Gartner Splits the 2014 Business Intelligence Magic Quadrant in Two
How the BI market split foreshadowed the modern analytics and AI platform landscape.
title: "Gartner Splits the 2014 Business Intelligence Magic Quadrant in Two" slug: "gartner-splits-2014-business-intelligence-magic-quadrant-two" description: "How the BI market split foreshadowed the modern analytics and AI platform landscape." datePublished: "2014-03-28" dateModified: "2026-03-15" category: "Business Intelligence" tags: ["Gartner", "business intelligence", "magic quadrant", "analytics"] tier: 3 originalUrl: "http://www.applieddatalabs.com/content/gartner-splits-2014-business-intelligence-magic-quadrant-two" waybackUrl: "https://web.archive.org/web/20140328195127/http://www.applieddatalabs.com:80/content/gartner-splits-2014-business-intelligence-magic-quadrant-two"
Gartner Splits the 2014 Business Intelligence Magic Quadrant in Two
We called it. In 2014, Gartner split their annual Magic Quadrant for Business Intelligence and Analytics Platforms into two separate reports, adding a new Magic Quadrant for Advanced Analytics Platforms. We had been arguing for over a year that the traditional BI vendors and the data discovery upstarts were playing fundamentally different games, and Gartner's decision validated that argument. What I didn't expect was that the market would keep splitting -- and that by 2026, the number of Gartner MQs covering analytics and AI would have multiplied into a whole ecosystem of reports.
What Happened in 2014
The split was dramatic. By pulling advanced analytics out of the BI Magic Quadrant, Gartner's main report became much more focused on ease of use -- what we'd been calling the shift from IT-driven BI to business-user-driven products. The results shuffled immediately.
Tableau was the clear winner. From the start, they'd been focused on ease of use for business intelligence, and the restructured MQ let that strength shine without being penalized for lacking the deep statistical capabilities of products like SAS. Panorama Software, Alteryx, and Birst also moved closer to the Leaders quadrant.
Meanwhile, the new Advanced Analytics MQ gave traditional stats-heavy platforms like SAS, IBM SPSS, and the R ecosystem their own competitive arena. The subtext was that these were really two different markets serving two different buyers -- business users who wanted to explore data versus data scientists who wanted to build models.
We also reminded readers of what we'd written previously about the hidden side of Gartner: that paying clients tend to get more analyst time and often more favorable assessments. That dynamic didn't change with the split. It just doubled the number of reports where it applied.
When Gartner split the BI Magic Quadrant in 2014, it confirmed what we'd been arguing: traditional BI and modern analytics were different markets. The split happened again with AI. The same pattern keeps repeating -- technology categories grow until they have to be broken apart.
The Splitting Kept Going
What I find most interesting about the 2014 split, looking back, is that it was just the beginning of a fracturing that hasn't stopped.
By 2020, Gartner had renamed the BI MQ to "Analytics and Business Intelligence Platforms" and the competitive picture had been completely redrawn. Tableau and Microsoft (Power BI) dominated the Leaders quadrant. The traditional vendors we'd tracked -- SAP BusinessObjects, IBM Cognos, Oracle BI, MicroStrategy -- had all drifted to the Challengers or Niche Players positions. The market had voted, and ease of use won decisively.
The Advanced Analytics MQ evolved into the "Data Science and Machine Learning Platforms" MQ, reflecting the shift from statistical analysis to machine learning. By 2024, Gartner was publishing separate analyses for AI Developer Technologies, Conversational AI Platforms, Generative AI, Cloud AI Services, and more.
Each split follows the same pattern we saw in 2014. A technology category grows until the participants are too diverse to evaluate on a single set of criteria. Gartner creates a new report, which creates a new set of Leaders and Challengers, which drives a new wave of enterprise purchasing decisions. The analyst influence we wrote about in 2014 has multiplied alongside the number of reports.
Traditional BI's Long Decline
The traditional BI vendors that dominated the pre-split Magic Quadrant have had a rough decade. SAP's analytics story has been a confusing progression through BusinessObjects, Lumira, Analytics Cloud, and SAC. IBM sold off much of its analytics business. Oracle's analytics products exist but have lost mindshare. MicroStrategy pivoted to a Bitcoin holding company (yes, really -- they spent over $4 billion on Bitcoin starting in 2020, which was a more exciting story than their BI software).
The exception is Microsoft, which straddled both sides of the market. Power BI competed on the modern analytics side while Azure ML and the broader Microsoft AI stack competed on the advanced analytics and AI side. Their integrated approach -- plus aggressive pricing -- made them the dominant player across multiple Gartner categories.
Where the Market Stands in 2026
Today I'd describe three distinct tiers in what used to be the unified "BI" market:
The first tier is reporting and dashboards -- the traditional BI use case. This is largely commoditized. Power BI owns the mass market. Tableau owns the premium segment. Everyone else fights for niches.
The second tier is analytics platforms -- self-service exploration, augmented analytics, AI copilots. This is where the competition is hottest and where the most innovation is happening. Databricks, Snowflake, and the cloud data platforms are pulling analytics capability into their own offerings, which threatens the standalone tools.
The third tier is AI/ML platforms -- model building, training, deployment, and monitoring. This is where the real growth is, and it's a completely different buyer (data science teams, ML engineers) with completely different evaluation criteria. SageMaker, Vertex AI, Azure ML, and Databricks compete here.
The progression from one unified market in 2013 to three distinct tiers in 2026 was predictable in hindsight. We saw the first split coming in 2014. The question now is whether AI capabilities will collapse these tiers back together or push them further apart.