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Gartner: Magic Quadrant For Business Intelligence And Analytics Platforms

Full Report here with registration

23 February 2015

Analyst(s): Rita L. Sallam, Bill Hostmann, Kurt Schlegel, Joao Tapadinhas, Josh Parenteau, Thomas W. Oestreich


Traditional BI market share leaders are being disrupted by platforms that expand access to analytics and deliver higher business value. BI leaders should track how traditionalists translate their forward-looking product investments into renewed momentum and an improved customer experience.

Market Definition/Description

This document was revised on 22 May 2015. The document you are viewing is the corrected version. For more information, see the Corrections page on

BI and Analytics Platform Capabilities Definition

The BI and analytics platform market is undergoing a fundamental shift. During the past ten years, BI platform investments have largely been in IT-led consolidation and standardization projects for large-scale systems-of-record reporting. These have tended to be highly governed and centralized, where IT-authored production reports were pushed out to inform a broad array of information consumers and analysts. Now, a wider range of business users are demanding access to interactive styles of analysis and insights from advanced analytics, without requiring them to have IT or data science skills. As demand from business users for pervasive access to data discovery capabilities grows, IT wants to deliver on this requirement without sacrificing governance.

While the need for system-of-record reporting to run businesses remains, there is a significant change in how companies are satisfying these and new business-user-driven requirements. They are increasingly shifting from using the installed base, traditional, IT-centric platforms that are the enterprise standard, to more decentralized data discovery deployments that are now spreading across the enterprise. The transition is to platforms that can be rapidly implemented and can be used by either analysts and business users, to find insights quickly, or by IT to quickly build analytics content to meet business requirements to deliver more timely business benefits. Gartner estimates that more than half of net new purchasing is data-discovery-driven (see "Market Trends: Business Intelligence Tipping Points Herald a New Era of Analytics"). This shift to a decentralized model that is empowering more business users also drives the need for a governed data discovery approach.

This is a continuation of a six-year trend, where the installed-base, IT-centric platforms are routinely being complemented, and in 2014, they were increasingly displaced for new deployments and projects with business-user-driven data discovery and interactive analysis techniques. This is also increasing IT's concerns and requirements around governance as deployments grow. Making analytics more accessible and pervasive to a broader range of users and use cases is the primary goal of organizations making this transition.

Traditional BI platform vendors have tried very hard to meet the needs of the current market by delivering their own business-user-driven data discovery capabilities and enticing adoption through bundling and integration with the rest of their stack. However, their offerings have been pale imitations of the successful data discovery specialists (the gold standard being Tableau) and as a result, have had limited adoption to date. Their investments in next-generation data discovery capabilities have the potential to differentiate them and spur adoption, but these offerings are works in progress (for example, SAP Lumira and IBM Watson Analytics).

Also, in support of wider user adoption, companies and independent software vendors are increasingly embedding traditional reporting, dashboards and interactive analysis into business processes or applications. They are also incorporating more advanced and prescriptive analytics built from statistical functions and algorithms available within the BI platform into analytics applications. This will deliver insights to a broader range of analytics users that lack advanced analytics skills.

As companies implement a more decentralized and bimodal governed data discovery approach to BI, business users and analysts are also demanding access to self-service capabilities beyond data discovery and interactive visualization of IT-curated data sources. This includes access to sophisticated, yet business-user-accessible, data preparation tools. Business users are also looking for easier and faster ways to discover relevant patterns and insights in data. In response, BI and analytics vendors are introducing self-service data preparation (along with a number of startups such as ClearStory Data, Paxata, Trifacta and Tamr), and smart data discovery and pattern detection capabilities (also an area for startups such as BeyondCore and DataRPM) to address these emerging requirements and to create differentiation in the market. The intent is to expand the use of analytics, particularly insight from advanced analytics, to a broad range of consumers and nontraditional BI users - increasingly on mobile devices and deployed in the cloud.

Interest in cloud BI declined slightly during 2014, to 42% compared with last year's 45% - of customer survey respondents reporting they either are (28%) or are planning to deploy (14%) BI in some form of private, public or hybrid cloud. The interest continued to lean toward private cloud and comes primarily from those lines of business (LOBs) where data for analysis is already in the cloud. As data gravity shifts to the cloud and interest in deploying BI in the cloud expands, new market entrants such as Salesforce Analytics Cloud, cloud BI startups and cloud BI offerings from on-premises vendors are emerging to meet this demand and offer more options to buyers of BI and analytics platforms. While most BI vendors now have a cloud strategy, many leaders of BI and analytics initiatives do not have a strategy for how to combine and integrate cloud services with their on-premises capabilities.

Moreover, companies are increasingly building analytics applications, leveraging a range of new multistructured data sources that are both internal and external to the enterprise and stored in the cloud and on-premises to conduct new types of analysis, such as location analytics, sentiment and graph analytics. The demand for native access to multistructured and streaming data combined with interactive visualization and exploration capabilities comes mostly from early adopters, but are becoming increasingly important platform features.

As a result of the market dynamics discussed above, for this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 13 critical capabilities across three categories - enable, produce and consume - in support of four use cases for BI and analytics. These capabilities support building an analytics portfolio that maps to shifting requirements from IT to the business. From delivery of insights to the analytics consumer, through an information portal often deployed centrally by IT, to an analytics workbench used by analysts requiring interactive and smart data exploration (see "How to Architect the BI and Analytics Platform"), these capabilities enable BI leaders to support a range of functions and use cases from system-of-record reporting and analytic applications to decentralized self-service data discovery. A data science lab would be an additional component of an analytics portfolio. Predictive and prescriptive analytics platform capabilities and vendors are covered in the "Magic Quadrant for Advanced Analytics Platforms."

See Note 1 for how capability definitions in this year's Magic Quadrant have been modified from last year to reflect our current view of the critical capabilities for BI and analytics platforms.

The 13 Critical Capabilities and Use Cases

Vendors are assessed for their support of four main use cases:

  1. Centralized BI Provisioning: Supports a workflow from data to IT-delivered-and-managed content.
  2. Decentralized Analytics: Supports a workflow from data to self-service analytics.
  3. Governed Data Discovery: Supports a workflow from data to self-service analytics to systems-of-record, IT-managed content with governance, reusability and promotability.
  4. OEM/Embedded BI: Supports a workflow from data to embedded BI content in a process or application.

Vendors are also assessed according to the following 13 critical capabilities. Subcriteria for each are listed in Note 2. How well Magic Quadrant Leaders' and Challengers' platforms support these critical capabilities is explored in greater detail in the "Critical Capabilities for BI and Analytics Platforms" (to be published shortly).

BI and Analytics Platform Capabilities for 2015Enable
  • Business User Data Mashup and Modeling: "Drag and drop," user-driven data combination of different sources and the creation of analytic models such as user-defined measures, sets, groups and hierarchies. Advanced capabilities include semantic autodiscovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage and data blending on varied data sources, including multistructured data.
  • Internal Platform Integration: A common look and feel, install, query engine, shared metadata, promotability across all platform components.
  • BI Platform Administration: Capabilities that enable securing and administering users, scaling the platform, optimizing performance and ensuring high availability and disaster recovery. These capabilities should be common across all platform components.
  • Metadata Management: Tools for enabling users to leverage the same systems-of-record semantic model and metadata. They should provide a robust and centralized way for administrators to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics/KPIs, and report layout objects, parameters and so on. Administrators should have the ability to promote a business-user-defined data mashup and metadata to the systems-of-record metadata.
  • Cloud Deployment: Platform as a service and analytic application as a service capabilities for building, deploying and managing analytics and analytic applications in the cloud, based on data both in the cloud and on-premises.
  • Development and Integration: The platform should provide a set of programmatic and visual tools and a development workbench for building reports, dashboards, queries and analysis. It should enable scalable and personalized distribution, scheduling and alerts, and workflow of BI and analytics content and applications via email, to a portal or to mobile devices. It should include the ability to embed and customize BI platform components in a business process, application or portal.
  • Free-Form Interactive Exploration: Enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed. This includes an array of visualization options that go beyond those of pie, bar and line charts, including heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of it.
  • Analytic Dashboards and Content: The ability to create highly interactive dashboards and content with visual exploration and embedded advanced and geospatial analytics to be consumed by others.
  • IT-Developed Reporting and Dashboards: Provides the ability to create highly formatted, print-ready and interactive reports, with or without parameters. IT-authored or centrally authored dashboards are a style of reporting that graphically depicts performance measures. This includes the ability to publish multiobject, linked reports and parameters with intuitive and interactive displays; dashboards often employ visualization components such as gauges, sliders, checkboxes and maps, and are often used to show the actual value of the measure compared with a goal or target value. Dashboards can represent operational or strategic information.
  • Traditional Styles of Analysis: Ad hoc query enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a reusable semantic layer to enable users to navigate available data sources, predefined metrics, hierarchies and so on. Online analytical processing (OLAP) enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write-back values to a database for planning and "what if?" modeling. This capability could span a variety of data architectures (such as relational, multidimensional or hybrid) and storage architectures (such as disk-based or in-memory).
  • Mobile: Enables organizations to develop and deliver content to mobile devices in a publishing and/or interactive mode, and takes advantage of mobile devices' native capabilities, such as touchscreen, camera, location awareness and natural-language query.
  • Collaboration and Social Integration: Enables users to share and discuss information, analysis, analytic content and decisions via discussion threads, chat, annotations and storytelling.
  • Embedded BI: Capabilities - including a software developer's kit with APIs and support for open standards - for creating and modifying analytic content, visualizations and applications, and embedding them into a business process and/or an application or portal. These capabilities can reside outside the application, reusing the analytic infrastructure, but must be easily and seamlessly accessible from inside the application, without forcing users to switch between systems. The capabilities for integrating BI and analytics with the application architecture will enable users to choose where in the business process the analytics should be embedded.
Magic Quadrant

Figure 1. Magic Quadrant for Business Intelligence and Analytics Platforms

Figure 1.Magic Quadrant for Business Intelligence and Analytics Platforms

Source: Gartner (February 2015)

Overview of Magic Quadrant Positioning

The vendors' positions in this Magic Quadrant reflect the current market transition.

The year 2014 has been another year of challenging execution for the market share leaders in the BI and analytics market, juxtaposed against strong execution by the data discovery vendors that are satisfying customers, meeting their buying requirements and delivering greater business value. Growing business user requirements for ease of use, support for users to conduct complex types of analysis, and a fast time to business benefits are not being well met by vendors that own the large, IT-centric installed base market share. Customers of IT-centric platforms that have a broad range of BI platform capabilities report using them narrowly, most often for production reporting. On the other hand, business-centric platforms such as Tableau, Qlik and other emerging vendors have a more narrow set of capabilities, but are used more broadly for a range of BI and analytics functions - including for reporting, for which they are not optimally suited, and for expanding use cases - primarily because they are easy to use and deploy.

The current BI and analytics market situation looks similar to the mainframe/workstation market in the late 1980s, which had a complete shift in requirements and buyers. For example, these shifts drove HP to a complete rethink and redesign of its computing platform strategy and architecture. Ultimately this market shift in requirements and buyers wiped out DEC, since it was not effective in adapting to the shift. The market share leaders in the BI and analytics market are now at a similar crossroads.

While data discovery platforms predominantly complement IT-centric systems-of-record deployments, they are being used for much of the new analytics project investments. The result has been increased marginalization of the installed-base vendors, which without competitive offerings have fewer opportunities for expanded growth.

Displacement of the incumbents by Tableau, Qlik and others are increasing in pockets, particularly in SMBs, although this trend is not yet mainstream. Gartner inquiries and survey data suggest that, increasingly, companies would like to expand their use of, and even standardize on, data discovery platforms for their larger enterprise BI deployments, but find that in many cases these platforms lack the necessary enterprise features in relation to governance, administration and scalability (among other things). The data discovery vendors continue to invest in capabilities to reverse these limitations.

If we begin to see large-scale displacements by these and other business-user-centric vendors, the market shift will be complete (see "Market Trends: The Collision of Data Discovery and Business Intelligence Will Cause Destruction"). Right now, the majority of buyers seem to be waiting to see if their enterprise-standard BI platform will deliver on the business-user-oriented capabilities they prefer to use to meet new analytics requirements beyond production reporting. The existence of separate systems-of-record reporting platforms and data discovery platforms can pose challenges for organizations attempting to govern, scale and support these different environments and pace layers (see "Applying Gartner's Pace Layer Model to Business Analytics"), with no single vendor fully addressing both.

It is very likely that 2015 will be a critical year in which democratizing access to analytics will continue to dominate market requirements and stress the need for governance. Next-generation data discovery capabilities that leverage advanced analytics, but hide its complexity to simplify business user data preparation and automate pattern exploration, are likely to be more important enablers. The extent to which these emerging capabilities and trends impact buying in 2015 and beyond will determine which existing and new vendors emerge from this market transition as market leaders.

The need for platforms to scale and perform for larger amounts of diverse data will also continue to dominate BI market requirements. At the same time, the ability to bridge decentralized business-user-led analytics deployments with those centralized to serve the enterprise will be a crucial ongoing challenge for IT and BI vendors. With the added complexities introduced by new data sources (such as the cloud, real-time streaming events and sensors, and multistructured data) and new types of analysis (such as link/network and sentiment analysis, and new algorithms for machine learning), new challenges and opportunities will emerge to integrate, govern and leverage these new sources to build business value. Leaders of BI initiatives will be under pressure to identify and optimize these opportunities and to deliver results faster than ever before.

This document presents a global view of Gartner's opinion of the main software vendors that should be considered by organizations seeking to use BI and analytics platforms to develop BI applications. Buyers should evaluate vendors in all four quadrants and not assume that only the Leaders can deliver successful BI implementations. Year-over-year comparisons of vendors' positions are not particularly useful, given the market's dynamics (such as emerging competitors, new product road maps and new buying centers); also, clients' concerns have changed since our last Magic Quadrant, particularly since we are in the middle of a significant shift in this market. It is also important to avoid the natural tendency to ascribe your personal definitions for Completeness of Vision and Ability to Execute to this Magic Quadrant. For the purposes of evaluation in this Magic Quadrant, the measures are very specific and likely to be broader than the axis titles may imply at first glance. Readers are encouraged to look at the Evaluation Criteria and Vendor Strengths and Cautions sections carefully to fully understand the nuances of vendor placement that may not be apparent in the Magic Quadrant graphic. For guidance on the Magic Quadrant evaluation process and on how to use a Magic Quadrant, see "How Markets and Vendors Are Evaluated in Gartner Magic Quadrants."

Gartner surveyed 2,083 users of BI platforms as part of the research for this report. Vendors are assessed on a number of key customer survey metrics referred to throughout this report (see Note 3 for how these are calculated). For a detailed explanation of vendor positioning, please see the Vendor Strengths and Cautions section and the Quadrant Descriptions section in this document.


The Microsoft BI and analytics product portfolio supports a diverse range of centralized and decentralized BI use cases and analytic needs for its large customer base. Organizations typically deploy SQL Server and SharePoint to support IT-developer-centric data management, reporting and administration requirements, while business-user-oriented, self-service data preparation and analysis needs are delivered by the Power BI components of the portfolio through Excel 2013 and Office 365. Business-user enablement is a clear focus of Microsoft's product road map and business model evolution - as evidenced by its new "freemium" Power BI product offering (currently in preview), which can be deployed as a stand-alone solution for business users to author and share analytic content without the need for Excel 2013 or an Office 365 subscription.

Microsoft's leadership position in the Magic Quadrant is primarily driven by a strong product vision and future road map, as well as a clear understanding of the market's desire for a platform that can support systems-of-record requirements and deliver easy-to-use data discovery capabilities, with support for promotability of business-user content and governance. Power BI has gained some traction, but has yet to gain widespread market acceptance due to the complexity of on-premises deployments and the relatively limited functionality currently delivered through the Office 365 cloud; barriers which Microsoft is trying to address with the new Power BI offering currently in preview and due to be released later in 2015. As Power BI matures and cloud adoption grows, Microsoft is positioned to leverage its large customer base (and capitalize on the already pervasive use of Excel and the existing SQL Server Analysis Services footprint in the market) to expand the breadth and depth of its deployments in organizations and increase its overall BI and analytics market share, if it can increase its focus on BI sales and marketing and overcome customers' structural barriers to adoption.

  • Overall cost of ownership and license cost remain the top reasons customers choose Microsoft (according to the survey). Microsoft has integrated good capabilities into Excel such as Power Query, Power Pivot, Power View and Power Map, which are included with existing enterprise license agreements. Additional cloud-based consumption and collaboration capabilities are currently available in Power BI through a subscription-based pricing model in Office 365. Microsoft recently announced a freemium license model for its new stand-alone Power BI offering, which includes Power BI Designer for content authoring, set to be officially released during 2015 and featuring a free tier for up to 1GB of data storage per user and a Power BI Pro option for up to 10GB available for $10 per user per month (significantly reduced from the Power BI version currently offered through an Office 365 subscription).
  • Many organizations already use Microsoft Excel extensively for data manipulation and presentation of information through spreadsheets, which gives Microsoft a strong foundation on which to build with Power BI and close the gap between it and the data discovery leaders. A key differentiator for Microsoft is its ability to deliver a range of business user capabilities, encompassing self-service data preparation with Power Query and Power Pivot, interactive visualization through Power View and Power Map and the ability to share with SharePoint and Office 365, which few vendors can claim without third-party support and partnership. Platform scalability is a strength of the Microsoft platform, which ranked highest for data volume accessed - with an average data size of 62TB, compared with an overall average of 14.2TB.
  • Microsoft reference organizations also report deployment sizes larger than those of any other vendor in this Magic Quadrant, with an average number of end users of 6,000 compared with an overall average deployment size of 1,554. With the release of the new stand-alone version of Power BI, business users will have access to built-in connectivity to on-premises SQL Server Analysis Services cubes, which will allow organizations to leverage existing data assets without having to move to replicate in the cloud and further unlock the value of existing multidimensional data structures.
  • Microsoft products scored well in its traditional areas of strength: BI administration and development, and integration and collaboration. They also scored highly on business-user data mashup - an area of investment for Microsoft with Power Pivot and Power Query.
  • Reorganization and new leadership at Microsoft appears to be positive for Microsoft BI. Since taking over, new CEO Satya Nadella has made support for Apple and Android devices, as well as cloud deployment, a high priority.
  • Microsoft's product portfolio is complex and includes many components, which can cause confusion for customers evaluating purchase options. The fact that many of the newer capabilities that are important to buyers in this market require current versions of Office, SQL Server and SharePoint adds to the complexity and represents a barrier to adoption for many organizations that are on older versions and are not yet willing to buy Office 365 and deploy BI and analytics in the cloud. For example, the workflow between components such as Power Query, Power View and Power Pivot is not yet completely seamless. Moreover, the role of SharePoint dashboards and Reporting Services has not been clearly articulated; Reporting Services is not supported in Azure. While it is a work in progress, Microsoft is attempting to address many of these limitations in the forthcoming stand-alone version of Power BI, which does not require Office 2013 or an Office 365 subscription and can access Analysis Services structures and content without physically moving underlying enterprise data to the cloud.
  • Microsoft had the highest percentage of customer references citing absent or weak functionality (for example, no drill-through capabilities in Power View) as a platform problem. This is consistent with last year's results; and while Microsoft is addressing weakness in mobility, analytic dashboards and free-form interactive exploration with Power BI, market awareness and adoption has been slow to materialize - as shown by minimal client inquiries since its launch in early 2014.
  • Microsoft's sales model continues to be a pain point for customers, who rated Microsoft fourth lowest for overall sales experience. Customers have historically found it difficult to engage directly with Microsoft during the sales cycle, which was a major complaint from IT but is an even greater concern for Microsoft as it attempts to appeal to business buyers that have high expectations of simplified license models and purchase options.
  • Microsoft has a large network of implementation partners and developers that are skilled in most aspects of traditional centralized BI deployments. However, customers may have difficulty finding external resources with experience in the newer Power BI stack, which requires a different set of skills and expertise than Microsoft's sweet spot of systems-of-record, developer-focused BI deployments.

Qlik is a market leader in data discovery. It sells two products, both based on an in-memory associative search engine. QlikView is a mature, self-contained, tightly integrated development platform used by IT or more technical users for building intuitive and interactive dashboard applications faster and easier than traditional BI platforms. Qlik Sense is a new platform (released during September 2014) that gives business users the ability to build their own dashboards while giving IT the ability to govern, manage, scale and embed them.

Qlik's position as a Leader in this Magic Quadrant is driven by strong vision around governed data discovery with the introduction of Qlik Sense and high level of market understanding, but execution around customer experience (particularly support) and the sales experience - combined with slower market momentum, particularly in the run-up to the Qlik Sense release - are concerns. Following the Qlik Sense release, interest in Qlik seems to have rebounded. During 2015, for continued momentum, Qlik must refocus on the customer experience as it transitions to effectively selling two products and matures Qlik Sense's functional capabilities through its newly introduced agile point release schedule (that is, three per year).

  • Qlik offers a highly interactive dashboard development product portfolio that spans business-user self-service, centralized dashboard application development, and IT needs for enterprise features to support governed data discovery. Qlik made the bold decision to invest in Qlik Sense, a completely new platform, to enhance its enterprise features and deliver on governed data discovery. Even though Qlik Sense is new to the market, with still limited adoption to date, Qlik has the highest percentage of survey customers (mostly using QlikView) reporting that they use the platform for governed data discovery. Qlik continues to build differentiation into the platform through both internal development and strategic acquisitions (for example, NComVA, DataMarket and NPrinting).
  • Ease of use, particularly for dashboard consumers, is a key reason customers report buying Qlik, in addition to low implementation time and effort - particularly compared with traditional BI platforms. Qlik also enables users to conduct a broader range of more complex types, of analysis - through intuitive interactive discovery - than most vendors in this Magic Quadrant. This combination has been a key driver of its success. Qlik offers free desktop versions of both of its products and this freemium model gives users a risk-free way to try out the product. Consistent with its use and reasons for purchase, Qlik scored well on analytics dashboards (responsive design and storytelling), free-form exploration (associative, smart search) and mobile - in support of both decentralized and governed data discovery use cases - both platform strengths.
  • Qlik has shown an increased penetration into the enterprise from last year. Although Qlik's average deployment size is at 791 users, below the overall average of 1,554, more than 62% of Qlik's customers consider it to be their BI standard (versus around 50% last year) and customer references report that it is among the top five in terms of being deployed broadly across departments and enterprisewide. As Qlik Sense is more broadly adopted, enterprise penetration should increase.
  • While many aspects of customer experience (see Note 3) declined, user enablement and availability of skills are exceptions. Reference customers rate Qlik in the top five for user enablement, which includes documentation, online training, tutorials, user communities and conferences, and for wide availability of skills. A particular strength is the Qlik Community, which offers an online collaboration hub and center of excellence for prospects, customers, partners and employees.
  • Partners, including SIs, resellers and now OEMs, have been more instrumental in Qlik's global awareness and growth strategy than for any other BI or stand-alone data discovery vendor. Qlik Sense's emphasis on open APIs should expand the opportunities for partners to build value-added applications with the platform.
  • Qlik offers two products with two different pricing models. While this dual product and pricing strategy expands options for customers, it may also be confusing as they try to decide when to deploy Qlik Sense versus QlikView, or in combination; how the different pricing models are managed and affect overall cost; and how and when to migrate. BI buyers will need to invest time in understanding the differences and to assess fit with their requirements.
  • Qlik Sense is a new, 1.0 product and platform that has a number of differentiators over QlikView and the rest of the market (for example, storytelling, smart visualizations and smart search), but is also a work-in-progress in a number of functional areas. This will result in limited adoption of Qlik Sense while Qlik enhances its capabilities to support interactive exploration for the business analyst, to be more competitive with other data discovery tools; and to extend its capabilities for advanced dashboard applications, to be more comparable to QlikView for those customers that want to move to Qlik Sense as their single platform. A focus on maturing the Qlik Sense platform may limit Qlik's bandwidth to invest in more forward-looking capabilities (such as smart data discovery, self-service data preparation, cloud and other) to differentiate/help the platform remain competitive in the future.
  • Qlik has not offered production reporting and its direct query to relational sources capabilities (versus in-memory only) have not been widely adopted. Customers have had to be prepared to mix and match for system-of-record reporting. This has limited Qlik's use as an enterprise standard when a customer wants to perform both data discovery and reporting in a single platform. Product scores reflect this previous direction, with weaker ratings in IT-developed reports and dashboards and traditional styles of analysis. However, Qlik has recently acquired (during February 2015) existing partner NPrinting - a report generation, distribution and scheduling application with Microsoft Office integration capabilities for QlikView (integration with Qlik Sense is on the road map) to address this limitation. NPrinting has over 1,000 current QlikView customers. The platform also scored lower than the overall average in BI administration, metadata management (Qlik Sense is better than QlikView for both) and for business user data mashup. Self-service data preparation is a future road map item for Qlik.
  • Qlik has had inconsistent sales execution during the past couple of years, potentially explained by its shift from selling to departments to supporting the needs of strategic enterprise deployments. However, this transition has taken a toll on how customers view their sales experiences with Qlik - for which it was rated in the bottom five of the vendors in this Magic Quadrant. This is often a source of concern during Gartner client inquiries. Perception of customer experience has also declined from last year, driven primarily by low customer support ratings (it was rated in the bottom four vendors). Customer experience matters to buyers, and a successful transition on both fronts is important if Qlik is to accelerate its market penetration.
  • Qlik has been later than a number of other Leaders to deliver a fully featured cloud offering. Qlik Sense's cloud-based sharing capabilities represent its first step. Qlik's customers put it in the top three for having no plans to adopt it for cloud BI.

Tableau's intuitive, visual-based data discovery capabilities have transformed business users' expectations about what they can discover in data and share without extensive skills or training with a BI platform. Tableau's revenue growth during the past few years has very rapidly passed through the $100 million, $200 million and $300 million revenue thresholds at an extraordinary rate compared with other software and technology companies.

Tableau has a strong position on the Ability to Execute axis of the Leaders quadrant, because of the company's successful "land and expand" strategy that has driven much of its growth momentum. Many of Gartner's BI and analytics clients are seeing Tableau usage expand in their organizations and have had to adapt their strategy. They have had to adjust to incorporate the requirements that new users/usage of Tableau bring into the existing deployment and information governance models and information infrastructures. Despite its exceptional growth, which can cause growing pains, Tableau has continued to deliver stellar customer experience and business value. We expect that Tableau will continue to rapidly expand its partner network and to improve international presence during the coming years.

  • Tableau has clearly defined the market in terms of data discovery, with a focus on "helping people see and understand their data." It is currently the perceived market leader with most vendors viewing Tableau as the competitor they most want to be like and to beat. At a minimum, they want to stop the encroachment of Tableau into their customer accounts.
  • Tableau rates among the top five vendors for aggregate product score, with particular strengths in the decentralized and governed data discovery use cases. In particular, analytic dashboards, free-form exploration, business-user data mashup and cloud deployment are platform strengths. Tableau's direct query access to a broad range of SQL and MDX data sources, as well as a number of Hadoop distributions, native support for Google BigQuery, Salesforce and Google Analytics has been a strength of the platform since the product's inception and often increased its appeal to IT versus in-memory-only options. As a result, customers report having slightly below-average deployment sizes, in terms of users, but among the highest data volumes (in this Magic Quadrant).
  • Tableau has managed its growth and momentum well. The company has been able to grow and scale without a significant impact on discounts extended (that is, these are very limited) or customer experience. Most technology companies struggle to manage this balance between growth and execution.
  • Tableau customers report among the highest scores in terms of breadth and ease of use along with high business benefits realized. Gartner inquiries and customer conversations reveal that Tableau users report an enthusiasm for the product as a result of being able to rapidly leverage insights from Tableau that have a significant impact on their business. Customers also report faster-than-average report development times.
  • Tableau is an R&D-driven company. It continues to invest in R&D at a higher pace (in terms of percentage of revenue - 29% in 2014) than most other BI vendors.
  • Tableau has a limited product line focused on data discovery. Organizations like to buy and manage fewer software assets and vendors. At some point many of the new generation of visualization and discovery tools that are bundled with other (competitor) applications may gain traction, particularly as they roll out smart data discovery and self-service data preparation differentiators.
  • IT-developed reports and dashboards, traditional styles of analysis, metadata management, development and integration, BI platform administration, embedded BI and collaboration are rated as weaker capabilities of the platform, making it less well suited for centralized and embedded use cases. When Tableau customers have advanced data preparation, production reporting, advanced analytics, distribution and alerting as requirements, they have to turn to third-party products and partner capabilities. This may also limit its ability for large-scale displacements, but not for large scale surrounding and marginalizing of IT and report-centric incumbents.
  • Tableau is the competitive target of most other vendors in this market. It faces competitive threats from every other vendor in the market that is also focused on delivering self-service data discovery and visualization capabilities, in an attempt to slow down Tableau's momentum.
  • Tableau offers limited advanced analytics capabilities. R integration has been recently added and is a major improvement for users needing more statistical and advanced capabilities. Other vendors, such as SAS, SAP and Tibco, have more advanced native capabilities.
  • Tableau's enterprise features around data modeling and reuse, scalability and embeddability - that enable companies to use the platform in a more pervasive and governed way - are evolving with each release, but are still more limited than IT-centric system-of-record platforms.
Other Vendors to Consider That Did Not Qualify for Inclusion

A number of interesting vendors participated in the Magic Quadrant process (with most identifying reference customers and providing information), but did not meet the criteria for inclusion in the Magic Quadrant itself. These vendors fall into the following categories:

  • Real-time process and operations intelligence
  • Hadoop-based data discovery
  • Collaborative decision making, collaborative BI and collaborative performance management
  • Integrated BI and corporate performance management
  • Cloud BI
  • New approaches to big data
  • Smart data discovery and natural-language generation
  • Search-based data discovery
  • Link/graph-based data discovery
  • Data discovery
  • Self-service data preparation
  • Other BI platform vendors worth considering
Real-Time Process and Operations Intelligence

Analytics for data of different velocities (real-time and batch) are an increasingly important driver of value from big data, particularly as demand for analytics applications that require combining streaming data with historical data - for a range of high-value applications (including operational intelligence, cybersecurity analytics, process intelligence and Internet of Things applications, such as preventive maintenance and asset optimization) - becomes more mainstream. Kofax and Splunk (mentioned here) as well as Datawatch and Tibco (Spotfire) support these emerging requirements.


Splunk has a unique set of capabilities and differentiators compared with other BI/analytics platform vendors in the market. The focus for Splunk is the management and real-time (as well as historical) analysis associated with high-volume, high-velocity and highly variable data that are typical of machine datasets/streams. It has the ability to collect, index, model, store and explore/analyze/visualize data from multiple machine-generated data sources as well as network data. Splunk enables correlation across multiple data sources including mobile data, supports schemas on the fly and has universal data forwarding capabilities. There's no back-end relational database management system and no need to filter then forward data. Splunk also supports the ability to perform data mashups across structured data in relational databases and machine-generated data. Support for an Open Database Connectivity (ODBC) interface enables delivery of machine data from Splunk to visualization tools.

Typical Splunk customers span multiple segments, such as healthcare, finance, government, online gaming and media, and use its products for a variety of use cases: security analytics, IT operations analytics, IoT analytics along with business analytics. Splunk has a unique pricing model, in the business analytics market anyhow, based on daily index volume. Splunk does not price by nodes/CPUs/users/servers; these elements are unlimited in the Splunk pricing model. As a part of the upcoming Hunk 6.2 release, Splunk's analytics product for data stored in Hadoop or NoSQL data stores, customers can purchase Hunk on an hourly consumption basis (running in AWS). Because of its real-time capabilities and ability to integrate a unique class of data with structured data sources, Splunk is often already deployed in an organization for operational use cases and used alongside to complement, rather than replace, existing BI and analytics platform investments.


Salesforce entered the BI platform market in October 2014 with its launch of Salesforce Analytics Cloud (Wave). The introduction follows Salesforce's acquisition of EdgeSpring, two years of development, and several earlier analytics initiatives. The platform offers an alternative to traditional BI platforms for customers willing to consider cloud deployment. It is most likely to appeal to customers who have most of their data in the cloud and want to augment it with on-premises data, including unstructured and semistructured data such as log data for customer-centric applications. The new platform's search index architecture enables customers to integrate Salesforce with non-Salesfore cloud and on-premises data from multistructured sources, although customers must still load data into the cloud rather than accessing it in place (which may be an inhibitor for enterprise buyers). Some of the cloud BI vendors - Birst and GoodData in particular - have a more robust and mature set of product capabilities than Salesforce's initial offering. The first release lacks certain features of data discovery and some traditional BI platforms - such as advanced data exploration for the business analyst, and geospatial and self-service data preparation, among others. Wave does, however, offer standard point-and-click interactive visualizations, dashboards and analysis that form the basis of packaged, closed-loop, front-office analytic applications. The platform is natively mobile, with an emphasis on smartphones (rather than tablets) and collaboration, which should appeal to a front-office LOB buyer. Salesforce Analytics Cloud has a robust partner ecosystem, that includes many ETL, predictive analytics vendors, and system integrators, and is also natively integrated with Salesforce security, metadata and collaboration, which should appeal to customers in the Salesforce installed base.

Evaluation Criteria Ability to Execute

Vendors are judged on their ability and success in making their vision a market reality that customers believe is differentiated and that they purchase. Delivering a positive customer experience, including sales experience, support, product quality, user enablement, availability of skills, upgrade/migration difficulty, also determines a vendor's Ability to Execute. In addition to the opinions of Gartner's analysts, the ratings and commentary in this report are based on a number of sources: customers' perceptions of each vendor's strengths and challenges, as gleaned from their BI-related inquiries to Gartner; an online survey of vendors' customers conducted during October 2014 (which yielded 2,083 responses); a questionnaire completed by the vendors; vendors' briefings, including product demonstrations, strategy and operations; an extensive RFP questionnaire inquiring how each vendor delivers specific features that make up the 13 critical capabilities (a toolkit with the RFP template will be published soon after this Magic Quadrant); a prepared video demonstration of how well vendor BI platforms address the 13 critical capabilities; and research.

Table 1. Ability to Execute Evaluation Criteria

Evaluation Criteria


Product or Service


Overall Viability


Sales Execution/Pricing


Market Responsiveness/Record


Marketing Execution

Not Rated

Customer Experience



Not Rated

Source: Gartner (February 2015)

Table 2. Completeness of Vision Evaluation Criteria

Evaluation Criteria


Market Understanding


Marketing Strategy


Sales Strategy


Offering (Product) Strategy


Business Model

Not Rated

Vertical/Industry Strategy




Geographic Strategy


Source: Gartner (February 2015)

Summary of Leaders Quadrant Positions

Vendors are positioned on the edges on the Leader's quadrant with significant white space in the middle, because no single vendor is executing on both current business user requirements to support larger and larger business-user-oriented yet governed deployments and innovating in preparation for the next generational shift in user experience. There are concerns that vendors that own the installed base market share will not be able to regain market momentum despite their investments in innovation.

Tableau and Qlik have market momentum because they are excelling at delivering on current market and customer experience requirements. They are satisfying customers for data discovery, are enabling easy, broader use, and are growing.

Customers place high value on ease of use, satisfaction with product features, sales experience, support, product quality, upgrade experience, user enablement, achievement of business benefits and supporting a range of analysis for all users. These vendors with the majority of the market momentum are focused on making it easier and simpler for more users to author content and explore and discover patterns in data wherever they are. They are executing on all or most requirements customers care most about and are growing from new analytics project investments, although enterprise features for governance, administration, embeddability and scalability are a work in progress (Qlik has introduced these in its new Qlik Sense platform, while Tableau is adding these incrementally with each new release to address this limitation).

From a vision standpoint, both Tableau and Qlik are largely evolving their capabilities by continuing to invest in making their platforms easier to use for a broader range of users, but have placed less emphasis on emerging growth areas like smart data discovery to further democratize access to analytics (see the Market Overview section for more detail on these emerging capabilities and trends) or on self-service data preparation (Tableau is planning limited capabilities as part of Tableau 9; Qlik is also planning to introduce some capabilities in a future release).

SAP, SAS and IBM are meeting most Completeness of Vision requirements and own a large portion of the installed base market share. They are investing aggressively to close gaps and regain momentum and differentiation through a next-generation, smart data discovery experience featuring self-service data preparation and automated pattern detection with natural-language query and generation for smart data discovery. They are also positioning their integration with their enterprise platforms to support governed data discovery as key differentiators. These vendors must translate their vision into renewed market momentum, including outside their installed bases, and improve their customer experience and delivery of business value to remain in the Leaders' quadrant in the future.

  • SAS has had better traction, adoption and customer experience than IBM and SAP as a result of its major commitment to SAS Visual Analytics, its data discovery capabilities, as its go-forward BI platform. SAS has leveraged its advanced analytics strengths into compelling differentiators around smart data discovery in Visual Analytics.
  • SAP has invested aggressively in Lumira with forward-looking capabilities around smart data discovery leveraging its KXEN acquisition and self-service data preparation. It also has a clearer road map than IBM on how Lumira integrates with the rest of its BI stack (Hana, SAP BusinessObjects).
  • IBM has a compelling vision for Watson Analytics - combining self-service data preparation, natural-language query generation and exploration, automatic pattern detection and prediction, and visual storytelling - that will likely drive future market requirements. However, its road map for how this capability will integrate with and breathe momentum back into IBM Cognos is less clear.

Microsoft, MicroStrategy, Oracle and Information Builders have many elements of Completeness of Vision, but are hampered by execution challenges.

  • Microsoft has delivered data discovery capabilities in Excel that have had some level of adoption, particularly in its own installed base. It has a strong product vision (particularly with natural-language query and self-service data preparation), and offers a better customer experience than the other megavendors. However, mediocre product scores and the lack of a strong BI and analytics marketing and sales focus - combined with the complexity of on-premises deployments and the relatively limited functionality currently delivered through the Office 365 cloud - has limited Microsoft's market traction and position to date. Its new updated Power BI product offering (currently in preview), which can be deployed as a stand-alone solution for business users to author and share analytic content without the need for Excel 2013 or an Office 365 subscription, may change this trajectory in the future.
  • Oracle has gained momentum in the cloud through embedded SaaS analytics in its Fusion applications and has introduced a BI Cloud Service. Oracle also is credited with product vision around multistructured and big data analytics. However, it has been late to invest in business-user-oriented capabilities. While Oracle continues to grow BI revenue into its installed base, these customers have rated it near the bottom across customer experience measures.
  • MicroStrategy offers a compelling product and vision for governed data discovery and has made a number of positive business changes, particularly around pricing, but recent disruptions from executive turnover and layoffs may have negatively impacted its customer experience and sales momentum. It has also impeded marketing efforts to competitively reposition the platform for mainstream business-user buying requirements where product investments have resulted in differentiation.
  • Information Builders is a longtime player with a strong enterprise platform for delivering information-centric applications targeted at a large number of business user consumers, both internal and customer-facing. While it has invested in governed data discovery, it is late to the market. Information Builders has visionary elements on the road map and has introduced early innovations to the market, but has achieved limited adoption outside of its core information-centric applications sweet spot. Like the most other IT-centric Leaders, growth and momentum has been limited and largely from expanding within its installed base.
Market Overview

Gartner's view is that the market for BI and analytics platforms will remain one of the fastest-growing software markets. The market (for BI platforms) grew 9% in 2013, and is projected to grow at a compound annual growth rate of 8.7% through 2018 (seeForecast: Enterprise Software Markets, Worldwide, 2011-2018, 4Q14 Update"), driven by the following market activity

Disclosure: The author is long DATA, MSFT, CRM.

Additional disclosure: May buy SPLK in next 72 hours