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16 top platforms for data science 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.” Such a platform “supports data scientists in the performance of tasks across the entire data and analytics pipeline. These include tasks relating to data access and ingestion, data preparation, interactive exploration and visualization, feature engineering, advanced modeling, testing, training, deployment and performance engineering.” With that in mind, the research firm has released a Magic Quadrant report that looks at 16 leading products in this space. The report was written by Gartner analysts Carlie J. Idoine, Peter Krensky, Erick Brethenoux, Jim Hare, Svetlana Sicular and Shubhangi Vashisth.
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Leaders
“Leaders have a strong presence and significant mind share in the data science and machine-learning market,” the Gartner analysts write. “They demonstrate strength in depth and breadth across a full exploration, model development and implementation process. While providing outstanding service and support, Leaders are also nimble in responding to rapidly changing market conditions. The number of data scientist professionals skilled in the use of Leaders' platforms is significant and growing. Leaders are in the strongest position to influence the market's growth and direction. They address all industries, geographies, data domains and use cases and, thus, have a solid understanding of, and strategy for, this market. Not only are they able to focus on executing effectively, based on current market conditions, but they also have solid and robust roadmaps to take advantage of new developments and advancing technologies in this rapidly transforming sector. They provide thought leadership and innovative differentiation, often disrupting the market in the process.”
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Alteryx
Alteryx is based in Irvine, California. “Alteryx offers a unified machine-learning platform, Alteryx Analytics, which enables citizen data scientists to build models in a single workflow,” according to Gartner. “In mid-2017, Alteryx acquired Yhat, a data science vendor focused on model deployment and management. Alteryx issued an initial public offering (IPO) on the New York Stock Exchange in early 2017, which strengthened its ability to invest in expanding and enhancing its platform's capabilities. 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.”
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H20.ai
H2O.ai is based in Mountain View, California. “H2O.ai offers an open-source machine-learning platform,” Gartner says. “For this Magic Quadrant, we evaluated H2O Flow, its core component; H2O Steam; H2O Sparkling Water, for Spark integration; and H2O Deep Water, which provides deep-learning capabilities. H2O.ai 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.”
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KNIME
KNIME is based in Zurich, Switzerland. “KNIME provides the fully open-source KNIME Analytics Platform, which is used by over 100,000 people worldwide,” according to Gartner. “KNIME offers commercial support and commercial extensions to boost collaboration, security and performance for enterprise deployments. In the past year, KNIME has introduced cloud versions of its platform for AWS and Microsoft Azure, paid more attention to data quality, expanded its deep-learning features, and converted some of its commercial capabilities to open source. KNIME is accelerating its product development and customer acquisition efforts. KNIME's platform is used by most industries and in most regions of the world. 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.”
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RapidMiner
RapidMiner is based in Boston, Massachusetts. “RapidMiner’s platform includes RapidMiner Studio, RapidMiner Server and RapidMiner Radoop, “ Gartner says. RapidMiner Studio is the model development tool, available as both a free edition and a commercial edition; it is priced according to the number of logical processors and the amount of data used by a model. With the free edition, customers get one logical processor and 10,000 rows of data. RapidMiner Server is designed for sharing, collaborating on and maintaining models. RapidMiner Radoop extends RapidMiner's execution directly into a Hadoop environment. 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.”
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SAS
SAS is based in Cary, North Carolina. “SAS provides many software products for analytics and data science,” the Gartner analysts write. For this Magic Quadrant, we evaluated SAS Enterprise Miner (EM) and the SAS Visual Analytics suite of products, which includes Visual Statistics and Visual Data Mining and Machine Learning. 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.”
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Challengers
“Challengers have an established presence, credibility, viability and robust product capabilities,” according to the Gartner analysts. “They may not, however, demonstrate thought leadership and innovation to the same degree as Leaders. There are two main types of Challenger. Long-established data science and machine-learning vendors that succeed because of their stability, predictability and long-term customer relationships. They need to revitalize their vision to stay abreast of market developments and become more broadly influential and innovative. If they simply continue doing what they have been doing, their growth and market presence may be impaired. Vendors well-established in adjacent markets that are entering the data science and machine-learning market with solutions that extend their current platforms for existing customers but are also a reasonable option for many potential new customers. As these vendors prove they can influence this market and provide clear direction and vision, they may develop into Leaders. They must avoid the temptation to introduce new capabilities quickly but superficially.”
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MathWorks
MathWorks is a privately held company headquartered in Natick, Massachusetts. “Mathwork’s two major products are MATLAB and Simulink, but only MATLAB met the inclusion criteria for this Magic Quadrant,” according to the Gartner analysts. “MathWorks remains a Challenger. Its Ability to Execute is aided by its sustained visibility in the general advanced analytics field, a significant installed base and strong customer relations, but impaired by average scores from reference customers for critical capabilities. Its Completeness of Vision is limited by its focus on engineering and high-end financial use cases, largely to the exclusion of customer-facing use cases like marketing, sales and customer service.”
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TIBCO Software
TIBCO Software is based in Palo Alto, California. “Building on its presence in the analytics and BI sector, TIBCO entered the data science and machine-learning market by acquiring the well-established Statistica platform from Quest Software in June 2017,” according to Gartner. “Additionally, in November 2017, TIBCO announced the acquisition of Alpine Data, a Visionary in the prior Magic Quadrant. In terms of Ability to Execute, this Magic Quadrant evaluates only TIBCO's ability with the Statistica platform. Other acquisitions by TIBCO contribute only to its Completeness of Vision. TIBCO enters this Magic Quadrant as a Challenger. The Statistica platform has a large and mature customer base, and received high scores for the three most typical use cases: business exploration, advanced prototyping and production refinement.”
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Visionaries
“Visionaries are typically smaller vendors or newer entrants representative of trends that are shaping, or have the potential to shape, the market,” according to Gartner. “There may, however, be concerns about these vendors' ability to keep executing effectively and to scale as they grow. They are typically not well-known in the market, and therefore often have low momentum, relative to Challengers and Leaders. Visionaries have a strong vision and supporting roadmap. They are innovative in their approach to addressing the needs of the market. Although their offerings are typically innovative and solid in the capabilities they do provide, there are often gaps in the completeness and breadth of their offerings. Visionaries are worth considering because they may:
  • Represent an opportunity to jump-start an innovative initiative.
  • Provide some compelling, differentiating capability that offers a competitive advantage as either a complement to, or a substitute for, existing solutions.
  • Be more easily influenced with regard to their product roadmap and approach.”
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Databricks
Databricks is based in San Francisco, California. “Databricks offers the Apache Spark-based Databricks Unified Analytics Platform in the cloud, Gartner analysts said. “In addition to Spark, it provides proprietary features for security, reliability, operationalization, performance and real-time enablement on Amazon Web Services (AWS). Databricks announced a Microsoft Azure Databricks platform for preview in November 2017, which is not considered in this Magic Quadrant because it was not generally available at the time of evaluation. 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.”
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Dataiku
Dataiku is headquartered in New York City and has a main office in Paris, France. “Dataiku offers Data Science Studio (DSS) with a focus on cross-discipline collaboration and ease of use,” Gartner analysts said. “Dataiku remains a Visionary — and a popular choice for many data science needs — 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 prior Magic Quadrant, due to comparatively poor breadth in terms of use cases and deficiencies in automation and data streaming. Dataiku's Ability to Execute has also decreased, due to some difficulties in operationalizing and scaling machine-learning models.”
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Domino
Domino (Domino Data Lab) is headquartered in San Francisco, California. “Domino offers the Domino Data Science Platform,” Gartner analysts said. “This 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. Founded in 2013, Domino is a recognized name in this market and continues to gain mind share among expert data scientists. Domino maintains its position as a Visionary. Its 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.”
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IBM
IBM is based in Armonk, New York. “IBM provides many analytic solutions,” according to Gartner analysts. “For this Magic Quadrant, we evaluated SPSS, including both SPSS Modeler and SPSS Statistics. Data Science Experience (DSX), a second data science and machine-learning offering, did not meet our criteria for evaluation on the Ability to Execute axis, but does contribute to IBM's Completeness of Vision. 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.”
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Microsoft
Microsoft is based in Redmond, Washington. “Microsoft provides a number of software products for data science and machine learning,” according to the Gartner analysts. “In the cloud, it offers Azure Machine Learning (including Azure Machine Learning Studio), Azure Data Factory, Azure Stream Analytics, Azure HDInsight, Azure Data Lake and Power BI. For on-premises workloads, Microsoft offers SQL Server with Machine Learning Services, which was released in September 2017 — after the cutoff date for consideration in this Magic Quadrant. Only Azure Machine Learning Studio fulfilled the inclusion criteria for this Magic Quadrant, although Microsoft's broader advanced analytics offerings did influence our assessment of its Completeness of Vision. 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.”
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Niche players
“Niche Players demonstrate strength in a particular industry or approach, or pair well with a specific technology stack,” Gartner analysts explained. “Some Niche Players demonstrate a degree of vision, which suggests they could become Visionaries. They are, however, often struggling to make their vision compelling, relative to others in the market. They may also be struggling to develop a track record of innovation and thought leadership that could give them the momentum to become Visionaries. Other Niche Players could become Challengers if they continue to execute in a way that increases their momentum and traction in the market.”
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Anaconda
Anaconda , formerly known as Continuum Analytics, is based in Austin, Texas. “Anaconda sells Anaconda Enterprise 5.0, an open-source development environment based on the interactive-notebook concept,” Gartner analysts explained. “It also provides a loosely coupled distribution environment, giving access to a wide range of open-source development environments and open-source libraries, mainly Python-based. Anaconda's strength lies in its ability to federate and provide a central access point for a very large number of Python developers who build machine-learning capabilities continuously. However, Anaconda has little or no control over those developers' efforts in terms of quality, dependability and sustainability. Anaconda nurtures a broad developer community through Anaconda Cloud. Anaconda's position as a Niche Player reflects its suitability for seasoned data scientists fluent in Python.”
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Angoss
Angoss which is based in Toronto, Canada, was acquired by Datawatch in January 2018. “It still appears as Angoss in this document due to the acquisition's lateness, relative to the Magic Quadrant process, and uncertain impact,” the Gartner analysts write. “This evaluation covers the following products: KnowledgeSEEKER, the company's most basic offering, aimed at citizen data scientists in a desktop context; KnowledgeSTUDIO, which includes many more models and capabilities than KnowledgeSEEKER; and the newly launched KnowledgeENTERPRISE, a flagship product that includes the full range of capabilities. Angoss has lengthy experience with banking customers. This underpins its ability to deliver to the banking sector and other sectors with similar data and analytical needs, such as insurance, transportation and utilities.”
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SAP
SAP is based in Walldorf, Germany. “SAP has yet again rebranded its platform: SAP Business Objects Predictive Analytics is now simply SAP Predictive Analytics (PA),” Gartner writes. “This platform has a number of components, such as Data Manager for dataset preparation and feature engineering, Automated Modeler for citizen data scientists, Predictive Composer for more sophisticated machine learning, and Predictive Factory for operationalization. SAP Leonardo Machine Learning and other components of the SAP Leonardo ecosystem did not contribute to SAP's Ability to Execute position in this Magic Quadrant. Over the past year, SAP has made good progress in several respects, but still lags behind in others. It is a Niche Player due to low customer satisfaction scores, a lack of mind share, a fragmented toolchain, and significant technological weak spots (in relation to the cloud, deep learning, Python and notebooks, for example), relative to others.
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Teradata
Teradata is based in San Diego, California. “The Teradata Unified Data Architecture (UDA) is an enterprise analytical ecosystem that combines open-source and commercial technologies to deliver analytic capabilities,” according to the Gartner analysts. “The UDA includes Aster Analytics, a Teradata database, Hadoop and data management tools. Although Teradata has strong operationalization capabilities, it still lacks a unified end-to-end technology platform. Teradata has maintained its intrinsic performance and reliability strengths, but its lack of cohesion and ease of use on the data science development side have impaired both its Ability to Execute and its progress on the Completeness of Vision axis. It remains a Niche Player.”