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Gartner’s 2020 Magic Quadrant for Data Science and Machine Learning Platforms named Alteryx, Dataiku, Databricks, MathWorks, SAS, and TIBCO as Leaders. The chart covered 16 vendors, with positions plotted as of November 2019; its report graphic is dated February 11, 2020. KDnuggets’ February 24, 2020 analysis also reported four new Leaders, two vendors moving from Leader to Visionary, and SAP’s removal from the chart. These are historical placements, not a current vendor shortlist.
Who was in each quadrant?
KDnuggets’ February 2020 summary lists 16 vendors across Gartner’s four quadrants:
| Quadrant | Vendors listed | Count |
|---|---|---|
| Leaders | Alteryx, Dataiku, Databricks, MathWorks, SAS, TIBCO | 6 |
| Challengers | IBM | 1 |
| Visionaries | DataRobot, Domino, Google, H2O.ai, KNIME, Microsoft, RapidMiner | 7 |
| Niche Players | Anaconda, Altair (identified in the KDnuggets article as former DataWatch/Angoss) | 2 |
The categories and movement descriptions here are those published in the chart and summarized by KDnuggets, not an independent assessment of vendor quality. The chart’s plotted positions are as of November 2019, rather than the date of the February 2020 report graphic. KDnuggets’ 2020 analysis and the reproduced Gartner chart provide those historical details.
What changed from the 2019 field?
KDnuggets reported that the chart returned to 16 vendors from 17 the year before, with no new entries and SAP dropped. It identified Alteryx, Dataiku, Databricks, and MathWorks as new Leaders, while SAS and TIBCO remained Leaders. Alteryx and Dataiku moved up from Challenger; KNIME and RapidMiner moved down from Leader to Visionary.
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Why the article says vendors moved
The explanations below are KDnuggets’ reading of Gartner’s vendor assessments. They are not independent product tests, and the full Gartner report is not available in the cited material.
- Alteryx: Its return to Leader from Challenger was linked to company and product vision, including process automation and “augmented DSML.” The article also mentioned its 2019 acquisitions of ClearStory Data and Feature Labs.
- Databricks: Its move to Leader was associated with execution, growth, its Apache Spark foundation, and its partner ecosystem.
- Dataiku: Its move from Challenger to Leader was associated with usability, vision, governance, and collaboration between technical and business roles.
- MathWorks: The product considered was MATLAB. The article highlighted adaptability, deep learning, reinforcement learning, and execution.
- KNIME: Its move from Leader to Visionary was attributed mainly to visibility and relative revenue growth.
- RapidMiner: Its move from Leader to Visionary was attributed mainly to slower relative growth.
How to read the axes—and what they do not tell you
The vertical axis is Ability to Execute; the horizontal axis is Completeness of Vision. Gartner describes Magic Quadrants as graphical positions of providers in a specific market using those two criteria. The publicly surfaced 2020 material does not provide a complete account of detailed weighting or individual scores, so quadrant placement should not be read as a numeric rating or a precise ordering of vendors. Gartner’s 2026 report abstract likewise describes the broad use of these dimensions, but does not establish later positions for the vendors on the 2020 chart.
Gartner’s notice, reproduced with the chart, says: “Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation.” Treat the chart as one input, not as a purchasing recommendation. A practical evaluation should also consider fit with your workflows, deployment needs, governance, collaboration, and execution requirements.
What the chart covered—and left out
KDnuggets described the 2020 field as commercial products and noted that open-source platforms such as Python and R were excluded, despite their widespread use by data scientists. The chart therefore represents selected vendors in Gartner’s defined market, not a complete inventory of the tools teams use for data science and machine learning.
The article names report-era offerings including SAS Visual Data Mining and Machine Learning, MATLAB, Data Science Studio from Dataiku, Watson Studio and related IBM offerings, Azure Machine Learning among Microsoft’s cloud components, Anaconda Enterprise, and Altair Knowledge Studio. These are historical names and descriptions from around 2020; product names, ownership, availability, and capabilities may have changed since then. Consult current first-party product information before making a present-day comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the 2020 placements are historical context
Gartner’s later research uses an evolving market frame. Its May 28, 2025 DSML report abstract describes platforms for building, customizing, and deploying AI models and highlights awareness of AI agents. The June 22, 2026 report abstract uses the title “AI Platforms for Data Science and Machine Learning” and describes end-to-end AI model and agent development and lifecycle management. Those later descriptions show a changed category framing; they do not say that any vendor from the 2020 chart retained a particular quadrant position.
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For a current buying decision, assess products against your organization’s needs and current product information. The 2020 chart can explain how Gartner and market commentators viewed the field at the time, but it cannot establish today’s relative product fit.
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