Magic Quadrant for Business Intelligence and Analytics Platforms

Gartner (


04 February 2016 | ID:G00275847

Analyst(s): Josh Parenteau, Rita L. Sallam, Cindi Howson, Joao Tapadinhas, Kurt Schlegel, Thomas W. Oestreich


The BI and analytics platform market’s multiyear shift from IT-led enterprise reporting to business-led self-service analytics has passed the tipping point. Most new buying is of modern, business-user-centric platforms forcing a new market perspective, significantly reordering the vendor landscape.

Strategic Planning Assumptions

By 2018, most business users and analysts in organizations will have access to self- service tools to prepare data for analysis as part of the shift to deploying modern BI platforms.
By 2018, most stand-alone self-service data preparation offerings will either have expanded into end-to-end analytical platforms or been integrated as features of existing analytics platforms.
By 2018, smart, governed, Hadoop-based, search-based and visual-based data discovery will converge in a single form of next-generation data discovery that will include self- service data preparation and natural-language generation.

Market Definition/Description

This document was revised on 8 February 2016. The document you are viewing is the corrected version. For more information, see the Corrections ( page on
During the past several years, the balance of power for business intelligence (BI) and analytics platform buying decisions has gradually shifted from IT to the business as the
long-standing BI requirement for centrally provisioned, highly governed and scalable
system-of-record reporting has been counterbalanced by the need for analytical agility and business user autonomy (see “How to Modernize Your Business Intelligence and Analytics Platform for Agility, Without Chaos” ). The evolution and sophistication of the self-service data preparation and data discovery capabilities available in the market has shifted the focus of buyers in the BI and analytics platform market — toward easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis.
This significant shift has accelerated dramatically in recent years, and has finally reached a tipping point that requires a new perspective on the BI and analytics Magic Quadrant and the underlying BI platform definition — to better align with the rapidly evolving buyer
and seller dynamics in this complex market. This Magic Quadrant focuses on products that meet the criteria of a modern BI and analytics platform (see “Technology Insight for
Modern Business Intelligence and Analytics Platforms” ), which are driving the vast majority of net new purchases in the market today. Products that do not meet the modern criteria required for inclusion in the Magic Quadrant evaluation (because of the upfront requirements for IT to predefine data models, or because they are enterprise-reporting centric) will be covered in our new Market Guide for enterprise reporting-based platforms.
This change in the focus of the BI and analytics Magic Quadrant should not be interpreted by organizations as a recommendation to immediately replace all existing reporting-based system-of-record BI technology with a modern platform featured in the current Magic Quadrant. In many organizations, the existing enterprise reporting systems are integral to day-to-day business processes, and these processes would be exposed to unnecessary risk if disrupted by an attempt to re-create them in a modern platform. However, the problem that most organizations have encountered with lackluster BI adoption relative to the level of investment during the past 20 years stems from the fact that virtually all BI- related work in that time frame has, until recently, been treated as system of record from inception to development to delivery. This traditional approach to BI addresses Mode 1 of the bimodal delivery model, because it supports stability and accuracy, but does not address the need for speed and agility enabled through exploration and rapid prototyping that is essential to Mode 2 (see “How to Achieve Enterprise Agility With a Bimodal Capability” ).
The shift in the BI and analytics market and the corresponding opportunity that it has created for new and innovative approaches to BI has drawn considerable attention from a diverse range of vendors. The list spans from large technology players — both those new to the space as well as longtime players trying to reinvent themselves to regain relevance
— to startups backed by enormous amounts of venture capital from private equity firms. A crowded market with many new entrants, rapid evolution and constant innovation creates a difficult environment for vendors to differentiate their offerings from the competition.
However, these market conditions also create an opportunity for buyers to be at the leading edge of new technology innovations in BI and analytics and to invest in new products that are better suited for Mode 2 of a bimodal delivery model than their predecessors.
Gartner’s position is that organizations should initiate new BI and analytics projects using a modern platform that supports a Mode 2 delivery model, in order to take advantage of market innovation and to foster collaboration between IT and the business through an
agile and iterative approach to solution development. The vendors featured in this year’s Magic Quadrant (and those highlighted in the Appendix) present modern approaches to promoting production-ready content from Mode 2 to Mode 1, offering far greater agility than traditional top-down, IT-led initiatives — and resulting in governed analytic content that is more widely adopted by business users that are active participants in the
development process. As the ability to promote user-generated content to enterprise-ready governed content improves, so it is likely that, over time, many organizations will
eventually reduce the size of their enterprise system-of-record reporting platforms in favor of those that offer greater agility and deeper analytical insight.

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