Gartner2019魔力象限-BI平台-行业趋势及排行原文版.pdf

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Licensed for Distribution Magic Quadrant for Analytics and Business Intelligence Platforms Published 11 February 2019 - ID G00354763 - 109 min read By Analysts Cindi Howson, James Richardson, Rita Sallam, Austin Kronz Modern analytics and BI platforms are now mainstream purchases for which key differentiators are augmented analytics and support for Mode 1 reporting in a single platform. This Magic Quadrant will help data and analytics leaders complement their existing solutions or move to an entirely new vendor. Strategic Planning Assumptions By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence, data science and machine learning platforms, and embedded analytics. By 2020, 50% of analytical queries either will be generated via search, natural language processing or voice, or will be automatically generated. By 2020, organizations that offer users access to a curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not. By 2020, the number of data and analytics experts in business units will grow at three times the rate of experts in IT departments, which will force companies to rethink their organizational models and skill sets. By 2021, natural language processing and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-ofﬁce workers. Market Deﬁnition/Description Modern analytics and business intelligence (BI) platforms are characterized by easy-to-use tools that support the full analytic workﬂow — from data preparation and ingestion to visual exploration and insight generation. They are most differentiated from traditional BI platforms by not requiring

signiﬁcant involvement from IT staff to predeﬁne data models or store data in traditional data warehouses (see “Technology Insight for Modern Analytics and Business Intelligence Platforms”). The emphasis is on self-service and agility. Most modern analytics and BI platforms also have their own self-contained in-memory columnar engine to ensure fast performance and support rapid prototyping, but many can optionally use existing modeled data sources. The growing use of data lakes and logical data warehouses dovetails with the capabilities of modern analytics and BI platforms that can ingest data from these less-modeled data sources (see “The Practical Logical Data Warehouse: A Strategic Plan for a Modern Data Management Solution for Analytics”). The crowded analytics and BI market includes everything from long-standing and large technology players to startups backed by venture capital and smaller, privately funded software vendors. Vendors of traditional BI platforms have evolved their capabilities to include modern, visual-based data discovery that also includes governance, and more recently, augmented analytics. Newer vendors continue to evolve the capabilities that once focused primarily on agility, by extending them to enable greater governance and scalability, as well as publishing and sharing. The holy grail is for customers to have both Mode 1 and Mode 2 capabilities (see Note 1) in a single, seamless platform that draws on existing assets but also has emerging best-of-breed capabilities. As disruptive as visual-based data discovery has been to traditional BI, a third wave of disruption has emerged in the form of augmented analytics, with machine learning (ML) generating insights on increasingly vast amounts of data. Augmented analytics also includes natural language processing (NLP) as a way of querying data and of generating narratives to explain drivers and graphics. Vendors that have augmented analytics as a differentiator are better able to command premium prices for their products (see “Augmented Analytics Is the Future of Data and Analytics”). This Magic Quadrant focuses on products that meet Gartner’s criteria for a modern analytics and BI platform (see “Technology Insight for Modern Analytics and Business Intelligence Platforms”). It is this type of platform that accounts for the majority of net new mainstream purchases in the analytics and BI platform market. Products that do not meet our criteria for a modern platform — either because of the upfront requirements for IT to predeﬁne data models, or because they are reporting-centric — are covered in our “Market Guide for Traditional Enterprise Reporting Platforms.” This Magic Quadrant refers throughout to composite measures of success based on a survey of vendors’ reference customers. Reference customers scored vendors on each of the metrics deﬁned in Note 2 (see also the Evidence section). Opinions from Gartner Peer Insights contributors are also factored into our assessments; these contributors are referred to as Gartner Peer Insights reviewers. The Five Use Cases and 15 Critical Capabilities of an Analytics and BI Platform

We deﬁne and assess product capabilities across the following ﬁve use cases: ■ ■ ■ ■ ■ Agile, centralized BI provisioning: Supports an agile IT-enabled workﬂow, from data to centrally delivered and managed analytic content, using the platform’s self-contained data management capabilities. Decentralized analytics: Supports a workﬂow from data to self-service analytics, and includes analytics for individual business units and users. Governed data discovery: Supports a workﬂow from data to self-service analytics to system of record (SOR), IT-managed content with governance, reusability and promotability of user- generated content to certiﬁed data and analytics content. OEM or embedded analytics: Supports a workﬂow from data to embedded BI content in a process or application. Extranet deployment: Supports a workﬂow similar to agile, centralized BI provisioning for the external customer or, in the public sector, citizen access to analytic content. We assess vendors on the 15 critical capabilities listed below. (Any changes to the previous year’s critical capabilities are listed in Note 3. Subcriteria for each capability are published in “Toolkit: Analytics and BI Platform RFP.” How well vendors’ platforms support these critical capabilities is explored in greater detail in “Critical Capabilities for Analytics and Business Intelligence Platforms.”) Infrastructure 1. BI platform administration, security and architecture: Capabilities that enable platform security, administering of users, auditing of platform access and utilization, and high availability and disaster recovery. 2. Cloud BI: 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. 3. Data source connectivity and ingestion: Capabilities that enable users to connect to structured and unstructured data contained within various types of storage platform (relational and nonrelational), both on-premises and in the cloud. Data Management 4. Metadata management: Tools enabling users to leverage a common semantic model and metadata. These 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 to report layout objects, parameters and so on. Administrators should have the ability to promote a business-user- deﬁned data mashup and metadata to the SOR metadata. 5. Data storage and loading options: Platform capabilities for accessing, integrating, transforming and loading data into a self-contained performance engine, with the ability to index data, manage data loads and refresh scheduling. 6. Data preparation: “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 machine learning-enabled semantic autodiscovery, intelligent joins, intelligent proﬁling, hierarchy generation, data lineage and data blending on varied data sources, including multistructured data. 7. Scalability and data model complexity: The degree to which the in-memory engine or in- database architecture handles high volumes of data, complex data models, performance optimization and large user deployments. Analysis and Content Creation 8. Advanced analytics for citizen data scientists: Enables users to easily access advanced analytics capabilities that are self-contained within the platform, through menu-driven options or through the import and integration of externally developed models. 9. Analytic dashboards: The ability to create highly interactive dashboards and content with visual exploration and embedded advanced and geospatial analytics for consumption by others. 10. Interactive visual exploration: Enables the exploration of data via an array of visualization options that go beyond those of basic pie, bar and line charts to include heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These features enable users to analyze and manipulate data by interacting directly with a visual representation of it, to display it as percentages, bins and groups. 11. Augmented data discovery: Automatically ﬁnds, visualizes and narrates important ﬁndings such as correlations, exceptions, clusters, links and predictions in data that are relevant to users without requiring them to build models or write algorithms. Users explore data via visualizations, natural-language-generated narration, search and natural language query (NLQ) technologies. 12. Mobile exploration and authoring: 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 and location awareness. Sharing of Findings 13. Embedding of analytic content: Capabilities including a software developer kit with APIs and support for open standards for creating and modifying analytic content, visualizations and applications, embedding them in 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 analytics and BI with the application architecture will enable users to choose where in the business process the analytics should be embedded. 14. Publish, share and collaborate on analytic content: Capabilities that enable users to publish, deploy and operationalize analytic content through various output types and distribution methods, with support for content search, scheduling and alerts. These capabilities enable users to share, discuss and track information, analysis, analytic content and decisions via discussion threads, chat and annotations. Overall Platform 15. Ease of use, visual appeal and workflow integration: The ease of administering and deploying the platform, creating content, and consuming and interacting with content, as well as the degree to which the product is visually appealing. This capability also considers the degree to which capabilities are offered in a single, seamless product and workﬂow, or across multiple products with little integration. Magic Quadrant

Figure 1. Magic Quadrant for Analytics and Business Intelligence Platforms Source: Gartner (February 2019) Vendor Strengths and Cautions Birst Birst provides an end-to-end data warehouse, reporting and visualization platform built for the cloud. Birst was an early cloud-native vendor, but also offers its product as an on-premises appliance running on commodity hardware. In 2017, Birst was acquired by Infor. It now operates as a stand-alone subdivision within Infor. Infor has been gradually replacing its packaged analytic applications for Infor’s business

application solutions (previously built with IBM Cognos) with Birst, dubbing the new applications Birst for CloudSuite. The Birst analytics and BI team, meanwhile, has gained a global footprint, additional sales representatives and data science talent. Birst’s new Smart Analytics capabilities, powered by Coleman AI, were on limited release in 4Q18. Birst is a Niche Player in this Magic Quadrant. Birst’s reference customers primarily use its platform for agile, centralized BI provisioning (58%). The next most common use is for decentralized analytics (45%). Strengths ■ Cloud-native support for Mode 1 and Mode 2: Birst provides data preparation, dashboards, visual exploration and formatted, scheduled reports on a single platform. Few other vendors support both usage styles in this way. The networked semantic layer enables business units to create data models that can then be promoted to the enterprise. A new orchestration capability enables this content to be replicated to multiple instances. Birst supports live connectivity to on-premises data sources. However, its rapid creation of a data model and all-in-one data warehouse that can draw on a range of storage options (Microsoft SQL Server Analysis Services, SAP HANA, Amazon Redshift) is a unique selling point. Sixty-one percent of Birst’s surveyed reference customers use Birst as their only analytics and BI standard. ■ ■ Vertical applications: Birst for CloudSuite now gives Infor ERP customers prebuilt extraction, transformation and loading (ETL), data models, and dashboards that are fully integrated into Infor business applications. For non-Infor data sources, Birst provides solution accelerators for speciﬁc domains, such as wealth management, insurance, sales and marketing. Global presence: With the acquisition by Infor, Birst has gained access to a global salesforce and additional R&D talent, particularly in the ﬁeld of data science. It now also has a physical presence in 44 countries, an increase from three in 2016 when Birst was a stand-alone vendor. Cautions ■ Workflow and performance: Although Birst offers a single platform, workﬂow across the authoring interfaces is not seamless. A large percentage of Birst’s reference customers (42%) highlighted poor performance (the highest ﬁgure for any vendor in this regard) and 26% complained about absent or weak functionality. Although Coleman AI marks some innovation from Birst, initial tests of the limited release software show a cumbersome workﬂow, and it rarely ﬁnds drivers for key metrics. ■ Poor operations: Quality and timeliness of support have been perennial problems for Birst, and this has not improved in the past year or under Infor’s ownership. Birst’s reference customers’ scores for support and product quality place it bottom, with 10% considering its operations poor or limited. Gartner Peer Insights reviewers were less negative, however, with Birst being only slightly below average for service and support.

■ Reduced relevance in analytics and BI market: There has been a decline in Gartner searches and inquiries related to Birst, in comparison with 2017. Reference customers’ responses indicate a decline in the product’s viability in this market (Birst was the only vendor for which there was negative sentiment in this regard, which suggests that customers do not see Infor’s ownership as a net positive). Birst points to double-digit growth in revenue, but it is unclear how much of this growth derives from only the Infor installed base. Birst also had the highest percentage of reference customers considering discontinuing use of its product. BOARD International BOARD International delivers a single, integrated system that provides BI, analytics and ﬁnancial planning capabilities. The company’s stated aim is to provide an “end-to-end decision-making platform.” BOARD continues to report strong revenue growth in excess of 50% year over year, with almost 20% of its revenue now coming from outside Europe, where the company originated. In January 2019, Board announced that private equity ﬁrm Nordic Capital will become a majority shareholder. The deal is expected to close in 1Q19. BOARD is a Niche Player in this Magic Quadrant. It successfully serves a submarket for centralized, single-instance analytics, BI and ﬁnancial planning and analysis (FP&A) solutions. BOARD has a narrow focus and the market’s awareness of this vendor is limited, although its regional adoption is growing. BOARD has successfully transitioned to a cloud/subscription-based model. Its reference customers reported that the most common use cases for BOARD’s system are agile, centralized BI provisioning (61%) and decentralized analytics (57%). Strengths ■ Uniﬁed analytics, BI and FP&A: BOARD is one of only two vendors offering a modern analytics and BI platform with integrated ﬁnancial planning and reporting functionality (the other being SAP, with SAP Analytics Cloud). As such, it is highly differentiated for the relatively small number of buyers looking to close the gap between BI and ﬁnancial processes. BOARD is also used for read-only analytics and BI deployments, although, in Gartner’s view, this is less commonly the case. BOARD’s new self-service reporting capabilities strengthen its Mode 1 support. ■ ■ Breadth of usage: BOARD ranks in the top third of vendors in this Magic Quadrant for complexity of analysis. Reference customers use BOARD for a wide range of BI tasks — from viewing reports, creating personalized dashboards and doing simple ad hoc analysis, to performing complex queries, undertaking data preparation and using predictive models. System integrator ecosystem: For a relatively small vendor, BOARD has a well-established and growing network of system integrator (SI) partners, which are helping to drive its growth, whether as inﬂuencers or resellers. BOARD offers them a reliable solution for the niche it operates in. Reference customers identify the main business beneﬁt of using BOARD’s platform

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