Magic Quadrant for Analytics and Business Intelligence Platforms
29 June 2026 - ID G00840721 - 45 min read
By Anirudh Ganeshan, Christopher Long, and 1 more
This Magic Quadrant assesses analytics and BI platforms as the market moves toward agentic AI, governed semantics, and AI-augmented decision support. Vendors are differentiated by execution, ecosystem alignment, and their ability to scale secure self-service and interoperable analytics.
Market Definition/Description
Analytics and business intelligence (ABI) platforms prepare, model, analyze and visualize data to support decision making. They deliver insights through AI-powered conversational experiences, interactive dashboards and classic reporting. They support collaboration between business and technical users for defining the dimensions, measures and business rules used to create and maintain semantic models. The platforms provide functionality for agentic analytics, where AI agents coordinate tasks across the data-to-insight workflow to automate insight delivery under governance and audit controls.
Analytics and business intelligence platforms integrate data from multiple sources, such as databases, spreadsheets, cloud services and external data feeds, to provide a unified view of data, breaking down silos and transforming raw data into meaningful insights. They also allow users to clean, transform and prepare data for analysis, in addition to creating data models that define relationships between different data entities.
Modern ABI increasingly embeds agentic analytics — AI agents that orchestrate tasks across the data‑to‑insight workflow (semiautonomously or autonomously) to accelerate insight delivery while keeping humans in the loop for oversight and strategy. This shift complements and, in part, displaces time spent in curated dashboards with automated, conversational and dynamically generated insights.
These platforms enable collaborative semantic models (consistent dimensions, measures and business rules), natural‑language query and automated insights that surface drivers, anomalies, clusters and forecasts with transparency and audit trails. Agent workflow orchestration coordinates data prep, analysis, visualization, narrative generation and action triggers under governance, lineage and policy‑as‑code controls, ensuring explainability and trust at scale.
Typical benefits of leveraging ABI platforms include:
- Insight velocity and cost‑to‑serve: AI agents automate repetitive data preparation and analysis, compressing cycle time from data to action and lowering operating costs compared with manual processes.
- Proactive, embedded experiences: Embedded, context‑aware analytics deliver narratives and recommendations inside business workflows, increasing adoption among nontechnical users.
- Governed autonomy and explainability: A robust semantic layer, lineage tracing, bias detection and human‑validation checkpoints build confidence in agent‑generated outputs and support compliance.
- Improved decision quality and confidence: These platforms provide timely, relevant and contextual insights that empower business users to make informed decisions, reducing reliance on intuition and increasing strategic alignment.
Mandatory Features
- Analytics governance: Refers to the set of capabilities that ensure secure, compliant and efficient management of analytics platforms. It includes controlling access, certifying content, managing life cycle policies, monitoring usage, optimizing performance, and enforcing auditability and policy-as-code to maintain trust and transparency across all analytics workflows.
- Data preparation: Supports drag-and-drop, user-driven combinations of data from different sources and the creation of analytic models, such as user-defined measures, data pipelines, sets, groups and hierarchies.
- Agentic insights: Agentic insights leverage AI agents to autonomously or semiautonomously surface insights such as anomalies, drivers, clusters and forecasts. These agents orchestrate tasks across the data-to-insight workflow, using active metadata and user feedback to deliver personalized, explainable insights under governance and audit controls.
- Conversational analytics: Enables users to interact with data through natural language — typed or spoken — and receive dynamically generated narratives and explanations. This capability supports intuitive querying and contextual storytelling, adapting responses to user roles, preferences and analytical complexity.
- Semantic modeling: An abstraction layer that supports both visual and code-based authoring of business logic, including entity-relationship modeling, hierarchies and calculated measures. It provides virtualization and headless access to govern data consistency across interactive dashboards, embedded analytics and agentic workflows. This capability unifies metrics with metadata context, enabling complex multifact joins, life cycle management, and the grounding required for GenAI and automated insights.
Optional Features
- Embedded analytics: These capabilities integrate analytical capabilities directly into business applications, websites and portals, enabling collaboration and communication of insights. They also support interactive and customizable reports and dashboards, offer robust API and SDK integration, allow data write-back to various sources and automate data-driven workflows to trigger business actions.
- Insight delivery: This capability provides a container for the content created by users or AI agents, such as dashboards, pixel-perfect and paginated reports that can be scheduled and shared with a large user community.
- Analytics catalog: Portal-like curation and collaboration of ABI content, enabling users to share, find, search, comment and certify dashboards, reports and datasets from a diverse range of platforms in one place.
Magic Quadrant
Vendor Strengths and Cautions
Alibaba Cloud
Alibaba Cloud is a Challenger in this Magic Quadrant. Its primary analytics and business intelligence (ABI) platform is Quick BI, which is actively transitioning from a traditional analytical tool to an AI-driven, agentic platform centered around its Smart Q assistant. While Alibaba Cloud maintains a dominant market presence in China, it continues to expand its global footprint across nine international regions. Its typical customer profile includes large enterprise organizations spanning the retail, automotive, and food and beverage sectors. Recent and future investments focus heavily on a “Data + AI” strategy, upgrading semantic layers, and deepening AI capabilities to move users from basic insights to autonomous business actions.
- Ecosystem synergies: Alibaba Cloud leverages its vast corporate ecosystem, including the DingTalk and Tmall platforms, to drive widespread user adoption. This provides organizations already utilizing Alibaba’s digital infrastructure with a seamless and scalable analytics deployment path.
- Flexible commercial pricing model: The vendor offers highly adaptable pricing options, and has introduced token-based consumption pricing. This allows organizations to economically scale their AI-driven workflows and agentic capabilities alongside traditional subscription models.
- Advanced analytical agents: The Smart Q assistant provides robust agentic capabilities, incorporating multistep attribution analysis and knowledge fusion across structured and unstructured data. This enables business users to execute complex statistical evaluations, such as RFM and DuPont models, directly within the platform.
- Geographic dependency: Despite ongoing international expansion efforts, Alibaba Cloud’s market momentum and community support ecosystem remain predominantly concentrated within China.
- Cross-platform interoperability: Quick BI supports third-party cloud sources and JDBC custom connections, but heterogeneous deployments may require additional setup and operational effort versus Alibaba-native use.
- BYOLLM limitations: Quick BI’s supported models vary by deployment. The domestic version defaults to regional models (e.g., Qwen, DeepSeek), while the international version supports selected models used outside the region (e.g., Gemini, ChatGPT); buyers should validate model availability for their target region.
Amazon Web Services
Amazon Web Services (AWS) is a Leader in this Magic Quadrant. Its analytics and business intelligence platform, Amazon Quick, focuses on delivering agentic AI capabilities, conversational analytics, and seamless integration with the broader AWS data ecosystem. AWS operates globally, serving a vast client base of midsize and large enterprises across the financial services, healthcare, manufacturing and retail sectors. Recent and future investments reflect a shift in AWS’ ABI roadmap beyond dashboards toward an AI-powered workspace and agentic workflows. Amazon Quick brings together Quick Sight (BI), Quick chat (customizable conversational agents), Quick Research, Quick Flows and Quick Automate, bridging the gap between insight discovery and operational action.
- Cloud ecosystem and scale: AWS integrates analytics with its native data and analytics stack, which facilitates large-scale deployments and the centralized management of multimodal data.
- Pricing and cost predictability: Amazon Quick offers organizations predictable cost planning and controlled scaling through its transparent, tiered pricing model that includes choices between user-based and capacity-based options, bundling AI capabilities into higher tiers.
- Agentic workflow orchestration: Amazon Quick enables users to bridge the gap between insights and autonomous execution by supporting natural-language-triggered, multistep agentic workflows that automate analysis and initiate operational actions across connected systems. This includes integrations with market tools such as Jira and ServiceNow, as well as industry-standard integrations such as OpenAPI and Model Context Protocol (MCP).
- Confined deployment options: While Amazon Quick can connect to leading cloud providers, it has richer flows for AWS data sources and services, which may reduce appeal for organizations operating in multicloud or non-AWS-centric analytics architectures and on-premises sources.
- Skill availability: Compared with more established enterprise ABI platforms, Amazon Quick has a smaller practitioner community, which may limit access to readily available skills.
- Fragmented administration: Administrators must rely on multiple AWS services outside Amazon Quick for monitoring, auditing and cost visibility, which may increase management overhead.
Databricks
Databricks makes its inaugural appearance as a Visionary in this Magic Quadrant. Its offering spans the Databricks Platform and the native Databricks AI/BI and Genie experience, positioning BI directly on the lakehouse rather than as a separate layer. Databricks operates globally, focusing on large enterprises, including many global organizations across key industries. Recent and future investments signal a shift to agentic analytics, expanding Genie (previously Databricks One) for business-user access and enhancement of agentic, multistep reasoning, and Agent Bricks for building operational AI agents.
- High-concurrency serverless scaling: Databricks uses serverless execution to autoscale workloads and support high user concurrency, combining precomputation and filtering techniques to help organizations deliver predictable performance without manual infrastructure management.
- Unified governance and security: The platform provides content security and governance through its Unity Catalog, which enforces granular, attribute-based access controls, row filters and column masks across all enterprise data assets to ensure consistent, centralized oversight.
- Advanced conversational analytics: Users can execute complex multistep analyses, such as forecasting and temporal analysis, because Genie translates natural language into governed SQL and visualizations, while maintaining conversational memory to adapt to interim results and answer open-ended and follow-up questions.
- Enterprise-focused sales strategy: Databricks prioritizes large enterprise deployments, which can raise adoption and cost barriers for smaller organizations seeking lightweight or stand-alone analytics solutions.
- Platform coupling: The analytics capabilities are tightly integrated with the broader Databricks platform, impeding deployment as a stand-alone ABI tool outside the platform’s unified data architecture.
- Operational reporting constraints: Databricks’ operational reporting is less mature than traditional ABI platforms, with stronger support for interactive dashboards than for pixel-perfect, paginated report delivery at scale.
Domo
Domo is a Challenger in this Magic Quadrant. Its product, Domo AI and Data Products Platform, provides data source connectivity to create interactive, embedded analytics experiences and data apps for both no-code and pro-code users. Domo has a global customer base across the Americas, Europe and Asia/Pacific, primarily serving midmarket and enterprise organizations across key industries. Recent and future investments focus on building an intent engine that gives AI agents contextual business memory, which enables users to move from static dashboards to dynamic, conversational AI interfaces that turn insights into actions.
- Low-code data preparation: Domo’s Magic ETL provides a visual, drag-and-drop experience to build and reuse dataflows, and recent updates allow governed datasets protected by Personalized Data Permissions (PDPs) to be used as inputs while maintaining row-level security policies.
- AI orchestration: Domo supports building AI agents with human review points, and its MCP Server enables tools such as Claude and ChatGPT to securely query Domo datasets and documents.
- Composable delivery model: Domo supports both internal analytics and external-facing embedded dashboards and portals through Domo Embed. It can also extend insights into actions using workflows and lightweight app patterns.
- Limited semantic modeling: Domo’s semantic modeling features are limited and require some manual model setup, but can leverage AI agents for tasks such as generating, refining, documenting, or validating definitions and relationships. Moreover, it does not currently support formal knowledge graph connections or internal usage pattern harvesting.
- Pricing and consumption model: The vendor’s consumption-based pricing impacts the customer’s ability to budget for credit expenses, though it includes access to the full feature set to test business value.
- Support and community: Gartner clients often report that support is slow and sometimes has an overwhelming amount of information. Additionally, the platform’s smaller user community can limit its adoption and support.
GoodData.AI
GoodData.AI is a Visionary in this Magic Quadrant. Its offerings, GoodData Cloud and GoodData Cloud Native, use a headless strategy to deliver a composable data application platform. It serves customers globally across the financial services, hospitality, communications, and industrial IoT sectors. Recent and future investments focus on building a comprehensive AI skills repository following its acquisition of Understand Labs, and introducing agent and workflow builders supported by its “analytics-as-code” approach to enhance automation and ensure strict governance.
- Headless semantic layer: The platform functions as a reusable, governed semantic layer that supports consistent metric definitions across dashboards, embedded applications and APIs, with strong support for white-labeling and programmatic control through SDKs.
- Enterprise-grade embedded deployment: The platform’s multitenant architecture and governance model are well suited for large-scale embedded analytics deployments, particularly in regulated industries requiring isolation and consistency across regions.
- Agentic extensibility: GoodData.AI provides an MCP-based agentic framework that supports external language models and third-party tool integration. AI agents can securely query governed semantic models across structured data and approved unstructured content, while operating within enterprise controls.
- Agentic orchestration constraints: GoodData.AI offers limited native support for autonomous, multistep workflow execution, often requiring custom integration through APIs or external orchestration tools to achieve end-to-end automation.
- Decreased market visibility: GoodData.AI shows lower Gartner inquiry volume and search activity compared with peers, which can limit awareness and consideration outside its core embedded analytics use cases.
- High technical adoption barriers: The platform’s analytics-as-code approach requires strong data engineering skills and mature governance, and its agentic AI capabilities are less turnkey than some buyers may expect.
Google is a Leader in this Magic Quadrant. Google’s Looker is a multicloud-architected ABI platform offering highly governed analytics with semantic layers, self-service visualizations, dashboards and an API-first approach. Looker operates globally, serving startups to large enterprises across industries. Recent and future investments focus on advanced reasoning for conversational analytics capabilities for both business users and developers, introducing native natural language queries, and a conversational analytics API with content guardrails to ensure governed, high-performance insights.
- Unified semantic layer: Looker utilizes LookML to create a centralized, code-based “single source of truth” that guarantees metric consistency and strict compliance across the organization. This governed semantic layer ensures that every downstream application and AI model consumes accurate, standardized definitions without the risk of conflicting data logic.
- Analytics governance: Looker excels in analytics governance through its semantic layer, hierarchical permissions, and a new certification framework that clearly signals content trust levels and enables admins to control who certifies assets. Comprehensive auditing, native Git-based version control, and customizable agents for monitoring platform usage ensure strong oversight and transparency across the analytics environment.
- Gemini 3 reasoning engine: The engine is Looker’s integrated intelligence layer, enabling complex strategic analysis for business users through advanced natural language abstract reasoning. For developers, it is embedded directly into the workflow to accelerate engineering with reliable LookML auditing and automated writing.
- Developer-centric perception: Looker is still widely perceived by Gartner clients as a tool primarily for developers and data engineers, though it has added self-service and natural language features that are appealing to business users.
- Google-centric value proposition: Looker is marketed most strongly as part of the Google Cloud analytics stack, so non-Google-centric buyers may see fewer differentiating ecosystem benefits, despite being able to deploy it with other data platforms and cloud environments.
- Reporting and distribution limitations: Looker emphasizes interactive analysis and embedded use cases, with more limited support for highly formatted, pixel-perfect reporting and complex batch distribution.
IBM
IBM is a Visionary in this Magic Quadrant. Its core offering, Cognos Analytics, supports enterprise reporting, semantic modeling, and self-service analytics across both cloud and on-premises environments. IBM primarily serves large global enterprises with demanding governance and scalability needs in highly regulated sectors requiring strict data control. Recent and future investments focus on integrating AI agents into daily workflows, supporting customer-provided large language models, and releasing Certified Containers for hybrid deployments, while continuing investment in IBM-managed cloud offerings.
- Deployment flexibility: IBM offers Cognos Analytics through parallel deployment options, including IBM-managed SaaS (Cloud Hosted single-tenant and Cloud On-Demand multitenant), and customer-managed deployments as stand-alone software or Certified Containers for Kubernetes across on-premises and public cloud environments.
- Enterprise reporting: The platform supports paginated and customized reporting for internal and external distribution at scale, with comparable capabilities across on-premises, containerized, and managed cloud deployments. AI agents can also assist with report creation.
- Prebuilt industry content: IBM provides prebuilt analytics content for sectors such as banking, healthcare and manufacturing. The platform delivers these capabilities through customizable data models, reporting templates and analytics workflows that align with specific departmental requirements while maintaining centralized enterprise governance.
- Market visibility: The vendor shows decreased visibility among Gartner clients compared to competitors, reflected in lower search volumes and client inquiries.
- Stand-alone analytics ecosystem: Though Cognos integrates with many enterprise applications, they lack a native enterprise application suite or digital workplace platform, which limits the vendor’s ability to drive organic adoption.
- Confined SaaS deployment options: Cognos Analytics SaaS is presently delivered on IBM Cloud, which may limit alignment with organizations standardizing on other hyperscalers. IBM has indicated plans to broaden availability, with AWS-based SaaS targeted for general availability in 2026.
Incorta
Incorta is a Niche Player in this Magic Quadrant. Its analytics and business intelligence offering focuses on rapid data modeling, data acquisition and visualization for complex enterprise applications like Oracle and SAP. Incorta’s presence is concentrated in North America and the Middle East, primarily serving large enterprises with complex ERP environments across key industries. Recent and future investments focus on positioning Incorta as a foundational data platform for enterprise AI, and expanding agentic capabilities through its Nexus platform and its marketplace of prebuilt domain applications.
- Accelerated time to value: Incorta streamlines data preparation by bypassing traditional data transformation processes with its Direct Data Mapping (DDM) engine, integrating complex operational data rapidly. This approach delivers fast operational insights and has fueled strong customer retention and business momentum.
- Deep domain-specific market fit: The vendor demonstrates a highly targeted product strategy by offering prebuilt Data Apps optimized for complex ERP ecosystems. These packaged solution accelerators feature predefined schemas and operational KPIs that rapidly deliver actionable insights for manufacturing, supply chain and finance departments.
- Advanced analytics governance: Incorta delivers governance controls through its certified business views, which serve as a single, trusted definition for enterprise analytics. The platform enforces fine-grained row- and column-level security natively or inherited from source systems, tracks end-to-end data lineage, and provides a crowdsourced trust layer to prevent multiple versions of the truth.
- Restricted geographic market presence: Despite strong growth, Incorta’s market momentum remains heavily concentrated, generating the vast majority of its revenue from North America and the Middle East. This limited global expansion, coupled with a smaller overall customer base, may present challenges for international organizations seeking localized support and skilled professionals.
- Platform positioning: Incorta overlaps with data management and warehouse automation use cases, which may increase competitive pressure as enterprise application vendors embed native ABI capabilities.
- Broader analytics ecosystem fit: Incorta’s strengths are most pronounced in operational and application-centric analytics, which may limit its appeal as a general-purpose BI platform for cross-domain or exploratory use cases.
Microsoft
Microsoft is a Leader in this Magic Quadrant. Its primary analytics offering is Power BI, which operates as a critical component of the Microsoft Fabric platform. Operations are highly global, and typical customers span all organizational sizes across nearly every industry, particularly those already invested in the Azure ecosystem. Recent and future investments focus on the Microsoft IQ initiative to provide contextual data for AI agents and on transitioning from standard semantic models to ontologies, enabling automated workflows and read/write capabilities directly within the analytics environment.
- Dominant market presence: Power BI is widely deployed, which can simplify access to internal skills, partner support and training. This can help accelerate rollout and ongoing support.
- Productivity application synergy: The platform is deeply integrated with productivity applications like Teams and Excel, which helps organizations standardizing on Microsoft infrastructure to drive user adoption and streamline enterprise deployments.
- Consolidated data and analytics: Microsoft Fabric unifies data engineering, warehousing and BI in one SaaS platform, reducing integration across fragmented stacks. Fabric capacity also provides access to the broader environment, including Real-Time Intelligence (RTI) and Fabric IQ.
- Dependence on the broader Fabric environment: Power BI’s full value is increasingly tied to Fabric capacity and broader platform adoption, with OneLake serving as the shared data foundation across workloads.
- Shared capacity management risk: Copilot usage is centralized on a designated Fabric capacity, requiring active user assignment and monitoring to manage consumption and maintain performance.
- Workspace proliferation risks: The ease of workspace creation and content publishing frequently leads to duplication of dashboards and reports, as well as fragmented semantic models, requiring administrators to implement strict life cycle management policies to prevent conflicting data definitions.
Oracle
Oracle is a Visionary in this Magic Quadrant. Oracle Analytics Cloud (OAC) and Oracle Analytics Server are the core ABI engines in Oracle’s analytics portfolio, underpinning both the Oracle AI Data Platform (AIDP) and Fusion Data Intelligence (FDI). Oracle operates globally, primarily serving enterprise organizations within the Oracle ecosystem across key industries. Recent and future investments focus on specialized AI assistants tailored to different roles, including analysts, business consumers and data engineers, to streamline conversational data exploration and automated data pipeline creation.
- Business application integration: Oracle’s FDI delivers prebuilt, role- and industry-specific analytics through OAC, integrating with Autonomous Data Warehouse to provide real-time and predictive insights within business workflows.
- AI enhancements: OAC provides role-specific AI assistants for analysts, business users and data engineers, with an emerging ambient AI experience that adapts to user context to explain insights and guide analysis through conversational and visual interactions.
- Multimodal data preparation: Oracle Analytics Cloud uses OCI Enterprise AI services to blend structured data with unstructured sources such as PDFs and images, automatically extracting and enriching content for analysis, reducing manual preparation effort and enabling faster insights.
- Platform coupling: OAC is often adopted within the broader Oracle ecosystem despite stand-alone availability, and is less frequently evaluated as an independent analytics layer in non-Oracle environments.
- Market visibility: OAC has relatively lower visibility among Gartner clients, reflected in fewer inquiries and Gartner.com searches than its competitors.
- Limited BYOLLM capability: The OAC AI Assistant and conversational features no longer support BYOLLM, relying instead on built-in models, Oracle Database API profiles, or the OCI Generative AI service.
Qlik
Qlik is a Leader in this Magic Quadrant. Its primary offering, Qlik Cloud Analytics, is delivered as a SaaS platform comprising Qlik Analyze, Qlik Predict, Qlik Answers and Qlik Automate. Qlik operates globally, primarily serving enterprise and midmarket organizations across all industries. Recent and future investments focus on a capacity-based pricing model; advancing its agentic analytics roadmap, reflected in Qlik Answers for exploring structured and unstructured enterprise data; and emphasizing data integration and data quality as core capabilities.
- Comprehensive agentic AI vision: Qlik Answers applies a coordinated, swarm-style agent architecture to support conversational analysis across structured and unstructured data, generate dashboards, and autonomously route alerts or trigger actions through Qlik Automate.
- Associative technology differentiation: Qlik’s in-memory analytics engine enables free exploration without preset query paths, helping users and AI agents uncover relationships and validate AI-driven queries while maintaining performance at enterprise scale.
- Deployment flexibility: Qlik supports on-premises, cloud and hybrid deployments, along with integration across a broad range of data sources and enterprise systems.
- Missing proprietary cloud ecosystem: Although Qlik adopts a flexible, cloud-agnostic approach, it lacks a proprietary cloud infrastructure service or widely adopted enterprise business application suite. This can limit adoption in hyperscaler-led consolidation efforts.
- Architectural cloud integration concerns: Qlik relies heavily on its proprietary in-memory caching layer to achieve scale for on-demand querying. Buyers pursuing a direct-query lakehouse strategy should assess potential trade-offs related to data duplication, caching dependency and ingestion bottlenecks.
- Externalized multimodel management: While the platform allows a BYOLLM via its MCP server, it lacks native controls for governing and routing these models. Organizations must manage multiple LLMs externally to explicitly control model invocation for specific tasks and workflows.
Salesforce (Tableau)
Tableau, a Salesforce company, is a Leader in this Magic Quadrant. Its core portfolio spans Tableau Cloud, Tableau Server, Tableau Desktop and Tableau Next, providing self-service visual analytics and AI-assisted exploration as a stand-alone platform with flexible deployment options. Tableau operates globally, serving organizations of all sizes across a wide range of industries. Recent and future investments focus on transitioning toward agentic analytics, leveraging Agentforce and Data 360 to embed AI directly within business applications, and evolving its semantic layer into a composable knowledge graph that enables AI agents to securely query data.
- Community engagement and adoption: Tableau’s large and active practitioner community supports faster onboarding through shared best practices, forums and reusable templates, reducing implementation time.
- Visual data preparation: Tableau Prep provides an interactive map of the data pipeline that automatically infers relationships and suggests cleaning operations. Users can view detailed data profiles alongside the workflow to instantly identify inconsistencies at each transformation step, accelerating semantics for data and AI readiness.
- Deployment flexibility: Gartner clients can choose between fully managed Tableau Cloud, self-managed Tableau Server deployed on-premises or in IaaS environments, or hybrid architectures using secure connectors, enabling alignment with regulatory, data residency and infrastructure requirements.
- Cost and licensing considerations: Gartner inquiries frequently reflect concerns about Tableau’s pricing and the layered packaging of capabilities across the Standard, Enterprise and Tableau+ options.
- Salesforce stack dependency: There is a customer perception that advanced agentic features delivered through Salesforce components, such as Agentforce and Data Cloud, could create lock-in. This provides an opportunity for the vendor to clarify its open and composable architecture message.
- Operational analytics limitations: Tableau remains optimized for interactive dashboards and agentic exploration, offering limited support for highly formatted, parameterized or large-scale scheduled reporting for operational and regulatory use cases.
SAP
SAP is a Visionary in this Magic Quadrant. With its SAP Analytics Cloud as part of SAP Business Data Cloud (BDC), it offers a cloud-native, multitenant platform providing analytics, data visualization, and distinctive planning and scenario analysis features for predictive forecasts. SAP operates globally, primarily serving enterprise organizations within the SAP ecosystem across key industries. Recent and future investments focus on zero-copy data sharing with third-party cloud platforms, integrating the Joule conversational AI assistant, and rolling out prebuilt analytics content supported by a growing catalog of certified data products.
- Conversational and agentic insights: Integration of the Joule AI assistant enables conversational access to insights, as well as automated explanations and guided analysis within SAP applications.
- Prebuilt industry analytics content: SAP offers prebuilt analytics content and curated data products for cross-functional domains such as finance, supply chain and HR, helping accelerate time to value and reduce implementation effort.
- Integrated D&A stack: SAP Analytics Cloud is embedded within SAP BDC, unifying analytics, planning and simulation with data management services such as SAP Datasphere and SAP HANA Cloud to deliver governed insights without managing separate tools.
- Limited stand-alone ABI mind share: SAP Analytics Cloud is less frequently shortlisted as an independent ABI platform, as buyers often evaluate it primarily in the context of the broader SAP application and data platform investments.
- Platform breadth: The broad scope of SAP BDC can lead to a perception of complexity and increase client evaluation, onboarding and change management effort, particularly for buyers seeking a focused analytics tool rather than an integrated data and analytics stack.
- Semantic modeling for non-SAP data: BDC supports semantic modeling on SAP data, including knowledge graph support with added context for natural language queries. However, it does not yet have the same breadth of capabilities on non-SAP data.
SAS
SAS is a Visionary in this Magic Quadrant. SAS Viya, with SAS Visual Analytics, enables data scientists, business analysts and IT professionals to collaborate across the ABI development life cycle. SAS operates globally, primarily serving large enterprises in regulated industries, with a strong presence in North America and Europe. Recent and future investments focus on advancing SAS Visual Analytics, introducing SAS Viya Copilot for AI assistance, and enhancing its MCP server, as well as extending Visual Analytics into operational workflows through SAS Viya Intelligent Decisioning.
- Versatile AI assistance: SAS Viya Copilot supports business analysts with natural language analytics and dashboard creation, while enabling data scientists and developers with code generation, pipeline development, and code explanations.
- Open architecture adoption: SAS supports open integration through its Agentic AI Accelerator, MCP server and Viya Workbench, enabling analytics to connect with external tools and agent frameworks.
- Industry credibility: SAS remains trusted in regulated industries, where its depth in statistics, governance and compliance supports defensible, enterprise-scale analytics.
- Platform complexity: Despite efforts to lower barriers through Viya Copilot, effective use of SAS still requires specialized expertise, increasing onboarding effort and limiting talent availability.
- High and inflexible pricing: Gartner inquiries frequently highlight concerns around SAS’ premium pricing, limited transparency in deal structures, and recurring price increases.
- Community and ecosystem scale: Compared with other ABI platforms, SAS has a smaller practitioner community focused on BI use cases, which can limit access to shared templates, peer guidance and informal learning resources.
ServiceNow (Pyramid Analytics)
ServiceNow (Pyramid Analytics) is a Visionary in this Magic Quadrant. It offers a deployment-agnostic analytics and business intelligence platform spanning the data life cycle, founded on machine-learning-driven data preparation. Pyramid Analytics operates globally, with a strong presence in North America and Europe, serving midmarket and enterprise organizations across key industries. Recent and future investments focus on vectorizing its analytics catalog for domain-specific conversational answers, introducing decision flows, and exposing competitive catalogs for cross-platform discovery.
ServiceNow closed its acquisition of Pyramid Analytics on 10 March 2026 during the research period for this MQ.
- Flexible deployment: Pyramid supports multiple deployment models, making it suitable for organizations with strict regulatory or data residency requirements.
- End-to-end analytics capability: Pyramid combines semantic modeling, reporting and data science in one environment to address a broad spectrum of analytical needs.
- Comprehensive data preparation capabilities: Pyramid combines visual drag-and-drop data pipelines with automated ML-driven suggestions (relationships, classifications, metrics) to support both business users and advanced engineers.
- Postacquisition execution clarity: Following its acquisition by ServiceNow, Gartner clients may encounter short-term uncertainty around prioritization, decision-making speed and how resources are allocated between stand-alone platform enhancements and deeper ServiceNow platform integration.
- Market visibility: Pyramid Analytics has relatively lower visibility among Gartner clients, reflected in fewer inquiries and Gartner.com searches than its competitors. Visibility may increase following its acquisition by ServiceNow, which brings broader market exposure.
- Ecosystem and mind share: Pyramid has a smaller ecosystem and community than larger ABI vendors, which can affect skills availability and partner coverage. Access to the broader ServiceNow partner ecosystem and training resources may help address these constraints.
Sigma
Sigma is a Niche Player in this Magic Quadrant. Its cloud-native analytics and business intelligence platform delivers a spreadsheet-like interface that executes queries directly against cloud data warehouses. Sigma’s presence is strongest in North America, with offices in London and Australia and an expansion into Asia/Pacific, serving midmarket and enterprise organizations. Recent and future investments focus on expanding artificial intelligence capabilities, introducing Sigma Assistant for natural language querying, and enabling users to build operational, agentic data applications that write back to the warehouse.
- Spreadsheet-style user experience: The platform’s spreadsheet-inspired interface lowers barriers for business users familiar with Excel-like workflows, supporting calculations, pivoting and ad hoc analysis directly on governed warehouse data.
- Cloud data stack fit: Sigma integrates tightly with major cloud data warehouses, making it attractive for organizations standardized on Snowflake, BigQuery or Databricks that are seeking analytics aligned with a lakehouse architecture.
- Warehouse-native data application vision: Sigma keeps analytics fully within the cloud data warehouse, querying data in place and extending this model into workflows that support AI-driven data applications capable of writing back and triggering operational actions.
- Expanding geographic market presence: Sigma’s customer base and go-to-market efforts remain strongest in North America, although its London and Australia offices support expansion for organizations seeking broader international coverage, localized support or regional account management.
- Cloud-native deployment: Sigma is a cloud-native SaaS platform hosted on AWS, Microsoft Azure and GCP, and designed to query supported cloud data platforms directly, with no self-managed or on-premises deployment option.
- Maturing agentic insight capabilities: While Sigma has expanded conversational capabilities, its agentic features were recently introduced at the time of evaluation and remain less-developed relative to some peers, particularly in areas such as diagnostic reasoning, automated clustering and proactive anomaly detection. They have seen rapid adoption, with more than 500 customers using Sigma Agents.
Strategy
Strategy (formerly MicroStrategy) is a Visionary in this Magic Quadrant. Strategy offers a multicloud analytics platform providing a semantic layer foundation, enterprise reporting, and advanced analytics with strong governance and composability. Recent and future investments focus on providing a rich open semantic layer supporting AI workloads. In support, Strategy recently joined the Open Semantic Interchange (OSI) initiative, added MCP support to enable external language models to securely query governed semantics, and introduced Mosaic Sentinel to strengthen governance, risk monitoring and cost oversight for AI-driven analytics. Strategy operates globally, with a strong presence across North America, Europe and Asia, primarily serving large enterprises with complex analytics requirements.
- AI-ready semantic layer foundation: Mosaic functions as a stand-alone, governed semantic layer that models entities, relationships and rules across any data source to provide a consistent business context across analytics.
- Enterprise analytics governance: Strategy supports secure, large-scale analytics delivery with full dev-to-prod life cycle management and centralized monitoring, auditing, and anomaly detection through Mosaic Sentinel.
- Open and interoperable architecture: Strategy provides extensive APIs and supports open standards, including OSI, enabling governed semantic reuse across data sources, BI tools, and AI platforms while reducing vendor lock-in.
- Cloud transition pressure: Strategy’s move toward full cloud adoption may increase migration pressure for customers still running legacy deployments.
- Data preparation gaps: Strategy offers limited visual, pipeline-based data preparation and constrained support for multimodal data such as video, audio and social sources.
- Cost and licensing considerations: Strategy recently introduced newer lower-priced options, including a multitenant offering for small businesses to provide a more accessible entry point. Even still, Strategy is generally perceived as a premium platform.
Tellius
Tellius is a Visionary in this Magic Quadrant. Its core platform delivers agentic analytics, combining conversational analytics with multistep reasoning within autonomous AI agents. Its operations are primarily located in the United States, targeting enterprise organizations with deep specialization in the pharmaceutical and consumer packaged goods sectors, and an expanding midmarket presence. Recent and future investments focus on advancing its autonomous workflow capabilities, a publicly available Model Context Protocol server (GA) and Kaiya Apps for custom interfaces, and advancing automated semantic modeling, unstructured data integration and purpose-built industry agents.
- Industry-specific analytic applications: The platform delivers purpose-built artificial intelligence agents and packaged semantic models tailored to vertical requirements, allowing organizations in sectors like pharmaceuticals and consumer goods, including a majority of the top-10 pharmaceutical companies, to accelerate time to value.
- Autonomous workflow orchestration: The system natively decomposes complex business objectives into multistep execution plans and supports scheduled and event-triggered agent workflows, utilizing specialized agents to process structured databases and unstructured documents into cohesive, actionable insights.
- Automated semantic modeling: Tellius’s Kaiya Architect agent autoprofiles new datasets, infers relationships and joins, and generates Business Views with semantic enrichment (for example, measures/dimensions, descriptions and synonyms), reducing manual setup during onboarding.
- Regional market presence: Tellius maintains a limited global footprint, with the vast majority of its revenue and support centers concentrated in the United States. Prospective buyers outside the Americas should carefully evaluate the vendor’s capacity to deliver effective international support.
- Decreased market momentum: The vendor exhibits lower market visibility and momentum compared to competing platforms, reflected in lower search data and fewer client inquiries.
- Product gaps: While the platform supports scheduled and parameterized reporting, it has more limited capabilities for large-scale, automated report distribution, compared to traditional enterprise reporting tools.
ThoughtSpot
ThoughtSpot is a Leader in this Magic Quadrant. It focuses on delivering self-service insights through natural language conversation interfaces and scaling of complex analysis across large datasets. ThoughtSpot operates globally, primarily serving large enterprises across healthcare, banking and technology. Recent and future investments focus on expanding a team of AI agents, such as Spotter, SpotterModel, SpotterViz and SpotterCode to automate end-to-end analytics workflows, including natural language querying, semantic modeling, dashboard creation and embedded analytics, and deepening integration with cloud platforms like Snowflake and Databricks.
- Strong conversational analytics: The platform processes multistep conversational analytics natively through its Spotter agent. The system breaks down natural language questions into transparent reasoning plans, retains conversational memory across interactions, and blends structured and unstructured data to answer complex analytical queries.
- External semantic layer connectivity: ThoughtSpot can query governed metrics/definitions from dbt Semantic Layer and Looker (LookML) via Open SQL with Snowflake Semantic Views in early access, and it integrates with Databricks Unity Catalog to inherit metadata and enforce access controls.
- Agent workflow orchestration: Spotter, SpotterModel, SpotterViz and SpotterCode facilitate the creation of automated AI workflows across the analytics life cycle. These workflows execute tasks like pipeline monitoring across external systems without manual intervention.
- Cost predictability of consumption license: ThoughtSpot offers multiple licensing models, including named user and ELAs. However, ThoughtSpot’s consumption-based licensing charges credits for query, which can make spending harder to forecast as adoption grows.
- Increasing natural language competition: The widespread market integration of large language models and natural language interfaces across competing platforms may make it difficult for the vendor to sustain its historical differentiation in search-driven analytics.
- Pixel-perfect reporting limitations: ThoughtSpot prioritizes agentic workflows over traditional, pixel-perfect reporting. Organizations needing pixel-perfect or bursted reports may require complementary reporting tools or custom development for regulatory or operational use cases.
Zoho
Zoho is a Niche Player in this Magic Quadrant. Its primary analytics and business intelligence offering is Zoho Analytics, focusing on enterprise self-service, data management and embedded analytics. It supports a global customer base primarily comprising small and midsize businesses, with growing adoption among enterprises across key industries. Zoho operates its own data centers to manage regional data sovereignty and compliance requirements. Recent and future investments focus on launching the MCP server and integrating multiple large language models.
- Deployment flexibility: Zoho offers multiple deployment models, allowing organizations to run Zoho Analytics in Zoho’s native cloud environment, on-premises environments, or across public hyperscalers like AWS, Azure and Google Cloud. This provides architectural adaptability for customers with strict data residency requirements.
- Governed artificial intelligence models: The platform allows administrators to manage multiple large language models, including the vendor’s proprietary engine and external providers like OpenAI. Administrators can exert granular control by configuring exactly which model powers specific features or user roles, ensuring compliance with corporate data policies.
- Embedded analytics ecosystem: The platform provides extensive white-labeling capabilities designed for independent software vendors and managed service providers. Organizations utilize these tools to integrate analytics seamlessly into custom applications and external portals.
- Lower Gartner visibility: Gartner search and inquiry data indicate that Zoho has lower visibility among Gartner clients than many other vendors in this research.
- Constrained multimodal support: The platform’s data integration capabilities remain focused on structured and semistructured data, with limited native support for richer multimodal sources such as images, video, audio, and social content.
- Enterprise adoption scale: Zoho supports a growing mix of SMB, midmarket and enterprise customers, but organizations should validate its fit for large-scale environments with high concurrency and complex administrative needs.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.
Added
- Databricks
Dropped
- Sisense
Inclusion and Exclusion Criteria
To qualify for inclusion in this Magic Quadrant, vendors must meet all of the following criteria:
- Offer a generally available software product that met Gartner’s definition of an ABI platform and offers at least five of the eight critical capabilities.
- Offer an ABI platform that is industry-agnostic, ensuring the solution is not confined to specific industry verticals.
- Demonstrate meaningful internal/stand-alone ABI adoption beyond embedded/OEM use, based on go-to-market focus, revenue contribution and production deployments.
Honorable Mentions
- Aible: Aible operates on an agentic analytics model, licensed per agent, per server or unlimited, rather than per user or per token. It automatically ensures data quality for generating insights, explores millions of variable combinations to discover actionable patterns, and offers recommendations. Business users can provide feedback on the agent’s reasoning steps to align them with specific terminologies, processes and preferences.
- Snowflake: Snowflake Cortex Agents enables natural-language Q&A over structured data by translating questions into governed SQL executed natively on Snowflake. Streamlit supports lightweight visual apps built on Semantic Views for self-service reporting, while Cortex Code is a Snowflake-native AI-powered coding agent, available in Snowsight and as a CLI, that helps developers build and manage these apps, semantic models and data workflows using natural language.
Evaluation Criteria
Ability to Execute
Product or Service: This criterion assesses how competitive and successful a vendor’s ABI platform product is with regard to the critical capability areas, in light of the vendor’s RFP response and video submission.
Overall Viability: This criterion concerns the organization’s financial status and model as it relates to ABI. It also considers existing and prospective customers’ views about the vendor’s likely future relevance.
Sales Execution/Pricing: This criterion covers the vendor’s capabilities in sales activities. It includes the overall evaluation, contract negotiation/flexibility with a vendor, and the value the customer receives.
Market Responsiveness/Record: This criterion addresses the extent to which a vendor has momentum and success in the worldwide market using a balanced set of measures.
Marketing Execution: This criterion was not rated separately because client decisions in this market are driven primarily by product capability and user experience, and excluding it maintains focus on factors directly relevant to buyer outcomes.
Customer Experience: This criterion concerns customers’ experience of working with a vendor after a purchase. Factors include the availability of quality third-party resources (such as integrators and service providers), the quality and availability of end-user training, and the quality of the peer user community.
Operations: This criterion concerns how well a vendor supports its customers and how trouble-free its software is.
Ability to Execute Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Product or Service | High |
Overall Viability | High |
Sales Execution/Pricing | Medium |
Market Responsiveness/Record | High |
Marketing Execution | NotRated |
Customer Experience | High |
Operations | High |
Source: Gartner (June 2026)
Completeness of Vision
Market Understanding: This criterion concerns how closely a vendor is aligned with the shifting needs of analytics buyers and how widely its customers use recent and emerging capabilities.
Marketing Strategy: This criterion considers whether a vendor has a clear set of messages that communicate its value and differentiation in the ABI platform market, and whether that vendor is generating awareness of its differentiation.
Sales Strategy: This criterion concerns the extent to which a vendor’s sales approach benefits from a range of options and drivers that encourage customers to evaluate its ABI platform.
Offering (Product) Strategy: This criterion assesses a vendor’s ability to support key trends that will create business value in the future. Existing and planned products and functions that contribute to these trends are factored into each vendor’s score for this criterion, based on its presented roadmap.
Business Model: This criterion was not evaluated separately as commercial structure is reflected within pricing and offering factors already captured in the assessment, ensuring focus on elements with direct impact on buyer decisions.
Vertical/Industry Strategy: This criterion assesses how well a vendor can meet the needs of various industries through templates or packaged data and analytics content.
Innovation: This criterion gauges the extent to which a vendor is investing in and delivering unique capabilities. It considers whether a vendor is setting standards for innovation that others are emulating.
Geographic Strategy: This criterion considers how well-represented a vendor is around the world.
Completeness of Vision Evaluation Criteria
| Evaluation Criteria | Weighting |
|---|---|
Market Understanding | High |
Marketing Strategy | High |
Sales Strategy | High |
Offering (Product) Strategy | High |
Business Model | NotRated |
Vertical/Industry Strategy | Low |
Innovation | High |
Geographic Strategy | Medium |
Source: Gartner (June 2026)
Quadrant Descriptions
Leaders
Leaders demonstrate a solid understanding of the key product capabilities and the commitment to customer success that buyers in this market demand. They couple this understanding and commitment with an easily comprehensible and attractive pricing model that supports proof of value, incremental purchases and enterprise scale. Buying decisions are made, or at least heavily influenced by, business users who demand products that are easy to buy and use. Business users require these products to deliver clear business value and enable the use of powerful analytics by those with limited technical expertise and without upfront involvement from the IT department or technical experts. In a rapidly evolving market featuring constant innovation, Leaders do not focus solely on current execution. Leaders ensure they have a robust roadmap to solidify their market position, and thus help protect buyers’ investments.
Challengers
Challengers are well-positioned to succeed in this market. However, they may be limited to specific use cases, technical environments or application domains. Their vision may be hampered by the lack of a coordinated strategy across various products in their portfolio. Alternatively, they may fall short of the Leaders in terms of effective marketing, sales channels, geographic presence, industry-specific content, and innovation.
Visionaries
Visionaries have a strong or differentiated vision for delivering a modern ABI platform. They offer deep functionality in the areas they address. However, they may have gaps when it comes to fulfilling broader functionality requirements, or they may have lower scores for customer experience, operations and sales execution. Visionaries are thought leaders and innovators, but they may be lacking in scale, or their ability to grow and still execute consistently may be questionable.
Niche Players
Niche Players do well in a specific domain (industry, vertical or use case), or they are good at meeting the ABI needs of organizations using a particular cloud stack. But they may have limited ability to surpass other vendors in terms of innovation or performance. They may focus on a specific domain or aspect of the ABI platform market, but lack deep functionality elsewhere. Alternatively, they may have a reasonably broad ABI platform, but limited implementation and support capabilities or relatively limited customer bases (in only a specific region or industry, for example).
Context
This Magic Quadrant assesses vendors’ capabilities on the basis of their execution in 2025 and future development plans. As vendors and the market are evolving, the assessments may be valid for only one point in time.
Readers should not use this Magic Quadrant in isolation as a tool for selecting vendors and products. They should treat it as one reference point among the many required to identify the most suitable vendor and product. When selecting a platform, they should use this Magic Quadrant in combination with Critical Capabilities for Analytics and Business Intelligence Platforms. We also recommend using Gartner’s client inquiry service.
Readers should not ascribe their own definitions of Completeness of Vision or Ability to Execute to this Magic Quadrant (they often incorrectly equate these with product vision and market share, respectively). The Magic Quadrant methodology uses a range of criteria to determine a vendor’s position, as shown by the Evaluation Criteria section above.
Market Overview
Analytics and business intelligence platforms prepare, model, analyze and visualize data to support enterprise decision making. These platforms deliver insights through AI-powered conversational experiences, interactive dashboards and classic reporting (see Will AI Kill BI?). They facilitate collaboration between business and technical users to define the dimensions, measures and business rules required to create and maintain semantic models. Modern platforms also provide functionality for agentic analytics, where AI agents coordinate tasks across the data-to-insight workflow to automate insight delivery under strict governance and audit controls.
Over the past year, the market has accelerated toward agentic analytics as vendors embed AI agents that coordinate tasks across the data-to-insight workflow (see Your BI Platform Isn’t Truly Agentic Yet). This shift has increased client emphasis on semantic layers that unify metrics, business rules and relationships to support explainability, multifact modeling, downstream interoperability and cross-platform integrations. Governance expectations have intensified simultaneously, as enterprises require policy-as-code controls, lineage tracing, privacy safeguards and mechanisms to validate agent-generated outputs and decisions.
Vendors are increasingly adopting open integration standards, such as Model Context Protocol, to securely expose governed enterprise data to external language models and third-party applications. Participation in initiatives like the Open Semantic Interchange further supports standardized metadata exchange across competitive data platforms
These market developments reflect a growing desire among organizations to reduce manual dashboard dependence (see How to Properly Integrate AI Agents Into Your BI Strategy). Clients expect automated insights, conversational experiences and agentic workflows to remain firmly grounded in trusted, governed data models. Additionally, the demand for embedded analytics and write-back functionality continues to rise. These capabilities allow business users to initiate operational workflows, update records and execute business actions directly from their analytical interfaces without switching applications.
Acronym Key and Glossary Terms
| ABI | Analytics and business intelligence |
| AutoML | Automated machine learning |
| BYOLLM | Bring your own large language model |
| D&A | Data and analytics |
| ERP | Enterprise resource planning |
| LLM | Large language model |
| MCP | Model Context Protocol |
| ML | Machine learning |
| SDK | Software development kit |
Gartner’s analysis in this Magic Quadrant is based on sources that include:
- Gartner analysts’ opinions of vendors.
- Customers’ perceptions of vendors’ strengths and challenges, drawn from ABI-related inquiries received by Gartner.
- Gartner Peer Insights data (see below).
- A questionnaire completed by vendors about their business.
- Vendor briefings covering differentiation, customer use cases and product roadmaps.
- An extensive RFP questionnaire inquiring how each vendor delivers the specific features that make up the 12 critical capabilities defined for this market.
- Video demonstrations of how vendors’ ABI platform products address the 12 critical capabilities profiled in the companion Critical Capabilities for Analytics and Business Intelligence Platforms.
- Externally sourced data on market momentum (e.g., job postings, videos on the web).
Gartner Peer Insights
Gartner Peer Insights reviews were considered for metrics relating to operations (service and support, and quality of technical support), customer experience (availability of third-party resources, quality/availability of end-user training, and overall experience), sales experience (pricing and contract negotiation), market responsiveness (value received), and market understanding (understanding customer needs).
Ability to Execute
Product/Service: Core goods and services offered by the vendor for the defined market. This includes current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization's financial health, the financial and practical success of the business unit, and the likelihood that the individual business unit will continue investing in the product, will continue offering the product and will advance the state of the art within the organization's portfolio of products.
Sales Execution/Pricing: The vendor's capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor's history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization's message to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This "mind share" can be driven by a combination of publicity, promotional initiatives, thought leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
Completeness of Vision
Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen to and understand buyers' wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.
Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service, and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor's approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature sets as they map to current and future requirements.
Business Model: The soundness and logic of the vendor's underlying business proposition.
Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including vertical markets.
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the "home" or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.