Gartner’s 2017 BI and Data Analytics Magic Quadrant Shows Microsoft Leading
Power BI is enjoying a tremendous momentum and unprecedented popularity. Just within this month, your humble correspondent has been teaching Power BI four times in a row. It looks like industry observers are taking notice of this momentum. As Kamal Hathi (General Manager, Microsoft BI) announced, the newly released Garner Magic Quadrant for Business Intelligence and Data Analytics gave Microsoft a very high score. The image below shows the Microsoft’s lift between last year and this year in the Gartner magic quadrant.
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I’m not surprised about the Qlik drop given they sold out the company. What’s still surprising to me is that Gartner ranked Tableau and Microsoft almost the same on the ability to execute. Although the report is not out yet, judging by the stub, Gartner used the same 14 criteria as last year, but added one more which is unknown at this point (probably real-time where Microsoft can score very high as well). Here are my comments on where Microsoft stands on these 14 criteria. You might also find my two-part blog about Tableau vs. Microsoft useful if you are tasked to compare vendors.
| Capability | Teo’s Rank for MS BI | Comments | |
| Infrastructure | BI Platform Administration Capabilities that enable scaling the platform, optimizing performance and ensuring high availability and disaster recovery | High | On premises or cloud, I think the MS BI Platform is second to none |
| 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 | High | Power BI supports both pure cloud and hybrid architectures | |
| Security and User Administration Capabilities that enable platform security, administering users, and auditing platform access and utilization | Medium | More work is required to support external users in Power BI, Power BI Embedded, and SSRS | |
| Data Source Connectivity Capabilities that allow users to connect to the structured and unstructured data contained within various types of storage platforms, both on-premises and in the cloud. | High | As of this time, Power BI supports close to 70 connectors to let you connect to cloud and on-premises data sources. No scripting required. | |
| Data Management | Governance and Metadata Management Tools for enabling users to share the same systems-of-record 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 report layout objects, parameters and so on. Administrators should have the ability to promote a business-user-defined data model to a system-of-record metadata object. | Medium | Power BI has done a good job to provide auditing and admin oversight but more work is required for proactive monitoring and improving its data governance capabilities |
| Self-Contained Extraction, Transformation and Loading (ETL) and Data Storage Platform capabilities for accessing, integrating, transforming and loading data into a self-contained storage layer, with the ability to index data and manage data loads and refresh scheduling. | Medium | SSIS is the most popular on-premises ETL tool. More work is required to bring similar capabilities in the cloud (I think Azure Data Factory is a step backwards) | |
| Self-Service Data Preparation The 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 | High | Power BI Desktop and Excel has a fantastic query editor (originated from Power Query) that scores big with business users. Tableau doesn’t have such native capabilities. Power BI and Excel have best of class self-modeling capabilities (much better than Tableau). Azure Query Catalog can be used for dataset autodiscovery. | |
| Analysis and Content Creation | Embedded Advanced Analytics Enables users to easily access advanced analytics capabilities that are self-contained within the platform itself or available through the import and integration of externally developed models. | High | Not sure what is meant here by “advanced analytics capabilities”. Power BI supports integration with R, Azure Machine Learning, clustering, forecasting, binning, but I might be missing something. |
| Analytic Dashboards The ability to create highly interactive dashboards and content, with visual exploration and embedded advanced and geospatial analytics, to be consumed by others | High | “Highly interactive dashboards and content” is what Power BI is all about. | |
| Interactive Visual 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, to include 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 | High | According to Gartner’s definition, Power BI should score high but more work is required on the visualization side of things, such as ability to drill through a chart point as we can do in SSRS. | |
| 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, location awareness and natural-language query | High | Native apps for iOS, Android and Windows to surface both Power BI and SSRS reports. | |
| Sharing of Findings | Embedding Analytic Content 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. | High | An Azure cloud service, Power BI Embedded allows you to do this with an appealing cost-effective licensing model. |
| Publishing Analytic Content Capabilities that allow users to publish, deploy and operationalize analytic content through various output types and distribution methods, with support for content search, storytelling, scheduling and alerts. | Medium | Power BI supports subscriptions and data alerts but we can do better, such as to allow an admin to subscribe other users. “Storytelling” can mean different things but I thought the integration with Narrative Science can fall into this category. | |
| Collaboration and Social BI Enables users to share and discuss information, analysis, analytic content and decisions via discussion threads, chat and annotations | High | Power BI supports this with workspaces and Office 365 unified groups. |
Of course, there are many competing definitions of what constitutes a BI and Analytics platform. Again, it looks to me that Gartner has predominantly focused on the self-service BI aspect of it (even there Microsoft should have scored higher) and ignored the SQL Server BI features and all the cloud BI-related products (Azure SQL Database, SQL Data Warehouse, Azure ML, Query Catalog, HDInsight, StreamInsight). If we take them in consideration, where will that dot be?
