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RapidMiner, KNIME, SAS, IBM Lead Gartner's MQ for Data Science Analytics

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Gartner gave its analysis of advanced analytics platforms a new name, but retained four of the same vendors as industry leaders for the second consecutive year.

RapidMinerKNIMESAS and IBM lead Gartner's Magic Quadrant for Data Science Platforms. The report, formerly called the Magic Quadrant for Advanced Analytics Platforms, was published yesterday.

Gartner analysts Alexander Linden, Peter Krensky, Jim Hare, Carlie J. Idoine, Svetlana Sicular and Shubhangi Vashisth, who co-authored the report, define data science platforms as: "A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solutions, and for incorporating those solutions into business processes, surrounding infrastructure and products."

The new name reflects changes in the data analytics market has changed since the previous analysis last March

Organizations want data science platforms that embrace open-source technologies and machine-learning science as they move from descriptive and diagnostic analytics to predictive and prescriptive approaches.

“The data and analytical needs of organizations continue to evolve rapidly,” they wrote. “Many organizations are extending the breadth of their analytical capability to include data science.”

Accenture Out, Dataiku In

The new definitions and analytics approaches led to some changes in this year's report. Although four vendors from last year retained their spots on the leaderboard, Quest — which Dell sold to Francisco Partners and Elliott Management Corp. in October — slipped from a leader to a challenger.

Gartner dropped Accenture, Lavastorm, Megaputer, Predixion Software and Prognoz from the report. It added Dataiku, Domino Data Labs,, MathWorks and Teradata.

Gartner classifies vendors in four categories:

  • Leaders (strong presence, significant mindshare): RapidMiner, KNIME, SAS, IBM
  • Challengers (established presence, credibility): MathWorks, Quest, Alteryx, Angoss
  • Niche Players (industry- and technology-specific strength): SAP, FICO, Teradata
  • Visionaries (typically smaller players, trend-setters): Microsoft (we said "typically"),, Dataiku, Domino Data Lab, Alpine Data 

Analysts factor in a vendor's “ability to execute” and “completeness of vision.”

Gartner has many requirements to get through these Magic Quadrant doors. It includes vendors that have generated revenue from data science platform software licenses and technical support that includes one of the following benchmarks:

  • At least 150 percent revenue growth from 2014 to 2015
  • At least 200 paying end-user organizations

Gartner Data science MQ
Magic Quadrant for Data Science PlatformsGartner (reprinted with permission)

About the 4 Leaders

Here’s a snapshot of the strengths and weaknesses of the four leaders, according to Gartner:

Learning Opportunities

RapidMiner: Algorithm, Community Strengths

Cambridge, Mass.-based RapidMiner features a graphical user interface-based data science platform whose strengths include platform breadth, ease of use for all data scientists and skill levels and its “large and vibrant” user community. Gartner authors cited its its large selection of algorithms, flexible modeling capabilities, data source integration and consequent data preparation but cautioned against its data usage cap and its weak global presence (one office in the US office, three in Europe), which “could limit worldwide growth.”

SAS: Strong Data Access, Model Development

Cary, N.C.-based SAS offers SAS Enterprise Miner and Visual Analytics Suite, the most cited toolset by Gartner clients. It has strong data access and preparation capabilities, and “excellent” data visualization and exploration capabilities. But Gartner cautioned the platforms can be tough to learn and sometimes lead to confusion about what to use and when.

KNIME: Flexibility, Reliable Sales

Zurich-based KNIME has an analytics platform that includes an open-source solution that is flexible, and the platform’s low total cost of ownership is intriguing to clients. KNIME also has strong data access and transformation, sales and vendor relationships and active communities. It has challenges in model management, scalability, exploring and visualizing data, according to Gartner researchers.

IBM: Committed to Open-Source

Armonk, N.Y.-based IBM's SPSS Modeler and SPSS Statistics have a strong customer base, commitment to open-source technologies, support for a broad range of data types and model management and governance make it a leader, according to Gartner. SPSS supports all leading Hadoop distributions, NoSQL DBMSs and other databases. But IBM’s breadth of analytics offering sometimes leads to confusion and some also see the platforms as “outdated and overpriced,” according to Gartner. 

‘State of Flux’ for Data Science

Gartner found a data science platforms in “a state of flux” and warned leaders are not immune to potential disruption because of advancements in machine learning, artificial intelligence, cloud and automation. New entrants can also be threats and Amazon, Baidu and Google are all ready to execute. Amazon and Google made Gartner’s “honorable mention” list in this Quadrant.

Open-source technologies like Apache Spark, a “foundation for most data-science advances,” may also contribute to market disruption, Gartner reported. Python, R and Scala also are prevalent open-source tech in the market.

Data science platforms generated almost double that of the overall BI and analytics software market in 2015, with a 20.8 percent rise bringing its total to $2.4 billion in constant currency, according to the 2016 Gartner report, "Market Snapshot: BI and Analytics Software, Worldwide, 2016” (subscription required).  

Gartner estimates predictive and prescriptive analytics will attract 40 percent of enterprises' new investment in BI and analytics technologies by 2020. Organizations still must have the right personnel in place to execute on the platforms, Gartner authors reported.

“Many data science platform vendors are innovative,” Gartner authors noted. “Nevertheless, organizations still face key challenges in terms of a lack of qualified staff with data science skills, long cycle times for building and deploying data science models, and difficult collaboration between business teams and data science teams.”