Magic Quadrant for Analytics and Business Intelligence Platforms
16 June 2025 - ID G00822218 - 64 min read
By Anirudh Ganeshan, Edgar Macari, and 3 more
Data and analytics leaders use ABI platforms to support the needs of IT, analysts and consumers. While integration with cloud ecosystems and business applications is a key selection requirement, platforms should also enable governance, interoperability and AI to automate the analytics process.
Market Definition/Description
This document was revised on 21 August 2025. The document you are viewing is the corrected version. For more information, see the Corrections page on gartner.com.
Gartner defines analytics and business intelligence platforms (ABI) as those that enable organizations to model, analyze and visualize data to support informed decision making and value creation. These platforms facilitate the preparation of data and the creation of interactive dashboards, reports and visualizations to uncover patterns, predict trends and optimize operations. By doing so, they empower users to collaborate and effectively communicate the dimensions and measures that drive their organization. The platforms may also optionally include the ability to create, modify or enrich a semantic model, including business rules.
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.
For more than 20 years, visual-exploration-based dashboards and reports have been the primary way users monitor and explore data. However, time spent in curated, predefined dashboards will be complemented and even displaced partially by automated, conversational and dynamically generated insights.
Modern ABI platforms are increasingly leveraging generative AI (GenAI) to enhance productivity for both developers and consumers of analytics content by augmenting the data-to-insight workflow through low- and no-code features. For developers, GenAI features include creating metrics, models, visualizations, reports and dashboards; for consumers, it focuses on extracting insights and meaning from data to support informed decision making.
Typical benefits of leveraging ABI platforms include:
- Operational efficiency: These platforms significantly reduce the time and effort required to gather, process and interpret information. By leveraging intuitive dashboards and reports to visualize complex data, stakeholders can quickly identify trends, uncover opportunities and address potential risks.
- Scalability and flexibility: Modern analytics and BI platforms are designed to scale with an organization’s growth. Whether dealing with increasing data volumes or expanding user bases, these platforms offer the flexibility to adapt to changing business needs.
- Enhanced collaboration and communication: Analytics and BI platforms foster a collaborative environment by providing shared access to data and insights. These platforms allow for the integration of data from different sources, breaking down silos, thus providing a unified view of the organization’s data.
- Managed governance and trust: ABI enhances data and analytics governance by allowing individual users to mask or encrypt data loaded into the platform using both row-level and object-level security, ensuring that protected and sensitive information remains hidden.
Mandatory Features
- Visualization: Supports custom visualizations, allows users to visualize and analyze graphs and integrates with charting libraries, supports geographic mapping, and recommends which visualization type to use for different data types.
- Content management: This capability allows administrators and D&A stewards to manage the security, certification and life cycle of analytics content.
- Reporting: This capability provides pixel-perfect, paginated reports that can be scheduled and shared with a large user community.
- Data preparation: Supports drag-and-drop, user-driven combination of data from different sources and the creation of analytic models, such as user-defined measures, data pipelines, sets, groups and hierarchies.
- Automated insights: The ability to apply machine learning (ML) techniques to automatically generate insights for end users; for example, identifying the most important attributes in a dataset, conducting time series forecasting and identifying clusters within datasets. These features also enable the incorporation of usage and feedback data from users to display content that is most relevant for their role and use case.
- Natural language query (NLQ): This capability enables users to ask questions of the data using terms that are either typed into a search box or spoken aloud.
- Data source connectivity: Capabilities that enable users to connect to and ingest data contained in various types of storage platforms, both on-premises and in the cloud.
- 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.
Common Features
- Metrics layer: A virtualized layer that allows users to create and define business metrics as code, govern those metrics from data warehouses, and serve the downstream analytics, data science and business applications.
- 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.
- Platform administration: These capabilities allow users to track platform usage statistics, manage costs by monitoring capacity or credits, and intervene by pausing or prioritizing jobs. They also measure query performance and provide feedback to content authors, offering recommendations to optimize datasets and reports to minimize inefficient or slow queries.
- Natural language generation (NLG): The ability to automatically produce written or spoken responses, allowing users to specify the level of detail, verbosity and tone. NLG also supports complex analyses, such as variance analysis and outlier detection, and it contextualizes narratives based on user personas by leveraging active metadata, background and preferences.
Magic Quadrant
Vendor Strengths and Cautions
Alibaba Cloud
Alibaba Cloud is a Challenger in this Magic Quadrant. Its Quick BI is focused on reporting and visualizations supported by data preparation and natural language query (NLQ), which enable insights by combining data accuracy objectives, integration, scalability, AI, business intelligence (BI) and agentic solutions. Its cloud-native approach offers Quick BI integrations with its cloud databases and tools (e.g., Dataphin for data management and Quick Audience for customer behavior insights) to provide a comprehensive enterprise data and analytics platform.
Alibaba Cloud has accelerated its geographic expansion in the past year, invested in a more global strategy, and now offers multilingual support, starting with English.
- AI and agentic enhancements: Alibaba Cloud heavily focuses on expanding agentic solutions, such as multiagent orchestration, advancing capabilities for large language model (LLM) integrations. It also offers AI features like conversational interface via SmartQ and NLQ capabilities to enhance user experience and analytics output.
- Enhanced focus on China: Alibaba Cloud offers specialized features for Chinese users, such as workbooks for complex report creation, and data integration and big screen visualization for data storytelling on large displays. These features differentiate Alibaba Cloud from other global vendors for prospective buyers in China.
- Ease of availability and flexible pricing: Quick BI is accessible through Alibaba Cloud and other platforms with extensive user bases, such as Taobao and DingTalk app market, offering customizable function packages and pricing models suitable for enterprises of various sizes.
- Limited global support: While global expansion is a core part of Alibaba Cloud’s future market strategy, it currently offers limited multilingual and localization support. Prospective buyers outside the Asia/Pacific market should verify Alibaba Cloud’s current and/or planned support offerings are sufficient for their region.
- Integration with non-Alibaba Cloud products: Integrating Quick BI with non-Alibaba Cloud products and services may yield technical challenges, such as compatibility of data formats and protocols, data synchronization, latency issues, the need to create complex data pipelines for consistency, and timeliness across platforms. Prospective buyers using diverse tools may face difficulties in achieving effective interoperability.
- Limited community support ecosystem outside China: Quick BI has a smaller global user community and fewer independent consultants or developers than other established BI platforms, offering limited community-driven resources (e.g., forums, tutorials and user-generated content) for talent development and troubleshooting.
Amazon Web Services
Amazon Web Services (AWS) is a Challenger in this Magic Quadrant. Its analytics and business intelligence (ABI) offering, QuickSight, attracts primarily customers vested in the broader AWS ecosystem because of its seamless integration with the AWS D&A stack, scalability, performance and competitive pricing model.
In 2024, AWS introduced scenario modeling in Amazon Q for QuickSight, enabling agentic trend analysis, forecasting and solution exploration. QuickSight also added support for unified insights from both structured and unstructured data, with plans to expand to additional formats, such as text, images and video.
- Integrated global cloud service: AWS offers QuickSight users seamless integration with its extensive D&A tools. Prospective buyers focusing on a single cloud service provider (CSP) can benefit from its integrated capabilities (e.g., Amazon’s Redshift, Athena, Data Catalog and SageMaker) to simplify and optimize their analytics infrastructure through vendor-native integration.
- Transparent pricing: Amazon QuickSight pricing starts at $3 per user per month for a reader license and offers capacity-based pricing models as an alternative. It offers Amazon Q’s Q&A capabilities as part of all reader or author licenses. This pricing flexibility helps customers avoid overprovisioning.
- Serverless architecture boosts scalability and performance: Amazon QuickSight’s serverless cloud setup, paired with SPICE, its data storage and calculation engine, ensures high performance and scalability by adapting to usage demands. SPICE processes analytical queries swiftly and reuses stored data without extra costs, serving as an efficient in-memory alternative to direct queries.
- Confined deployment options: Amazon QuickSight is optimized for deployment on the AWS cloud. However, extending its deployment to on-premises, containerized or hybrid environments can be complex. While QuickSight can integrate with data from other cloud services and on-premises solutions (often through Amazon Athena), prospective buyers should consider the implications of its confined deployment option.
- Universal metric layer barriers: AWS QuickSight has limitations as a comprehensive metrics layer. While it provides external access to datasets through JSON API calls, metrics are linked to specific datasets and can’t be accessed separately. Additionally, its lack of native connections restricts its effectiveness as a semantic layer for integrating various platforms.
- Lack of business application suite: AWS experiences challenges in its sales strategy due to the absence of a comprehensive suite of widely adopted business applications, affecting its ability to achieve market penetration for Quicksight. Despite a strong vision for agentic analytics, AWS’s product strategy for QuickSight’s governance capabilities relies on separately licensed AWS products, which cost extra. Users have the option to utilize Amazon DataZone, a separately licensed product, for more complex governance scenarios.
Domo
Domo is a Challenger in this Magic Quadrant. Its product, Domo, delivers rapid and scalable access to essential data resources, and its comprehensive data integration, visualization and collaboration enable users to connect disparate data sources and create interactive dashboards. Its target users are line-of-business owners, knowledge workers and frontline users, and its user-friendly interface with no- and pro-code features appeals to both technical and nontechnical users.
Domo is investing in secure and transparent AI workflows to allow businesses to manage AI models and leverage prebuilt or custom agents to interact with data in natural language.
- Data connection and preparation capabilities: Its Magic ETL delivers robust data preparation capabilities, allowing users to create, visualize and monitor entire data pipelines, and repeatable and reusable analytic models. It also offers over 1,000 native connectors to major data sources, many of which include write-back capabilities.
- Integration with portals and applications: Domo Everywhere empowers users to build and deliver analytics across various applications (e.g., Microsoft 365 and Microsoft Teams), extending core features to end consumers as if they were native components of their own products. Its public embed option allows easy analytics integration into public-facing web properties and business applications through simple embedded snippets, which may appeal to prospective buyers seeking a lightweight implementation.
- AI agents: Domo allows users to create agentic solutions in its environment using AI services in workflows. Its product vision for future enhancements includes an agent builder that allows users to choose LLMs, upload and vectorize key reference documents, and provide context to teach the agent how it should behave.
- Limited geographic presence: Domo is based in the United States and maintains a network of support centers, including one in the U.S., three in Asia/Pacific and two in Europe. Despite its widespread customer base across the U.S., Europe and Asia/Pacific, prospective buyers should assess Domo’s ability to deliver effective global support.
- Complex consumption-based model: Domo’s consumption-based pricing model makes estimating solution costs complex. During Gartner inquiries and in Peer Insights reviews, Domo customers and prospective buyers cited a need for guidance and clarification on understanding its pricing structure and considerations for evaluating its offerings.
- Limited NLQ features: Domo’s natural language query features are limited, missing predictive suggestions and autotyping capabilities. Furthermore, implementing advanced forecasting models may require coding in R and Python for sophisticated algorithms such as ARIMA.
GoodData
GoodData is a Niche Player in this Magic Quadrant. It has two offerings, GoodData Cloud Native (deployments on cloud or via Kubernetes, including on-premises) and GoodData Cloud (SaaS), and it employs a headless strategy closely aligned with DevOps methodologies. GoodData’s BI interface is highly composable, allowing components such as dashboards, filters, visualization tools and the AI chatbot to be embedded through code, iframes or API integration for tailored user experiences. Its “analytics-as-code” approach allows users to enhance automation and streamline the analytics development process while ensuring governance.
GoodData has continued to carve out a distinct niche among clients seeking to establish and integrate a metrics layer as a universal semantic framework within their ecosystems. It also continues to evolve its market position beyond traditional BI to a composable data application platform.
- Composability-first focus: GoodData’s business model and features follow the analytics-as-code tenet and cater to the composability market with its ability to integrate and align analytics content development with the application development life cycle. Native features, including FlexConnect, make it an ideal tool for connecting disparate data and developing analytics products available through a zero-copy architecture.
- Headless vision: GoodData focuses on the market’s need for a centralized metrics layer that ensures consistency of metric definitions and maps to business objectives. Its open semantic layer is accessible for various downstream analytics use cases, positioning GoodData as an agnostic modeling tool in the metrics layer market.
- Third-party interoperability: GoodData’s integration with third-party applications distinguishes it from other vendors in this Magic Quadrant. Notably, GoodData’s catalog integration consumes the metadata of competitor BI tools for universal search and discovery. This integration allows GoodData to support a broader range of use cases by extending its capabilities through the partner network and fitting into a technology ecosystem.
- Adoption requirements: Adopting an analytics-as-code strategy to scale business intelligence within an organization requires significant data engineering and governance maturity. Prospective buyers should assess their organizational readiness to capitalize on GoodData’s offerings fully.
- Product gaps: GoodData’s platform has limitations in key advanced areas. Its augmented analytics capabilities, including key driver analysis, outlier detection and natural language generation (NLG), are still evolving and lack advanced functionalities, with some features still in trial phases. Furthermore, its data storytelling capabilities are weak, particularly for advanced reporting and visualization, potentially making it less appealing for buyers seeking robust features in these specific domains.
- Evolving market momentum: Gartner Peer Insights data indicates GoodData has a lower willingness-to-recommend metric compared to other vendors in this research. Reviewers also cite high licensing costs as a key factor in purchasing decisions. Additionally, GoodData exhibits relatively lower numbers in Gartner’s search and inquiry data.
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 that allows composability. It delivers a unified enterprise BI platform with Studio and Gemini in Looker. Individual users and small teams can use Looker Studio at no cost or connect it to the platform to leverage its governance and semantic layer functionalities, with Gemini included in all versions.
In the past year, Google enhanced its self-service analytics capabilities by incorporating Studio in Looker and introducing Looker Conversational Analytics, which allows business users to interact easily with data. Google also upgraded Looker’s visuals and launched a preview of its new mobile experience. Looker now offers a responsive canvas that scales according to the viewer’s device size, and a new Looker mobile app unifies all analytics content.
- Analytics governance and platform administration: Google’s Looker excels in certifying, watermarking and controlling access to analytics content, allowing programmatic users to isolate data sources via API. Its support for multiple development, testing and production instances, complemented by comprehensive APIs, allows seamless content movement across environments, and users can easily analyze the platform’s performance and health with Explore mode and built-in dashboards.
- Robust open architecture and semantic layer: Looker’s open architecture and API-first approach acts as the single source of truth, guaranteeing data accuracy, consistency and governance. Its open semantic layer enables deep integration with other ABI platforms, Google Connected Sheets, Microsoft Excel and custom applications, optimizing existing investments. Additionally, Looker’s embedded analytics features allow integration of interactive visualizations and data experiences directly into any application, offering users and prospective buyers access to precise and consistent data and enhanced interactive data capabilities.
- Simplified pricing model: Google offers Free and Pro versions of Looker Studio, enabling customers to start as Free users and transition to a Looker Studio Pro Edition as their governance and modeling needs evolve. GenAI pricing is integrated into the user license, eliminating unpredictable costs or complex per-LLM call models.
- Premium pricing: Looker’s pricing is relatively high compared to other vendors analyzed in this research. In Gartner Peer Insights reviews and during Gartner inquiries, some Looker customers cited high pricing as a barrier to large-scale deployments.
- Market awareness: Looker is often perceived as a tool primarily for developers, despite efforts to offer capabilities aimed at business users such as NLQ, data visualization and reporting. Its robust governance and API-first approach cater more to users with technical expertise, potentially limiting its accessibility and appeal to nontechnical business users seeking straightforward, self-service analytics solutions.
- Developing consumer-focused capabilities: Looker has made progress in consumer-focused capabilities through Looker Studio, Gemini features, and improved sharing via Google Workspace. However, it still trails other ABI platforms in delivering a fully collaborative experience for curating, sharing, and autogenerating insights — areas that remain on the roadmap.
IBM
IBM is a Visionary in this Magic Quadrant. Its Cognos Analytics delivers consistently strong reporting and insights capabilities, supported by customization and optimization options to enable enterprisewide business roles and personas. It offers comprehensive analytics cataloging with IBM Analytics Content Hub and flexible deployment and scaling across cloud, on-premises, and hybrid/multicloud environments. Notably, IBM is one of the few vendors evaluated in this research that still emphasizes and invests in on-premises products.
IBM’s recent innovations include AI assistants and agents, which allow enterprise consumers and creator personas (e.g., data scientists, developers, business analysts and data explorers) to create data models, dashboards and highly paginated reports with a traditional BI interaction or conversational analytics.
- Robust and customizable enterprise reporting: Cognos Analytics enables users to produce highly paginated and pixel-perfect enterprise reports. Users can build highly formatted context-specific reports in custom layouts and share with their internal and external stakeholders in various ways (e.g., bursting), with filters, formats and language customizations.
- AI investments and vision: IBM is building on its investments in AI in the coming year with IBM watsonx BI, a generative AI (GenAI) analytics agent within Cognos Analytics. IBM watsonx BI is intended to enable a wide range of analytics and reporting capabilities to streamline analytics processes for traditional business consumers with descriptive, diagnostic, predictive and prescriptive analytics insights and recommendations using natural language.
- Vertical industry strategy: IBM supports various industries with tailored solutions, including risk management in insurance and banking, patient care optimization in healthcare, production monitoring in manufacturing, and performance tracking in marketing, operations and sales. These solutions would be highly valuable for prospective buyers with industry-specific requirements.
- Lagging in AI-enabled data preparation support: While IBM Cognos Analytics offers advanced data preparation capabilities — including robust modeling through its proprietary Framework Manager and self-service features via data modules — it falls slightly behind in AI-enabled data preparation compared to some peers. However, these AI-driven enhancements are on the product roadmap, with IBM actively investing in generative AI and automation to close the gap.
- Reduced market momentum: IBM exhibits decreased market momentum, reflected in fewer reviews on Gartner Peer Insights and relatively lower figures in Gartner search data. This suggests that fewer clients are showing interest in IBM’s products. Users on Gartner Peer Insights also frequently cite high licensing costs as a significant factor influencing their decisions.
- Lack of sales adoption drivers: Despite its broad D&A offerings, IBM lacks a native digital workplace application (e.g., Google Workspace, Microsoft 365 and Zoho Workplace) or its own enterprise application (e.g., Oracle, Salesforce, SAP). However, it integrates with collaboration platforms like Microsoft Teams and Slack.
Incorta
Incorta is a Niche Player in this Magic Quadrant. It is focused on rapid data modeling capabilities for enterprise applications like Oracle, Salesforce and SAP. Beyond its data warehouse automation features, Incorta offers data acquisition, management, and visualization and reporting functionalities.
Incorta’s recent innovations in 2024 include the addition of Copilot, an OpenAI ChatGPT integration that brings GenAI to various parts of the platform, including generating dashboard insights and storyboards using natural language. The integration allows nontechnical users to easily generate insights, data stories and narratives on ERP and business application data.
- Accelerated time to value: Incorta streamlines data preparation by bypassing ETL processes, using its Direct Data Mapping engine to integrate and harmonize data from multiple sources without transformations or reshaping, preserving the original data fidelity. This approach enables rapid creation of business-ready datasets, loading data into memory in its compressed columnar form to handle large volumes and complex queries, thus enabling quick insights and decision making.
- Live operational data with frequent updates: Incorta provides frequent incremental data refreshes at scale, delivering operational data every 15 minutes. This low-latency data availability is important for tasks such as inventory analysis and financial period close, offering customers the agility to respond promptly to changing conditions.
- Domain-specific analytics: Incorta offers packaged data applications for systems like Oracle, Salesforce, SAP and Workday. These applications feature prebuilt business schemas and analytic dashboards that incorporate industry standards and best practices. These applications support complex use cases, such as finance, supply chain management and human resources, enabling quick setup and analytics for accounts payable, inventory management and more.
- Limited augmented capabilities: Incorta’s augmented functionalities, like NLQ, are limited. Users must understand specific syntax for Incorta Copilot prompts, and business-specific synonyms require manual management. This may not fully meet the needs of prospective buyers seeking robust NLQ capabilities.
- Competition from integrated analytic solutions: Incorta’s primary business involves integration with large enterprise applications. As enterprise application vendors increasingly embed comprehensive ABI platforms into their solutions, Incorta’s data warehouse automation platform may become less competitive for prospective buyers.
- Low market visibility: Incorta’s smaller customer base could make it challenging to find skilled professionals. While Incorta’s product minimizes the need for many administrators, prospective buyers should consider whether talent availability will affect their ability to utilize Incorta fully.
Microsoft
Microsoft is a Leader in this Magic Quadrant, integrating Power BI into Microsoft Fabric. Power BI licensing is divided into per-user licensing and capacity subscription. Per-user licensing includes Power BI Free for basic use, Power BI Pro for team collaboration, and Power BI Premium Per User for advanced features. Capacity subscriptions include Power BI Premium and Power BI Embedded, offering dedicated resources for large organizations and developers. Consuming Power BI reports without paid per-user licenses requires Fabric SKUs F64 and above.
In January 2025, Microsoft launched CoreAI – Platform and Tools, advancing AI initiatives and enhancing Power BI’s analytics, security and user experience.
- Dominant market presence: Compared to other ABI platforms, Power BI has a dominant market presence. This makes finding internal skills, external consultants and training material easy. Moreover, Power BI is an approved product in most organizations, which enables easy expansion through existing deployments.
- Integrated D&A stack: Power BI is part of Microsoft Fabric, a D&A ecosystem that includes OneLake, Data Integration, Data Warehouse, Apache Spark, Data Science, operational databases and real-time capabilities. This product integration across the D&A stack meets customers’ preferences by eliminating the need to cobble together multiple best-of-breed tools.
- Prominent Copilot brand: Copilot for Power BI provides intelligent assistance to boost productivity in typical D&A workflows, including visualization, calculating metrics, documenting metadata and finding insights such as key drivers and anomalies. While other ABI platforms offer these technologies, Microsoft’s Copilot brand is more widely recognized.
- Pricing increases: In April 2025, the price increased for Power BI Pro Per User (from $10 to $14 per month) and Premium Per User (from $20 to $24 per month). Moreover, while volume discounts negotiated in Microsoft Enterprise Agreements applied to Power BI Premium capacities in the past, they are not guaranteed as customers shift to Fabric licensing. Gartner has also noticed a spike in inquiries from customers confused by the bundle of Power BI in Fabric.
- Limited to the Azure Stack: While Microsoft does offer direct query, import and mirroring capabilities to data lakehouses on other platforms, these functions only run on Azure. This limitation affects potential customers who have invested in another hyperscaler’s cloud platform.
- Workload isolation: Power BI’s inability to isolate workloads limits users’ ability to control workload requests, the most commonly cited challenge by its customers in Gartner inquiries in 2024. If an organization has multiple workspaces running on a single F64 tenant, one workspace can consume an oversized portion of the 64 capacity units shared with the other workspaces, limiting customers’ ability to control workload requests. Microsoft is addressing this with Surge Protection.
Oracle
Oracle is a Leader in this Magic Quadrant. Oracle Analytics Cloud (OAC) and its on-premises version, Oracle Analytics Server, are components of its broader D&A stack named Oracle Data Intelligence. It offers comprehensive integration of analytics, business applications, AI, and data management and integration.
It recently introduced the Oracle Analytics AI Assistant, which enables users to leverage natural language for insights, complex visualizations and dashboard assembly. While the Analytics AI Assistant comes with its own GenAI model, it also supports third-party models like OpenAI via its bring-your-own LLM capability. In addition to the Analytics AI Assistant, Oracle bolstered its vertical analytical application focus, particularly in healthcare and life sciences.
- Business application integration: Oracle Analytics’ integration with the Fusion Data Intelligence platform provides packaged data integration, data models and analytics content for most horizontal applications, such as finance, CRM and supply chain management. Its content is also embedded in business application workflows to support decisions with data-driven insights.
- AI enhancements: Oracle’s analytics offerings are infused with AI and ML capabilities to provide users with advanced tools for data preparation, analysis and visualization. Its platform supports NLQs and generates insights using GenAI models, allowing users to interact intuitively with data. These AI enhancements improve the analytical process by delivering automated insights and enabling sophisticated data storytelling.
- Market-leading data preparation: OAC excels in data preparation with its intuitive interface and advanced features. It autogenerates statistics on data quality and distribution, uses semantic profiling with natural language processing and ML to identify patterns, and offers interactive visual tiles for data insights. OAC provides automated data repair recommendations and obfuscation for personally identifiable information (PII), enhancing data security. The new magic wand icon for alerts feature notifies users of enhancements, allowing easy updates, while the Auto Insights feature leverages ML to identify valuable data insights, making it a powerful tool for business users.
- Limited use cases beyond Oracle ecosystem: Customers that don’t use Oracle’s cloud infrastructure, data management, or Fusion applications may find limited value in OAC as a stand-alone product. Although Oracle provides access to non-Oracle applications, Gartner has observed minimal adoption among customers outside the Oracle ecosystem.
- Operational challenges for small businesses: OAC may be costly and complex for small and midsize businesses (SMBs), especially without substantial investment in other Oracle products. Some SMBs have reported operational difficulties due to the product’s complexity, highlighting the need for significant enterprise-level investment. SMBs using NetSuite may find Oracle NetSuite Data Intelligence, which packages Oracle Analytics with a prebuilt analytics model and content, a better fit.
- Resource availability: While Oracle has tried to enhance the availability of OAC resources, some customers have cited challenges in finding knowledgeable personnel familiar with OAC and Oracle Cloud Infrastructure (OCI). Prospective buyers should ensure they have sufficient resources and talent to utilize OAC and OCI fully.
Pyramid Analytics
Pyramid Analytics is a Visionary in this Magic Quadrant. Pyramid offers a modern ABI platform that spans the entire data life cycle, and its architecture is founded on ML-driven data preparation and data wrangling. It facilitates data discovery and sharing through dashboards and reports that business users create in a low-code/no-code setting. The deployment-agnostic platform offers flexible hosting on major cloud platforms and on-premises.
Pyramid has continued integrating AI features across its platform, enhancing data preparation, modeling and query capabilities. The AI-driven Smart Model and Model Pro tools facilitate intuitive data integration and transformation for users with varying levels of technical expertise.
- NLQ features: Pyramid’s NLQ features adaptive AI that refines responses based on user feedback, ensuring continuous improvement in query handling. The platform offers real-time query suggestions and type-ahead features, enhancing user experience by predicting relevant questions and providing contextualized suggestions tailored to individual user interactions.
- Accessible metrics layer: Pyramid’s metrics layer leverages AI-assisted processes to suggest metric code and help create analytical models. The platform also provides a low-code/no-code interface for metrics development while allowing code-centric developers to create metrics using PQL and MDX languages or through REST APIs. It ensures flexibility and integration with diverse analytical environments via REST API, SDK and OData queries, although it does not offer custom-made native connectors to other ABI platforms.
- Multipersona data prep experiences: Pyramid provides four experiences for preparing data for analytics. They are the one-click, quick Smart Modeling tool for augmented model building, the quick Direct Model experience for a semantic data model, the Model Lite experience as a step-by-step wizard, and an advanced ETL-like process for proficient end users. All interfaces produce a reusable data model that can be optionally shared across multiple reports and projects.
- Lack of market reach: Despite recent improvements in market presence, Pyramid Analytics’ platform continues to experience a lack of awareness among clients seeking low-code/no-code ABI solutions, as indicated by Gartner inquiries. This impacts its visibility and consideration in industry evaluations, and Gartner rarely sees it included on competitive shortlists.
- Training and resources: In Gartner’s Peer Insights reviews, a few Pyramid users report difficulties accessing support and resources for troubleshooting, as well as challenges with the tools and user experience (UX) that are hard to learn and use. Pyramid is making updates to its Learning Management System to enhance the self-service learning experience for users.
- Limited catalog interoperability: Pyramid Analytics lacks capabilities to integrate analytic content such as reports, dashboards, metrics and visuals from competing analytics and BI platforms in its analytics catalog. Most organizations have a variety of ABI platforms and are looking to organize analytic content from various sources in a way that is easy to consume.
Qlik
Qlik is a Leader in this Magic Quadrant. Qlik Cloud Analytics is offered as a SaaS platform and includes Qlik Sense, Qlik Predict and Qlik Automate. Qlik provides a comprehensive platform that integrates AI and automation across the data life cycle, from data ingestion to analytics to decision making.
Qlik recently introduced a new agentic AI framework to enhance user experiences, enabling features like conversational analytics, automated authoring and autoML. It also added a new application, Qlik Answers, designed to provide insights from unstructured data and facilitate real-time decision making. Moreover, Qlik acquired Upsolver in January 2025 for real-time streaming and no-code data preparation capabilities to simplify the data wrangling process.
- Renewed customer success: Qlik demonstrates strong customer satisfaction and retention rates, as evidenced by Gartner Peer Insights reviews, where many users are willing to recommend the product. This willingness indicates prospective buyers might want to explore other Qlik offerings, such as Qlik Talend Cloud, based on their business needs. However, while Qlik has the opportunity to upsell these products, many renewals continue to concentrate primarily on analytics.
- Associative model differentiation: Qlik’s associative model creates an associative index of all data loaded into the system, thus allowing users to explore data freely without being constrained by preset paths or queries. The associative model is visible through Qlik’s color coding to help users quickly find insights.
- Cloud- and application-agnostic: Qlik is offered as a service across all major clouds and integrates with many major enterprise cloud applications. Thus, it can be a reliable choice for prospective buyers with multicloud implementations and a diverse range of enterprise applications.
- Absence of a cloud or application ecosystem: Although Qlik adopts a cloud-agnostic approach and forms partnerships, its lack of a proprietary cloud infrastructure service or business applications (such as CRM/ERP) constrains its sales strategy for enterprise adoption in some organizations. Prospective buyers should verify that Qlik can provide seamless integrations with their current D&A ecosystems.
- Questionable cloud integration: Qlik doesn’t offer a serverless architecture. Instead, it relies on its own in-memory caching layer to scale, unlike native cloud data platforms that are designed for on-demand, elastic querying and optimized to process data requests. Prospective buyers with an aggressive data lakehouse strategy should vet any concerns about importing and creating data silos with Qlik.
- Limited natural language query: Qlik does not offer significant functional differentiation in NLQ capabilities, such as reasoning and analytic understanding, suggestions and type ahead, compared with other vendors evaluated in this Magic Quadrant.
Salesforce (Tableau)
Tableau, a Salesforce company, is a Leader in this Magic Quadrant. It is distinguished by its capabilities in visual-based data exploration, facilitating business users’ data access, preparation, analysis and presentation. With a global operational footprint, Tableau serves a diverse range of clients.
In 2025, Tableau introduced Tableau Next, an agentic analytics platform built on an open lakehouse architecture that unifies structured, unstructured, and streaming data. It features an AI semantic layer for interpreting data in a business context and uses agents to suggest and curate semantics. The platform delivers visualizations embedded in users’ workflows, supports conversational analytics through agents and connects insights to actions via an integrated workflow engine.
- Strong automated insights and data preparation features: Tableau’s robust automated insights and data preparation capabilities empower users to analyze data more effectively. Key features include Einstein Discovery for key driver analysis, Data Guide for correlation analysis, and Tableau Pulse for proactive tracking of trends and outliers across collaboration platforms such as Slack, MS Teams and email. Tableau also streamlines data blending and transformation for comprehensive analysis.
- Flexibility and scalability: Tableau supports flexible deployment across major cloud service providers such as Azure, Alibaba Cloud, Google Cloud and AWS. However, potential buyers should note that this flexibility is specific to Tableau Server when deployed in a virtual machine (VM) on another CSP, which can leverage Tableau LangChain for agentic capabilities. Tableau Cloud, the SaaS application, is solely available on AWS. Tableau also supports operations across data lakes, on-premises, cloud and hybrid environments, allowing organizations to use their existing data assets and respect their governance policies efficiently.
- Strong user community: Tableau customers frequently highlight the platform’s strong user community, service expertise, and product functionality and performance as critical factors in their decision making, according to Gartner Peer Insights reviews. Its community-driven knowledge sharing provides significant value, especially for new Tableau users. The community is an active partner in driving Tableau’s product strategy and innovation.
- Product gaps: Tableau’s analytics catalog functionality does not support content integration from competing BI platforms, NLQ capabilities lack real-time type-ahead and predictive suggestions, and it does not offer native write-back capabilities, requiring users to rely on third-party plug-ins.
- High license costs: According to Peer Insights reviews and discussions during Gartner inquiries, some Tableau customers mention Tableau’s pricing as a significant factor in their purchasing decisions. They also cite the complexity of the product portfolio included in the cloud package as a concern. Last year’s addition of the Tableau+ license, as well as additional simplified pricing coming later this year, may alleviate some of these concerns.
- Industry-agnostic solution: Although Tableau offers a variety of out-of-the-box intelligent analytics and industry-specific solutions, it is often perceived as an industry-agnostic platform. While Tableau Accelerators, intelligent apps and industry resources provide tailored dashboards and applications, they may not fully address the unique requirements of specialized sectors without further customization.
SAP
SAP is recognized as a Visionary in this Magic Quadrant. With its SAP Analytics Cloud, it offers a cloud-native, multitenant platform excelling in analytics content governance, data visualization and automated insights. Its planning and scenario analysis features for predictive forecasts and simulations are distinctive among vendors in this market.
In 2025, SAP integrated SAP Analytics Cloud into the SAP Business Data Cloud (BDC), a fully managed SaaS solution that unifies SAP data and connects with third-party sources. This integration includes SAP Datasphere, SAP Business Warehouse (BW) modernization, managed SAP Databricks, and Business Data Cloud intelligent applications. Consequently, SAP no longer markets SAP Analytics Cloud and SAP Datasphere as separate products.
- Strong platform administration: SAP Analytics Cloud offers real-time feedback on widget performance, helping designers improve how stories load and function. It also includes a system for managing and organizing data assets, allowing users to tag and control access to content based on its status and certification. Administrators can efficiently manage content workflows, track usage statistics and recommend content by leveraging insights to optimize utilization and identify improvement areas.
- Strong integration and reduced total cost of ownership (TCO): SAP Analytics Cloud, now part of BDC, is deeply integrated within the SAP ecosystem. It reduces the TCO by running live queries on SAP data sources without moving data and preserving data authorizations through role-based authentication. BDC supports closed-loop analytics and planning workflows, harmonizing SAP and non-SAP data into semantic models, and enhancing ABI with SAP Business AI for reliable insights.
- Tailored industry applications: SAP’s Business Data Cloud offers prebuilt analytical applications (Intelligent Applications) designed for specific industry use cases and lines of business such as finance, supply chain, HR, procurement and customer experience. Users can access a wide range of curated data products and over 300 packages of analytics and BI content for more than 20 industries from SAP and its partners.
- Limited cloud options: SAP BDC currently supports AWS Cloud, but support for Google Cloud and Microsoft Azure is still on the roadmap in 2025. Clients using SAP Analytics Cloud on Google Cloud Platform and Azure will continue to receive support and will be transitioned to BDC on these platforms in the future. Prospective buyers should determine SAP’s ability to support non-AWS Cloud platforms, if applicable.
- Low momentum outside of SAP ecosystem: While SAP is distinguished by its ecosystem, its adoption is notably limited outside this framework. Based on Gartner inquiries, organizations that do not operate within a SAP-centric application or data environment rarely consider SAP Analytics Cloud a viable option.
- Lack of functional differentiation: SAP Analytics Cloud offers consistent capabilities but does not provide a significant functional differentiation. The primary interest in SAP Analytics Cloud stems from its integration within the SAP ecosystem and its planning and scenario analysis features for predictive forecasts and simulations, though it has notable gaps in AI-enabled data preparation and analytics catalog interoperability.
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, enabling quick and accurate insight generation that supports the whole enterprise.
In the past year, SAS continued to advance SAS’s Viya Visual Analytics capabilities and expanded SAS Viya Workbench, enabling on-demand self-service capabilities for data preparation, exploratory analysis and model development. It also invested in productivity and performance for advanced analytics capabilities with a strategic focus and commitment to trustworthy AI. SAS Viya continued to embrace open architectures and open standards with AI at the core of its future vision.
- AI and agentic as core approach: AI and agentic capabilities are at the core of SAS’s approach to analytics and future vision. SAS Viya Copilot enhances user productivity with conversational analytics throughout the analytics life cycle, helps users choose ML models, builds GenAI apps and provides workflow enablement with built-in explainable AI using NLG. These features allow prospective buyers to simplify complex analytics tasks and improve overall workflow efficiency.
- Commitment to governance, privacy and trust: SAS’s strategy foundation is built on a commitment to AI responsibility, intelligent profiling of PII and sensitive data, and bias avoidance to ensure fairness with data privacy, data quality and model interpretability. It automatically generates privacy icons that identify data with potential privacy concerns, enabling users to review the data before using it in analytics reports or models.
- Enterprise reporting and forecasting: SAS Viya offers extensive functionality for highly formatted, parameterized and pixel-perfect enterprise reporting, with automatic built-in composite AI techniques. SAS Viya also provides advanced analytics capabilities and a simple drag-and-drop interface for creating forecasts. It tests seven forecasting models before choosing the best fit, accompanied by NLG summaries that explain the confidence level, the chosen model and any significant underlying factors.
- Pricing transparency and increases: During Gartner inquiries, clients frequently expressed concerns about the consistent pattern of price increases, the lack of transparency in deal structures and SAS’s limited flexibility on product pricing.
- Metrics layer capabilities: SAS lags other vendors evaluated in this Magic Quadrant for metrics layer capabilities; metrics are created in views and are not accessible independently. While SAS’s product roadmap includes stronger data management features through AI assistance, prospective buyers should verify that SAS’s current offerings meet their functionality requirements.
- Limited adoption: Market adoption of SAS Visual Analytics has been limited, according to customer reviews during Gartner inquiries and in Peer Insights. Its lack of a major business application or public cloud ecosystem restricts its potential customer base and market penetration, which may hinder prospective buyers’ ability to earn executive buy-in compared to other vendors in this Magic Quadrant.
Sigma
Sigma is a Niche Player in this Magic Quadrant. Sigma, a cloud-based ABI solution, empowers business analysts to make decisions quickly and accurately. Sigma delivers an intuitive, spreadsheet-like UI that helps users who are familiar with Excel transition to the product quickly. It also supports live querying through its AI Query feature, as well as SQL and Python.
Sigma is anchoring its future vision on AI, data applications, operations reporting and embedded analytics. Sigma has made significant investments in talent and technologies over the past two years, and it is relatively new in the market with a growing customer base.
- Spreadsheet BI differentiation: Sigma’s spreadsheet-like ABI product is a significantly differentiated approach to enabling business analytics, directly responding to opportunities opened by the expansion of cloud data platforms. Sigma’s approach deemphasizes visualizations for data and calculation manipulation, which may be ideal for prospective buyers in finance, sales and other spreadsheet-intensive functions.
- Performance and scalability: Sigma’s Alpha Query engine enhances cloud-scale performance through distributed query processing, allowing exploration of large datasets while managing compute costs and latency. Its architecture maintains data security by keeping data within the warehouse and adhering to cloud data warehouse security controls.
- Embedded analytics: Analysts and business users can embed dashboards, charts and reports built in Sigma in internal business applications, SaaS applications, analytics as a service or public websites. End users can also personalize their dashboards and bookmark changes, essentially creating new assets independently.
- Lack of enterprise cloud ecosystem: While Sigma connects to major cloud data warehouse providers, optimizes connections through federation and performs writeback to data sources, it lacks an all-in single cloud ecosystem. This lack of a unified ecosystem may be undesirable for some prospective buyers.
- Competitive market landscape: Sigma is a relatively newer entrant in the ABI market compared to some of the more well-established vendors in this Magic Quadrant, which may limit its recognition and adoption in specific industries or regions. Potential buyers should carefully assess how Sigma’s features align with their strategic needs to maximize its unique capabilities.
- Less mature NLQ and NLG: Sigma’s NLG capabilities are limited in narrative automation, as it does not yet support automatic narrative generation across full dashboards and data models, although these features are still on the roadmap. Additionally, Sigma’s NLQ capabilities lack real-time query suggestions and type-ahead features, restricting user efficiency and experience in query generation.
Sisense
Sisense is a Niche Player in the Magic Quadrant. Its platform includes Fusion, an analytics as a platform (AnPaaS) offering. Sisense has advanced its composable analytics development with the Compose SDK, which decouples query composition, data exploration and visualization processes. Additionally, the Sisense Knowledge Graph captures user analytic inquiries and usage, serving as organizational memory by aggregating query usage for easy analysis.
In the past year, Sisense focused on enhancing its capabilities as a tool for embedded analytics and AI-driven insights, supporting both self-service augmented analytics and embedded applications within a hybrid cloud environment.
- Focus on embedded analytics: Sisense provides embedding capabilities through its Compose SDK, enabling integration and customization of data visualizations within applications and portals. It also supports headless queries and offers a library of interactive components for flexible analytics deployment. These features benefit users and prospective buyers by enabling analytics integration in existing systems, enhancing user experience and providing customized solutions tailored to business needs.
- Robust data preparation capabilities: Sisense features a semantic layer that allows users to combine multiple data sources using drag-and-drop functionality and proprietary algorithms. Its support for data profiling provides comprehensive statistics on data quality and distribution to help users assess and improve their data models. Additionally, Sisense offers data inference capabilities that suggest transformations and cleaning processes, facilitating efficient data preparation and ensuring high-quality analytics outputs.
- Deployment flexibility: Sisense offers deployment options in managed cloud, hybrid or self-hosted environments, ensuring enterprise-level security and compliance. For organizations with users across multiple geographic regions, Sisense can provision separate environments in different data centers to provide regional data residency and enhance performance for a globally distributed user base.
- Limited content management and cataloging: Sisense’s content management and analytics catalog capabilities are limited by the absence of automated catalog population and interoperability with other tools, which restricts efficient catalog creation and integration. The platform lacks comprehensive features for tagging, certifying and managing analytic content, impacting the organization and accessibility of data assets.
- Absence of data or application ecosystem: While Sisense is offered as a service and interacts effectively with cloud technologies, it lacks a broader data or application ecosystem. This lack of a comprehensive system can be a disadvantage compared to ABI solutions from cloud and business application providers, which benefit from being part of larger cloud ecosystems and cloud-based business applications.
- Lowered search momentum: Sisense is experiencing reduced market visibility, as indicated by fewer overall reviews on Peer Insights and lower Gartner search and inquiry data compared to its competitors evaluated in this research. Additionally, its user community is less engaged than larger industry players, resulting in limited peer-to-peer interactions and knowledge sharing.
Strategy
Strategy, formerly known as MicroStrategy, is a Visionary in this Magic Quadrant. Strategy ONE is an integrated, multicloud-based platform that provides BI, data visualization and advanced analytics capabilities with strong governance and composability. It remains committed to its vision of “Intelligence Everywhere” by offering composable analytics and AI-infused capabilities, including NLQ, automated insights and metrics layer.
In 2024, the company rebranded from MicroStrategy to Strategy to enhance its market perception as an innovator in the analytics space and highlight its Bitcoin Treasury strategy as part of its efforts toward financial independence. Beyond rebranding, the company launched Strategy AI to enhance AI-driven analytics.
- Composable analytics: Strategy provides top-tier embedded analytics capabilities, enabling technical users to seamlessly integrate Strategy resources into portals and applications. It also features a robust metrics layer that facilitates the creation, definition, governance and distribution of business metrics across downstream analytics, data science and business applications.
- Analytics governance: Delivering enterprise analytics content at scale securely and in a governed manner remains a hallmark of Strategy. Its content management and platform administration capabilities ensure robust scalability and control for business users and administrators.
- New branding: The rebranding of Strategy aims to address its lack of visibility in a competitive market filled with emerging players and innovative offerings. Although too soon to evaluate the rebrand’s success, the initiative represents a noteworthy effort to counter perceptions of being a legacy vendor. For prospective buyers, this new branding could indicate that Strategy is evolving to remain competitive and positioning itself as an innovative solution provider.
- Product gaps: Strategy’s platform offers room for improvement in some areas, such as clustering, which currently requires manual effort. The analytics catalog functionality lacks interoperability to connect with third-party tools, relying on manual Python scripting to integrate analytics content. NLG does not use metadata or user preferences for contextualization.
- Lack of surrounding data or application ecosystem: Strategy offers its platform on major clouds, including AWS, Azure, Google, and StackIT, and integrates well with other cloud technologies. However, it lacks the inherent go-to-market advantages of ABI solutions from cloud providers with proprietary cloud ecosystems and business applications. Many organizations depend on a robust vendor ecosystem’s technical and commercial synergies when selecting ABI platforms. Such dependencies can impact Strategy’s competitiveness.
- High license cost and steep learning curve: Strategy implemented a new simplified, all-inclusive predictable pricing model in early 2024. However, in Gartner’s Peer Insights reviews and during inquiries, some legacy clients cited high license cost, a complex licensing model and steep learning curve as key decision factors.
Tellius
Tellius is recognized as a Visionary in this Magic Quadrant. Its product is efficient at addressing “what,” “why” and “how” questions to deliver actionable insights. For “what” questions, the platform facilitates code-free querying through NLQ and automated visualization. For “why,” it employs automated insights to uncover hidden drivers and trends, presented in a user-friendly narrative and visual format. AutoML enhances manual processes to address “how” questions.
In the past year, Tellius invested in advancing its GenAI-powered insights engine to integrate unstructured data with structured datasets, enabling users to leverage diverse textual data. It has also introduced a revamped conversational AI interface and two-way chat with enterprise data, as well as AI agents and agentic workflows for expediting multistep analysis. It has refined its focus on high-value verticals like life sciences and consumer packaged goods (CPG) with targeted use cases.
- Strong NLQ and automated insights: Tellius’s AI-powered assistant, Kaiya, is powered by the company’s composite AI engine to enable conversational AI, NLQ, NLG and agentic analytics capabilities. These capabilities generate dynamic, text-based summaries for individual charts, entire Vizpad dashboards and specific insights aligned with user-defined analytical priorities. Kaiya allows users to refine summaries interactively, adjusting tone and length through text or voice commands. Kaiya also leverages common dimensions for contextual filtering and enables personalized query interpretations, enhancing relevance and accuracy over time.
- Vertical industry strategy: Tellius offers prebuilt templates, models and workflows designed explicitly for the pharmaceutical and CPG industries. These solutions address cases such as field sales force effectiveness, brand analytics, market access and category management. Strategic partnerships with companies like ZS and Indegene enhance domain-driven analytics and provide robust implementation support. However, this vertical-specific approach may limit the applicability and flexibility for prospective buyers in industries outside these sectors.
- Ease of use and customer centricity: In Gartner Peer Insights reviews, Tellius customers cite ease of use, alongside customer focus, as key deciding factors when choosing Tellius. It prioritizes customer satisfaction through structured engagement strategies, such as regular check-ins and success reviews, while actively gathering feedback via surveys and Net Promoter Score (NPS) to assess usability and impact.
- Emerging geographic strategy: Tellius is headquartered in the U.S., with three support centers in the U.S, one in Asia/Pacific and one in Europe. While Tellius has customers distributed across the Americas, Europe and Asia, prospects should carefully evaluate the vendor’s ability to provide global support. The majority of its revenue is based in the U.S.
- Product gaps: Tellius’s metrics layer lacks API integration for creating and managing complex calculations, publication workflows and derived metric development. The platform’s metric layer does not demonstrate its capability to serve as an agnostic layer for third-party ABI, DSML and SaaS applications. Moreover, AI-enabled data prep features are limited to suggestions for synonyms and descriptions, indicating a need for enhanced automation and integration.
- Reduced market momentum: Tellius exhibits lower market momentum, as reflected by fewer overall reviews on Peer Insights and decreased Gartner search and inquiry numbers. This suggests that Tellius is less frequently considered in competitive buying decisions compared to its peers evaluated in this research. Its slower momentum might also indicate limited talent availability in the market, which may be challenging for prospective buyers to overcome.
ThoughtSpot
ThoughtSpot is a Leader in this Magic Quadrant. It offers self-service insights through natural language conversation with a consumer-friendly UX, ability to handle complex analysis and scalability for analyzing large datasets.
In the past year, ThoughtSpot introduced Spotter, its agentic analytics feature that allows users to perform conversational analytics. Spotter can integrate into users’ preferred platforms, business applications, productivity tools and custom agents. It also launched Analyst Studio, providing a unified space for data teams to prepare data for AI and analytics, manage cloud costs and drive strategic impact.
- Automated insights and natural language query capabilities: ThoughtSpot provides robust automated insights through SpotIQ, which automatically searches for anomalies in a dashboard and visually highlights them, prompting the user to run key driver analysis on the anomaly. The platform also supports NLQs via Spotter, allowing users to ask questions and receive contextual responses through multistep analysis and reasoning, utilizing synonyms, predictive text and chatbot integration.
- Increasing market interest: ThoughtSpot is experiencing a notable rise in Gartner search and inquiry volumes, reflecting a growing market interest in the product. This surge is driven by the increasing adoption of natural language features, particularly with advancements in GenAI technologies. The rising interest in ThoughtSpot may allow prospective buyers to earn executive buy-ins more easily than other vendors evaluated in this Magic Quadrant.
- Embedded analytics product offering: Through Smart Apps, users can embed ThoughtSpot’s AI-driven analytics in their apps to take actions, write back to systems or trigger workflows — all within the app interface. ThoughtSpot also offers a low-code platform and developer playground for easy integration and customization.
- Product gaps: ThoughtSpot could enhance its analytics catalog capabilities, particularly in terms of interoperability with other vendors to integrate analytics content like reports and dashboards. Additionally, while it supports clustering through scikit-learn, SpotIQ requires manual intervention to identify clusters.
- Pure-play ABI vendor: ThoughtSpot integrates well with leading vendors like Databricks, dbt and Snowflake. However, it lacks its own comprehensive business application or cloud platform ecosystem, which may be suboptimal for buyers seeking a more integrated solution to maximize and streamline their existing investments and resources.
- Differentiation in a crowded market: ThoughtSpot is recognized for its capabilities in NLQ and automated insights features such as key driver analysis and outlier detection. However, sustaining this differentiation is challenging as many ABI platform vendors now integrate LLMs and offer natural-language-first interfaces. Prospective buyers should evaluate ThoughtSpot’s offerings in the context of their business requirements to maximize its unique capabilities.
Zoho
Zoho is a Niche Player in this Magic Quadrant. Zoho Analytics emphasizes data preparation and visualization and offers a marketplace of prebuilt analytical apps tailored for business analysts. Leveraging AI across the platform, Zoho provides advanced analytics, agentic AI assistants, automated insights and autoML. Integrated DSML capabilities enable the creation of ML models with a no-code assistant and custom models with Python Code Studio. Zoho has a global presence in terms of support centers and customers.
In the past year, Zoho introduced the BI Fabric feature, enabling organizations to create a unified portal that consolidates reports and dashboards from other BI applications, such as Tableau and Power BI.
- Domain-specific analytics: Zoho delivers prebuilt analytics for sales, marketing, HR, finance, help desk and ERP, featuring data integration, domain models and prepackaged reports. For instance, its ManageEngine Analytics Plus provides IT analytics with integrations for over 200 IT-specific data sources. Additionally, Zoho offers marketplace plug-ins for platforms such as Shopify, JIRA and ServiceNow, enabling advanced analytics tailored to each platform.
- Low cost compared to value: According to Gartner Peer Insights reviews, Zoho customers frequently highlight the platform’s low license costs as a key factor in their purchasing decision. This may provide an attractive option for small to midsize organizations.
- Proficiency in data preparation and embedded analytics: Zoho Analytics offers features for precise data modeling, such as automatic relationship detection and manual customization. It also enables AI-driven suggestions for data types, relationships and enrichment, aiding in efficient data preparation. For embedded analytics, it allows integration with business applications, supporting interactive reports and dashboards. Zoho Analytics also facilitates embedding through APIs and SDKs, enabling access across external portals and applications.
- Product gaps: Zoho Analytics has constraints in analytics content certification, with watermarks applicable solely to PDF reports. Although users need to manually transfer content across development, staging and production environments, the platform streamlines this through its Linked Workspace feature, which detects changes and compiles them as ”pending updates” for review and approval. Additionally, its NLG capabilities are confined to single-view insights without support for comprehensive dashboard or data model analysis.
- Reduced search momentum: Fewer numbers in Gartner search and inquiry data suggest that Zoho is experiencing reduced visibility among Gartner clients compared to other vendors analyzed in this research.
- Decreased advanced analytics deployments: Although primarily known for self-service analytics, Zoho Analytics introduced DSML capabilities in September 2024, which are now increasingly adopted by its customer base.
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
Sigma met the inclusion criteria for 2025 and was added.
Dropped
Spotfire was excluded from this year’s Magic Quadrant due to its strategic pivot toward Industrial Analytics. The platform now emphasizes specialized use cases in engineering, scientific research and financial services, diverging from the broader business analytics landscape.
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 eight of the 12 critical capabilities.
- Offer an analytics and business intelligence platform that is industry-agnostic, ensuring the solution is not confined to specific industry verticals.
- Rank among the top 20 organizations in the Customer Interest Index defined by Gartner for this Magic Quadrant.
To calculate customer interest, Gartner uses a proprietary method that includes data inputs such as:
- Gartner customer search, inquiry volume and Peer Insight trend data.
- Social media followers and product videos posted.
In line with Gartner’s Magic Quadrant methodology, the number of vendors covered is limited to 20. There are many more ABI platform vendors that are not covered in this research.
Honorable Mentions
Here are some other vendors to consider. Some are tangential to the ABI platform market. Others offer comprehensive ABI platforms but did not meet the inclusion requirements for this Magic Quadrant.
- Aible: Aible operates on an agentic analytics model, licensed per agent rather than per user. It automatically ensures data quality for generating insights, explores millions of variable combinations to discover patterns, and offers recommendations. Users can provide feedback on the agent’s reasoning steps to align them with specific terminologies, processes and preferences.
- Board: Board differentiates itself by providing an enterprise planning platform (analytics, FP&A, consolidation and reporting, commercial planning, supply chain planning and HR and workforce planning) that supports business processes different from those provided by vendors of competing ABI products.
- Databricks: Databricks AI/BI is an advanced business intelligence product that enhances data analysis by understanding the semantics of your data. Built on an agentic architecture, it leverages various AI technologies to extract insights from the entire data life cycle, including ETL processes and data lineage. It provides a full-featured dashboard and reporting solution, AI/BI dashboards for traditional BI tasks and AI/BI Genie, a conversational interface for natural language analysis. The product integrates into Databricks’ Data Intelligence Platform to provide unified governance and optimized costs and performance.
- dbt Labs: dbt provides a consistent, governed and unified metrics and logic layer that integrates with various ABI platforms. Its offering goes beyond the semantic layer to enable analytics engineers to build, test and deploy data models across a distributed environment.
- FanRuan Software: FanRuan is one of the largest ABI vendors in China, where its traditional, report-centric BI product, FineReport, is widely used. FanRuan’s new FineBI product offers self-service, visually driven BI via an on-premises deployment model.
- Metabase: Metabase is an open-source platform for business intelligence and data analytics. It allows users to interact with their data by asking questions in natural language and receiving insightful answers. Additionally, Metabase can be integrated into applications.
- Snowflake: Snowflake Cortex Analyst is a fully managed, LLM-powered feature that answers business questions based on structured data in Snowflake. Users can ask questions in natural language and receive direct answers without writing SQL, using a convenient REST API for integration. It simplifies developing conversational self-service analytics by generating accurate text-to-SQL responses, executed against Snowflake’s scalable engine for optimal performance and lower TCO, while ensuring robust security and governance.
Evaluation Criteria
The Ability to Execute criteria used in this Magic Quadrant are as follows (for the sources of information that informed Gartner’s evaluations using these criteria, see the Evidence section):
- 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.
- 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
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 2025)
The Completeness of Vision criteria used in this Magic Quadrant are as follows (for the sources of information that informed Gartner’s evaluations using these criteria, see the Evidence section):
- 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.
- 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
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 2025)
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 2024 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
This year, the analytics and business intelligence (ABI) platforms market experienced significant changes driven by generative AI (GenAI) disruptions. Vendors focused on innovations in AI agents and agentic capabilities, such as natural language query (NLQ) and natural language generation (NLG). These advancements allow users to generate reports, insights and technical outputs like data models and dashboards using natural language prompts through AI assistants. For more details, see How GenAI is Transforming Analytics and Business Intelligence Platforms (client access may be required) and GenAI Is a Disruptor for Analytics and Business Intelligence Platforms. The pursuit of GenAI capabilities has lowered the barrier to adoption for ABI platforms, democratizing ABI functionalities and making them more accessible to a broader audience.
Simultaneously, vendors from adjacent markets, including database management systems (DBMS) and AI-native applications, are ambitiously introducing more ABI features, indicating that business intelligence is no longer the sole channel for analytics. This expansion suggests that market share may become increasingly contested by these new entrants. The presence of hyperscalers and major cloud ERP and CRM application providers continues to raise concerns about vendor lock-in. Some incumbent vendors have introduced vendor-supported end-to-end data and analytics (D&A) ecosystems, combining various services for a unified user experience.
Microsoft continues to dominate the market in user adoption with the growth of its Power BI cloud service, driven by its bundling with Microsoft 365 and Microsoft Fabric at reduced prices, and its integration with Microsoft Teams for remote work support. However, dedicated specialist vendors are countering Microsoft’s dominance by integrating with broader D&A ecosystems and leveraging their independence from major cloud providers as competitive differentiators, alleviating fears of vendor lock-in and offering customers greater flexibility and choice.
Vendors offering clear pricing models and transparency are better positioned to attract customers, as GenAI technologies can introduce complex cost structures due to computational demands. Content consumers are increasingly becoming creators, composing low-code or no-code automation workflows and applications. As the market evolves, the focus is shifting from delivering datasets and dashboards to providing enriched, contextualized insights that enhance decision making and drive business value.
Acronym Key and Glossary Terms
| ABI | Analytics and business intelligence |
| ARIMA | Autoregressive integrated moving average |
| autoML | Automated machine learning |
| CSP | Cloud service provider; |
| D&A | Data and analytics |
| DSML | Data science and machine learning |
| ETL | Extraction, transformation and loading |
| FP&A | Financial planning and analysis |
| GenAI | Generative AI |
| LLM | Large language model |
| MDX | Multidimensional eXpressions |
| ML | Machine learning |
| NLG | Natural language generation |
| NLQ | Natural language query |
| PII | Personally identifiable information |
| PQL | Process Query Language |
| REST | Representational State Transfer |
| SDK | Software development kit |
| SKU | Stock keeping unit |
| TCO | Total cost of ownership |
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.