Self-Service BI Platformsv1.0

Zero to Insight, ASAP

Table of Contents

  1. Summary
  2. Market framework
  3. Maturity of Categories
  4. Considerations for Choosing and Implementing Self-Service BI
  5. Key Players
  6. Near-term Outlook
  7. Key Takeaways
  8. About Andrew Brust


Self-Service Business Intelligence (SSBI), while somewhat nebulous in its definition, is a clear product category that over the past few years has gained significant market share and mindshare. This is especially true with organizations looking to liberate their data from IT-managed silos, leverage their power users to do more with the data already in their systems, and allow for additional data sources to be brought in and blended in for analysis. For many organizations, the result has been to give frontline analysts, as well as executives, beautiful, modern ways of interacting with and visualizing data. SSBI also facilitates deriving insights from that data and, finally, making these capabilities accessible to all levels of the organization, technical and non-technical, both in-cloud and on-premises.

In this report, we explore SSBI products and technologies, outline the key differentiating factors between the major offerings, review major incumbent vendors, and identify the most promising contenders. We also describe how organizations can take advantage of the SSBI paradigm to satisfy their users and the challenges they face.

Key findings:

  • Self-service BI is a rapidly evolving sector with new capabilities added at a quick pace. Numerous BI vendors have made significant innovation investment around data access, blending and mashup, visualizations, data “storytelling,” AI-generated insights, and embedded analytics.
  • Most vendors offer a variety of capabilities with various levels of success at integration. We found that the major areas on offer include (1) modern data visualizations; (2) a variety of connectors to disparate on-premises and cloud data sources; (3) data blending, mashup and visual extract, transform and load (ETL) aimed at non-technical users; (4) embedding, programmability, and data science platform integration aimed at developers; (5) natural language/conversational search of the data; (6) AI-generated insights and visualizations; (7) mobile capabilities for iOS and Android; and (8) on premises, public, private, and hybrid cloud deployments.
  • There is no canonical, universally agreed upon definition of self-service BI. Although, to most people, it is the combination of ease-of-use, prolific data connectivity and blending, user-friendly data visualizations, and reduced need for IT involvement. Vendors attempt to define SSBI according to their own strengths, which can lead to confusion in the market, as organizations grapple with both the technology and the cultural/process changes required to implement it successfully.
  • Self-service BI puts the business user at its core and tailors its products and marketing pitch to reducing, or even eliminating, traditional IT from the equation.
  • There is a clear set of incumbents in the marketplace, with Tableau historically having the most mindshare; and other BI players, such as Microsoft, Qlik, and TIBCO having modernized their offerings in order to compete.
  • The new formidable set of contenders include ThoughtSpot, Zoomdata, Looker, and SiSense. They differentiate themselves in key areas of UX, conversational search, and AI-generated insights that both pose threats to the established vendors and may influence their future offerings.
  • Vendors are evolving their products at a fast pace, to close any functionality gaps and position themselves as holistic platforms with as many capabilities as possible. Vendors can no longer compete as simply data visualization or ETL platforms. As such, they are all slowly converging to a similar slate of functionality over the long term.

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