Silver BlogGainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms

We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx,, and Domino.

Gartner keeps changing the names for this report (and by implication the market segment) - the latest 2018 version, published Feb 23, 2018, is called "Magic Quadrant for Data Science and Machine-Learning Platforms" (with an old-fashioned dash between Machine and Learning). In 2017 it was "MQ for Data Science Platforms", and in 2014-2016 it was "MQ for Advanced Analytics Platforms". This change reflects the rapid changes in the industry, both in terms of content and capabilities. and the evolving branding which reflects the growth of AI and Machine Learning.

Gartner defines a data science and machine-learning platform as:
A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products.
The changes in 2018 are quite significant, as we explain below.

The 2018 report evaluated 16 analytics and data science firms over multiple criteria and placed them in 4 quadrants, based on completeness of vision and ability to execute.

Note that while open source platforms like Python and R play an increasingly important role in the Data Science market, Gartner research methodology does not include them.

Gartner MQ Data Science ML Platforms 2018
Fig. 1: Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms

Firms covered:
  • Leaders (5): KNIME, Alteryx, SAS, RapidMiner,
  • Challengers (2): MathWorks, TIBCO Software (new)
  • Visionaries (5): IBM, Microsoft, Domino Data Lab, Dataiku, Databricks (new)
  • Niche Players (4): SAP, Angoss, Anaconda (new), Teradata
Three new firms were added in 2017: TIBCO Software, Anaconda, and Databricks.

Three others present in 2017 MQ were dropped: FICO, Quest, and Alpine Data. We note that Alpine Data firm and Quest's Statistica assets were acquired by TIBCO, which appears in this MQ in a spot close to where Quest was in 2017.

As we did in our previous post Gartner 2017 MQ for Data Science Platforms: gainers and losers, we compared the latest 2018 Magic Quadrant with its previous version. Below we examine the changes, gainers, and losers.

Gartner 2018 Vs 2017 MQ for Data Science and ML Platforms
Fig 2: Gartner Magic Quadrants for Data Science and Machine Learning Platforms compared, 2018 vs 2017

Fig 2 shows a comparison of 2017 MQ (greyed background image) and 2018 MQ (foreground image), with arrows connecting circles for the same firm. Arrows are colored green if the firm position improved significantly (further away from origin), red if the position became weaker. Green circles indicate new firms, while red Xs mark vendors dropped in 2018.


For the first time since 2014 we have a change here. IBM, which belonged to the leaders in the past, dropped to visionaries, based on lower ability to execute. KNIME moves significantly ahead in Completeness of Vision axis, SAS moved back on the same axis, and RapidMiner dropped a little on the ability to execute.

Two firms joined the leaders for the first time in 2018:, moved up from Visionaries and Alteryx, moved up from Challengers.

Here are short summaries for each firm. For full report, see pointers below.

KNIME provides the open-source KNIME Analytics Platform, with over 100K users worldwide. KNIME offers commercial support and extensions for collaboration, security and performance for enterprise deployments. In 2017, KNIME added cloud versions of its platform for AWS and Microsoft Azure, improved data quality features, and expanded deep-learning abilities.

Gartner writes
The vendor demonstrates a deep understanding of the market, a robust product strategy and strength across all use cases. Together, these attributes have solidified its place as a Leader.

Alteryx platform enables citizen data scientists to build models in a single workflow. In 2017 Alteryx had a successful IPO and later acquired Yhat, a data science vendor focused on model deployment and management.

Gartner writes:
Alteryx has progressed from the Challengers quadrant to the Leaders quadrant. This is thanks to strong execution (in terms of both revenue growth and customer acquisition), impressive customer satisfaction, and a product vision focused on helping organizations instill a data and analytics culture without needing to hire expert data scientists.

SAS provides many software products for analytics and data science. For this MQ Gartner evaluated SAS Enterprise Miner (EM) and the SAS Visual Analytics suite of products.

Gartner writes:
SAS remains a Leader, but has lost some ground in terms of both Completeness of Vision and Ability to Execute. The Visual Analytics suite shows promise because of its Viya cloud-ready architecture, which is more open than prior SAS architecture and makes analytics more accessible to a broad range of users. However, a confusing multiproduct approach has worsened SAS's Completeness of Vision, and a perception of high licensing costs has impaired its Ability to Execute. As the market's focus shifts to open-source software and flexibility, SAS's slowness to offer a cohesive, open platform has taken its toll.

RapidMinerplatform includes RapidMiner Studio model development tool, (available as both a free edition and a commercial edition), RapidMiner Server and RapidMiner Radoop.

Gartner writes:
RapidMiner remains a Leader by delivering a well-rounded and easy-to-use platform to the full spectrum of data scientists and data science teams. RapidMiner continues to emphasize core data science and speed of model development and execution by introducing new productivity and performance capabilities. offers open-source machine-learning platform, including H2O Flow, its core component; H2O Steam; H2O Sparkling Water, for Spark integration; and H2O Deep Water, which provides deep-learning capabilities.

Gartner writes: has progressed from Visionary in the prior Magic Quadrant to Leader. It continues to progress through significant commercial expansion, and has strengthened its position as a thought leader and an innovator.


  • MathWorks remains a Challenger, aided by its high visibility in the advanced analytics field, large installed base and strong customer relations. However it received poor scores from some customers and its Completeness of Vision is limited by its focus on engineering and high-end financial use cases.
  • TIBCO Software (new) entered this market by acquiring the well-known Statistica platform from Quest Software in June 2017. In Nov 2017 it also acquired Alpine Data, a Visionary in 2017 MQ. For Ability to Execute, this MQ evaluated only TIBCO ability with the Statistica platform. Other acquisitions by TIBCO contribute only to its Completeness of Vision.

  • IBM provides many analytic solutions. For this MQ, Gartner evaluated SPSS Modeler and SPSS Statistics, but not Data Science Experience (DSX), which did not meet Gartner criteria for evaluation on the Ability to Execute axis. Gartner writes
    IBM is now a Visionary, having lost ground in terms of both Completeness of Vision and Ability to Execute, relative to other vendors. IBM's DSX offering, however, has potential to inspire a more comprehensive and innovative vision. IBM has announced plans to deliver a new interface for its SPSS products in 2018, one that fully integrates SPSS Modeler into DSX.
  • Microsoft provides multiple products for data science and machine learning. For In-Cloud computing, these include Azure Machine Learning, Azure Data Factory, Azure Stream Analytics, Azure HDInsight, Azure Data Lake and Power BI.
    For on-premises computing, Microsoft offers SQL Server with Machine Learning Services (released in September 2017 - after the cutoff date for this MQ). Only Azure Machine Learning Studio fulfilled the inclusion criteria for this MQ. Gartner writes
    Microsoft remains a Visionary. Its position in this regard is attributable to low scores for market responsiveness and product viability, as Azure Machine Learning Studio's cloud-only nature limits its usability for the many advanced analytic use cases that require an on-premises option.
  • Domino (Domino Data Lab) Data Science platform is an end-to-end solution for expert data science teams. The platform focuses on integrating tools from both the open-source and proprietary-tool ecosystems, collaboration, reproducibility, and centralization of model development and deployment. Domino remained in Visionary quadrant but significantly improved its ability to execute. Gartner writes
    Domino ... Ability to Execute, though improved, is still hampered by weaker functionality at the beginning of the machine-learning life cycle (data access, data preparation, data exploration and visualization). Over the past year, however, Domino has demonstrated the ability to win new accounts and gain traction in a highly competitive market.
  • Dataiku offers Data Science Studio (DSS) with a focus on cross-discipline collaboration and ease of use. Gartner writes
    Dataiku remains a Visionary ... by enabling users to start machine-learning projects rapidly. Its position for Completeness of Vision is due to its collaboration and open-source support, which are also the focus of its product roadmap. Its overall Completeness of Vision score is lower than in the previous MQ, due to comparatively poor breadth in terms of use cases and deficiencies in automation and data streaming.
  • Databricks (new) offers the Apache Spark-based Databricks Unified Analytics Platform in the cloud. It also provides proprietary features for security, reliability, operationalization, and performance. Gartner writes
    Databricks is a new entrant to this Magic Quadrant. As a Visionary, it draws on the open-source community and its own Spark expertise to provide a platform that is easily accessible and familiar to many. In addition to data science and machine learning, Databricks focuses on data engineering. A 2017 Series D funding round of $140 million gives Databricks substantial resources to expand its deployment options and fulfill its vision.
Niche Players
  • SAP again rebranded its platform from SAP Business Objects Predictive Analytics to simply SAP Predictive Analytics. It remains a Niche Player due to low customer satisfaction scores, a lack of mind share, a fragmented toolchain, and significant gaps in technology in cloud, deep learning, and Python.
  • Angoss was acquired by Datawatch in January 2018, but still appears as Angoss in this document due to the acquisition lateness. Angoss has loyal customers, but remains a Niche Player as it is still perceived as a vendor for desktop environments.
  • Anaconda (new) offers Anaconda Enterprise 5.0, an open-source development environment based on the interactive-notebook concept. It also provides a distribution environment, giving access to a wide range of open-source development environments and open-source libraries.
  • Teradata offers Teradata Unified Data Architecture, an enterprise analytical ecosystem that combines open-source and commercial technologies to deliver analytic capabilities. It remains a niche player due to its lack of cohesion and ease of use on the data science development side.
You can download the Gartner 2018 Magic Quadrant report for Data Science and Machine Learning platforms from Domino,, Alteryx, Dataiku, and probably other vendors favorably mentioned in this report.

You can also see a related 2018 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms.