New Gartner Magic Quadrant for Advanced Analytics Platforms
How the advanced analytics market evolved from 2014 platforms to modern AI solutions.
title: "New Gartner Magic Quadrant for Advanced Analytics Platforms" slug: "new-gartner-magic-quadrant-advanced-analytics-platforms" description: "How the advanced analytics market evolved from 2014 platforms to modern AI solutions." datePublished: "2014-04-04" dateModified: "2026-03-15" category: "Business Intelligence" tags: ["Gartner", "analytics", "magic quadrant", "platforms"] tier: 3 originalUrl: "http://www.applieddatalabs.com/content/new-gartner-magic-quadrant-advanced-analytics-platforms" waybackUrl: "https://web.archive.org/web/20140404013124/http://www.applieddatalabs.com:80/content/new-gartner-magic-quadrant-advanced-analytics-platforms"
New Gartner Magic Quadrant for Advanced Analytics Platforms
In 2014, Gartner created a brand-new Magic Quadrant for Advanced Analytics Platforms, and I wrote that the real story wasn't the new report itself but what it signaled about where the industry was headed. Gartner defined advanced analytics as "the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to BI are unlikely to discover." I added that this IT-driven type of analytics was becoming more niche, and that the true innovators were the companies integrating advanced analytics into the background of their products. That turned out to be exactly right -- and the implications played out over the next decade in ways I should have bet money on.
The 2014 Prediction That Held Up
The most important thing I wrote in 2014 was this: "The next step for BI is going to be integration; people don't want more to do, they want better results from what they already do." I predicted that the future wasn't giving people separate advanced analytics tools but embedding intelligence into the applications they already used.
That prediction described exactly what happened. SAS, the dominant player in the 2014 Advanced Analytics MQ, sold software that required specialized training and dedicated analysts. It produced good results for the work you put in. But the market moved toward products that helped people get better results while doing less work -- AI features baked into CRMs, ERPs, marketing platforms, and BI tools.
The 2014 MQ featured SAS, IBM SPSS, KNIME, RapidMiner, and a collection of specialized statistical platforms. It was an enterprise software category dominated by vendors who'd been selling to data scientists and statisticians for decades. It also included emerging open-source options like R and early mentions of Python's data science ecosystem.
In 2014, I wrote that people don't want more tools, they want better results from what they already do. That was the trajectory for the entire analytics industry. SAS charged you $100K for a license and required a statistics degree. Python was free and eventually easier.
SAS's Decline and Open Source's Rise
The biggest story of the advanced analytics market between 2014 and 2026 is the decline of SAS Institute. For decades, SAS was the undisputed king of advanced analytics. Government agencies, pharmaceutical companies, banks, and insurers ran on SAS. It was expensive, it required specialized skills, and it worked.
Then Python and R happened. Not overnight -- both languages had been around for years -- but the ecosystem around them matured rapidly after 2014. Pandas, scikit-learn, TensorFlow, PyTorch, and the Jupyter notebook ecosystem gave data scientists free tools that could match or exceed SAS's capabilities. By 2020, surveys consistently showed Python as the most popular language for data science, and SAS's market share was shrinking in every segment except legacy government contracts.
SAS responded by investing in cloud offerings and AI capabilities, but the momentum had shifted. A generation of data scientists trained on Python simply never learned SAS. The talent pipeline dried up, and with it, the customer pipeline. IBM SPSS followed a similar trajectory, declining from a major player to an afterthought as Python ate the statistical computing market.
KNIME and RapidMiner -- the open-source and low-code analytics platforms from the 2014 MQ -- survived by occupying a middle ground between full code (Python) and full proprietary (SAS). They have loyal user bases but never broke into the mainstream the way Python did.
Cloud ML Platforms Took Over
The real successor to the "Advanced Analytics Platforms" category isn't another Magic Quadrant. It's the cloud ML platform wars.
Amazon SageMaker (launched 2017) made it possible to build, train, and deploy ML models entirely within AWS. For organizations already on AWS, it became the default choice. Google Vertex AI (launched 2021, succeeding AI Platform) did the same for Google Cloud, with strong integration with TensorFlow and later Gemini. Azure Machine Learning covered the Microsoft ecosystem and benefited from the same bundling strategy that made Power BI dominant.
Databricks is the most interesting player because it came at the problem from the data engineering side rather than the model building side. Their Lakehouse platform combines data storage, processing, and ML training in a single environment. By 2024, Databricks was valued at $43 billion and had become the de facto platform for organizations that wanted to do both data engineering and ML without stitching together separate tools.
The common thread is integration. The standalone "advanced analytics platform" that you installed on premises, connected to your data, and used to build models -- the thing the 2014 MQ evaluated -- has been absorbed into cloud platforms that handle everything from data storage to model serving. Exactly what I predicted in 2014 when I said integration was the next step.
What This Means for Enterprise AI
If you're building AI capabilities today, the choice of platform matters less than it did in 2014. The cloud ML platforms have converged on similar feature sets: managed notebooks, automated ML, model registries, deployment pipelines, monitoring. The differentiators are ecosystem fit (which cloud are you on?), team skills (does your team know PyTorch or TensorFlow?), and operational maturity (can you actually deploy and monitor models in production?).
The last point is where I see most organizations struggle. Building a model in a notebook is the easy part. Getting it into production, keeping it accurate over time, monitoring for drift and bias, and maintaining governance -- that's the operational infrastructure that separates experiments from business value. The 2014 MQ didn't evaluate any of that. Neither do most of today's analyst reports.