Survey Analysis: Customers Rate Their BI Platform Vendor, 2014 9 October 2014 ID:G00262301 Analyst(s): Rita L. Sallam, Josh Parenteau VIEW SUMMARY Based on customer surveys conducted on BI and analytics platforms, this research will alert BI leaders to the customer experiences of professionals across 42 vendors. Overview Key Findings Customer experience matters. A vendor's customer experience rating, measured as the combination of scores for product quality, customer support and sales experience, has a strong relationship to how customers view its future. Megavendors continue to be judged as below average by all respondents on many measures of customer success ease of use, functionality and overall customer experience, albeit for the largest and most complex deployments with global systems record reporting. Data discovery vendors and many small independents with above average ratings for ease of use and enabling more complex types of analysis tend to deliver stronger business benefits than other vendor types. They also tend to have much of the market growth momentum. With the exception of Microsoft, the megavendors plus MicroStrategy have among the highest enterprise standardization rates in the largest companies. Data discovery vendors are deployed in large companies, but are often complements that ultimately threaten the enterprise standard as these vendors enhance their enterprise features and expand their footprints through viral use. Recommendations Assess the measures that directly influence customer satisfaction with a BI vendor: support quality, product quality, upgrade difficulty, sales experience, ease of use and achievement of business benefits, as supplements to an evaluation of functionality, integration and cost of ownership requirements during vendor selection. Consider user enablement programs that drive user success in vendor selection decisions. Look beyond large suppliers there are many choices for standardizing on an enterprise BI platform, depending on company and deployment size, and the regional, vertical and functional requirements of the deployment. Talk to references (and Gartner) for a candid view of customer experiences. TABLE OF CONTENTS CONTENTS Survey Objective Data Insights TABLES Table 1. FIGURES Figure 1. Customer Experience and Plans for the Future Customer Experience and Key Buying Requirements Customer Experience and Upgrade Difficulty Customer Experience With BI Platform Integration Methodology Vendor List by Category Number of References Included in Survey Analysis by Vendor Category and Vendor EVIDENCE The survey was conducted over a four week period in 4Q13, and hosted and executed by Gartner. Survey results were used as an input to the Gartner Business Intelligence Platform Magic Quadrant 2014 research. This research provides details on how survey respondents are currently investing in, or plan to invest in, new and emerging BI and analytic platform capabilities. NOTE 1 BI PLATFORMS MAGIC QUADRANT 2014 INCLUSION CRITERIA To be included in the Magic Quadrant graphic, software vendors had to meet all of the following criteria: Each had to generate at least $15 million in total BI related software license revenue annually. (Gartner defines total BI related software license 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 the case of vendors that also supply transactional applications, each had to show that its BI platform is used routinely by organizations that do not use its transactional applications. Each had to deliver at least 12 out of the 17 capabilities in the BI and Analytics Platform Capabilities Definition (see Note 6 and, for further details, "Magic Quadrant for Business Intelligence and Analytics Platforms," OEM components from other vendors were not included). Each had to receive a minimum of 30 survey responses from customers that use its platform in a production environment. NOTE 2 SPECIFIC VENDOR EXCLUSION References responding on behalf of Domo have been excluded from this survey analysis report. As there were 14 Domo references, the overall number of references reported in some 2014 Magic Quadrant materials of 1,655 has been reduced by 14 from 1,669 for the purpose of this analysis. NOTE 3 COMPLEXITY OF ANALYSIS CALCULATION Composite complexity of analysis/usage is a weighted average score based on percentage of respondents reporting use of the platform. Activities are weighted as follows: Viewing static reports = 1 Monitoring performance via a scorecard = 1 Viewing parameterized reports = 2 Doing simple ad hoc analysis = 3 Interactive exploration and analysis of data = 4 Doing moderately complex to complex ad hoc analysis = 5 Using predictive analytics and/or data mining models = 5
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Number of References Included in Survey Analysis by Product Magic Quadrant Vendors Customer Experience Versus Sales Experience and View of the Vendor's Future Other Vendors Customer Experience Versus Sales Experience and View of the Vendor's Future Magic Quadrant Vendors Support and Software Quality Versus Customer View of the Vendor's Future Other Vendors Support and Software Quality Versus Customer View of the Vendor's Future Magic Quadrant Vendors BI Success Score Versus Customer View of the Vendor's Future Other Vendors BI Success Score Versus Customer View of the Vendor's Future Magic Quadrant Vendors BI Success Score Versus Customer Likelihood to Discontinue Use Versus Customer View of the Vendor's Future Other Vendors BI Success Score Versus Customer Likelihood to Discontinue Use Versus Customer View of the Vendor's Future All Vendors by Category Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits Magic Quadrant Vendors Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits Versus Deployment Size Other Vendors Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits Versus Deployment Size Magic Quadrant Vendors by Category Ease of Use Versus Revenue Growth Versus Market Share Versus Complexity of Analysis Magic Quadrant Vendors by Category Ease of Use Versus Revenue Growth Versus Market Share Versus Achievement of Business Benefits Magic Quadrant Vendors Enterprise Standardization Versus Company Size Versus Deployment Size Other Vendors Enterprise Standardization Versus Company Size Versus Deployment Size Percentage of Customers Using Magic Quadrant Vendors for a Range of BI Activities Percentage of Customers Using Other Vendors for a Range of BI Activities Magic Quadrant Vendors Integration Score Versus Product Quality Versus Upgrade Difficulty Score Other Vendors Upgrade Difficulty Versus Integration Score Versus Product Quality Score Magic Quadrant Vendors Upgrade Difficulty Versus Support Score Versus Product Quality Score Other Vendors Upgrade Difficulty Versus Support Score Versus Product Quality Score User Enablement by Magic Quadrant Vendor User Enablement by Other Vendor Ability to Integrate with Complementary BI Capabilities Integration Score: Ability to Embed and Customize BI Platform Components in an Application or Portal Integration Score: Ability to Promote Business User Generated Data Mashups to the System of Record Integration Score: Across BI Platform Components Integration Score: A Common Security Model and Administration Application Components Across the Platform Integration Score: A Common Semantic Layer Across Components NOTE 4 CALCULATION OF BUSINESS BENEFITS SCORE The business benefits score is an average of scores on 10 different benefit areas, scored by respondents on a scale of 1 to 7 (where 1 to 2 = poor, 3 to 5 = average, and 6 to 7 = outstanding). This score is normalized to a scale of 1 to 10. The business benefits score components are: Make better information available to more users Expand types of analysis Ability to make better and faster decisions Improve customer satisfaction Link key performance indicators (KPIs) to corporate objectives Increase revenue Reduce other non IT costs Reduce external IT costs Reduce line of business head count Reduce IT head count NOTE 5 CALCULATION OF BI PLATFORM INTEGRATION SCORE The BI Platform Integration areas below are scored by respondents on a scale of 1 to 7 (where 1 to 2 = poor, 3 to 5 = average, and 6 to 7 = outstanding). This score is normalized to a scale of 1 to 10. The BI Platform Integration Score is an average of the seven integration areas scored by customer survey respondents. Integration across platform components Integrated and common front end tools for example, consistent user interface and menus, and users' ability to easily leverage authored content from one tool to the next Integrated semantic/metadata layer for example, unified and fully integrated/leveraged across BI platform tools Ability to promote business user data mashup to systems of record semantic layer Ability of the BI platform to integrate with complementary BI capabilities for example, parts of the stack such as data integration, search, content management, enterprise applications, collaboration, BAM and BPM Common security model and administration application across components Ability to embed and customize BI platform components in another application and/or portal NOTE 6 CAPABILITY DEFINITIONS Information Delivery Reporting: Provides the ability to create highly formatted, print ready and interactive reports, with or without parameters. Figure 32. Survey Objective Integration Score: Integrated and Common Front End Tools Each year, Gartner evaluates the business intelligence (BI) and analytics platforms market with the ultimate objective of publishing Magic Quadrant research, scrutinizing those results. Part of this process is a large user survey of vendor supplied references and other organizations. This includes IT, business or hybrid IT business leaders disclosing their experiences with their vendor's BI and analytics products, as well as how those products have contributed to overall business success. The purpose of this research is to utilize the extensive Magic Quadrant customer survey data that Gartner collects annually to provide additional insight into the platform problems and limitations that references have indicated, as well as to assess the future state of each BI vendor. For a detailed categorization of the vendor segments used throughout this analysis, see Table 1. Table 1. Vendor List by Category Vendor Category Vendors Dashboards: A style of reporting that graphically depicts performance measures. Includes the ability to publish multi object, 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. Ad hoc report/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. Microsoft Office integration: Sometimes, Microsoft Office (particularly Excel) acts as the reporting or analytics client. In these cases, it is vital that the tool provides integration with Microsoft Office, including support for native document and presentation formats, formulas, charts, data "refreshes" and pivot tables. Advanced integration includes cell locking and writeback. Cloud BI Birst Mobile BI: Enables organizations to develop and deliver content to mobile devices in a publishing and/or interactive mode, and takes advantage of
Data Discovery Leaders Large Independents Megavendors Open Source Small Independents Other Vendors BI = business intelligence GoodData Tableau Tibco Spotfire Qlik Information Builders MicroStrategy SAS Institute IBM Microsoft Oracle SAP Actuate (BIRT) Jaspersoft (acquired by Tibco Software) Pentaho Alteryx arcplan Bitam Board International Infor Logi Analytics Panorama Software Prognoz Pyramid Analytics Salient Management Targit Yellowfin Adaptive Insights Advizor Solutions Chartio Dimensional Insight Dundas Data Visualization eq Technologic InetSoft Jedox Jinfonet Software (JReport) Lavastorm Analytics Phocas Software SiSense Software AG (JackBe) SpagoBI Strategy Companion The 2014 Magic Quadrant customer survey results used in this analysis included a total of 1,589 responses that are used in all vendor and vendor category level views. Total survey responses received from: Vendor provided references 1,470 (93%) BI users from Gartner BI Summits 33 (2%) Respondents from last year's survey 86 (5%) Figure 1 shows a breakdown of survey responses by vendor category and vendor, for reference purposes. mobile devices' native capabilities, such as touchscreen, camera, location awareness and naturallanguage query. Analysis Interactive visualization: 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. Search based data discovery: Applies a search index to structured and unstructured data sources and maps them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface. This is not the ability to search for reports and metadata objects. This would be a basic feature of a BI platform. Geospatial and location intelligence: Specialized analytics and visualizations that provide a geographic, spatial and time context. Enables the ability to depict physical features and geographically referenced data and relationships by combining geographic and location related data from a variety of data sources, including aerial maps, geographic information systems and consumer demographics, with enterprise and other data. Basic relationships are displayed by overlaying data on interactive maps. More advanced capabilities support specialized geospatial algorithms (for example, for distance and route calculations) as well as the layering of geospatial data onto custom base maps, markers, heat maps and temporal maps, supporting clustering, geofencing and 3D visualizations. Embedded advanced analytics: Enables users to leverage a statistical functions library embedded in a BI server. Included are the abilities to consume common analytics methods such as Predictive Model Markup Language (PMML) and R based models in the metadata layer and/or in a report object or analysis to create advanced analytic visualizations (of correlations or clusters in a dataset, for example). Also included are forecasting algorithms and the ability to conduct "what if?" analysis. Online analytical processing (OLAP): Enables users to analyze data with fast query and calculation performance, enabling a style of analysis known as "slicing and dicing." Users are able to navigate multidimensional drill paths. They also have the ability to write back values to a database for planning and "what if?" modeling. This capability could span a variety of data architectures (such as relational, multidimensional or hybrid) and storage architectures (such as disk based or in memory). Integration BI infrastructure and administration: Enables all tools in the platform to use the same security, metadata, administration, object model and query engine, and scheduling and distribution engine. All tools should share the same look and feel. The platform should support multitenancy. Metadata management: Tools for enabling users to leverage the same systems of record semantic model and metadata. They should provide a robust and centralized way for administrators to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics/kpis, and report layout objects, parameters and so on. Administrators should have the ability to promote a business user defined data mashup and metadata to the systems of record metadata. Business user data mashup and modeling: The code free, "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. Development tools: 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
Figure 1. Number of References Included in Survey Analysis by Vendor Category and Vendor scalable and personalized distribution, scheduling and alerts for BI and analytics content via email, to a portal and to mobile devices. Embeddable analytics: Tools including a software developer's kit with APIs for creating and modifying analytic content, visualizations and applications, and embedding them into a business process and/or an application or portal. These capabilities can reside outside the application and reuse 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. Collaboration: Enables users to share and discuss information, analysis, analytic content and decisions via discussion threads, chat and annotations. Support for big data sources: The ability to support and query hybrid, columnar and array based data sources, such as MapReduce and other NoSQL databases (graph databases, for example). Support could include direct Hadoop Distributed File System (HDFS) queries or access to MapReduce through Hive. BI = business intelligence We included original equipment manufacturer (OEM) references in the actual 2014 survey but, as their responses were not factored into the Magic Quadrant scoring process, they will thus be excluded from all survey analysis notes this year. The exception is "Survey Analysis: BI and Analytics Spending Intentions, 2014," which details market spending patterns not included in the Magic Quadrant scoring methodology. We have included vendors in this research with at least 12 or more customer survey responses, which may not have met the inclusion criteria for an actual position within the Magic Quadrant. As such, these are reported as "other" vendors in the figures depicted throughout the analysis. Please see Note 1 for the inclusion criteria for a position on the "Magic Quadrant for Business Intelligence and Analytics Platform," and Note 2 for specific vendor exclusions. For the product level views, the total number of responses included in the analysis is 1,551. Products that did not have a significant enough number of responses to accurately assess were excluded, but are included in the vendor and vendor category aggregated views and factored into the Magic Quadrant scoring and positioning. Figure 2 shows the number of responses received for each product included in the analysis, for consideration when evaluating the survey results reported for each of the product level views. It is important to note that, because the product level views include fewer responses than the vendor or vendor category views, overall survey average results reported in this analysis for the same metric will be different in product views versus those reported for vendor or vendor category. Figure 2. Number of References Included in Survey Analysis by Product
BI = business intelligence Data Insights Customer Experience and Plans for the Future While platform ease of use and product functionality are key buying criteria, customers also care about their overall customer experience. For the purpose of this research, we measure this as product quality, customer support and sales experience. Figures 3 and 4 show that these measures for both Magic Quadrant and "other" vendors also have a relationship to how customers view a vendor's future. Vendors in the upper right hand quadrant of each figure that have orange dots are above average in all three measures. With the exception of Microsoft, megavendors do not fare well on customer experience measures, and their customers are concerned about their future. Vendors that are below average in customer and sales experience but, despite this, have an above average customer view of their future, such as SAP (BusinessObjects BI Platform 4.1 only), Tibco Spotfire, SiSense and Jedox, tend to have a customer base that buys into their future vision and appears willing to accept a subpar customer experience at least in the short term. Moreover, small, high growth vendors such as SiSense often experience growing pains as they rapidly staff sales and support to fuel growth. Generally speaking, the quality of the sales and support experience is related to the experience level of people in those positions, so it is typically lower in a rapidly growing startup that is adding new employees to these positions. In general, product quality can also be affected when vendors introduce functionality at a rapid cadence in order to build differentiation into the product they sometimes release a product to differentiate before it may be fully ready from a quality perspective. A number of "other" vendors such as Kofax (Altosoft), Dimensional Insight, Phocas, Chartio and Dundas scored positively on customer experience, but customers are concerned about their future. This could reflect concerns over longevity, viability or narrowness of scope. Figure 3. Magic Quadrant Vendors Customer Experience Versus Sales Experience and View of the Vendor's Future
BI = business intelligence; MQ = Magic Quadrant Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed. n = 1,551 (see Figures 1 and 2) Figure 4. Other Vendors Customer Experience Versus Sales Experience and View of the Vendor's Future Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed.
n = 1,551 (see Figures 1 and 2) Figures 5 and 6 show a similar relationship between customer ratings for product support and product quality and their view of their vendor's future. Only three of the 46 products rated for this research, SAP BusinessObjects BI 4.1, Board and Jedox, are ranked below average for both product quality and support but customers have a positive view of their future. Six vendors Salient, Bitam, Information Builders, Dundas, Chartio and Dimensional Insight have above average support and product quality ratings, but these do not translate into a positive view of their future, perhaps due to concerns about longevity and viability. Figure 5. Magic Quadrant Vendors Support and Software Quality Versus Customer View of the Vendor's Future BI = business intelligence; MQ = Magic Quadrant Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed. n = 1,551 (see Figures 1 and 2) Figure 6. Other Vendors Support and Software Quality Versus Customer View of the Vendor's Future
Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed. n = 1,551 (see Figures 1 and 2) A vendor's overall BI success score, which adds its aggregate product and performance scores to customer experience, has a weaker relationship to a customer's view of the vendor's future than customer experience alone. Figures 7 and 8 show that six vendors out of the 42 Magic Quadrant and "other" vendors included in this research have above average customer ratings for their future, despite having below average BI success scores. While seven vendors of the 42 have above average BI success scores, their customers have a negative view of their future. This may indicate that product related functional measures are less important than customer experience measures when shaping a customer's view of the vendor's future. Figure 7. Magic Quadrant Vendors BI Success Score Versus Customer View of the Vendor's Future
BI = business intelligence; MQ = Magic Quadrant BI Success Score is an aggregate measure that includes aggregate product score, customer experience (product quality and support), sales experience and platform performance score. n = 1,551 (see Figures 1 and 2) Figure 8. Other Vendors BI Success Score Versus Customer View of the Vendor's Future BI = business intelligence BI Success Score is an aggregate measure that includes aggregate product score, customer experience (product quality and support), sales experience and platform performance score.
n = 1,551 (see Figures 1 and 2) Figure 9 shows that, of the Magic Quadrant vendors, most with below average BI success scores also have a high likelihood that customers will discontinue use in the next one to three years; and almost all those that customers rate with above average BI success scores and a below average likelihood to discontinue use are viewed positively in terms of their future. The relationship is less with "other" vendors, as seen in Figure 10. There may be strong reasons other than BI success measures to discontinue use and that affect the customer's view of the vendor's future, such as standardizing on another platform or concerns about the vendor's long term viability. Note that SAS is most likely to see customers discontinue use of any Magic Quadrant vendor. This could be due to the growing adoption of R and other options such as Python, as well as increased business user access to advanced analytics from traditional BI and data discovery platforms. Figure 9. Magic Quadrant Vendors BI Success Score Versus Customer Likelihood to Discontinue Use Versus Customer View of the Vendor's Future Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed. BI = business intelligence; MQ = Magic Quadrant BI Success Score is an aggregate measure that includes aggregate product score, customer experience (product quality and support), sales experience and platform performance score. n = 1,551 (see Figures 1 and 2) Figure 10. Other Vendors BI Success Score Versus Customer Likelihood to Discontinue Use Versus Customer View of the Vendor's Future
Note: The orange color shape indicates an above average score of how the vendor's future is viewed; the blue color shape indicates a below average score of how the vendor's future is viewed. BI = business intelligence BI Success Score is an aggregate measure that includes aggregate product score, customer experience (product quality and support), sales experience and platform performance score. n = 1,551 (see Figures 1 and 2) Customer Experience and Key Buying Requirements Making complex types of analysis more accessible to a broader range of users in the organization has become a key buying driver for most new deployments and projects. Figures 11, 12 and 13 show that vendor categories and individual Magic Quadrant and "other" vendors with above average ratings for ease of use tend to deliver a higher achievement of business benefits for customers. These vendors also tend to enable users to conduct more complex types of analysis with the platform. In general, vendors that support this combination, such as the data discovery vendors, are experiencing the highest market momentum. Most traditional, IT centric vendors are rated by their customers in the bottom left hand quadrant as their larger, mostly report centric deployments are perceived to deliver less business value. Systems of record reporting has become a minimum requirement for most companies, but it does not enable them to use information to differentiate or innovate, as do capabilities that enable users to do more types of analysis by themselves to find insights in data that can create value for the business. Enabling a broader range of users to conduct more advanced types of analysis translates into customers being able to derive more business value from their deployments. These results highlight the importance for IT centric vendors with systems of record reporting to rapidly enhance their platforms' ease of use and expand accessibility to a wider range of analytics capabilities, in order to address new buying requirements and stay relevant in the market. Figure 14 shows how market growth relates to ease of use, complexity of analysis supported and market share. Figure 15 shows how market growth relates to ease of use, achievement of business benefits and market share. It is clear that customers reward these attributes in Magic Quadrant vendors, as they are highly related to a company's growth. Figure 11. All Vendors by Category Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits
Note: The orange color shape indicates an above average complexity of analysis score (see Note 3); the blue color shape indicates a below average complexity of analysis score. BI = business intelligence n = 1,551 (see Figures 1 and 2) Figure 12. Magic Quadrant Vendors Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits Versus Deployment Size
Note: The orange color shape indicates an above average complexity of analysis score (see Note 3); the blue color shape indicates a below average complexity of analysis score. MQ = Magic Quadrant n = 1,551 (see Figures 1 and 2) Figure 13. Other Vendors Ease of Use Versus Complexity of Analysis Versus Achievement of Business Benefits Versus Deployment Size
Note: The orange color shape indicates an above average complexity of analysis score (see Note 3); the blue color shape indicates a below average complexity of analysis score. n = 1,551 (see Figures 1 and 2) Figure 14. Magic Quadrant Vendors by Category Ease of Use Versus Revenue Growth Versus Market Share Versus Complexity of Analysis
n = 1,551 (see Figures 1 and 2) Figure 15. Magic Quadrant Vendors by Category Ease of Use Versus Revenue Growth Versus Market Share Versus Achievement of Business Benefits
n = 1,551 (see Figures 1 and 2) Figure 16 shows that, of the Magic Quadrant vendors, all the megavendors (with the exception of Microsoft) plus MicroStrategy have among the highest enterprise standard rates in the largest companies. Data discovery vendors such as Tableau, Qlik and Tibco Spotfire are also deployed in large companies, but are complements that ultimately threaten the enterprise standard as they enhance their enterprise features and expand their footprint through viral use. Vendors in the bottom right quadrant are often considered the enterprise standards for smaller organizations and deployments. Figure 16. Magic Quadrant Vendors Enterprise Standardization Versus Company Size Versus Deployment Size
Note: The orange color shape indicates an above average deployment size in terms of number of users; the blue color shape indicates a below average deployment size in terms of number of users. BI = business intelligence; MQ = Magic Quadrant n = 1,551 (see Figures 1 and 2) Figure 17. Other Vendors Enterprise Standardization Versus Company Size Versus Deployment Size Note: The orange color shape indicates an above average deployment size in terms of number of users; the blue color shape indicates a below average deployment size in terms of number of users. BI = business intelligence
n = 1,551 (see Figures 1 and 2) Figures 18 and 19 show how customers are using Magic Quadrant and "other" vendors' platforms. As the same user can leverage multiple capabilities, the total percent of use across all types of analysis can be greater than 100% for widely used platforms. The vendors with the highest use total across categories support the broadest range of analytic styles. Of the Magic Quadrant vendors, Bitam, Panorama, Prognoz, GoodData, Birst, Tableau and MicroStrategy are among the highest, while arcplan, SAP, SAS, Oracle, Microsoft and Pyramid Analytics support the most narrow use. Of the "other" vendors, SpagoBI, Chartio, Phocas and Advizor support the broadest use, while customers of Kofax (Altosoft), Adaptive Insights and Jinfonet (JReport) report among the narrowest use. In general, Magic Quadrant vendors support broader use than "other" vendors. Figure 18. Percentage of Customers Using Magic Quadrant Vendors for a Range of BI Activities Figure 19. Percentage of Customers Using Other Vendors for a Range of BI Activities
Customer Experience and Upgrade Difficulty Figures 20 and 21 show a strong relationship for both Magic Quadrant and "other" vendors between product integration ratings, product quality scores and upgrade/migration difficulty, a key measure in customer experience and BI platform ownership cost over time. Vendor products in the bottom left quadrant have below average scores for both integration and product quality, and above average migration difficulty scores. Figure 20. Magic Quadrant Vendors Integration Score Versus Product Quality Versus Upgrade Difficulty Score
Note: The orange color shape indicates above average upgrade/migration difficulty; the blue color shape indicates below average upgrade/migration difficulty. BI = business intelligence; MQ = Magic Quadrant n = 1,551 (see Figures 1 and 2) Figure 21. Other Vendors Upgrade Difficulty Versus Integration Score Versus Product Quality Score Note: The orange color shape indicates above average upgrade/migration difficulty; the blue color shape indicates below average upgrade/migration difficulty. n = 1,551 (see Figures 1 and 2)
Figures 22 and 23 show that strong product support and product quality contribute to easier upgrades. Only two of 46 products earned below average support scores and above average product quality and migration difficulty. Only three earned above average support and product quality, yet were rated above average for upgrade/migration difficulties. It is clear that most vendors with below average upgrade difficulty also have above average product support and product quality across the Magic Quadrant and "other" sets; the inverse is also true. Figure 22. Magic Quadrant Vendors Upgrade Difficulty Versus Support Score Versus Product Quality Score Note: The orange color shape indicates above average product quality; the blue color shape indicates below average product quality. BI = business intelligence; MQ = Magic Quadrant n = 1,551 (see Figures 1 and 2) Figure 23. Other Vendors Upgrade Difficulty Versus Support Score Versus Product Quality Score
Note: The orange color shape indicates above average product quality; the blue color shape indicates below average product quality. n = 1,551 (see Figures 1 and 2) User enablement is an important element of deployment success. Figures 24 and 25 show the Magic Quadrant and "other" vendor scores on a range of vendor activities that support users. BI leaders should consider user enablement in vendor selection decisions. Figure 24. User Enablement by Magic Quadrant Vendor
n = 1,551 (see Figures 1 and 2) Figure 25. User Enablement by Other Vendor
n = 1,551 (see Figures 1 and 2) Customer Experience With BI Platform Integration Platform integration is key to easing upgrade difficulty and overall platform quality. Figures 26 to 32 highlight the primary dimensions of platform integration. Figure 26 shows ratings for both Magic Quadrant and "other" vendors on their ability to integrate with complementary BI capabilities (for example, parts of the stack such as data integration, search, content management, enterprise applications, collaboration, business activity monitoring [BAM] and business process management [BPM] see Note 5). Of the Magic Quadrant vendors, Birst, Prognoz, Panorama, Pentaho scored at the top, while Infor, Oracle, Pyramid and GoodData scored at the bottom. Of the "other" vendors, Lavastorm, Kofax (Altosoft), Dimensional Insight and Software AG (JackBe) ranked at the top, while Strategy Companion, Chartio, eq Technologic and Advizor scored at the bottom. Figure 26. Ability to Integrate with Complementary BI Capabilities
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Figure 27 shows integration scores for the ability to embed and customize BI platform components. Magic Quadrant vendor customers rated Panorama, Birst, Prognoz, Tableau and Logi Analytics at the top, and Infor, Oracle, IBM and Targit at the bottom. Among "other" vendors, Software AG (JackBe), Kofax (Altosoft), Jinfonet (JReport) and Dundas scored at the top, while Chartio, Advizor, Strategy Companion and SiSense ranked at the bottom. Figure 27. Integration Score: Ability to Embed and Customize BI Platform Components in an Application or Portal
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Data discovery capabilities are proliferating among enterprises and more business users are creating their own business user data mashup models, either for personal/departmental use or as a rapid prototype for IT to turn into systems of record content. As a result, the ability for a platform to promote such usergenerated models to sanctioned content becomes critical. Figure 28 shows how customers rate their Magic Quadrant and "other" vendors on this integration measure. Of the Magic Quadrant vendors, Panorama, Bitam, Birst and Tableau are ranked at the top, while Oracle, arcplan, SAP, Infor and IBM are at the bottom. Lavastorm, Kofax (Altosoft) and Dimensional Insight scored at the top of "other" vendors, while SpagoBI, Strategy Companion and Jinfonet (JReport) ranked at the bottom. Figure 28. Integration Score: Ability to Promote Business User Generated Data Mashups to the System of Record
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Platforms built from the ground up tend to be the most integrated across platform components. Figure 29 highlights that, of the Magic Quadrant vendors, Birst, Panorama, Logi Analytics, Tableau and MicroStrategy scored the highest, while Infor, Oracle, Actuate and SAP ranked at the bottom. Of the "other" vendors, Dimensional Insight, Phocas and Lavastorm rated highly for integration across platform components, while SpagoBI, Strategy Companion and Advizor ranked at the bottom. Figure 29. Integration Score: Across BI Platform Components
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Figure 30 shows customer ratings for Magic Quadrant and "other" vendors' common security model and administration application capabilities that span across platform components. This integration attribute is a driver of administration costs a key component of BI platform ownership costs. Of Magic Quadrant vendors, Birst, Logi Analytics, Panorama, Bitam and Prognoz ranked the highest, while Infor, GoodData, Alteryx, Pentaho and Actuate were rated at the bottom. Of the "other" vendors, Dimensional Insight, Dundas and Adaptive Insights ranked at the top, while SiSense, SpagoBI and Strategy Companion were rated at the bottom. Figure 30. Integration Score: A Common Security Model and Administration Application Components Across the Platform
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. A common semantic layer across BI platform components is a key ingredient both to enable a single source of the facts, and for reuse and governance. Figure 31 shows that Birst, Panorama, Bitam and MicroStrategy were rated the highest of the Magic Quadrant vendors, while Infor, SAS, Actuate and arcplan ranked at the bottom. Of the "other" vendors, Dimensional Insight, Lavastorm and Kofax (Altosoft) were rated at the top, while SpagoBI, Jinfonet (JReport) and Strategy Companion ranked at the bottom. Figure 31. Integration Score: A Common Semantic Layer Across Components
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Making it easy for users to learn how to use the BI platform is key to adoption and the achievement of business benefits. An integrated and common set of front end tools is an important basic element to help users learn and get benefits from a BI platform. Having to learn multiple tools is a significant barrier to user enablement and success. Figure 32 shows that, of the Magic Quadrant vendors, Panorama, Birst, Logi Analytics, Tableau and Bitam ranked the highest, while SAP, Actuate, Infor, Pentaho and Oracle were rated at the bottom. Of the "other" vendors, Phocas, Dimensional Insight and Lavastorm scored highest, while SpagoBI, eq Technologic and Jinfonet (JReport) were rated at the bottom. Figure 32. Integration Score: Integrated and Common Front End Tools
n = 1,551 (see Figures 1 and 2) See Note 5 for an overview of integration categories. Methodology The online survey was developed and hosted by Gartner to support the BI platform Magic Quadrant analysis. More than 3,763 unique companies were invited to participate. Vendor provided references (direct customers and OEMs), participants in Gartner's BI summit conferences globally and respondents from last year's survey also provided data. To ensure the integrity of the survey data, each survey response was verified by company respondent email. For survey responses from nonidentified email accounts such as Gmail or Yahoo, the respondent was contacted to provide a company email address, role and other contact information, all of which were further vetted and ultimately included (this amounted to fewer than five responses). Only completed surveys were included in the results. 2014 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. or its affiliates. This publication may not be reproduced or distributed in any form without Gartner s prior written permission. If you are authorized to access this publication, your use of it is subject to the Usage Guidelines for Gartner Services posted on gartner.com. The information contained in this publication has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information and shall have no liability for errors, omissions or inadequacies in such information. This publication consists of the opinions of Gartner s research organization and should not be construed as statements of fact. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see Guiding Principles on Independence and Objectivity. About Gartner Careers Newsroom Policies Site Index IT Glossary Contact Gartner