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Magic Quadrant for Analytics and Business Intelligence Platforms


Augmented capabilities are becoming key differentiators for analytics and BI platforms, at a time when cloud ecosystems are also influencing selection decisions. This Magic Quadrant will help data and analytics leaders evolve their analytics and BI technology portfolios in light of these changes.

Strategic Planning Assumptions

  1. By 2022, augmented analytics technology will be ubiquitous, but only 10% of analysts will use its full potential.

  2. By 2022, 40% of machine learning model development and scoring will be done in products that do not have machine learning as their primary goal.

  3. By 2023, 90% the world’s top 500 companies will have converged analytics governance into broader data and analytics governance initiatives.

  4. By 2025, 80% of consumer or industrial products containing electronics will incorporate on-device analytics.

  5. By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.

Market Definition/Description

  1. Modern analytics and business intelligence (ABI) platforms are characterized by easy-to-use functionality that supports a full analytic workflow — from data preparation to visual exploration and insight generation — with an emphasis on self-service and augmentation. For a full definition of what these platforms comprise and how they differ from older BI technologies, see “Technology Insight for Ongoing Modernization of Analytics and Business Intelligence Platforms.”

  2. Vendors in the ABI market range from long-standing large technology firms to startups backed by venture capital funds. The larger vendors are associated with wider offerings that includes data management features. Most new spending in this market is on cloud deployments.

  3. ABI platforms are no longer differentiated by their data visualization capabilities, which are becoming commodities. Instead, differentiation is shifting to:

  4. Integrated support for enterprise reporting capabilities. Organizations are interested in how these platforms, known for their agile data visualization capabilities, can now help them modernize their enterprise reporting needs. At present, these needs are commonly met by older BI products from vendors like SAP (BusinessObjects), Oracle (Business Intelligence Suite Enterprise Edition) and IBM (Cognos, pre-version 11).

  5. Augmented analytics. Machine learning (ML) and artificial intelligence (AI)-assisted data preparation, insight generation and insight explanation — to augment how business people and analysts explore and analyze data — are fast becoming key sources of competitive differentiation, and therefore core investments, for vendors (see “Augmented Analytics Is the Future of Analytics”).

  6. ABI platform functionality includes the following 15 critical capability areas (these have been substantially updated to reflect the refocus on enterprise reporting and the increased importance of augmentation):

  7. Security: Capabilities that enable platform security, administering of users, auditing of platform access and authentication.

  8. Manageability: Capabilities to track usage, manage how information is shared and by whom, perform impact analysis and work with third-party applications.

  9. Cloud: The ability to support building, deploying and managing analytics and analytic applications in the cloud, based on data both in the cloud and on-premises, and across multicloud deployments.

  10. Data source connectivity: Capabilities that enable users to connect to, and ingest, structured and unstructured data contained in various types of storage platforms, both on-premises and in the cloud.

  11. Data preparation: Support for drag-and-drop, user-driven combination of data from different sources, and the creation of analytic models (such as user-defined measures, sets, groups and hierarchies).

  12. Model complexity: Support for complex data models, including the ability to handle multiple fact tables, interoperate with other analytic platforms and support knowledge graph deployments.

  13. Catalog: The ability to automatically generate and curate a searchable catalog of the artefacts created and used by the platform and their dependencies

  14. Automated insights: A core attribute of augmented analytics, this is the ability to apply ML techniques to automatically generate insights for end users (for example, by identifying the most important attributes in a dataset).

  15. Advanced analytics: Advanced analytical capabilities that are easily accessed by users, being either contained within the ABI platform itself or usable through the import and integration of externally developed models.

  16. Data visualization: Support for highly interactive dashboards and the exploration of data through the manipulation of chart images. Included are an array of visualization options that go beyond those of pie, bar and line charts, such as heat and tree maps, geographic maps, scatter plots and other special-purpose visuals.

  17. Natural language query: This enables users to query data using business terms that are either typed into a search box or spoken.

  18. Data storytelling: The ability to combine interactive data visualization with narrative techniques in order to package and deliver insights in a compelling, easily understood form for presentation to decision makers.

  19. Embedded analytics: Capabilities include an SDK with APIs and support for open standards in order to embed analytic content into a business process, an application or a portal.

  20. Natural language generation (NLG): The automatic creation of linguistically rich descriptions of insights found in data. Within the analytics context, as the user interacts with data, the narrative changes dynamically to explain key findings or the meaning of charts or dashboards.

  21. Reporting: The ability to create and distribute (or “burst”) to consumers grid-layout, multipage, pixel-perfect reports on a scheduled basis.

Magic Quadrant

  1. Figure 1. Magic Quadrant for Analytics and Business Intelligence Platforms Source: Gartner (February 2020)

Vendor Strengths and Cautions

Alibaba Cloud

  1. Alibaba Cloud, a new entrant to this Magic Quadrant, is a Niche Player. As yet, it competes only in Greater China, but it has global potential.

  2. Alibaba Cloud is the largest public cloud platform provider in China. It offers data preparation, visual-based data discovery and interactive dashboards as part of its Quick BI platform. It is available as a SaaS option running on Alibaba Cloud’s own infrastructure or as an on-premises option on Apsara Stack Enterprise.

  3. With release 3.4, Quick BI broadened its enterprise reporting functionality, thus reinforcing its strong focus on the needs of its local market.

  1. Support for Mode 1 (centralized) and Mode 2 (decentralized): In addition to Mode 2, self-service, visual-based data discovery capabilities, Quick BI provides Mode 1 capabilities such as Microsoft Excel-like reporting and write-back with form-based submission. Many of the organizations attracted to Quick BI are first-time customers with low levels of maturity in analytics. As a ABI platform that can meet both traditional and modern needs, Quick BI is suitable for them.

  2. Operations: According to the reference customers Gartner surveyed, Alibaba Cloud is operating well. They were very positive about the overall experience, service and support, and the migration experience delivered by Alibaba Cloud. Most would recommend Quick BI to others.

  3. Wider data offering: Quick BI is a core product within the Alibaba Data Middle Office offering, which is a productized version of the data and analytics technology built by Alibaba for its e-commerce business. This is driving market traction — Alibaba Data Middle Office is the most frequent topic raised by users of Gartner’s client inquiry service who are interested in deploying a data and analytics platform in Greater China. Alibaba sees Quick BI as key to its plan to execute its overall business strategy to develop its ecosystem and win new business for other Alibaba Cloud products, such as Dataphin (for data management) and Quick Audience (for customer insights and marketing automation).

  1. Geographical presence: Alibaba is a China-focused vendor, with a very limited installed base elsewhere. The quality of documentation and training materials for Quick BI available in Mandarin is not matched by those available for the same product in other languages.

  2. Functional maturity: Quick BI is a new product and its functional capabilities are relatively weak, compared with those of the other vendors in this Magic Quadrant. This is especially the case in terms of automated insight, data storytelling and data source connectivity. Reference customers indicated that they use Quick BI for simple BI tasks, with most viewing static reports or parameterized dashboards, rather than undertaking more complex self-service analysis.

  3. Support for wide deployment: Reference customers identified Quick BI’s inability to support large numbers of users and its cost as limitations to wider deployment in their organizations.


  1. Birst is a Niche Player in this Magic Quadrant. Its strategy and appeal are led by the aim of meeting the needs of the wider Infor installed base.

  2. Birst provides an end-to-end data warehouse, reporting and visualization platform built for the cloud. It also offers its product as an on-premises appliance on commodity hardware. Since 2017, Birst has operated as a stand-alone subdivision of Infor. Judging by inquiries from Gartner customers, most organizations that consider using Birst are Infor customers.

  3. In 2019, Birst extended its visual analytics capability with the guided Birst Visualizer, further developed its Smart Insights augmented analytics functionality and its core enterprise-readiness capabilities. Birst 7 brings together Mode 1 (centralized) and Mode 2 (decentralized) analytics in a single platform through a common interface.

  1. Metadata-powered cloud BI: Birst provides data preparation, dashboards, visual exploration and formatted, scheduled reports on a single cloud-native platform. Birst’s networked semantic metadata layer enables business units to create models that can be promoted to the wider enterprise. Birst supports live connectivity with on-premises data sources and rapid creation of a data model and all-in-one data warehouse on a range of storage options (Microsoft SQL Server, SAP HANA, Exasol and Amazon Redshift).

  2. Vertical applications: Birst for CloudSuite gives Infor ERP customers prebuilt extraction, transformation and loading (ETL), data models, and dashboards that are fully integrated into Infor business applications. For non-Infor data sources, Birst provides solution accelerators for specific domains, such as wealth management, insurance, sales and marketing.

  3. Global capability: As part of Infor, Birst has a physical presence in 44 countries. Its software supports complete localization of the entire Birst platform, including at the application layer, in over 40 languages, in the metadata model and in user-generated content.

  1. Performance: Most of Birst’s reference customers named poor performance as a problem they had encountered in their deployment, and identified this as a concern regarding wider deployment. This finding is consistent with feedback gathered for the 2019 edition of this Magic Quadrant. Poor responsiveness is an inhibitor of user adoption for any modern ABI product.

  2. Customer support: Providing high-quality and timely support has long been a problem for Birst. Birst’s reference customers’ view its software quality and support quality as ongoing inhibitors of wider use.

  3. Self-service usage: Although Birst now offers improved data visualization functionality, relatively few customers use it for self-service. Judging from reference customers, Birst is overwhelmingly used for Mode 1 static and parameter-driven reporting, rather than Mode 2 requirements.

BOARD International

  1. BOARD International is a Niche Player in this Magic Quadrant. It predominantly serves a submarket for financially oriented BI.

  2. BOARD positions itself as a vendor of an “end-to-end decision-making platform” and defines its leading go-to-market targets as organizations using IBM, Oracle and SAP enterprise reporting tools. The company has transitioned to a hosted cloud model, and seen strong growth in the U.S., which now accounts for around one-quarter of its global license revenue.

  3. In May 2019, BOARD introduced version 11 of its platform, based on a reengineered in-memory calculation engine that replaces its long-standing multidimensional online analytical processing (MOLAP) approach. The investment made by Nordic Capital early in 2019 is evident in BOARD’s head count growth — up almost 25% in a year — and its sharper marketing.

  1. Unified analytics, BI, and financial planning and analysis (FP&A): BOARD is one of only two vendors in this Magic Quadrant to offer a modern ABI platform with integrated FP&A functionality. As such, it is highly differentiated for buyers looking to close the gap between BI and financial processes.

  2. Breadth of analytics: The reference customers surveyed for this research use BOARD for a wide range of analytic tasks. This illustrates its platform’s breadth of capabilities, which range from Mode 1 reporting and simulation using write-back, to predictive analytics using the Board Enterprise Analytics Modelling (BEAM) statistical function library.

  3. System integrator ecosystem: BOARD has a well-established network of system integrator (SI) partners. These are helping to drive its growth and giving it presence, by proxy, outside the nine countries where it has significant direct operations, namely the U.S., Switzerland, the U.K., Italy, Germany, Australia, France, Benelux and Spain.

  1. Recognition outside finance departments: In most cases, BOARD enters a company via the finance department, its brand being well known there. Persuading nonfinance end users to use its platform as an alternative to better known BI platforms may prove difficult. None of the reference customers we surveyed said BOARD was their sole enterprise BI standard.

  2. Product direction: BOARD is not innovating as quickly as its competitors. Although it offers some augmented analytic capabilities in the BOARD Cognitive Space, particularly for automated forecasting, its vision is lagging behind that of the market as a whole in the key areas of openness and consumerization.

  3. Customer experience: BOARD’s reference customers were relatively unenthusiastic about their experience of working with the company. In particular, they identified issues with the product migration experience and the quality of the software product.


  1. Domo is a Niche Player in this Magic Quadrant. Its focus on business-user-deployed dashboards and ease of use characterize its appeal.

  2. Domo’s cloud-based ABI platform offers over 1,000 data connectors, consumer-friendly data visualizations and dashboards, and a low/no-code environment for BI application development. Domo typically sells directly to business departments, such as marketing and sales, that are attracted to its platform’s ease of use and fast time to deployment.

  3. In the fourth quarter of 2019, Domo announced a strategic partnership with Snowflake, a leading cloud data platform provider, to offer a native API integration and joint go-to-market strategy. In the second quarter of 2019, Domo announced a package of 20 data connectors to Amazon Web Services (AWS) services including Simple Storage Service (S3), Redshift, Athena, Aurora, DynamoDB and CloudWatch. In the first quarter of 2019, Domo announced its Business Automation Engine (BAE), an orchestration layer that coordinates event-based workflows and helps Domo move from descriptive to prescriptive analytics.

  1. Customer satisfaction: Domo scored well in several areas of Gartner’s reference customer survey, including overall vendor experience and product quality. All of Domo’s reference customers indicated they would recommend its product.

  2. Renewed business momentum: Domo’s subscription revenue increased by over 25% between the first nine months of 2018 and the first nine months of 2019.

  3. Speed of deployment: Domo’s ability to connect quickly to enterprise applications enables rapid deployment. Domo’s connectivity is differentiated in that it maintains API-like connectors that can respond dynamically to changes in source-side schemas.

  4. Preparation for consumer-centric analytics: Since 2010, Domo has been competing with a consumer-centric approach in a market almost exclusively focused on “power users,” but new market dynamics emphasizing the “analytic consumer” and the “empowered analyst,” should help it.

  1. Standardization rate: Comparatively few of Domo’s reference customers consider it their sole enterprise ABI platform standard. However, this is likely because Domo is often deployed by lines of business — in isolation from IT — for domain-specific analysis in the areas of marketing, finance and supply chain. This finding is consistent with customer reference feedback received in 2019.

  2. Geographic presence: Although Domo’s platform supports multiple languages (English, Japanese, French, German, Spanish and Simplified Chinese), the company has a direct presence in only four countries: the U.S., Japan, the U.K. and Australia. This impairs its perception as a viable option for enterprises based in other countries.

  3. Marketing differentiation: Domo’s most used capability remains its easy-to-use management dashboards. Few of Domo’s surveyed reference customers were using its product for complex analysis (predictive analytics in particular). The market is moving away from dashboards and, although Domo’s product roadmap acknowledges this development, its brand is not associated with the shift toward augmented analytics.


  1. Dundas, a Niche Player, is a new entrant to this Magic Quadrant. Greatly evolved from its origins as provider of a chart engine for developers, it now offers a fully featured platform.

  2. The Dundas BI platform enables users to visualize data, build and share dashboards, create pixel-perfect reports, and embed and customize analytics content. It is built on Dundas’ in-memory engine and built-in data warehouse. Dundas sells to large enterprises, but specializes in embedded BI, with 70% of its revenue coming from OEMs that extend, integrate, customize and embed Dundas BI in their applications.

  3. In 2019, Dundas introduced its new in-memory engine, added point-and-click trend analysis, a new natural language query capability, support for Linux (in addition to Windows), and an application development environment for highly customized analytic applications.

  1. Embedded BI specialty: With fully open APIs, Dundas specializes in highly customized and embedded analytics use cases. By drawing on its mature global partnership program, which includes e-learning and certifications, customers can enhance web portals and on-premises and SaaS offerings with Dundas reporting and dashboards, and build highly customized data applications from scratch.

  2. Integrated traditional reporting and modern ABI: Within Dundas BI, users can create pixel-perfect reporting content in the same environment that is used for drag-and-drop dashboard and self-service design.

  3. Customer support, sales and overall experience: Gartner Peer Insights reviewers and surveyed reference customers for Dundas view Dundas positively across these three measures. They also praised its training and user community. As a small vendor in a crowded market, Dundas advertises its provision of a personalized customer experience as a core differentiator.

  1. Cube focus: Although Dundas offers a patented in-memory engine and built-in data warehouse, its platform’s reliance on a cube architecture can become a limitation as data size and diversity grows. A higher proportion of Dundas customers identified inability to handle large data volumes as a platform limitation than was the case with any other vendor in this Magic Quadrant.

  2. Product vision: Dundas has made limited investments in augmented analytics features, although some data preparation features and expanded investments in NLP are on its roadmap. These investments may help address the concerns of the reference customers who identified “difficulty of use” as a limitation of its platform.

  3. Market recognition and geographical presence: As a small, niche vendor, Dundas is focused on North America, Europe and Australia. Despite having 2,500 customers, Dundas is not well known beyond its installed base.


  1. IBM is a Niche Player in this Magic Quadrant. IBM Cognos Analytics is primarily of interest to existing IBM Cognos customers who are looking to modernize their ABI use.

  2. IBM Cognos Analytics supports the entire analytics life cycle, from discovery to operationalization. For augmented analytics Cognos Analytics now supports statistically significant differences/insights, time series forecasting, key driver detection, NLP and NLG. As Cognos Analytics is an upgrade from earlier versions of Cognos, it brings formatted, production-style reporting for Mode 1, along with visual-based exploration and agility for Mode 2 ABI.

  3. In the fourth quarter of 2019, IBM released a Cognos Analytics Cartridge for Cloud Pak for Data, which uses the Red Hat OpenShift Container Platform for both analytic deployments and DataOps. Also in the fourth quarter, IBM introduced a Cognos Analytics and Planning Analytics offering, paving the way for unified planning, “what if?” analysis and reporting.

  1. Comprehensiveness of functionality: IBM Cognos Analytics is one of the few offerings that includes enterprise reporting, governed and self-service visual exploration, and augmented analytics in a single platform. In addition, as existing IBM Cognos Framework Manager models and reports from earlier versions can be used in the single environment, there is a migration path and the ability to use existing content.

  2. Product vision: Visionary elements on IBM’s roadmap include a social insights add-on, AI-driven data preparation, and analytic quality scores for data sources. A big part of the vision is to unify planning, reporting and analysis in a common portal that offers “what if?” planning, Mode 1 reporting, and predictive models and forecasts.

  3. Deployment options: IBM offers a variety of deployment options to meet all customer requirements. These include on-premises, cloud (IBM-hosted cloud and IBM OnDemand Cloud Service), “bring your own license” for any of the major infrastructure as a service (IaaS) platforms (Microsoft Azure, Google, AWS), and the Cloud Pak for Data.

  1. Loss of momentum and perception as innovator: IBM is no longer acting as a disruptor, but instead playing catch-up. Interest in IBM Cognos from Gartner clients failed to rebound in 2019, judging from their inquiries and searches.

  2. Rareness as sole enterprise standard: IBM Cognos Analytics is rarely the sole enterprise-standard platform for ABI. Less than one-fifth of IBM’s reference customers considered it to be their only enterprise standard.

  3. Prices: Prices for IBM Cognos Analytics Standard, Plus and Premium, at $15, $35 and $70 per user per month respectively, are in line with those of other independent BI specialists, but significantly higher than those of other large cloud providers. Consequently, IBM struggles to be competitive in new deals — that is, when Cognos Analytics is not already the incumbent platform.

Information Builders

  1. Information Builders is a Niche Player in this Magic Quadrant. Its WebFOCUS Designer is of most interest to its installed base and little evaluated in competitive sales cycles of which Gartner is aware.

  2. Information Builders sells the integrated WebFOCUS ABI platform, as well as individual components thereof. WebFOCUS Designer (formerly InfoAssist+) includes components from the WebFOCUS stack that are intended to satisfy modern self-service ABI needs.

  3. In 2019, Information Builders focused on reengineering its UI and moving the overall product to a cloud-first, microservice-based architecture aimed at shortening the time to value for users.

  1. External and large-scale deployments: Information Builders is well-known for deploying externally facing analytic applications at scale — sometimes to thousands of users. Almost half of its surveyed reference customers stated they had deployments for over 1,000 users. No reference customer had encountered problems with WebFOCUS Designer’s ability to support large user numbers or large data volumes.

  2. Prepackaged analytic apps: Information Builders provides prebuilt assets and customizable data models designed for a variety of horizontal and vertical areas, such as the banking, healthcare, insurance, law enforcement, visual warehouse/facilities management, retail, public and higher education sectors.

  3. Support for complex data and modern appeal: A core strength of Information Builders is data connectivity and integration of a variety of data sources, including real-time data streams. The redesigned UI of WebFOCUS Designer combines visual data discovery, reporting, dashboard creation and interactive publishing capabilities with mobile content and an in-memory engine.

  1. Marketing and sales strategy: In late 2019, Information Builders began offering a full SaaS option for those wishing to deploy in the cloud without managing their own data center or cloud instance. However, although new and improved augmented functionality is being delivered and appears on Information Builders’ roadmap for 2020, its reference customers reported low utilization of augmented analytics capabilities such as automated insights, and of natural language query (NLQ) and NLG.

  2. Performance and ease of use: Information Builders’ reference customers identified poor performance as the issue they most often encounter with WebFOCUS Designer. Ease of use also remains a challenge for this vendor, although it has improved from previous years.

  3. Innovation and product strategy: Although Information Builders’ product roadmap shows drastic improvements to the existing platform, its overall vision and product strategy are not entirely differentiated from those of its competitors. Information Builders is perceived as more of a fast follower than a market disruptor that others need to copy.

Logi Analytics

  1. Logi Analytics is a Niche Player, owing to its dedicated focus on the embedded analytics segment and appeal to developers.

  2. The Logi Analytics Platform is focused solely on embedded analytics and application teams. The bulk of its revenue comes from OEM software and service vendors. Logi Analytics’ platform provides embedded dashboard, reporting and end-user authoring. Logi Predict provides an embeddable workflow to produce predictive models.

  3. Logi acquired Jinfonet Software and its JReport product for pixel-perfect operational reporting in February 2019. It acquired Zoomdata for its streaming data in June 2019. Each year, it produces two major releases and offers frequent minor releases as service packs.

  1. Embedded BI and OEM practice: Logi continues to focus on application teams. It offers a full set of APIs to enable organizations to build sophisticated analytics within apps or websites. It has also designed a dedicated OEM practice with a sales team and pricing to align with its go-to-market strategy.

  2. Platform openness: Logi reinforces its vision for platform openness with an improved microservices architecture. Its adaptive security approach can adopt existing security infrastructure in multitenant environments. Reference customers view Logi’s integration and deployment capabilities as strengths.

  3. Actionable advanced analytics: Logi offers a predictive analytics solution to embed advanced capabilities directly inside existing applications. It also supports the ability to group data into logical segments with incremental data. These capabilities enable users to take actions based on analytic results without leaving the application.

  1. Narrowness of usage: Judging from the reference customers surveyed, few Logi users conduct ad hoc analysis, whereas a very high proportion use its product for viewing static reports, data integration, preparation, and accessing parameterized reports and dashboards.

  2. Product vision: Logi has invested in a broad set of visionary capabilities in terms of openness, but not ones aligned with the consumerization and automation trends identified by Gartner as key market drivers.

  3. Natural language capabilities: Logi has been slow to react to the shift to augmented analytics capabilities. As yet, it offers no built-in NLG features.


  1. Looker is a Challenger in this Magic Quadrant for the first time. Its pending acquisition by Google both increases its market visibility and raises questions about its future integration into Google’s portfolio.

  2. Looker offers modern ABI reporting and dashboard capabilities using an agile, centralized data model and an in-database architecture optimized for various cloud databases.

  3. Looker’s product enhancements in 2019 included integration with Slack, a redesigned dashboard experience and content organization structure, as well as automated model generation capabilities that convert SQL scripts into Looker data models. In addition, Looker has enhanced its developer tools and introduced a new developer portal, API sandbox and marketplace.

  4. Note: Google announced a plan to acquire Looker in June 2019, but at the time of writing the acquisition is not complete. As such, Looker is evaluated on its own merits, although the public announcement of Google’s interest has inevitably improved Looker’s market visibility and reference customers’ views of its viability as a supplier.

  1. In-database design: Unlike most competing solutions, Looker’s offering does not require in-memory storage optimizations. Rather, it leaves data in the underlying database and uses its LookML modeling layer to apply business rules. This enables power users and data engineers to model data and then reuse data and calculations in other applications in a trusted and consistent way. This approach exploits the performance and scalability of the underlying database and supports data source flexibility. Looker’s key differentiator is native support for cloud-based analytic databases, particularly Amazon Redshift and Athena, Google BigQuery, Microsoft Azure and Snowflake, which Google has committed to maintain postacquisition.

  2. Embedded uses and customer development: The developer is a key persona for Looker. It offers extensive APIs, SDKs, developer tools and workflow integration support for end-user organizations and OEMs that want to create and embed analytics in application workflows, portals and customer-facing applications.

  3. Customer experience: Reference customers scored Looker positively for support, product quality, and migration experience. Gartner Peer Insights reviewers have similar views and assess Looker favorably for the availability and quality of partner resources and for its user community and training.

  1. Power user skill requirement for data modeling: In contrast to the point-and-click and augmented approach taken by competing solutions, which are targeted at enabling less skilled users, Looker’s data modeling requires coding. Its product lacks data preparation capabilities for visually manipulating data.

  2. Narrowness of product vision: A comparatively high proportion of Looker’s reference customers identified absent or weak functionality as a limitation of its platform. Missing from Looker’s roadmap are key elements that are needed if a company is to compete in a market transitioning to AI-automated, augmented analytics and natural-language-driven, consumer-like experiences. Investments in these may, however, be announced, once the acquisition by Google closes.

  3. Geographic presence: Currently, Looker has a direct presence in only four countries: the U.S., the U.K., Ireland and Japan. This is a drawback for organizations that want a direct relationship with Looker in other countries.


  1. Microsoft is a Leader in this Magic Quadrant. It has a comprehensive and visionary product roadmap and massive market reach through its Microsoft Office channel.

  2. Microsoft offers data preparation, visual-based data discovery, interactive dashboards and augmented analytics in Power BI. It is available as a SaaS option running in the Azure cloud or as an on-premises option in Power BI Report Server. Power BI Desktop can be used as a stand-alone, free personal analysis tool. Installation of Power BI Desktop is required when power users are authoring complex data mashups involving on-premises data sources.

  3. Microsoft releases a weekly update to its cloud service, which added hundreds of features in 2019. Recent additions include decomposition tree visuals, LinkedIn data connectivity and geographic mapping enhancements.

  1. “Viral” spread: Although the price of Power BI Pro, at $10 per user per month, has helped the product’s market traction, this is secondary to its inclusion in Office 365 E5, which makes it “self-seeding” in many organizations. Prompts in other Microsoft Office products, like Excel, encouraging users to “visualize in Power BI” increase its exposure further — its reference customers claimed more deployments with more than 1,000 users than those of any other vendor in this Magic Quadrant. Power BI is now almost always mentioned by users of Gartner client inquiry service who ask about ABI platform selection.

  2. Product capabilities: For years following its 2013 launch, Power BI was a “follower” product that had only to be “good enough,” given its price. That is no longer the case — and with the releases in 2019, the Power BI Pro cloud service overtook most of its competitors in terms of functionality. It outstripped many by including innovative capabilities for augmented analytics and automated ML. AI-powered services, such as text, sentiment and image analytics, are available within Power BI and draw on Azure capabilities. The vast majority of Microsoft’s surveyed reference customers would recommend Power BI without qualification.

  3. Comprehensiveness of product vision: Microsoft continues to invest in a broad set of visionary capabilities and to integrate them with Power BI. This aligns well with the openness, consumerization and automation trends identified by Gartner as key market drivers.

  1. On-premises version: Compared with the Power BI Pro cloud service, Microsoft’s on-premises offering has significant functional gaps, including dashboards, streaming analytics, prebuilt content, natural language Q&A, augmentation (what Microsoft calls Quick Insights) and alerting. None of these functions are supported in Power BI Report Server.

  2. Azure-only: Microsoft does not give customers the flexibility to choose a cloud IaaS offering. Its offering runs only in Azure.

  3. Connectivity: Power BI offers a very wide range of data connectors, but feedback from users of Gartner’s client inquiry service indicates that the query performance of on-premises data gateways is variable and requires effort to optimize. Connectivity to SAP BW and HANA direct queries is problematic — a known issue that Microsoft is working on. Customers generally choose to load data into Power BI instead, which is more performant.


  1. MicroStrategy is a Challenger in this Magic Quadrant. It is extremely strong functionally and has released innovations recently, but its limited market momentum and recognition outside its installed base hinder wider adoption.

  2. MicroStrategy offers one of the most comprehensive ABI platforms, supporting both Mode 1 and Mode 2 analytic and reporting requirements. Its core analytic product family for data connectivity, data visualization and advanced analytics is supplemented by complementary mobile, cloud, embedded and identity analytics products.

  3. MicroStrategy’s semantic graph is central to a new category of content, which the company calls HyperIntelligence. HyperIntelligence overlays and dynamically identifies predefined insights within existing applications. In another significant recent development, MicroStrategy has opened up its semantic layer to competing ABI platforms. This breaks a long-standing tradition in the ABI platform sector that emphasizes a more proprietary architecture. We expect both HyperIntelligence and the open architecture to be imitated by MicroStrategy’s competitors.

  1. Consumer-friendly design focus: HyperIntelligence is among the most innovative product features to appear in the ABI platform space in the past two years. It puts the analytic consumer at the center of the design experience and brings analytic content into the workflow of web, office application and mobile users.

  2. Use as enterprise standard: MicroStrategy’s reference customers were twice as likely to select it as the sole enterprise standard ABI platform in their organization, in comparison with the average across vendors in this Magic Quadrant. In line with this finding, almost all of MicroStrategy’s reference customers upgraded their product in the prior year, which indicates its importance to their operations.

  3. Stability of integrated product: MicroStrategy does not acquire codebases. All new developments are built organically. This leads to more stable, less buggy code, especially when compared to competitors that fill product gaps with acquisitions. A high proportion of MicroStrategy’s reference customers indicated they had encountered no problems using its platform.

  1. Cost of software: Half of MicroStrategy’s reference customers identified the cost of its software as a barrier to wider deployment, as compared with the market average of around one-fifth across all vendors.

  2. Business momentum: Compared with the vendors it competes with, MicroStrategy has little traction with new customers. Although it is making a profit, total product licenses and subscription services were relatively flat, at $79 million, when comparing the first nine months of 2019 to the corresponding period in 2018.

  3. Lack of advantage of stack ABI solutions: Much of the momentum in the ABI platform market comes from the shift to deployment on cloud stacks, as well as to cloud-based business applications. Although MicroStrategy’s platform interacts well with other technologies, ABI solutions that are owned by cloud and business application vendors have a go-to-market advantage.


  1. Oracle is a Visionary in this Magic Quadrant, for the first time since re-entering it in 2017. Its continued focus on augmented analytics is now coupled with an improved go-to-market approach.

  2. Oracle’s very broad ABI capabilities are available both in the Oracle Cloud and on-premises. Oracle Analytics Cloud (OAC) offers an integrated design experience for interactive analysis, reports and dashboards.

  3. During 2019, Oracle simplified its product packaging to three offerings, including a new offering for analytic applications, introduced new, competitive pricing, and revamped its OAC customer success organization and customer and partner communities. It also continued to enhance its innovative augmented analytics and NLP integration with OAC and collaboration tools, such as Slack and Microsoft Teams, and added an analytics catalog.

  1. Augmented analytics and robust NLG: Oracle has implemented augmented analytics capabilities across its platform earlier than most other vendors, and reference customers reported broad use of its augmented analytics features. OAC also features NLG with adjustable tone and verbosity in English and French (eight more languages are on Oracle’s roadmap). It is the only platform on the market to support NLQ in 28 languages.

  2. Product vision: Oracle continues to invest aggressively in augmented analytics capabilities and consumer-like, conversational user experiences, including chatbot integration coupled with autogenerated insights. These are central to OAC and Day by Day, Oracle’s mobile app.

  3. Full-stack enterprise cloud: Oracle offers an end-to-end cloud solution, including infrastructure, data management, analytics and analytic applications with cloud data centers in almost all regions of the world. During 2019, Oracle made significant investments in its Oracle Analytics for Applications, which offers native integration, packaged augmented analytics and closed-loop actions for Oracle’s ERP, human capital management, supply chain, customer experience and NetSuite products.

  1. Oracle Cloud and Oracle application-centric: Although OAC can access any data source, it runs only in the Oracle Cloud, and packaged analytic applications are available only for Oracle enterprise applications at the time of writing.

  2. Market awareness: Oracle has a competitive product, but its differentiators are not well known, and Oracle is not considered as frequently as the Leaders in competitive evaluations known to Gartner.

  3. Rebuilding of customer perception: Oracle is in “rebuilding mode” and making significant investments to reestablish the perception that it is a trusted enterprise ABI partner to its existing customers and the broader market. However, changing hearts and minds takes longer than changing products. This effort is a work in progress.

Pyramid Analytics

  1. Pyramid Analytics is a Niche Player in this Magic Quadrant. It is growing by attracting organizations that want an on-premises, private cloud or hybrid deployment, instead of a public cloud-based platform.

  2. Pyramid offers an integrated suite for modern ABI requirements. It has a broad range of analytical capabilities, including data wrangling, ad hoc analysis, interactive visualization, analytic dashboards, mobile capabilities and collaboration in a governed infrastructure. It also features an integrated workflow for system-of-record reporting. Pyramid has now fully decoupled from Microsoft (on which it had relied) and is increasing awareness of its brand, growing new markets and audiences, and making strategic communications about its platform-agnostic offering.

  3. The release of Pyramid v2020 brings sophistication and simplicity to technical and nontechnical users alike. Additionally, the adaptive augmented analytics platform now covers the entire data life cycle out-of-the-box, from ML-based data preparation to automated insights and automated ML model building.

  1. Range of use cases: Pyramid supports agile workflows and governed, report-centric content within a single platform and interface. Its solution is well-suited to governed data discovery, with features such as BI content watermarking, reusability and sharing of datasets, metadata management and data lineage.

  2. Augmentation: Augmented features such as Smart Discovery, Smart Reporting, Ask Pyramid (NLQ), AI-driven modeling, automatic visualizations and dynamic content offer powerful insights to all users, regardless of skill level.

  3. Ease of deployment and administration, with single workflow: Reference customers scored Pyramid higher than most vendors for overall experience with the tool. They particularly value the platform’s single integrated workflow that supports all ABI use cases.

  1. Product vision and innovation: Pyramid has made significant progress over the past few years, but is still only catching up with the visionary elements delivered by competitors.

  2. Availability of market resources: Reference customers for Pyramid scored it below the average for the availability and quality of third-party resources such as integrators and service providers. They also gave a below-average score for the quality of Pyramid’s peer user community.

  3. Lack of market recognition: 2018 and 2019 were retooling and transition years for Pyramid. In 2020, it is focused on market growth and product differentiation — something that Pyramid has struggled to communicate in a crowded market.


  1. Qlik is a Leader in this Magic Quadrant. Its strong product vision for ML- and AI-driven augmentation is clear, but so is its lower market momentum, relative to its main competitors.

  2. Qlik’s lead ABI solution, Qlik Sense, runs on the unique Qlik Associative Engine, which has powered Qlik products for the past 20 years. The engine enables users of all skill levels to combine data and explore information without the limitations of query-based tools. Qlik’s cognitive engine adds AI/ML functionality to the product and works with the Associative Engine to offer context-aware insight suggestions and augmentation of analysis.

  3. Qlik continues to enhance its platform’s microservices-based architecture and multicloud capabilities. A full SaaS version of Qlik Sense Enterprise is available and forms the basis of Qlik’s new SaaS-based trial experience. Qlik introduced “associative insights” in June 2019 as an augmented analytics capability that uses Qlik’s cognitive engine to uncover otherwise hidden insights. Qlik’s acquisition of Attunity, whose product remains stand-alone, broadens the data integration capabilities of the Qlik ecosystem.

  1. Flexibility of deployment: Qlik was one of the first vendors to offer a seamless end-user experience and management capabilities across multicloud deployments. The flexibility to deploy on-premises, or with any major cloud provider, or to use a combination of both approaches, or to utilize Qlik’s full SaaS offering, remains a focus of Qlik’s vision.

  2. Expansiveness of platform capabilities: Qlik’s portfolio of offerings spans a number of phases in the analytics life cycle. Qlik Sense delivers self-service visual data discovery capabilities for analysts or business users, while also supporting developer-embedded analytics from the same platform. Qlik Data Catalyst is used for cataloging and additional governance. Also, although Qlik Data Integration Platform (formerly Attunity) is a stand-alone offering, it adds powerful integration and data movement capabilities under the Qlik umbrella.

  3. Augmentation and data literacy: The associative insights capability uses Qlik’s unique “associative experience” to automatically uncover insights on data that may otherwise have been missed by query-based tools. While users of the augmented features may be nonanalyst personas, Qlik’s Data Literacy Project helps users of all levels, Qlik customers or not, to better understand and utilize data.

  1. Momentum: After a period of realignment in 2019, Qlik is now adding staff again. The company made some visionary moves in 2019: technology acquisitions, major product releases and refreshed migration offerings. However, relative to other Leaders its momentum remains low, judging by Gartner’s search and client inquiry data and a range of other indicators. Furthermore, less than half of Qlik’s reference customers said it supplied their enterprise-standard ABI tool.

  2. Product migration: Despite Qlik’s emphasis on providing support and dedicated resources for customers moving from QlikView to Qlik Sense, surveyed reference customers for this vendor identified the migration experience as a key concern, relative to those of all other vendors.

  3. Self-service usage: Although Qlik Sense is designed to support visually driven self-service, Qlik’s reference customers reported that most of their users are consuming parametrized dashboards. That said, Qlik’s core associative experience offers an alternative way to automatically uncover insights, which may reduce need for some forms of self-service.


  1. Salesforce is a Visionary in this Magic Quadrant. It remains strongest in terms of augmented analytics functionality, but other vendors are catching up. Furthermore, questions about how Salesforce Einstein Analytics and Tableau will be positioned for the future are creating uncertainty among customers.

  2. Einstein Analytics is available in three packages, which differ in price and functionality. Einstein Analytics Plus — the comprehensive product — offers Einstein Prediction Builder, Sales Analytics, Service Analytics, Analytics Studio, Data Platform, Einstein Discovery and Einstein Data Insights. Salesforce’s midtier product, Einstein Analytics Growth, offers a more limited bundle of Sales Analytics, Service Analytics, Analytics Studio, and Data Platform. And Salesforce’s lowest price offering, Einstein Predictions, offers Einstein Predictive Builder and Einstein Discovery.

  3. On 1 August 2019, Salesforce completed its acquisition of Tableau — the most significant market change of the year. The addition of Tableau gives Salesforce enormous customer, product and channel momentum, but it also introduces uncertainty. Salesforce already had a very robust product line that heavily overlapped with Tableau’s. Moreover, Salesforce Einstein Analytics customers indicate strong satisfaction, which has prompted many to ask why Salesforce needed to make the $15 billion acquisition.

  4. Note: Salesforce announced a plan to acquire Tableau in June 2019. The acquisition was completed on 1 August 2019. However, the U.K. Competition and Markets Authority (CMA) implemented a “hold separate” order, which required Salesforce and Tableau to operate separately, pending a review. The CMA lifted this order at the end of November 2019. As a result, product and company integration plans were not developed and available to share with Gartner in time for consideration for this Magic Quadrant. As such, representing the joined offering as one entity was not warranted, nor would it be useful to readers at this point. Salesforce and Tableau are therefore represented separately in this Magic Quadrant.

  1. Embedded analytics: Einstein Analytics is much more likely to be embedded in business applications — commonly Salesforce’s own apps — than other ABI platforms. This propensity for embedded deployment, coupled with strong workflow integration, will become a major factor in customers’ selection decisions.

  2. Literacy of partner ecosystem and developer marketplace: Salesforce has over 36,000 Einstein Analytics community members, and their numbers have been growing at 50% to 75% a year. Moreover, Salesforce Trailhead provides an effective way to measure the literacy of community members.

  3. Support for large deployments: According to the survey of reference customers, almost one- quarter of Salesforce’s respondents had deployments with more than 5,000 users, which is much higher than the survey average.

  1. Threat to augmented analytics lead: In 2019, Salesforce’s competitors made significant progress in closing the gap with Einstein Analytics’ differentiation in terms of augmented analytics. Salesforce is defending its position by innovating. In 2019, it added functionality such as a public API enabling consumption of Einstein Discovery predictions in any application, and unique capabilities like bias protection that warn users of unintentional bias in predictions they create using Einstein Analytics.

  2. Cost: With Einstein Analytics Plus, Einstein Analytics Growth and Einstein Predictions prices starting at $150, $125 and $75 per user per month, respectively, Salesforce’s Einstein Analytics is one of the highest-priced platforms on the market.

  3. Rarity of use as enterprise standard: Surveyed reference customers for Salesforce indicated that Einstein Analytics is unlikely to be deployed as the enterprise standard. It tends to be used with Salesforce CRM applications alone, other ABI tools being used for enterprisewide analytics.


  1. SAP is a Visionary in this Magic Quadrant, thanks to its improved product functionality and strong vision, but it remains of interest predominantly to the wider base of SAP enterprise application users.

  2. SAP Analytics Cloud is a cloud-native multitenant platform with a broad set of analytic capabilities. Most companies that choose SAP Analytics Cloud already use some SAP business applications.

  3. In 2019, SAP continued to strengthen the product’s core capabilities in data connectivity (via SAP Cloud Platform Open Connectors) and visualization. It also added a set of open APIs to support the OEM/embedded use case for the first time. As the strategic analytics offering for all SAP applications and data sources, SAP Analytics Cloud is offered as the embedded analytics and planning solution for the SAP Intelligent Suite, which includes SAP SuccessFactors, SAP C/4HANA and SAP S/4HANA.

  1. Closed-loop capability: SAP Analytics Cloud’s integrated functionality for planning, analytical and predictive capabilities in a unified, single platform differentiates it from almost all competing platforms. The associated SAP Digital Boardroom is attractive to executives as it supports “what if?” analyses and simulations.

  2. Augmented and advanced analytics: Reference customers for SAP Analytics Cloud scored its advanced analytics functions highly. SAP included augmented analytics in its design approach some years ago, and SAP Analytics Cloud offers strong functionality for NLG, NLP and automated insights. SAP has continued to develop its augmented capabilities by adding support for automated time-series analysis and explainable findings.

  3. Breadth of capability: SAP offers a library of prebuilt content that is available online for SAP Analytics Cloud. This content covers a range of industries and line-of-business functions. It includes data models, data stories and visualizations, templates for SAP Digital Boardroom agendas, and guidance on using SAP data sources. Additionally, SAP Analytics Cloud now forms part of a wider data portfolio that includes the new SAP Data Warehouse Cloud.

  1. Perception by potential users: SAP’s brand has long been associated with traditional BI, and the legacy of that is a perception among potential users that does not reflect SAP Analytics Cloud’s capabilities. The effort of convincing internal users that SAP Analytics Cloud is worth considering puts it at a disadvantage versus the competition.

  2. Scale of user deployments and number of data sources: Consistent with data gathered for the 2019 edition of this Magic Quadrant, SAP Analytics Cloud user deployments (although growing) were among the smallest reported by reference customers surveyed for the present edition. They were also connected to a relatively low number of data sources. These findings may be not indicative of the entire SAP Analytics Cloud installed base, however.

  3. Cloud-only focus: SAP Analytics Cloud runs in SAP data centers or public clouds (on AWS infrastructure). For organizations that want to deploy a modern ABI platform on-premises, SAP Analytics Cloud is not a viable choice. SAP Analytics Cloud can, however, connect directly to on-premises SAP resources (SAP BusinessObjects Universes, SAP Business Warehouse and SAP HANA) for live data and to non-SAP data sources for data ingestion as a hybrid option.


  1. SAS is a Visionary in this Magic Quadrant. This status reflects its robust product and global presence, as well as its challenges in terms of marketing and price perception.

  2. SAS offers Visual Analytics on its new cloud-ready and microservices-based platform, SAS Viya. SAS Visual Analytics is one component of SAS’s end-to-end visual and augmented data preparation, ABI, data science, ML and AI solution.

  3. In 2019, SAS significantly enhanced its augmented analytics capabilities. These now include automated suggestions for relevant factors, and insights and related measures expressed using visualizations and natural language explanations. They also include automated predictions with “what if?” and AI-driven data preparation suggestions. Additionally, SAS has enhanced Visual Analytics’ location intelligence capabilities and introduced a new SDK.

  1. End-to-end platform vision: SAS’s compelling product vision is for customers to prepare their data, analyze it visually, and build, operationalize and manage data science, ML and AI models, in a single integrated, visual and augmented design experience (with progressive licensing). Moreover, with Visual Analytics, SAS is the only vendor in this Magic Quadrant to support text analytics natively.

  2. Augmented analytics: SAS is investing heavily in automation, with augmented analytics present across its entire platform. Investment areas include voice integration with personal digital assistants, chatbot integration and homegrown NLG, with outlier detection on the roadmap.

  3. Global reach with industry solutions: SAS is one of the largest privately held software vendors, with a physical presence in 47 countries and a global ecosystem of system integrators. Visual Analytics forms the foundation of most of SAS’s extensive portfolio of industry solutions, which includes predefined content, models and workflows.

  1. Market perception: Although SAS now supports the open-source data science and ML ecosystem and has introduced a new SDK for SAS Visual Analytics, it was slow to respond to the open-source trend. This slowness has contributed to a market perception that SAS is expensive and proprietary, which has been a barrier to broader market consideration outside SAS’s installed base.

  2. Sales experience: Despite new capability-based and metered pricing options introduced in 2019, Gartner Peer Insights reviewers rate SAS comparatively poorly for pricing and contract flexibility. Moreover, a relatively high percentage of SAS Visual Analytics reference customers identified cost as a limitation to broader deployment in their organization.

  3. Migration challenges: SAS Viya represented a major redesign of the user experience and platform to create a more open environment. Although SAS has continued to improve its utilities to make migration easier, reference customers continued to view migration as challenging. SAS has also received relatively low scores and write-ups for product quality and support by reference customers and Gartner Peer Insights reviewers.


  1. Sisense is a Visionary in this Magic Quadrant, one best known for its success with the embedded use case. Its new emphasis on serving the “builder” buyer persona represents a change in marketing focus and market segmentation as it seeks competitive advantage.

  2. Sisense provides an ABI platform that supports complex data projects by offering data preparation, analytics and visual exploration capabilities. Half of its ABI platform customers use the product in an OEM form.

  3. Sisense 8.0.1 was released in September 2019 with new ML capabilities to uncover hidden insights and suggest new visualizations. In May 2019, Sisense acquired Periscope Data to reinforce its advanced analytics capabilities. Integration of the products has begun and will continue over the coming months.

  1. Native cloud BI: Sisense was early to rearchitect its offering as a cloud-native analytics platform. It can run on modern scalable cloud applications such as Docker containers and Kubernetes orchestrations. The underlying components can be elastically scaled within Sisense’s managed cloud offering or the client’s deployment.

  2. Support for complex data mashups: Sisense has continued to add augmented data preparation capabilities to ElastiCube, the vendor’s proprietary caching engine that uses both in-memory and in-chip processing for fast performance. By introducing augmented text deduplication, Sisense can automatically deduplicate incorrect or misaligned data and group similar strings.

  3. Use as enterprise standard: Sisense’s reference customers strongly consider it their sole enterprise ABI platform standard. An improved, open, single stack with entirely browser-based interface helps customer penetration.

  1. Depth of use: Responses from Sisense reference customers indicated that a high proportion of customers manage complex data projects, but that a low proportion of Sisense users do moderately complex or highly complex ad hoc analysis and discovery, relative to the market norm.

  2. Narrowed marketing focus: Sisense’s acquisition of Periscope has sharpened its focus on power users and developers, whom it terms “builders,” and the integration of Periscope gives Sisense additional capabilities to enable data professionals to perform data science and analytics. But although this sharpened focus brings differentiation, it represents a somewhat narrower, less consumer-oriented vision than that of Sisense’s key competitors.

  3. Deployment size: Although Sisense has continued to grow its strategic accounts and has some very large deployments with thousands of active users, deployment sizes (judged by number of users) remain small, in comparison to other vendors. The percentage of reported deployments for over 500 people was low.


  1. Tableau is a Leader in this Magic Quadrant. It offers a visual-based exploration experience that enables business users to access, prepare, analyze and present findings in their data. It has powerful marketing and expanded enterprise product capabilities, but there is some uncertainty about its direction as part of Salesforce.

  2. In 2019, Tableau significantly broadened the scope of its product offerings, particularly their augmented analytics and governance capabilities. For augmented analytics, Tableau introduced both Ask Data and Explain Data to provide natural language query and automated insights. For governance, Tableau improved Tableau Prep Builder (which comes with Tableau Creator) and introduced Tableau Prep Conductor to schedule and monitor data management tasks. Tableau Prep Conductor comes bundled with Tableau Catalog as part of the Data Management Add-on. Tableau also introduced the Server Management Add-on, which provides server management, content migration and workload optimization. Tableau also moved a significant portion of its customer base to the cloud with Tableau Online.

  3. On 1 August 2019 Salesforce completed its acquisition of Tableau. This acquisition creates opportunities and challenges for Tableau. Salesforce bolsters Tableau in three key emerging areas of the ABI Platform market: AI, Cloud, and embedded analytics. However, Tableau had already made significant progress in all three areas prior the acquisition, and must now reconcile a complicated and overlapping product roadmap.

  4. Note: Salesforce announced a plan to acquire Tableau in June 2019. The acquisition was completed on 1 August 2019. However, the U.K. Competition and Markets Authority (CMA) implemented a “hold separate” order, which required Salesforce and Tableau to operate separately, pending a review. The CMA lifted this order at the end of November 2019. As a result, product and company integration plans were not developed and available to share with Gartner in time for consideration for this Magic Quadrant. As such, representing the joined offering as one entity was not warranted, nor would it be useful to readers at this point. Salesforce and Tableau are therefore represented separately in this Magic Quadrant.

  1. Customer enthusiasm: Customers demonstrate a fanlike attitude toward Tableau, as evidenced by the more than 20,000 users who attended its 2019 annual user conference. Reference customers scored Tableau well above the average for the overall experience. These users serve as strong champions for Tableau.

  2. Ease of visual exploration and data manipulation: Tableau enables users to ingest data rapidly from a broad range of data sources, blend them, and visualize results using best practices in visual perception. Data can easily be manipulated during visualization, such as when creating groups, bins and hierarchies.

  3. Momentum: Tableau grew its total revenue to just over $900 million through the first quarter of 2019, and achieved 14% growth from the first six months of 2018 to the first six months of 2019. Tableau remains a constant presence on evaluators’ shortlists and continues to expand within its installed base. The reference customers surveyed had mostly upgraded to Tableau’s latest version and expressed positive views about the migration experience.

  1. New risks in a changing market: Tableau dominated the visual data discovery era of the ABI platform market, but as the market moves toward the augmented era, new entrants may prove disruptive. So far, however, Tableau has made sound choices in terms of balancing short-term and long-term product roadmap priorities.

  2. Governance: Despite new data and server management product releases that added governance and administrative capabilities in 2019, perceptions of weak governance and administration persist among some of Tableau’s reference customers. These are also evident during some Gartner inquiry calls.

  3. Sales experience, contracting and cost: Negotiating with Tableau has always had its pros and cons. In general, customers like Tableau Viewer as a lower-cost option for analytic consumers, and they are accommodating Tableau’s push toward subscription pricing. However, with the Server Management Add-on and Data Management Add-on, Tableau customers will be faced with a la carte pricing, which means they should expect to pay extra for new functionality.


  1. ThoughtSpot is a Leader in this Magic Quadrant. Its innovative search-first approach remains attractive and continues to be emulated, but its differentiation is becoming slim.

  2. ThoughtSpot differentiates itself with its search-based interface, which supports analytically complex questions with augmented analytics at scale.

  3. In 2019, ThoughtSpot raised an additional $248 million in Series E funding, to bring its total venture capital investment to $554 million. During the year, it deepened its augmented analytics capabilities, introduced new AI-driven crowdsourced-driven recommendations, and added new autonomous monitoring of business metrics. Importantly, ThoughtSpot also added an in-database query option, initially for Snowflake and subsequently for Amazon Redshift, Google BigQuery and Microsoft Azure Synapse.

  1. Specialism in search and AI at scale: Given ThoughtSpot’s search feature, and use of NLP as the primary interface for querying data, questions can be posed by typing or speaking. ThoughtSpot supports analytically complex questioning of large amounts of data, with more than one-third of its reference customers analyzing over 1 terabyte of data. SpotIQ, ThoughtSpot’s augmented analytics capability, discovers anomalies, correlations and comparative analysis between data points without the need for coding.

  2. Consumer-oriented and augmented product vision: ThoughtSpot is prioritizing the building of Facebook-like consumer experiences into its platform. Priorities include continuous feeds of related content (for example, to monitor individual headline metrics), automated anomaly detection and proactive alerting.

  3. Market awareness: Despite ThoughtSpot’s relatively small size, the market’s awareness of its search-based value proposition is high. This vendor is shortlisted by most of the customers who make use of Gartner’s client inquiry service when prioritizing NLP and augmented analytics features.

  1. Gaps in data preparation, visual exploration and dashboards: ThoughtSpot’s software typically complements other products, and does not cover the full spectrum of ABI requirements. Data must be prepared and cleansed using third-party tools to either load data into ThoughtSpot’s massively parallel processing engine or into a high-performance database like Snowflake for in-database processing. The software does not allow users to readily manipulate data into groups or bins without using formulas, and dashboards are basic, lacking rich mapping features. Relatively few reference customers viewed ThoughtSpot as their only enterprise standard for ABI.

  2. Limited global reach, ecosystem and user community: Relative to the other Leaders in this Magic Quadrant, ThoughtSpot has a limited international presence, but one that is growing. Its partner ecosystem is a work in pro