For the first time in several years the name of this highly anticipated Gartner MQ for Data Science and Machine Learning Platforms report was essentially the same as last year. In 2018 the report was named “MQ 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 2016 “MQ for Advanced Analytics Platforms”. Perhaps the emerging stability of the name reflects certain maturity in how the field of Data Science and Machine Learning is perceived. But there is no stability in the industry, with many changes among the vendors rankings, as you will see below.
What changed in 2019?
The 2019 report evaluated 17 vendors (one more than in previous years) and placed them as always in 4 quadrants, based on completeness of vision ( vision for short) and ability to execute ( ability for short).
Note that Gartner included only vendors with commercially licensable products. Pure open-source platforms like Python and R, even though very popular with Data Scientists and Machine Learning professionals, were not included.
Fig. 1: Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms (as of Nov 2018)
- Leaders (4): KNIME, RapidMiner, TIBCO Software, SAS
- Challengers (2): Alteryx, Dataiku
- Visionaries (7): Mathworks, Databricks, H2O.ai, IBM, Microsoft, Google (new), DataRobot (new)
- Niche Players (4): SAP, Anaconda, Domino, Datawatch (Angoss)
Two new firms were added in 2019 report: Google and DataRobot.
One firm present in 2018 MQ was dropped: Teradata.
As we did previously (see Gainers and Losers in 2018 MQ, Gartner 2017 MQ Gainers and losers), we compared the latest Magic Quadrant with its previous version. Below we examine the changes, gainers, and losers.
Fig 2: Gartner Magic Quadrants for Data Science and Machine Learning Platforms compared, 2019 vs 2018
Fig 2 shows a comparison of 2018 MQ (greyed background image) and 2019 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 2 new firms (Google and DataRobot), while red X marks vendor dropped this year (Teradata).
We see that KNIME and RapidMiner remained in strong Leadership position, SAS dropped on ability but still among the leaders, and TIBCO, which acquired several analytics firms recently, joined the leaders for the first time. Alteryx moved to Challengers quadrant and was joined by Dataiku who made a big improvement on ability axis. MathWorks improved significantly on vision and is closing on the Leaders.
3 Year Comparison
We can see longer term trends more clearly if we examine the major firms that appeared in all 3 recent years – see Fig. 3.
Fig 3: Gartner Magic Quadrants for Data Science and Machine Learning Platforms compared for 3 years, 2017, 2018, 2019
Alteryx improved on ability in both years but remains a challenger.
Dataiku had a big drop in 2018 and a big improvement on ability in 2019.
IBM dropped significantly in both years along both axes and moved from Leaders to Visionaries.
H2O.ai moved up to leaders in 2018 but dropped back to visionaries in 2019.
KNIME moved forward in Vision in both years and is a strong leader.
MathWorks dropped on ability in 2018 but improved significantly on vision in 2019.
Microsoft dropped a little on ability in each year.
RapidMiner has remained essentially in the same place in leaders quad.
SAS fell both on vision and then on ability, but remained among leaders.
Here is a very brief summary of the firms in the current 2019 Gartner MQ
This quadrant used to have the same 4 firms (IBM, SAS, RapidMiner, and KNIME) in 2014-2017, but it changed in 2018 and changed again this year.
Rapidminer moved a little up in the Ability. Gartner says
RapidMiner remains a Leader by striking a good balance between ease of use and data science sophistication. Its platform’s approachability is praised by citizen data scientists, while the richness of its core data science functionality, including its openness to open-source code and functionality, make it appealing to experienced data scientists, too.
KNIME moved down on Ability axis but forward on Vision. Gartner says
With a wealth of well-rounded functionality, KNIME maintains its reputation for being the market’s “Swiss Army knife.” Its for-free and open-source KNIME Analytics Platform covers 85% of critical capabilities, and KNIME’s vision and roadmap are as good as, or better than, those of most of its competitors.
SAS dropped on Ability. Gartner writes:
SAS retains its long-held status as a Leader. Although the company faces threats on multiple fronts from other large vendors, maturing disruptors and open-source solutions, it retains a strong presence in the market.
TIBCO Software joined the leaders and improved both on Ability and Vision axes. Gartner writes:
Through the acquisition of enterprise reporting and modern BI platform vendors (Jaspersoft and Spotfire), descriptive and predictive analytics platform vendors (Statistica and Alpine Data), and a streaming analytics vendor (StreamBase Systems), TIBCO has built a well-rounded and powerful analytics platform.
Alteryx moved from a Leader to a Challenger position, due to its perceived lack of innovation. Gartner writes that nevertheless
Alteryx’s emphasis on making data science accessible to citizen data scientists and others across the end-to-end analytic pipeline is resonating in the market
Dataiku has made a huge improvement on Ability axis. Gartner writes
Dataiku’s appearance in the Challengers quadrant is principally due to its strong execution and strengthening capabilities in relation to scalability.
Databricks’ Apache Spark-based Unified Analytics Platform combines data engineering and data science capabilities that use a variety of open-source languages.
Databricks remains a Visionary by providing support for the end-to-end analytic life cycle, hybrid cloud environments and accessibility for a wide variety of users.
DataRobot platform automates key tasks, enabling data scientists to work efficiently and citizen data scientists to build models easily.
Google offers a rich ecosystem of AI products and solutions, ranging from hardware (Tensor Processing Unit [TPU]) and crowdsourcing (Kaggle) to world-class ML components for processing unstructured data like images, video and text. Google is also one of the pioneers of automated ML (with Cloud AutoML).
H2O.ai has lost some ground in Ability to Execute relative to other vendors in this Magic Quadrant, largely due to comparatively low scores from reference customers for several critical capabilities.
IBM remains a Visionary, but has lost ground in terms of both Completeness of Vision and Ability to Execute, relative to other vendors.
MathWorks has strengthened the coherence of the MATLAB platform for its engineering-focused audience by seamlessly integrating advanced functionality for the treatment of unconventional data sources (images, video and IoT data).
Microsoft remains a Visionary, having maintained a strong commitment to breadth and ease of open-source technology integration and excellence in relation to deep learning. Azure Machine Learning is not an option for the many data science teams and use cases that require a strictly on-premises product.
SAP, Anaconda, Domino, Datawatch (Angoss)
You can download the Gartner 2019 Magic Quadrant report for Data Science and Machine Learning platforms from Dataiku, DataRobot, and probably other vendors favorably mentioned in Gartner report.
Article by channel:
Everything you need to know about Digital Transformation
The best articles, news and events direct to your inbox
Read more articles tagged: Machine Learning