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Gold BlogLeaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms


The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.



Gartner has released last week its highly-anticipated report and magic quadrant (MQ) for Data Science and Machine Learning Platforms (DSML) and you can get copies from several vendors - see a list at the bottom of this blog. In previous years, the MQ name kept changing but the 4 leaders remained the same. Now the name has remained the same as in 2019 MQ and 2018 MQ reports, reflecting a more mature understanding of the DSML field, but the contents, especially the leader quadrant, have changed dramatically, reflecting accelerating progress and competition in the field.

The 2020 MQ report went back to evaluating 16 vendors (down from 17 last year), placed as usual in 4 quadrants, based on completeness of vision (vision for short) and ability to execute (ability for short).

We note that the report included only vendors with commercial products, and did not consider open-source platforms like Python and R, even though those are very popular with Data Scientists and Machine Learning professionals.

Gartner MQ 2020: Data Science and Machine Learning Platforms
Fig. 1: Gartner Magic Quadrant for Data Science and Machine Learning Platforms, as of Nov 2019.

Vendors covered (in alphabetical order in each quadrant):

  • Leaders (6): Alteryx, Dataiku, Databricks, MathWorks, SAS, TIBCO
  • Challengers (1): IBM
  • Visionaries (7): DataRobot, Domino, Google, H2O.ai, KNIME, Microsoft, RapidMiner
  • Niche players (2): Anaconda, Altair (former DataWatch/Angoss)
No new entries were added in 2020 report compared to last year, and one firm was dropped: SAP.

As we did previously, we compared this latest Magic Quadrant with its previous year version - see Fig. 2.

Gartner Mq Data Science Ml Platforms 2019 Gainers


Fig. 2: Gartner MQ for Data Science and Machine Learning Platforms, 2020 vs 2019 changes
Green arrows correspond to vendors improving their position or moving to the leaders quad, red - losing position, yellow - side moves.

This year there are so many changes in MQ that it is hard to see all the arrows, so we break the above chart into 2 parts: 1: Leaders, 2: Challengers and Visionaries.

Here are the 6 vendors in the leaders quad, two that have been leaders last year (SAS and TIBCO) and 4 new ones (Alteryx, Dataiku, Databricks, MathWorks) - see Fig. 3.

Gartner MQ 2020: Data Science and Machine Learning Platforms, Leaders
Fig. 3: Leaders in Gartner MQ for Data Science and Machine Learning Platforms

Leaders



The six leaders are: (in alphabetical order)
  • Alteryx returned to its 2018 place in Leader quad (from Challenger in 2019) based on showing strong company and product vision, especially in process automation and what Gartner calls "augmented DSML". Alteryx continues to have higher revenue growth than almost all other vendors in this MQ and also made significant international expansion. In 2019 it made two acquisitions: ClearStory Data and Feature Labs.
  • Databricks which is based on Apache Spark, offers Unified Data Analytics Platform spanning data science, ML and data engineering. It moved to the leaders quad based on its strong execution, and growth and a partner ecosystem of over 500 companies. Databricks is also a leader in enabling ML scalability.
  • Dataiku core product is Data Science Studio. It moved from a challenger to a leader this year, based on ease of use, vision, data governance, and ability to support collaboration for multiple user types - from data engineers and data scientists to business users and so-called "citizen data scientists".
  • MathWorks MATLAB was considered for this MQ. MathWorks moved to the leaders quad this year, based on its adaptability, use of latest technologies such as Deep Learning and Reinforcement Learning, and strong ability to execute. MATLAB is a fully integrated platform supporting data prep, model building, simulation, and deployment.
  • SAS has many products, but this MQ evaluated SAS Visual Data Mining and Machine Learning. SAS remained in leader quad where it has been since the first MQ for this field. According to Gartner, SAS products have a high-degree of enterprise readiness and deliver high business value to customers. While open-source alternatives are a competitive threat, SAS remains a strong competitor.
  • TIBCO has built a powerful platform (TIBCO Data Science) over the last several years via acquisitions, including BI firms (Jaspersoft and Spotfire) and analytics/data science vendors (Insightful, Statistica, and Alpine Data). TIBCO has remained in the leader quadrant as it continues to integrate its broad portfolio.
Next we look at entries in Challenger and Vi firms that did not make it into the leaders quad, but has improved their positions.

Gartner MQ 2020: Data Science and Machine Learning Platforms, Challengers and Visionaries
Fig. 4: Challengers and Visionaries in Gartner MQ for Data Science and Machine Learning Platforms

Visionaries

The Visionaries quad has 7 firms:

  • DataRobot DSML platform provides automation across the entire analytics process, enabling business users and "citizen" data scientists do the data analysis they need. DataRobot also offers pre-sale and post-sale support. The firm has recently acquired Cursor, a data collaboration platform, ParallelM, an MLOps platform, and Paxata, a data preparation platform. DataRobot shows significant growth in revenue, number of users, and brand name recognition.
  • Domino offers industrial strength and rich platform for end-to-end DSML in the cloud or onsite. Domino can serve as a central DSML platform in enterprises with large and independent data science groups. Gartner writes "Domino is positioned as a Visionary in this MQ largely because of its product progress and roadmap, both of which manifest the vendor's deep DSML market understanding."
  • Google has been actively expanding its AI/ML offerings, and currently has Google Cloud AI as its main DSML platform. It includes Cloud AutoML, BigQuery ML, and TensorFlow. Google also has its own TPU hardware and massive cloud infrastructure. Google was not ranked among leaders because it Cloud AI Platform is not a stand-alone product.
  • H2O.ai offers commercial AutoML product H2O Driverless AI and also supports its open-source product H2O-3 (aka "Sparkling Water"). H2O.ai provides global customer support and also a community slack channel. H2O has maintained its Visionary position.
  • KNIME offers open-source KNIME Analytics Platform and a commercial extension, KNIME Server, with advanced functions including collaboration, automation and deployment. KNIME dropped from Leaders quad to Visionary mainly because of its lower visibility and slow revenue growth relative to other vendors evaluated by Gartner.
  • Microsoft core product for this MQ is Azure Machine Learning (Azure ML), along with supporting products Azure ML Studio, Azure Data Factory, Azure HDInsight, Azure Databricks, Power BI and other related components. Microsoft in Visionary quad, with advances in both Vision and Ability. However, its Ability to Execute is limited by a cloud-first approach and coherence issues.
  • RapidMiner offers RapidMiner Studio as a free edition and as a commercial edition, and also RapidMiner Server an enterprise extension for deploying and maintaining models and collaboration. RapidMiner supporting portfolio includes RapidMiner Real-Time Scoring, RapidMiner Radoop, Rapidminer Auto Model, and more. RapidMiner has large and active user community. This year RapidMiner dropped from Leaders quad to Visionary mainly because of its slower growth relative to other vendors in this MQ.

Challengers

Only one firm is in the Challengers quad:

  • IBM main product considered for this MQ is Watson Studio, along with numerous supporting products including Watson Machine Learning, SPSS (Modeler and Statistics), IBM Decision Optimization for Watson Studio, and IBM Streams. Gartner writes
    "Improving its product bundling and corresponding go-to-market approach will compound IBM's laudable efforts to revamp its offering and to keep pace with increasingly fierce competition from both large and small vendors."

Niche Players

Finally, there are two firms in the Niche quad:

  • Anaconda product evaluated for this MQ is Anaconda Enterprise, a data science development environment based on the interactive notebook concept. Anaconda supports a very large and engaged community of open-source and enterprise users. Anaconda remains a Niche Player in this MQ. It is well-suited to expert data scientists who use Python or R and can leverage a continuous stream of new open-source capabilities. However, Anaconda platform lacks automation and suffers from the complexities of navigating the frequently changing open-source capabilities.
  • Altair (DataWatch/Angoss). In Dec 2018 Altair has completed its acquisition of Datawatch, which in turn has acquired Angoss in Jan 2018. The firm main DSML product is called Altair Knowledge Works and the main product considered for this MQ is Knowledge Studio. Altair is a niche player because of the risks and uncertainties from the product second acquisition in 2 years.


We also note that SAP was dropped from 2020 MQ.

You can get 2020 Gartner MQ report for Data Science and Machine Learning platforms from several vendors mentioned in the report, including Alteryx, Dataiku, Databricks, Domino, RapidMiner, and TIBCO.

See also our analysis for previous years

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