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.
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.
Vendors are assessed for their support of four main use cases:
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).
Source: Gartner (February 2015)
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.
A more detailed assessment of product capabilities for vendors positioned in the Leader’s and Challenger’s quadrant (as well as those vendors that were in the Leader’s quadrant last year) are included in the forthcoming “Critical Capabilities for BI and Analytics Platforms.”
The Alteryx platform offers a subscription-based analytics platform targeted at business users. Alteryx’s tools include easy-to-use advanced data preparation capabilities, location analytics and extensive integration with the R analytics ecosystem.
Alteryx maintains its position in the Visionaries quadrant due to its ability to support business users with an integrated set of intuitive tools for data preparation, embedded advanced analytics and application sharing as well as deep geospatial analytics. Instead of being one more reporting and dashboard tool in the BI space, this vendor takes a unique approach to the market — broadening the range of users capable of developing analytic processes.
Birst has defined the pioneering vision of what a set of cloud BI and analytics capabilities should look like. It has a unique two-tier data architecture coupled with a BI and analytics platform and allows customers to keep their data on-premises if they so choose.
Birst is a Challenger in this Magic Quadrant, primarily because of its product strategy and innovations and because it has adapted its strategy, organization and focus as the market for cloud BI has evolved. It has grown the business at a good pace in execution/delivery and customer satisfaction.
Board delivers a single, integrated platform, which provides programming-free BI, analytics and corporate performance management (CPM) capabilities. The focus is to deliver a central, unified and consistent information platform as a basis for BI and performance management applications. Europe is Board’s main market and it has subsidiaries in Europe, North America and Asia/Pacific, and supports clients in South America though partners.
Board is positioned as a Niche Player in this Magic Quadrant. (It is also part of the Magic Quadrant for CPM suites, where it was positioned as a Visionary player in 2014). It serves the submarket for unified BI and CPM platforms, which are centralized and mostly IT-driven deployments. Board is well-positioned in this submarket and has achieved strong growth for several consecutive years.
Datawatch offers an interactive data discovery platform (desktop and server) specializing in visually analyzing streaming and multistructured data. It is based on a combination of technologies: Monarch, a mature product for structuring data from PDFs and semistructured and unstructured sources that was primarily used by customers to migrate, or otherwise leverage, data in legacy reports; and Panopticon (acquired 2013), a visual-based data discovery platform well-suited for real-time dashboards for analyzing streaming time series data in addition to historical data.
Datawatch entered this Magic Quadrant for the first time this year, in the Niche Players quadrant. Strong scores for customer experience and a differentiated product and vision for data discovery on real-time and multistructured data have contributed to its position.
GoodData is a cloud BI and analytics specialist, providing a comprehensive multitenant, cloud-only platform that includes data integration, a fully managed data warehouse repository, front-end BI tools and packaged applications. Its customers can also expect a broad range of data connectors — namely, to cloud-based data sources such as social media — and a vendor able to work directly with business users and support the full BI environment without much involvement from internal IT teams.
GoodData has a strong position in the Niche Players quadrant because it was early to the cloud BI market and has gained market traction in the deployment of embedded and digital marketing use cases as well as the integration of social media data sources.
IBM offers a broad range of enterprise-grade BI, performance management and advanced analytics platform capabilities, complemented by a deep services organization that is ready to implement them in solutions for any domain, industry or geography. IBM Cognos is an integrated BI platform with capabilities for Web-based ad hoc query, report and dashboard authoring and consumption, OLAP, scorecarding, production reporting, scheduling, alerting, data discovery and mobile.
IBM has demonstrated innovation and has a compelling vision for the future with Watson Analytics, its next-generation data discovery capability, but has faced challenges in the core BI business to deliver a positive customer experience and meet important business-user-centric market requirements. High marks for sales strategy and industry and geographic reach also bolster IBM’s position on this Magic Quadrant. In 2015, IBM must translate its vision into market momentum and improve its customer experience to retain its position in the Leaders quadrant.
Information Builders is a leader in the market for BI and analytics platforms. It sells multiple components of its integrated WebFOCUS BI and analytics platform (AppStudio, Info-Discovery, InfoAssist, BI Portal, Server, Active Technologies, Magnify, Mobile Faves, Performance Management Framework and RStat). In addition to the traditional reports and dashboards for senior management, WebFOCUS is frequently used by IT developers to create analytic applications for operational workers and information consumers — inside and outside the firewall.
Information Builder’s position as a Leader in this Magic Quadrant is driven by its positive track record in creating pervasive BI solutions for a high volume of mainstream users, particularly in customer-facing scenarios. Its WebFOCUS platform is well-architected and offers a broad array of functionality; however, its execution is hampered by perpetually modest growth and customer adoption. After a slow start, Information Builders made significant progress in the self-service/data discovery area with the release of InfoDiscovery; however, Gartner needs to see how well this product is adopted during 2015. Information Builders’ lower-than-expected customer experience scores (see Note 3) from the customer reference survey prevent it from rising higher in the Leaders quadrant.
Logi Analytics’ BI platform is composed of two distinct products, Logi Info and Logi Vision. Logi Info delivers a wide range of functionality typically used by IT developers to deliver analytic content such as reports and dashboards to end users. Logi Info is also used extensively by organizations in an OEM capacity to embed analytic content in websites and applications, and by end-user organizations to extend BI access externally to customers, partners and suppliers. Logi Vision is a relatively new data discovery tool, launched in January 2014, and offers business users and analysts the ability to prepare and analyze data and share findings using Logi’s differentiated collaboration capabilities. Integration between the two products is a “work in progress,” but there is a clear road map to improve interoperability between Logi Info and Logi Vision — through the development of a common underlying data hub and new application services — during 2015.
With the introduction of Logi Vision, to address business users’ buying requirements for data discovery, coupled with a product road map aimed at addressing analytics governance concerns, Logi is again positioned as a Challenger. In addition to creating the framework needed to offer governed data discovery capabilities through the platform, Logi has introduced innovative social collaboration and crowdsourcing functionality into the Logi Vision product, which contributed to its improved product vision rating this year.
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.
MicroStrategy offers an enterprise-grade and organically grown end-to-end BI platform that is well-suited to large and complex system-of-record reporting and governed data discovery requirements. It has a compelling product and vision for large-scale governed data discovery and has invested early in cloud and mobile BI; however, its execution during the past year has been poor and the company is regrouping.
Executive turnover and the announced restructuring may have contributed to eroding the customer experience at a time when the company must distance itself from its “old BI” brand and convince the market that it has a compelling platform for the future. MicroStrategy has made key executive hires and is refocusing and repositioning to leverage its differentiators into renewed momentum in 2015. We need to see execution at MicroStrategy during 2015, if it is to remain a Leader in the Magic Quadrant in the future.
The OpenText (Actuate) iHub 3.1 platform supports an integrated suite of reporting and analysis tools (Designer Pro, Analytics) in the Business Intelligence Reporting Tool (BIRT) product family, supporting the needs of most user types from developers to information consumers and business analysts. In particular, Actuate is known for delivering highly personalized data-driven applications in customer-facing external use cases. It has also built up a large population of application developers in the Eclipse community to embed Actuate software into their applications.
Actuate is positioned in the Niche Players category because it primarily succeeds in one dominant use case — embedded analytical applications for developers. Actuate has invested in a visual data discovery and analytics offering, BIRT Analytics, but the product has yet to be widely adopted. Moreover, Actuate’s customer reference scores have brought down its scores for Ability to Execute and Completeness of Vision.
Oracle has a very large and diverse set of capabilities provided by the many products in its BI and analytics portfolio, which is most often used for large-scale enterprise deployments. Its products range from hardware to software platforms, and include Oracle BI Foundation Suite, more than 80 prebuilt BI applications, Oracle Endeca Information Discovery and Oracle Essbase — most of which are available on the Oracle Exalytics Engineered System. The product portfolio also includes Oracle Transactional Business Intelligence, a family of embedded SaaS analytics offerings in the Oracle Fusion SaaS applications; a newly announced and generally available (September 2014) BI Cloud Service; and Oracle Big Data Discovery (now generally available). Additional BI and analytics-related products (but not included in this Magic Quadrant evaluation) include Oracle Database 12c, Hyperion Financial Management, Oracle Advanced Analytics, Hyperion Planning, and Hyperion Profitability and Cost Management, and the family of Oracle Big Data products.
Oracle’s position on the Magic Quadrant reflects a relatively fragmented product vision compared with other large vendors, and low scores in the Gartner customer satisfaction survey. Oracle must translate its investments in improved usability, business-user-driven data discovery and cloud into higher business value for customers and an improved customer experience to be competitive across its wide range of products and competitors and to remain in the Leaders quadrant in the future.
Panorama Software’s Panorama Necto suite innovatively combines social BI with enterprise features to deliver a unique and guided interactive and data discovery experience that is collaborative and automatically highlights important insights to the user.
Panorama is in the Visionaries quadrant because of its product vision, innovation and strong market understanding. It has delivered a next-generation yet governed data discovery experience based on a unique combination of social and collaboration features together with smart data discovery. However, limited sales, marketing, partnering and geographic presence hinder its growth potential.
Pentaho is transforming from an open-source-based BI platform into a big data and embedded analytics specialist, enabling significantly more complex use cases. Pentaho’s data integration (PDI) and analytics components (Weka and Data Science Pack) are at the core of this transformation, delivering tight integration with Hadoop and other NoSQL databases as well as support for advanced analytics with R. Pentaho is positioned to the right in the Niche Players quadrant, due to its focus and innovation in the big data analytics space.
On 10 February 2015, Hitachi Data Systems announced its intention to acquire Pentaho. Hitachi Data Systems plans to continue to operate Pentaho as a separate business unit and to embed Pentaho into its portfolio of big data and analytics products and services, with a particular emphasis on solutions for the IoT. The acquisition is expected to close by June 2015.
The Prognoz BI platform (Prognoz Platform) consists of a highly integrated, end-to-end suite of components used for the development, deployment and administration of tightly coupled data warehouse and BI solutions. The platform supports the full spectrum of analytic capabilities, including traditional reporting and dashboarding, interactive visualization and advanced analytics such as time series analysis, modeling and forecasting. Prognoz also offers a wide range of industry- and domain-specific analytical applications built on top of the platform, which are typically productized versions of solutions built for customers who leverage the extensive services capabilities of Prognoz.
While Prognoz does offer a wide range of capabilities, and despite investments in a global strategy, its success and primary customer base remains concentrated in Eastern Europe and the platform has yet to gain significant awareness and traction in other regions of the world — which is a major execution-related reason for its placement in the Niche Players quadrant again this year. From a Completeness of Vision perspective, the product road map currently lacks some critical forward-looking components — around smart data discovery and business-user data preparation — that we expect to drive buying requirements in the future and that are being planned and developed by competing BI and analytics vendors.
Pyramid Analytics is an emerging and well-funded vendor positioned as a Niche Player for the second consecutive year. Targeted primarily at customers using the Microsoft BI stack, its platform, BI Office, is a Web-based, end-to-end BI platform offering a range of analytic capabilities for self-service BI, interactive analysis and visualization, while keeping all data and metadata within the BI Office repositories to support governance. BI Office is an integrated platform for relational in-memory or multidimensional analysis and visualization. The suite incorporates: Data Discovery (formerly bioXL) for ad hoc querying, OLAP analysis and interactive visualizations; Dashboards (formerly bioPOINT) for dashboards and KPI scorecards; and Publisher (formerly bioWRITER) for report publishing, alerting and distribution. Advanced analytics needs are addressed with a newly introduced integration of R, which replaces the previously announced bioMINER module. BI Office Online, which was introduced during 2014, is a public cloud offering that delivers the same end-to-end BI functionality as the on-premises platform.
Pyramid Analytics’ strong customer experience scores, and its product and vision for governed data discovery are key drivers of its Magic Quadrant placement.
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).
Salient Management Company offers its Collaborative Intelligence Suite, which provides deep data discovery capabilities that are used by its clients for an array of analytic solutions including: revenue management, merchandising and category optimization, supply chain optimization, margin analysis, and others.
Salient is positioned in the Niche Players quadrant because it provides one dominant use case — data discovery for business analysts and power users. While Salient is strong in this area, it has only recently released a dashboard product that would increase the breadth of features in the information delivery area. Moreover, it has very limited adoption — primarily in North America — and doesn’t have a large number of users in each customer site. Despite its limited growth and adoption, Salient scored particularly well on some of the key aspects of our customer reference survey.
SAP delivers a broad range of BI and analytic capabilities: for large IT-managed enterprise BI deployments with the SAP BusinessObjects BI platform; and for decentralized data discovery deployments with SAP Lumira. This is complemented with the SAP Hana in-memory data platform. Companies often choose SAP as their enterprise BI standard, particularly if they also standardize on SAP for ERP applications.
SAP’s position in the Leaders quadrant is primarily based on two aspects. SAP is investing heavily in a visionary product direction with SAP Lumira and has good product scores that have improved with the new release, and it has introduced its simplification strategy for the BI platform components. However, SAP continuously gets below-average scores in almost all areas of customer experience and business benefits achieved, and has had limited success to date in addressing business-user data discovery requirements. SAP must translate its visionary investments into momentum and an improved customer experience to remain a Leader in the future.
SAS offers vast array of integrated components within its Business Intelligence and Analytics suite that combines deep expertise in statistics and predictive modeling with innovative visualization enabled by powerful in-memory processing capabilities. SAS Visual Analytics is the flagship product in the suite for delivering interactive, self-service analytic capabilities at an enterprise level — extending the reach of SAS beyond its traditional user base of power users, data scientists and IT developers within organizations. SAS also leverages its range of platform components and expertise in various industries to offer a wide range of vertical- and domain-specific analytic applications.
SAS is again a Leader this year as it continues to build momentum with SAS Visual Analytics, which was released in 2012 and has gained some traction in the market against the data discovery leaders through product differentiation and a more accessible pricing model (with a lower entry point than initially offered). SAS also continues to demonstrate very strong vision in many areas such as the expansion of both smart data discovery capabilities and embedded advanced analytics within SAS Visual Analytics, seamless navigation between SAS Visual Analytics and SAS Visual Statistics, and integration across other core analytic components of the platform in order to address enterprise requirements for governed data discovery.
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.
Targit’s Decision Suite offers a single, integrated and comprehensive BI and analytics platform. Decision Suite focuses on meeting IT demand with respect to data management and data governance, while attracting a wide range of end users with an emphasis on ease of use and a consistent user experience. Targit is headquartered in Denmark with offices in 12 countries, including the U.S., Brazil, Australia and India.
Targit is positioned as a Niche Player with its Decision Suite platform. Its primary market is the Microsoft customer base, with about 80% of its customers using Microsoft SQL server, ERP or CRM. Targit further strengthened its position in this market segment by enhancing its integration with Microsoft SharePoint, AX and CRM and building vertical-specific solutions together with its partner base.
Tibco’s BI platform consists of two distinct products covering a wide range of analytic capabilities. Spotfire is a leading data discovery and interactive visualization product that offers business users and analysts the ability to access, combine, prepare and visualize data in the form of highly interactive analytic dashboards. Spotfire also offers advanced analytic capabilities through integration with Tibco’s Enterprise Runtime for R (TERR), and is a leader in geospatial, location analytics, and real-time use cases. Tibco’s second product, Jaspersoft, was acquired in April 2014, to expand the platform’s range of analytic capabilities beyond data discovery to include key strengths of the Jaspersoft product — embedded analytics and production reporting.
Tibco has two products that are both highly rated by customers, but continued poor execution of Spotfire in a data-discovery-led market is the primary driver of Tibco’s downward move from the Leaders to Visionaries quadrant this year. While other data discovery leaders have enjoyed recent success and growth using a “land and expand” business-focused sales model, Tibco has continued to try to sell Spotfire to IT using the same enterprise sales model it has for middleware, which has so far resulted in a missed opportunity to be a major player in the data discovery space.
Yellowfin delivers a set of innovative capabilities in collaboration features, storytelling and mobile, and a tightly integrated set of tools — from data integration to dashboards. Founded in Australia in 2003, Yellowfin is a BI vendor that continues to invest in a user-friendly interface to empower business users.
Yellowfin is positioned in the Niche Players Quadrant of the Magic Quadrant. Collaboration and social BI, mobile and location intelligence have been successful ways to differentiate its platform from competitors.
We review and adjust our inclusion criteria for Magic Quadrants and MarketScopes as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant or MarketScope may change over time. A vendor’s appearance in a Magic Quadrant or MarketScope one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.
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:
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.
Kofax acquired Altosoft in February 2013 to add process intelligence and embedded analytics to its application stack. While Altosoft’s code is packaged as Kofax Analytics, the vendor continues to operate as a wholly owned subsidiary, directly selling its Insight 5.0 platform. Insight provides a single integrated platform from data integration, to in-memory data storage, to dashboard delivery with no coding or scripting required. Unlike most BI tools, Altosoft Insight is process-aware. Insight provides an understanding of an entire business process to pinpoint process steps in which waste, loss and inefficiencies are taking place. This includes processes that span multiple systems of record. No business process management, workflow or process modeling tools are required. During the past year, Kofax grew from 500 to 700 customer organizations with strong practices in the healthcare and financial services verticals. In Insight 5.0 (its latest release) Kofax has added continuous simulation predictive analytics for process intelligence and has rewritten the platform to entirely support HTML5, eliminating previous Microsoft Silverlight dependencies. Another innovation is Kofax’s patent-pending MapAggregate distributed in-memory architecture that overcomes the problem of being limited to the RAM of a single physical server. Now, Altosoft Insight can distribute in-memory data across multiple servers.
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.
The ever-increasing amount and diversity of data has given rise to the need for and use of NoSQL databases, such as Hadoop (increasingly leveraging Spark) to store, manage and query large amounts of data economically. However, the rarity and the specialized nature of the skills needed to manage Hadoop clusters, generate MapReduce queries and find insights in these new sources of data have inhibited mainstream adoption, particularly among business users. Hadoop-based data discovery enables business users to explore and find insights across diverse data (such as clickstreams, social, sensor and transaction data) that is stored, managed and processed in Hadoop, increasingly with Spark. It enables users to directly query the Hadoop Distributed File System (HDFS) without the extensive modeling required by traditional SQL-based approaches, the specialized skills to generate custom MapReduce, Hive or Pig queries, or the performance penalty and lack of interactivity of querying Hadoop through Hive. Platfora, Datameer, FICO (which acquired Karmasphere) L-3 and the Eligotech are Hadoop-centric data discovery vendors whose tools are designed to address this challenge. These companies are banking on Hadoop with Spark increasingly supplanting the data warehouse as the primary data management repository and platform within enterprises. We believe that, for the foreseeable future, Hadoop and other NoSQL data stores are more likely to complement the systems-of-record data warehouse. A range of vendors are emerging to address requirements for data exploration on Hadoop. These include database and data warehouse suppliers (such as IBM, Microsoft and Oracle, which just released is Oracle Big Data Discovery platform); BI companies accessing Hadoop through Hive and HBase or though native integration with HDFS (like Pentaho and MicroStrategy); and Hadoop distribution vendors themselves (like Cloudera Impala) that are attempting to make SQL more accessible and “performant” against Hadoop. Emerging standards and vendor support for Spark are also enhancing platform capabilities for interactive exploration.
Datameer provides a big data analytics platform. Built to work natively within Hadoop, Datameer provides integration, data preparation, analytics and visualization capabilities with the promise of no coding and a familiar spreadsheet interface. Datameer optimizes analytical computations based on the analytics tasks, available system resources, and attributes of the datasets. The company provides an integrated environment designed to work with a Hadoop repository (customers can pick their own distribution); point-and-click data integration; a spreadsheet interface with prebuilt functions for data cleansing, transformation and analysis; a visual drag-and-drop environment to build and deliver dashboards and infographics; prebuilt applications capable of performing automated analytic processes (such as building a customer segmentation or finding correlated products); and drag-and-drop data mining algorithms. By using a native Hadoop engine instead of translating SQL through Hive, the product is able to handle large data volumes and use unstructured and semistructured information from data sources such as mainframes, databases, file systems, cloud services, social media and log files. Although relatively young, the company has strong reference customers and has built an interesting network of technology partners. With Datameer 5.0, the vendor introduces a more intelligent execution engine that leverages Hadoop’s Yarn and in-memory processing to dynamically select the optimal compute framework for each step in the analytic process.
Eligotech released its Hadoop-based data integration and search-based data discovery platform in 2Q13. Its core product, Harpoon, hides the complexity of Hadoop by enabling users to search, navigate and analyze data directly via a Google-like interface. Data can be imported from any relational database and from data sources of any structure. While the platform offers a set of visualizations, the extensible platform is built on top of Hadoop APIs and provides a full set of REST APIs to support integration with external applications, such as the workflow management systems and BI tools on which organizations may have standardized. Although relatively new code base, Eligotech produced two significant releases this year: Harpoon 2.5 (released in July 2014) bolstered Eligotech’ security by enabling better authorization on data and operations; and Harpoon 2.6 (released in October 2014) provides a visual data lineage capability, job scheduler, on-chart drill down, and ensures that the underlying HDFS permissions will be used in all Harpoon sessions.
FICO is a well-known provider of advanced analytics software and services. It acquired Karmasphere in April 2014, and is bringing it to market as FICO Big Data Analyzer, a product that is part of its Decision Management Suite. This native Hadoop application for analysts and business users, provides access to big data and enables data transformation, exploration, analysis and discovery of insights. The inclusion of data management, visualization capabilities and advanced analytics features creates an integrated analytics environment that will appeal to regular business users looking for self-service capabilities in analytics, but may also be used by data scientists for first-level insight generation. The outputs of the tool can also be embedded in business applications through the capabilities of FICO Decision Management Suite. Use cases for this technology range from credit scoring, banking originations, new customer onboarding and fraud detection, among others. In its first releases, Big Data Analyzer is helping FICO expand the types of decision management solution to include a broader set of use cases. Over time, it plans to help extend access, transformation, analysis and visualization of big data sources to a broader range of business users — beyond FICO’s traditional customer base of statisticians and high-end data scientists.
L-3 acquired Data Tactics in 2014. Its Big Data Ecosystem (BDE) is a cloud, Hadoop-based data discovery platform that enables users to quickly and efficiently fuse vast amounts of data from disparate structured, unstructured and semistructured sources. Its business-analyst- and data-scientist-oriented user interface allows users to conduct geospatial analytics, interactive visualization and data exploration, and predictive and prescriptive analysis leveraging its R integration. BDE currently provides federal government customers, including intelligence agencies, with data fusion and intelligence in a secure platform featuring National Security Agency (NSA)-grade security. It also has customers in the energy and oil, financial services and telecom industries.
Platfora, is a well-funded startup that made its big data analytics platform generally available in March 2013. The platform enables business analysts to conduct multistructured data exploration on very large datasets in Hadoop. Platfora directly accesses the Hadoop file system without needing specialized skills to write custom MapReduce, Hive, Pig or SQL queries, and then exposes the raw data in memory “lenses” so that users can identify and interactively explore the data in their Hadoop clusters. Platfora leverages both MapReduce and Spark to execute processing of raw data in Hadoop and its own columnar data store and engine to execute real-time queries in-memory. The ability to access non-Hadoop data sources using connectors is new in 2014. Platfora also provides business user tools for visualizing and interacting with data in dashboards and in autorecommended best-fit interactive charts for data discovery with a focus on customer analytics, security analytics and the IoT use cases. Platfora continues to extend the platform with additional embedded advanced analytics capabilities (such as advanced segment, behavioral, geospatial and graph analysis), including enabling users to identify entities of interest and their relationships (such as which customers bought product X and then product Y) within a certain time period, and then to iteratively segment them in order to find further patterns in their behavior. Platfora is enhancing its native on-Hadoop self-service data discovery and smart data preparation to reduce the time to insight for the analyst and data scientist accessing data in Hadoop.
Collaborative decision making and collaborative and social BI offers potential to finally close the gap between BI and decision making by facilitating intelligent collaboration, sharing and capture of the interactive decision process to enable more transparent, high-quality decisions. Many BI vendors in this Magic Quadrant (such as Panorama, Qlik, Tableau and Yellowfin) include collaboration and storytelling as key features of their products. Decisyon offers a collaborative decision-making platform that was built from the ground up, along with integrated BI and performance management. As a result, it is taking an innovative approach.
With recent funding and an expanding team of seasoned BI executives and professionals, Decisyon is expanding beyond its traditional European customer base, with a focus on North America. Its core product, Decisyon 360 unifies collaboration, social, analytics, planning and execution in a unique collaborative and social BI and analytics platform. Underpinning its differentiated approach is a smart social and data environment known as the “social workspace,” where users collaborate on data, tasks, decisions and analytic content, with native and integrated mobile workflow, planning and execution as part of the BI and analytics and performance management process. It also features embedded data integration (supporting structured, unstructured and streaming data). Decisyon offers its Decisyon 360 platform for the development of custom collaborative BI and performance management applications. It also sells packaged solutions for operational BI and process intelligence, planning and intelligent manufacturing operations, and supply chain. Its social CRM product provides social media intelligence and is extensible into enterprise social customer care and marketing. It is also well-suited for IoT applications. Partners have also built solutions for the banking and financial services, telecom, life sciences, pharmaceutical, retail and automotive industries using the platform and it is expanding its relationships with system integrators. Decisyon’s multitenant architecture can be deployed on customers’ premises or in the cloud — as a complement to enterprise BI and performance management platforms, when analytics and performance management-centric collaboration and social capabilities are required; or as a complete end-to-end solution for enterprise collaborative BI and performance management.
Adaptive Insights offers a 100% cloud-based platform combining BI and CPM capabilities through its comprehensive Adaptive Suite platform consisting of five key elements: planning, discovery, consolidation, reporting and integration. Adaptive Insights targets the suite at business users and has leveraged a “land and expand” approach to sales execution that has grown its customer base by 32% since 2013 (to greater than 2,500 in 2014). In October 2014, Adaptive Insights announced the addition of Adaptive Revenue which extends the capabilities of the suite to include revenue planning and sales analytics. The addition of Adaptive Revenue, with its integration with Salesforce and ERP systems, enhances the ability of the suite to deliver a 360-degree view of business performance that enables customers to manage the entire revenue life cycle.
arcplan’s unified BI and performance management platform incorporates a complete set of BI platform capabilities, along with budgeting, planning and forecasting. arcplan has two major support centers in the U.S. and Germany. The arcplan 8 platform is composed of three components: arcplan Enterprise for reporting, dashboards and scorecards; arcplan Edge for budgeting, planning and forecasting; and arcplan Engage for ad hoc analysis and interactive exploration — which includes arcplan Excel Analytics. With the introduction of HTML5 to the platform, arcplan applications are available on mobile devices too. arcplan started to offer support for R in 2014, to address the growing demand for embedded advanced analytics. It is expanding beyond its deep heritage as a BI provider within the SAP installed base by extending its native support to other data sources. The platform provides native APIs for SAP BW and SAP Hana, as well as IBM Cognos TM1 and Oracle Essbase, to exploit the information in megavendors’ systems (with the potential for a lower total cost of ownership). Positioned as a complementary front-end for information delivery, arcplan’s offering also supports systems such as Microsoft SQL Server and Microsoft SQL Server Analysis Services, plus those from Teradata and Kognitio, among others. The company has extended its support for data sources during 2014 by adding Salesforce (for CRM) and LucaNet.
Bitam offers an integrated enterprise performance management platform that spans traditional BI capabilities (Artus), financial planning (Ektos), and strategic planning (Stratego) in a unified solution suite. Customers choose Bitam because of its integrated BI and performance management capabilities and its ease of use. Bitam’s cloud offering, KPI Online represents the vast majority of new business opportunities for Bitam versus its on-premises offering. Bitam continues to invest in cloud computing initiatives, including the creation of prebuilt vertical-market solutions that appeal primarily to the small or midsize business (SMB) market, which is most receptive to cloud deployment options. Bitam’s customer base is heavily concentrated in Latin America, though expansion into Western Europe (particularly Spain), North America and Asia continued in 2014. However, the vendor is not well known outside Mexico and South America, where most of its customers are concentrated.
Jedox offers an integrated platform (Jedox 5.1) for reporting, planning, and analysis. With offices in Germany (Freiburg, Frankfurt and Düsseldorf), France (Paris) and the U.S. (Boston), Jedox is now running in over 1,200 organizations and used by over 100,000 users. The Jedox platform is based on an in-memory OLAP database for reporting and planning. Data is loaded into the OLAP engine from transactional systems using the Jedox integration offering. The platform is architected to support three front ends: Microsoft Excel, Web and Mobile. The Microsoft Excel add-in front end is the most widely used but all three support write-back. Only 15% of Jedox customer references mention poor performance as an issue. To bolster performance with high volumes of data Jedox supports Nvidia Graphic Processing Unit (GPU) technology for ultrafast aggregations. In addition to the blending of planning and analysis with true write-back functionality and the in-memory engine, another differentiator of the Jedox platform is its integration with Microsoft Office — in particular its ability to convert Microsoft Excel workbooks into multidimensional OLAP data cubes with reusable ETL processes (data-driven modelling). Overall, Jedox is used fairly narrowly within organizations against small data volumes, which suggests that it is usually deployed departmentally in larger organizations. To spark greater adoption and user growth Jedox released a managed cloud service in July 2014.
Cloud BI is an emerging trend that will likely be accelerated by the entrance of Salesforce into the market and greater investment by traditional on-premises vendors. Vendors such as Chartio, DataHero and the well-funded startup Domo also offer solutions to address this growing requirement.
Startup Chartio offers a cloud-based solution targeted at business users. It tries to address the need for easy-to-use tools capable of blending data from multiple data sources and producing dashboards without IT support. The range of data sources includes options such as: Amazon Redshift, Google BigQuery and Windows Azure Cloud, for cloud-based data; as well as databases such as Microsoft SQL, Oracle, MySQL and PostgreSQL, for on-premises data. It also offers connectivity to data sources such as Google Analytics, Salesforce, Stripe, Twilio and Zendesk. The software hides the complexity of integration with data sources, creating what the company calls a metadata warehouse and allowing the use of a simple drag-and-drop interface to produce dashboards. Although growing fast, Chartio is still a small vendor with limited geographic reach, but the cloud nature of its offering may help overcome this limitation and expand faster than traditional on-premises vendors.
DataHero launched its self-service cloud-based analytics platform for cloud-based data sources in 2013. It is designed specifically to empower business users to be able to import and combine data and create charts without any prior technical background. The platform’s built-in Data Decoder automatically detects the data structure and suggests optimal visualizations for the user based on the data. DataHero has prebuilt connectors to many enterprise cloud services and technology that automatically normalizes the data across those services, eliminating the need for custom ETL. Using DataHero, users can create visualizations, pin them into dashboards and do advanced analytics (initially cohort analysis) using drag and drop. Charts include icons that indicate the lineage of each chart. DataHero is mostly used by departments and SMBs, with an average deployment size of 10 users and largest deployment size of around 100 users.
Guided by a group of seasoned executives mostly from Omniture, Domo is using its substantial funding ($250 million to date) to build a cloud BI platform with a focus on dashboards targeted at making it easier for business users to find insights in their data and share and collaborate on findings, particularly on mobile devices. The company primarily sells to LOBs, where it offers LOB-specific packaged dashboard components and applications. The platform features point-and-click data mashup capabilities and connectors for more than 100 data sources, with native integration to a broad set of applications and to Salesforce, which is one of its most popular connectors. The company has been very secretive, with initial releases of the product to date requiring customers to sign nondisclosure agreements to view and try the product. This should change in April 2015 when the company has its official public launch. Domo is launching its product into an increasingly crowded cloud BI market that now includes Salesforce, early startups and now on-premises vendors that are investing heavily in the cloud. Selling into the Salesforce installed base has been a strong opportunity for this company.
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.
Handling an exponentially growing volume of data is not a new problem for BI professionals, but it has become a significant hindrance to deriving insights for all relevant data assets as the type and number of sources available for analysis grow exponentially. Vendors such as Sisense are trying to find new ways to address this challenge.
Sisense offers an integrated, performance optimized end-to-end platform that empowers business users to join and analyze large datasets and share insights via interactive dashboards through a drag-and-drop browser-based interface. Sisense uses its In-Chip and ElastiCube technologies to deliver scalability and performance that exceeds in-memory technology capabilities. Sisense has gained traction in the market through rapid expansion both in terms of new clients and increased utilization in existing accounts. This has resulted in the tripling of its revenue for the fourth consecutive year in 2014. Version 5, released in April 2014, accelerated adoption within the Sisense customer base and generated significant net new business with the introduction of in-browser authoring, a new user experience, real-time collaboration and improved mobile capabilities. Sisense released version 5.5 in August 2014, to bolster the platform’s enterprise features around security, load balancing and high availability, to satisfy IT requirements and promote more widespread adoption and acceptance as an enterprise standard BI platform. In order to accommodate the rapid growth it has experienced in recent years, Sisense moved its North American headquarters to a new Wall Street location and doubled the size of its R&D center in Israel. Sisense received $30 million in Series C venture funding during June 2014. This should help the company build on its success, and expand its awareness and presence in the BI and analytics market.
Smart data discovery facilitates the discovery of hidden patterns in large, complex datasets, without building models or writing algorithms or queries and helps users focus on what insights are most important. It goes beyond data discovery because less-skilled data scientists (citizen data scientists) can benefit from advanced analytics (to highlight and visualize important findings, correlations, clusters, links or trends in data that are relevant to the user), with user interaction and exploration via interactive visualizations, search and natural-language query technologies. Natural-language generation is also being used by Automated Insights, BeyondCore and DataRPM to assist business users in focusing on relevant insights and interpreting the results for them in their context. The combination of smart data discovery and natural language query and generation is likely to have a significant impact on the next generation BI user experience and the market in general.
Automated Insights’ product, Wordsmith, is a patented natural-language generation engine for transforming structured data into a narrative with actionable insights. Automated Insights believes that data visualizations don’t tell the full story. Wordsmith dynamically identifies the most relevant insights, patterns, context and trends in large multistructured datasets, prioritizes them based on value, and delivers a personalized narrative for each user that can be used in combination with visualizations. Style, format and even tone are configurable. Instead of creating one story for a million readers, Automated Insights can create 1,000,000 stories, each customized for an audience of one. Automated Insights has a number of high-profile clients including Comcast, Samsung, and Edmunds.com. Wordsmith publishes millions of fantasy football stories each week of the Yahoo Fantasy Football season. The Associated Press, also an investor in the company, has automated more than 4,000 earnings reports using the engine. Within a BI context, companies use Wordsmith’s custom narratives to highlight the most important insights for each user, who can then access the underlying reports for more detail. Customers report that the engine makes users more efficient at finding relevant insights, as an alternative to manual analysis and as a complement to data discovery. The company reports that most of its BI customers use the product to create narratives on top of analysis done in either Tableau or SAS. Automated Insights is a cloud-based solution hosted in AWS, requiring some upfront configuration. The company has a team of data scientists that assists users with the initial tuning.
BeyondCore offers highly innovative and differentiated analytics software that combines smart pattern discovery, automated insight detection and prescriptive recommendations to business users and analysts. Since it entered the market with its first product release in 2013, following eight years of R&D, BeyondCore has sought to address the shortage of data scientists in organizations — by automating the upfront analysis required to find meaning and relevance in data and allowing business users to focus on the most appropriate and actionable information. The software analyzes every intersection of data automatically, grays out statistically insignificant findings displayed in interactive visualizations and uses an animated narrative to report key findings and insights to the business user. This concept of being the “zeroth step” in analysis is a key differentiator and offers unique product positioning for BeyondCore as it seeks to build awareness and mainstream adoption in a very crowded BI and analytics landscape. Throughout 2014, BeyondCore has aggressively developed and released several product iterations, further demonstrating its understanding of the BI and analytics market and its trajectory. Most significant of these product enhancements is the addition of prescriptive recommendations based on the analysis of datasets optimizing for the outcome that a business user wants to improve. The innovative way in which the product translates analysis and insight into recommendations for action — through identification of opportunities for new revenue generation, cost reduction and risk mitigation — to deliver clear and measurable value to customers, positions BeyondCore as an extremely disruptive force in the BI and analytics market.
DataRPM’s Smart Machine Insights data discovery platform automatically models and performs statistical analysis on data in its Hadoop infrastructure. The platform’s machine-learning algorithms automatically find and deliver the most important findings and insights to users in optimized visualizations and narratives. This reduces the time, effort and skill needed for manual data discovery and accelerates time to actionable insight for a broader range of users. The system also allows business users to initiate queries and explore results using natural language. The Instant Answers computational search engine significantly reduces the need for traditional data modeling by automatically discovering and inferring semantics and entity relationships in diverse data. Any user can use Google-like natural-language query to discover and analyze the indexed data visually. Results can be shared with other users or embedded in other business applications and websites. DataRPM is aggressively updating its platform to establish an early presence in the smart data discovery space. The platform can be deployed on-premises or in the cloud.
Natural-language query technology will become an increasingly important means of delivering analytics to mainstream business users. A search index architecture is also an alternative approach to ingesting and modeling data for analysis, that reduces the time and complexity of traditional approaches. Microsoft Power BI (Q&A) and IBM Watson Analytics offer natural language query capabilities while new vendors DataRPM (featured in the smart data discovery section), Incorta and ThoughtSpot also hope to reduce time to deployment and expand user access to analysis.
Incorta (founded by Oracle BI and Endeca product development executives), offers a search-based data discovery platform that enables business users to create analytic content from data access, mashup and aggregation to dashboard development and interactivity from an iPad or from a Web-based (HTML5) user interface. The platform supports rapid time to deployment by removing the time and complexity of traditional data integration and modeling. Its search index architecture and data ingestion technology rapidly loads large and diverse datasets by removing the need for complex ETL and data warehouse indexes, aggregate tables and star schemas — with calculations running, instead, directly against normalized data. Users build queries and refine analysis using search. Users can also upload Microsoft Excel and CSV files from email as data sources in the Incorta iPad application. The search architecture also supports the combination of structured, unstructured and semistructured data for analysis. Incorta emphasizes its enterprise features around security subsetting, scalability (customers claim they can run calculations on millions of records with subsecond performance), and governance (every action by a user is recorded). The platform also supports embeddability with a rich set of APIs. Incorta can be deployed on-premises or in the cloud running on AWS. The company has a small number of early customers that report positive results using the platform as an alternative to reduce time to insight to traditional BI platforms.
ThoughtSpot (founded by a team of executives and engineers from Google, Nutanix, Oracle and Yahoo) introduced its search-based BI on-premises appliance product in October 2014, to make it easy for business users (not just analysts) to build reports using a Google-like search experience. In the initial version, previously modeled data is loaded and indexed in ThoughtSpot’s search engine, which it calls “relational search” for access by business users. It offers users consumer experiences like pinboards (think Pinterest) to create stories with data. It also gives the user a summary of data lineage information within the business user views, including information about data sources, calculations and filters that make up the views. Customers often use the product for business-user-driven reporting as a complement to more analyst-oriented data discovery tools such as Tableau. The road map includes enhancements for data access and integration as well as to the visualization capabilities.
Large datasets have structure and relationships between entities of interest (for example, people, places, things, interactions) in the data that can be inferred algorithmically. All data within and often external to an enterprise is related in some way, but these relationships are often obscured when data is separated in different data repositories. Vendors that visually represent the links or relationships in highly dense data (link- or graph-based data discovery) are addressing this challenge by providing new ways for users to easily find hidden and relevant patterns in data. Centrifuge Systems, Palantir Technologies and SynerScope are three such vendors.
Centrifuge provides a link-based data discovery platform that is particularly useful for finding hidden patterns in large, multistructured, complex and often seemingly unrelated datasets. Its fully browser-based platform has the ability to ingest and unify structured and unstructured data (such as Hadoop data, documents, Web-based data and machine data), perform interactive link analysis and visualization (path identification, link-ups, bundling, animated temporal views and geospatial views), as well as capabilities that let users share and publish findings. Centrifuge grew out of the intelligence and defense communities and has expanded into federal civilian agencies and law enforcement. It is also building a commercial customer base in the financial services, life sciences (pharmaceutical), healthcare, cybersecurity and supply chain sectors. It often competes with Palantir Technologies and IBM i2, and is planning to double in size during the next year. Partners include Splunk, YarcData, MarkLogic and a number of major Hadoop distributions.
Palantir offers a next-generation analytical platform that blends machine learning with human intuition, enabling end users to intuitively ask questions of the data using their own mental model. While it is evolving its front-end presentation skills, most of the Palantir code base is focused on solving back-end problems — integrating massive amounts of structured and unstructured data. Palantir’s architecture uses several modules that complement each other. For example, Raptor is used to store hundreds of millions to billions of records of structured and unstructured data such as large repositories of articles and reports. Phoenix stores billions to trillions of records of mostly structured data such as network logs. Hercules pushes Palantir more in the direction of smart data discovery, using machine learning to investigate vast amounts of data to find unknown anomalies and associations within that data. Indeed, the variety of technology used by Palantir makes it possible to classify the vendor in one of a number of categories including Graph, Search, Hadoop, and Smart Data Discovery. Also, given that most customer engagements include approximately four weeks of implementation by a small team of engineers, Palantir could be classified as a business analytic service provider. The vendor has grown from its roots in fraud detection and national security/intelligence work to a broader array of vertical industries. This has resulted in significant growth for the company in the past few years. Palantir really isn’t a competitor to most of the vendors on this Magic Quadrant; instead it is a complement and focuses on more complex analytic solutions and is therefore not seen as a mainstream analytic provider like the first generation of data discovery vendors.
SynerScope released its two core products, Marcato and Legato, in 2012. Marcato is a visual analysis platform that represents networks and relationships between entities in data, so that users can identify hidden patterns across structured and unstructured data without specialized skills. Entities are things such as people, places, products, transactions, events, companies, claims, policies, emails, social interactions, sentiment and files. Legato ingests data, generates metadata from both structured and unstructured files, and loads data into an in-memory database for fast interactive analysis. SynerScope can work with many leading databases; notably, it is an SAP partner and integrates with SAP’s Hana in-memory database. SynerScope’s initial customers are using the product for claims analysis and fraud detection in the insurance industry, as well as for analytics of mobile phone calls and cyber security. The new server version of the product (added in 2014) can be deployed on-premises or in the cloud.
A Bell Labs spinoff, Advizor specializes in interactive data visualization and predictive modeling and provides a fully in-memory data discovery engine for advanced information blending and exploration. By making extensive use of color and regression algorithms to identify information items, and employing a library of sophisticated visualizations, the tool delivers highly interactive dashboards that appeal to business users looking to find correlations, spot trends and uncover insights hidden in their data. Advizor has long been in partnership with Information Builders and HP ArcSight, both of which offer its products on an OEM basis. Advizor’s direct business specializes in the fund-raising, healthcare, higher education and manufacturing sectors. Advizor is repositioning its product as a high-end discovery tool, bridging the gap between basic information exploration and advanced analytics.
Self-service data preparation platforms enable business users to reduce the time and complexity of preparing data for analysis in a governed and reusable way. They feature capabilities like visual data flow building and automation, semantic autodiscovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage and data blending on varied data sources, including multistructured data. Many of these platforms also offer automated machine-learning algorithms that visually highlight the structure, distribution, anomalies and repetitive patterns in data — with guided business-user-oriented tools to resolve issues and enhance data. In addition to Alteryx and an increasing number of BI vendors focusing on this high-value capability, ClearStory Data, Lavastorm Analytics and Zoomdata offer alternatives.
ClearStory targets non-technical, LOB users that need fast answers, which ClearStory enables via its system-driven data blending capabilities that “infer and harmonize” disparate data to generate fast, visual analysis. Companies combining private and external data can see holistic insights about their customers, their market, and their competitors. ClearStory offers interactive and collaborative data story telling with discovery-based visualizations that are enriched by automatically harmonizing external/subscription and internal data sources. For internal data, ClearStory’s data Inference capabilities speed data access and preparation from enterprise data warehouses, relational database management systems, Hadoop, cloud applications, and files. ClearStory has partnerships with data providers such as point-of-sale data providers, Kantar Media, D&B, Nielsen, Weather Analytics and DataSift, and is working with IMS Health in the pharmaceuticals industry. Native availability of this content provides users with easy access to these third-party data sources. New data sources can be added via ClearStory’s external data API. ClearStory offers data preparation and automated data blending capabilities called Intelligent Data Harmonization (built on Spark for speed and scale) as an integrated part of the platform and a user’s analytic workflow. This capability uses data mining techniques to identify data relationships across many different data sources. It also suggests to the user relevant data sources that could be used to either improve the quality of the data and/or enrich the analysis in ClearStory applications/stories. ClearStory’s export capabilities can also be used to export raw or harmonized datasets for integration into most data discovery, BI, and data science platforms. .
Lavastorm Analytics specializes in operational analytics where ad hoc information exploration and automation of continuous analysis are key requirements. The company provides a tool that enables business users to work, in a graphical interface, on building analytic workflows that connect to multiple data sources, integrate and transform data, apply analytic models, output reports and integrate with external tools such as Tableau, QlikView and Tibco Spotfire for further information visualization. The workflows built are similar to visual ETL scripts, but instead of IT experts working in structured processes to load the corporate data warehouse, it is handled by business users and can be used for ad hoc analysis of disparate data sources — from spreadsheets to data warehouses. The company has deep experience in the telco and financial services industries, supporting large data volumes, performing complex analysis and operating in demanding operational environments. The product is offered in different editions, with desktop and server options — ranging from a free version for an individual user analyzing file-based data to a corporate setup for handling terabytes of information.
Note that Lavastorm has met most of the MQ inclusion criteria but is more narrowly focused on data preparation than other MQ vendors.
Zoomdata offers innovative capabilities for the exploration of data sources — in particular for real-time and streaming data. It has connectors typically used for big data, such as Hadoop HDFS, Spark, MongoDB, Amazon Redshift and Cloudera Impala; search-based services such as Solr and Cloudera Search; streaming data such as Twitter or Amazon Kinesis; as well as support for traditional data repositories such as Microsoft SQL, Oracle and PostgreSQL. Zoomdata delivers a unique microquery approach to large datasets that improves the sharpness of results with time — just like a picture being downloaded from the Web is rendered in multiple steps that add additional detail. This allows business users to have an immediate approximation of the results, so they can proceed with their exploration process without delay, while the platform retrieves additional information in the background to sharpen the results. Another interesting feature is what Zoomdata calls “data DVR” — which allows reporting on real-time streaming data with comparisons with historic data, and the ability to rewind or fast-forward the information being rendered. All the information is rendered using a broad range of visually compelling D3-based data visualizations that business users can handle without IT support.
A number of other small vendors may be worthy of consideration, depending on requirements, although they did not meet the criteria for inclusion in this year’s Magic Quadrant.
AFS Technologies offers Discovery G2, an end-to-end data visualization and data discovery solution that delivers tailored, individual insights and analytics targeted at business users in the field such as sales reps or customer service personnel, or in aggregate for decision makers in the consumer goods industry. The platform can extract information from ERP systems, industry data sources including Nielsen, trade promotion management systems, warehouse management systems and various budgeting tools and spreadsheets, and use this data for a range of management analysis including sales, post-trade spending, warehouse efficiency and general financial. AFS has more than 200 customers that leverage the platform’s preconfigured starting templates to deliver customized results reports for sales analyses, post-trade spend analyses, standard warehouse management and financial reports to reduce time to deployment.
Antivia is a privately held company that began with an emphasis on collaboration across BI tools and later focused more on the SAP BusinessObjects platform, where its products helped overcome many of the limitations in SAP’s dashboard product (Xcelsius, rebranded as Dashboards in SAP BI 4.0). In 2013, the company released DecisionPoint, which is data-source-agnostic, positioning itself as a dashboard and mobile solution beyond the SAP customer base. DecisionPoint has its own client-side microcube engine to provide drill-down and pivot within a dashboard; it also supports multiple universe formats (UNV and UNX) as data sources, along with other relational sources such as Teradata, Oracle, SQL Server, Netezza, big data databases (such as Hadoop), and flat files. In contrast to the SAP dashboard tools, which require scripting, DecisionPoint is an all drag-and-drop dashboard design. It also offers native iPad support.
Dimensional Insight delivers a wide range of BI capabilities through its Diver Solution, spanning data collection, data assembly and information delivery deployed either on-premises, through an appliance or as a cloud based SaaS solution. Diver Solution’s data collection tier enables connection to a wide range of source systems and the ability to integrate with the ETL capabilities of the platform. The data assembly tier is the core of Diver Solution, where its multidimensional data model is built and enhanced with business rules in preparation for analysis. Information delivery capabilities offered within the platform include reporting and dashboarding, data discovery and visualization, and MS Office integration with mobile capabilities delivered through DiveTab. Dimensional Insight has leveraged its deep vertical industry and domain expertise to offer several prebuilt “Advisor” solutions built upon the Diver Solution platform. Dimensional Insight’s extensive expertise in the healthcare provider vertical is evident in its support for three such purpose-built applications aimed at organizations in the segment: DI Surgery Advisor, DI Physician Performance Advisor and DI Meaningful Use Compliance Advisor. DI Sales Advisor and DI Program Advisor are used to deliver information and key metrics to sales and support teams. DI General Ledger (GL) Advisor allows for analysis and interaction with financial data and the DI Pricing Advisor is used by CPG providers to effectively manage price and maximize revenue, margin and profitability.
Privately owned, Dundas Data Visualization started out as a charting-engine technologies company whose OLAP, chart, map and gauge components for ASP.NET, SSRS, SharePoint and Windows Forms were purchased by Microsoft in 2007. Dundas then evolved into a dashboard vendor with its first release of Dundas Dashboard in 2009, which provides interactive visualization functions, a rich set of APIs and a built-in C# scripting engine for data presentation customization and extensibility. To expand its market opportunity, in October/November 2014, Dundas introduced Dundas BI, an end-to-end BI platform focused on self-service. It is Web-based and includes a data warehouse and ETL, data discovery, reports, dashboards, and mobile capabilities. Customers report choosing Dundas for its functionality and ease of use for end users, with most using Dundas’s dashboard and interactive visualization capabilities. Dundas also offers a design Professional Services offering to assist customers in implementing BI and dashboarding best practices.
eQ Technologic (eQ) delivers BI capabilities through eQube Business Intelligence (eQube-BI), a component of its eQube enterprise software information infrastructure platform. Primarily, eQube-BI provides enterprise “visibility” leading to actionable insight in the product life cycle management (PLM) domain extending across the enterprise to ERP/supply-chain, Management Execution Systems (MESs), Asset Lifecycle Management (ALM), planning systems, warranty systems, often combining streaming sensor data with product requirements, product engineering data, warranty systems, and manufacturing data. EQ’s customers are predominantly in the aerospace and defense, automotive and machinery, shipbuilding, high-tech and consumer goods industries — often combining streaming sensor data with product, warranty, systems and data. The platform offers over 100 BI templates and cubes “out of the box” that are specific to industrial companies. It is sold both directly to customers and via partnerships, such as those with Siemens PLM Software, which markets and resells eQube-BI as Teamcenter Reporting and Analytics. EQube-BI is used primarily for rapid prototyping and iterative development of in-memory cubes that can be developed without the need for a data warehouse or datamart. Customers have the option of using the eQube-BI cubes developed during the prototyping process to build a data warehouse, if needed, for other applications. The company introduced its eQube 3D Insight module to allow customer to visualize the 3D model of their product, with color coding (red/yellow/green) based on different parameters such as number of changes, velocity of change, inventory levels, supply chain bottlenecks and warranty claims. Reference customers identified data access and integration, integration with enterprise applications, and integration with information infrastructure as their main reasons for choosing eQube-BI. Data integration and the ability to create a single view of data — combining several disparate data sources in-memory and honoring the security of the underlying applications — is a clear strength of eQube-BI, but responses to our survey indicated that the product is difficult to use for developers, administrators and end-users alike, with these identified as factors limiting wider deployment. Revamping the user interface, expanding advanced analytics and creating a big data solution for the IoT are on the company’s road map.
iDashboards delivers capabilities used to build and consume highly interactive dashboards that can be deployed either on-premises or in the cloud. The platform offers an easy-to-use, code-free interface for accessing cloud and enterprise data, and displaying analytic content and KPIs on intuitive dashboards for end-user consumption. The iDashboards content can be consumed via mobile devices using any HTML5-compliant browser with mobile application options for Android tablet and iPad/iPhone users. Special display solution licensing is available for rendering of dashboards on screens in common areas such as offices, production facilities or boardrooms for shared KPI consumption and monitoring. The iDashboards product line also offers various predefined packaged solutions for popular cloud-based sources, such as Salesforce, Google Analytics, Facebook and Twitter, with prebuilt connectors, transformation logic and a suite of prebuilt dashboards displaying common metrics typically tracked and analyzed for each source. In addition to end-user organizations that use iDashboards to create and deliver dashboards to internal and external users, a growing number of independent software vendors have formed OEM partnerships with iDashboards to embed content in analytic solutions.
InetSoft is a U.S.-based company that specializes in easy-to-use interactive dashboards targeted at expanding end-user adoption plus pixel-perfect report generation, scheduling, and bursting. Business user data mashup is also a core feature of the platform. InetSoft has a large number of OEM customers and also serves enterprises in different geographies with support centers in the U.S. and China (the product is localized to Mandarin in addition to English). In 2014, InetSoft announced native mobile applications for Android and iOS and improved its support for big data sources. Through its product enhancements, InetSoft is trying to address a wide range of capabilities — from traditional reporting to OLAP-based analysis, to ad hoc information exploration and analytic dashboards.
Infor Business Intelligence (BI) is the basis of an end-to-end platform that encompasses BI, analytics and performance management capabilities. Infor has a global presence and major support centers in EMEA, the U.S. and Asia/Pacific. Infor BI includes an in-memory multidimensional OLAP database; Web front ends, such as Infor BI Dashboards/Motion Dashboards for data presentation and analysis; a feature-rich Microsoft Excel-based interface; a data integration tool; and a modeling tool. Infor BI offers out-of-the-box integration and packaged industry- and domain-specific analytic solutions for Infor ERP applications. In addition, it is frequently deployed in the SAP ERP installed base, primarily as a result of the former MIS Alea’s relationship with SAP. Infor continues to enhance the Infor BI platform. In 2014, Infor introduced a metalayer, embedded in the platform, now combining multidimensional and relational ad hoc analysis and reporting capabilities. It continues to invest in improving the mobile app and the in-memory processing performance.
Jinfonet Software delivers JReport — a Java-based BI platform originally developed for embedding in third-party applications by OEMs. Over time, the product has evolved into a general-purpose platform intended for use by end-user companies. However, according to the company, 86% of customers still use the product for embedded BI and 62% of customers are OEMs. It is also optimized to be scalable and fault-tolerant. The current version (JReport 13) features improvements to geospatial, performance and visualizations. JReport 12 enhanced dashboards, interactive visualization and mobile capabilities, and support for big data sources — MongoDB, Hadoop (through Hive), and Amazon Redshift. Jinfonet’s product strengths are its embedding capability, through its customizable architecture for integration with host applications, and a high-performance reporting engine (JReport). Enhancements to usability and investment in the cloud are on the company’s near-term road map.
Looker offers tools for business-user-driven data transformation and exploration. Its purpose is not to compete with traditional BI tools, but to facilitate analytic processes between data analysts and end users. Looker is invested in making their customers successful with using the product. It provides experienced data analysts that help their customers with unlimited support in the use of the tool. The platform’s key differentiator is LookML — Looker’s data modeling language — which lets tech-savvy business users generate in-database queries, with optimized performance, without depending on IT. Repositories such as Teradata Aster, Microsoft SQL Server, Oracle, Amazon Redshift, SAP Hana, Google BigQuery, Apache Spark and Hadoop (through Cloudera Impala), can thus be queried, through LookML, to generate insights. A number of customizable visualizations can also be applied to the information extracted from the data sources and be used to create reports. The sales focus of the company has been driven by an internal team and partner sales team and has been centered in the U.S. Looker recently opened its operations in Europe with an office in London and has customers across Europe, Asia and Latin America. Looker has plans to continue to expand its reach through its partner network, with companies such as Amazon, IBM, Cloudera, Snowplow Analytics and Teradata.
Manthan offers a comprehensive BI and advanced analytics cloud-based platform focused on retail. The Manthan Analytics Platform, which offers from data integration through to reporting, dashboards, interactive visualizations and predictive and prescriptive analytics (optimization engines and decision frameworks), is the foundation for its packaged retail analytics applications for merchandise, customers and supplier/wholesaler analytics. Making insights from advanced analytics accessible to business users in retail is a differentiator for Manthan. According to its customer references, customers choose Manthan because of its functionality and ease of use and for its specialized vertical expertise.
Manthan met all of the inclusion criteria for a dot position in the MQ, but was excluded from the top 24 because of its focus on packaged applications versus a platform, which is the main focus on this MQ. However, Retail customers looking for unified set of packaged applications supporting range of descriptive, diagnostic, predictive and prescriptive analytics should consider Manthan.
Phocas currently offers a subscription-based BI product and is migrating to a SaaS model with a cloud-first approach to development. Multitenancy, the ability to blend data from cloud and on-premises data sources, and a full Web user experience make it particularly suited to a cloud BI environment, although many of its customers still prefer to use their traditional on-premises offering. Phocas offers a range of capabilities, including dashboards, basic data discovery and collaboration features, geospatial rendering of information, advanced analytics models (such as shopping basket analysis and profit optimization) and mobile BI — with desktop-like data exploration capabilities through an HTML5 interface. The company is investing in simplifying its customer experience, which is paying off in terms of customers rating Phocas highly for ease of use and achievement of business benefits. Phocas is also concentrated in the delivery of vertical solutions for different industries and roles, providing an extensive list of data connectors to business applications that help simplify deployments. The product is localized in several languages and currently used by over 1,000 customers.
SpagoBI is a totally open-source BI solution offered as a single version that should ease deployment and support in comparison with similar solutions. Its core markets go beyond Western Europe (where it originated in an IT consultancy firm) to include regions where OEM solutions and open-source are popular — such as the U.S., Latin America, and Central and Eastern Europe. The company is headquartered in Italy, but has offices in the U.S., Brazil, Argentina, the Republic of Serbia and Belgium, as well as a network of more than 100 partners. The product is now on version 5.1 (released in January 2015) and has expanded data connectivity toward big data and analytic capabilities, with new solutions for OLAP analysis, self-service in-memory cockpits, data mining with R, social listening and what-if analysis. SpagoBI’s reference customers report a breadth of use that is slightly above average, but claim that the product’s ease of use, and the complexity of analysis that can be achieved with it, are below the average (for this Magic Quadrant survey). There is also feedback from customers that the product is difficult to implement, but this is in-line with what the open-source market has offered over the years. It is still too early to understand to what extent the new version, 5.1 will impact these assessments.
Strategy Companion’s Analyzer platform is a self-service BI solution ideally suited for Microsoft SQL Server Analysis Services (SSAS)-based solution development, with several deployment options available: Analyzer Enterprise for internal BI delivery through on-premises deployment; Analyzer SaaS, which is a multitenant option used by cloud and SaaS application providers to build and host external solutions; and Analyzer OEM, which is used by customers to embed content in applications using open standards offered by the Analyzer platform. Analyzer Mobile is based on HTML5 and can render content on most mobile devices including iPads, iPhones, BlackBerry, Android and Windows phones using role-based security profiles defined within Analyzer. Strategy Companion leverages a unique data analysis feature in Analyzer called “Recombinant BI,” which is named from the combination of Recombinant DNA and Genetic Recombination. This patent-pending functionality extends the capabilities beyond traditional OLAP, by allowing users to combine new data as needed and answer new questions on their own without a reliance on IT to curate the data needed for analysis. Customers report using Analyzer for relatively complex analytic tasks to achieve business benefits that exceed the overall survey average.
Zucchetti first launched a BI platform offering (Infinity) in 2007, to complement its broad array of business applications (for HR, CRM, and document management). Zucchetti now delivers BI capabilities through its InfoBusiness platform to approximately 6,000 customers, many of which also use its ERP and HR solutions. In fact, 80% of customers using Zucchetti for BI are integrating it with either Zucchetti ERP or Zucchetti HR; 20% are using it as a stand-alone BI platform. Based on Gartner’s other research, it is evident that the InfoBusiness platform is targeted at descriptive use cases, offering reporting and parameterized dashboards, and some ad hoc analysis capabilities to consumers. Zucchetti plans a product launch of Infinity Analytics during 2H15. The platform also integrates collaboration capabilities, enabling users to share BI content. Other recent improvements to the platform include the ability to model multiple data views and the support of 64-bit platforms. Zucchetti is using a business model of 55% indirect, 45% direct to grow its BI business beyond its current business application focus.
The number of vendors on this year’s Magic Quadrant is limited to 24. We ranked vendors that met all the inclusion criteria based on a combination of the criteria listed below.
Vendors that offer specific industry or domain analytic applications only are excluded from consideration because this Magic Quadrant highlights BI and analytics platforms that are used to build analytic applications for any industry or domain.
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 biscorecard.com research.
* These criteria are scored partly or wholly on the basis of input from the Magic Quadrant customer survey.
Product or Service
Source: Gartner (February 2015)
Vendors are rated on their understanding of how market forces can be exploited to create value for customers and opportunity for themselves. The Completeness of Vision ratings and commentary in this report are based on the same sources described in the Ability to Execute section.
When determining Completeness of Vision for the Offering (Product) Strategy criterion, Gartner evaluated vendors’ ability to support key trends that will drive business value in 2015 and beyond:
Expanding analysis to more users:
New types of data source and analysis:
New business models and sources of revenue:
Existing and planned products and functions that contribute to the above trends were factored into each vendor’s score for Offering (Product) Strategy in Completeness of Vision.
* These criteria are scored partly or wholly on the basis of input from the Magic Quadrant customer survey.
Offering (Product) Strategy
Source: Gartner (February 2015)
Leaders are vendors that are strong in the breadth and depth of their BI platform capabilities, and can deliver on enterprisewide implementations that support a broad BI strategy that delivers business value. Leaders articulate a business proposition that resonates with buyers, supported by viability and operational capability to deliver on a global basis. Smaller vendors, which may lack strong scores for geographic or vertical/industry strategy, may still be Leaders due to the strength of their market understanding, capabilities and road maps (to enable business users more easily to find relevant insights in data), market momentum, and excellent execution on key product, customer and sales experience measures.
Important to Leader positioning:
Less important to Leader positioning:
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.
Microsoft, MicroStrategy, Oracle and Information Builders have many elements of Completeness of Vision, but are hampered by execution challenges.
Challengers are well-positioned to succeed in the market. However, they may be limited to specific use cases, technical environments or application domains. Their vision may be hampered by a lack of coordinated strategy across the various products in their platform portfolios, or they may lack the marketing efforts, sales channel, geographic presence, industry-specific content and awareness of the vendors in the Leaders quadrant.
The large number of vendors in the Challenger, Visionary, and Niche quadrants with specialized strengths suggests opportunities for customers to find a match for their requirements beyond the largest vendors. Most of these vendors offer differentiated capabilities for addressing business user driven and emerging requirements.
Visionaries have a strong and unique vision for delivering a BI platform. They offer depth of functionality in the areas they address. However, they may have gaps relating to broader functionality requirements. Visionaries are thought-leaders and innovators, but they may be lacking in scale, or there may be concerns about their ability to grow and provide consistent execution.
The large number of vendors in the Challengers, Visionaries, and Niche Players quadrants with specialized strengths suggests opportunities for customers to find a match for their requirements beyond the largest vendors. Most of these vendors offer differentiated capabilities for addressing business-user-driven and emerging requirements.
Niche Players do well in a specific segment of the BI platform and analytics market, such as reporting, dashboarding, collaboration, embeddability or big data integration, or have a limited capability to innovate or outperform other vendors. They may focus on a specific domain or aspect of BI, but are likely to lack depth of functionality elsewhere. They may also have gaps relating to broader platform functionality or have less-than-stellar customer feedback. Alternatively, Niche Players may have a reasonably broad BI platform, but limited implementation and support capabilities; or relatively limited customer bases, such as in a specific geography or industry. In addition, they may not yet have achieved the necessary scale to solidify their market positions.
The large number of vendors in the Challenger, Visionaries, and Niche Players quadrants with specialized strengths suggests opportunities for customers to find a match for their requirements beyond the largest vendors. Most of these vendors offer differentiated capabilities for addressing business-user-driven and emerging requirements.
Readers should not use this Magic Quadrant in isolation as a tool for vendor selection. Gartner has defined the BI and analytics market broadly. We include a variety of products that span a range of buyers and use cases. Consider this Magic Quadrant to be more of a summary of Gartner’s research on this market. When making specific tool selection decisions, use it in combination with our Critical Capabilities, Survey Analysis research, and Strengths, Weaknesses, Opportunities and Threats (SWOT) publications, as well as our analyst inquiry service. Moreover, readers should be careful not to ascribe their own definitions of Completeness of Vision or Ability to Execute to this Magic Quadrant, which often map narrowly to product vision and market share, respectively. The Magic Quadrant methodology factors in a range of criteria in determining position, as defined in the Evaluation Criteria section.
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:
By 2018, data discovery and data management evolution will drive most organizations to augment centralized analytic architectures with decentralized approaches.
By 2017, most data discovery tools will have incorporated smart data discovery capabilities to expand the reach of interactive analysis.
By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis.
By 2017, most business intelligence and analytics platforms will natively support multistructured data and analysis.
Through 2016, less than 10% of self-service business intelligence initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business.
|AWS||Amazon Web Services|
|CPG||consumer packaged goods|
|CPM||corporate performance management|
|ETL||extraction, transformation and loading|
|HDFS||Hadoop Distributed File System|
|IoT||Internet of Things|
|KPI||key performance indicator|
|LOB||line of business|
|OLAP||online analytical processing|
|SMB||small or midsize business|
1 Gartner defines total software revenue as revenue that is generated from appliances, new licenses, updates, subscriptions and hosting, technical support, and maintenance. Professional services revenue and hardware revenue are not included in total software revenue (see “Market Share Analysis: Business Intelligence and Analytics Software, 2013”).
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 in 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 (see updated toolkit); a prepared video demonstration of how well vendor BI platforms address the 13 critical capabilities; and biscorecard.com research.
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/key performance indicators (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 & 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 or centrally authored dashboards are a style of reporting that graphically depicts performances measures. 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 to 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. 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, 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 customer survey composite success measures are referenced throughout the report. Customer survey participants scored vendors on each metric on a scale of 1 to 7 (where 1 to 2 = poor, 3 to 5 = average, and 6 to 7 = outstanding).
Below is a reference for how these composite metrics are calculated:
Product/Service: Core goods and services offered by the vendor for the defined market. This includes current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization’s financial health, the financial and practical success of the business unit, and the likelihood that the individual business unit will continue investing in the product, will continue offering the product and will advance the state of the art within the organization’s portfolio of products.
Sales Execution/Pricing: The vendor’s capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor’s history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization’s message to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This “mind share” can be driven by a combination of publicity, promotional initiatives, thought leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
Market Understanding: Ability of the vendor to understand buyers’ wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen to and understand buyers’ wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.
Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service, and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor’s approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature sets as they map to current and future requirements.
Business Model: The soundness and logic of the vendor’s underlying business proposition.
Vertical/Industry Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including vertical markets.
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the “home” or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.