as part of its SAS Viya platform.

1. Strategy. SAS positions its governance capabilities in the context of its analytics heritage, emphasizing data readiness for AI and decisioning. While the platform showcases foundational governance features, SAS doesn’t aim to compete directly with governance-first vendors. Its roadmap includes enhancements to data quality monitoring, glossary workflows, and AI governance, but current capabilities are largely SAS-centric and focus mainly on technical users. SAS fails to articulate its vision and innovation strategy clearly, and its community and pricing transparency lag behind competitors.
2. Capabilities. SAS supports data stewards and analysts with integrated tools for cataloging, profiling, and ensuring data quality, enabling them to align governance with analytics workflows. However, gaps in advanced governance features — such as semantics, complex lineage visualization, and AI governance — limit its ability to provide automation and actionable insights for complex use cases. While the platform supports classification, glossary linking, and data masking, features like dashboards and policy enforcement are still in development or require custom configuration. SAS’s strength lies in its integration with the broader Viya ecosystem and support for analytics-driven governance.
3. Customer feedback. Customers appreciate SAS’s data quality and profiling tools and its ability to support analytics workflows. They note gaps in collaboration features, policy management, and marketplace functionality. SAS didn’t provide reference customers for this evaluation.
4. Forrester’s take. SAS is best suited for organizations that have invested in the SAS ecosystem and need integrated data quality and governance to support analytics and AI initiatives.
   
   View SAS’s detailed scorecard.

Vendor Offerings

Forrester evaluated the offerings listed below (see Figure 3).

Figure 3 - Evaluated Vendors And Product Information

Evaluation Overview

We evaluated vendors against three categories:

1. Current offering. Each vendor’s position on the vertical axis of the Forrester Wave graphic indicates the strength of its current offering.
2. Strategy. Placement on the horizontal axis indicates the strength of the vendors’ strategies, including elements such as vision and innovation.
3. Customer feedback. A halo on a vendor’s marker indicates above-average customer feedback relative to the other evaluated vendors. A double halo indicates outstanding customer feedback: We consider the vendor to be a Customer Favorite. As part of this evaluation, we speak with up to three customers of each vendor. We also consider customer input from our previous research.

Vendor Inclusion Criteria

Each of the vendors we included in this assessment has:

1. A standalone data governance solution that integrates into open data infrastructures. We didn’t consider vendors whose data governance solutions primarily focus on their proprietary platform, solution, or infrastructure. The solutions in this evaluation can be implemented and used outside of the vendor’s own ecosystem.
2. Broad, enterprise-level support for data governance functionalities. The vendor natively provides all core functionalities for this space and has a demonstrated track record for supporting large enterprises. We didn’t include vendors that provide an offering solely in one data governance segment (e.g., data security, privacy and compliance, data strategy and process solutions, analytics and insights support, data sharing and commercialization).
3. Coverage of at least two major geographies. We only considered vendors that have business presence and revenue in at least two major geographic areas (e.g., North America, Europe, and/or Asia Pacific).
4. More than $25 million in annual category revenue. The vendor must have at least $25 million in annual revenue from its data governance solutions product in the last four quarters to participate in this Forrester Wave.
5. Mindshare among Forrester’s enterprise clients. Forrester clients frequently mention the product as one they’re considering prior to a purchase. We have heard about the product from our clients in the form of inquiries, advisories, consulting engagements, and other interactions over the past year. Other vendors mention this vendor as a competitor.

Other Notable Vendors

The Forrester Wave evaluation is an assessment of the top vendors in the market; it doesn’t represent the entire vendor landscape. You’ll find more information about this market and additional vendors that Forrester considers to be notable for enterprise clients in our corresponding report: The Data Governance Solutions Landscape, Q1 2025.

Supplemental Material

The Forrester Wave Methodology

A Forrester Wave is a guide for buyers considering their purchasing options in a technology marketplace. To offer an equitable process for all participants, Forrester follows The Forrester Wave™ Methodology to evaluate participating vendors.

In our review, we conduct primary research to develop a list of vendors to consider for the evaluation. From that initial pool of vendors, we narrow our final list based on the inclusion criteria. We then gather details of product and strategy through a detailed questionnaire, demos and briefings, and interviews with customers (vendors may provide up to three reference customers; we also consider feedback from other customers we’ve spoken with). We use those inputs, along with the analyst’s experience and expertise in the marketplace, to score vendors, using a relative rating system that compares each vendor against the others in the evaluation.

We include the publishing date (quarter and year) clearly in the title of each Forrester Wave report. We evaluated the vendors participating in this Forrester Wave using materials they provided to us by April 25, 2025, and did not allow additional information after that point. We encourage readers to evaluate how the market and vendor offerings change over time.

In accordance with our vendor review policy, Forrester asks vendors to review our findings prior to publishing to check for accuracy. We score vendors that met our defined inclusion criteria but declined to participate in or contributed only partially to the evaluation in accordance with our vendor participation policy and publish their positioning along with those of the participating vendors.

IBM and SAS declined to participate in the full Forrester Wave evaluation process. For vendors that are not full participants, Forrester uses primary and secondary research in its analysis. For example, we might use public information, data gathered via briefings, and independently sourced customer interviews to score the vendor. We may ask the vendor for an abbreviated briefing and/or to provide reference customers. We may also rely on estimates to score vendors.