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The Leaders<\/h2>
\u201cLeaders have a strong presence and significant mind share in the market,\u201d according to Gartner. \u201cResources skilled in their tools are readily available. Leaders demonstrate strength in depth and breadth across a full development and implementation process. While maintaining a broad and long-established customer base, Leaders are also nimble in responding to rapidly changing market conditions, driven by the overwhelming interest in data science across all industries and domains. Leaders are in the strongest position to in\u0192luence the market's growth and direction. They address all industries, geographies, data domains and use cases. This gives them the advantage of a clear understanding and strategy for the data science market.\u201d<\/p>

IBM<\/h2>
IBM is based in Armonk, NY. \u201cAs a large enterprise software vendor, IBM offers a wide array of analytics solutions,\u201d Gartner says. \u201cFor this Magic Quadrant, we evaluated SPSS Modeler and, to a lesser extent, SPSS Statistics. IBM is again a Leader. Its position on the Ability to Execute axis has dropped slightly, as its attention is split between SPSS Modeler and its new data science platform, IBM Data Science Experience (DSx). DSx is not evaluated in this Magic Quadrant, but it does strengthen IBM's position on the Completeness of Vision axis.\u201d<\/p>

KNIME<\/h2>
KNIME ("Konstanz Information Miner") is based in Zurich, Switzerland. \u201cIts open-source KNIME Analytics Platform is a fully functional and scalable platform for advanced and expert data scientists,\u201d Gartner says. \u201cIts commercial offering provides extended value-added proprietary tools, service layers and support. KNIME's platform is used in a number of industries. Its strongest presence is in manufacturing and life sciences, but it also has a strong presence in the financial services, education, government and retail sectors. KNIME remains a Leader and a popular choice for data science needs. Its strong Ability to Execute is attributable to solid interactions with customers via its sales team, its responsiveness and its community support.\u201d<\/p>

RapidMiner<\/h2>
RapidMiner is based in Boston, MA. \u201cIt offers its RapidMiner GUI-based data science platform for the full spectrum of data scientists, including beginners,\u201d Gartner explains. \u201cIt also offers access to the core open-source code for expert data scientists who prefer to program. RapidMiner's data science studio is available both as a free edition and as a commercial edition, which offers additional functionality for working on larger datasets and connecting to more data sources. RapidMiner is again a Leader, owing to its market presence, the volume of client inquiries that Gartner receives about it, its user community, and its well-rounded product that addresses most data science use cases well.\u201d<\/p>

SAS<\/h2>
SAS is based in Cary, NC. \u201cIt provides a vast array of software products for analytics and data science,\u201d Gartner says. \u201cThis evaluation covers SAS Enterprise Miner (EM) and the line of SAS products with names starting with the word \u2018Visual,\u2019 such as Visual Statistics and Visual Data Mining and Machine Learning, which we refer to collectively as the Visual Analytics suite (VAS). SAS's focus is now interactive modeling in its VAS, but it continues to support its traditional programmatic approach (for Base SAS). SAS's delivery of these capabilities has enabled it to retain a strong position in the Leaders quadrant.\u201d<\/p>

The Challengers<\/h2>
\u201cChallengers have an established presence, credibility, viability and robust product capabilities,\u201d according to Gartner. \u201cThey may not, however, demonstrate thought leadership and innovation to the same degree as Leaders. There are two main types of Challenger: 1. Long-established data science 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 in\u0192luential. If they simply continue doing what they have been doing, their growth and market presence may decrease. 2. Vendors well-established in adjacent markets that are entering the data science market with solutions that can reasonably be considered by most customers.\u201d<\/p>

Alteryx<\/h2>
Alteryx is based in Irvine, CA. \u201cIt offers a data science platform geared toward citizen data scientists,\u201d says Gartner. \u201cThe platform's self-service data preparation capabilities and advanced analytics enable business users to blend data from internal and external sources and then to analyze it using predictive, prescriptive tools, using the same UI in a single work\u0192low. Integration with open-source R enables expert data scientists to extend functionality by creating and running custom R scripts. Alteryx also offers a cloud-based analytics gallery for collaboration, sharing and version control of work\u0192lows. This year, Alteryx is at the bottom right of the Challengers quadrant. Its move out of the Visionaries quadrant is due partly to solid customer growth, which has resulted in a higher score for Ability to Execute.\u201d<\/p>

Angoss<\/h2>
Angoss is based in Toronto, Ontario, Canada. \u201cIt provides a suite of visual-based advanced data mining and predictive analytics tools for expert data scientists and citizen data scientists. It also offers prescriptive analysis and optimization capabilities that can be used to solve complex decision-making problems. Although its overall scores from reference customers for product satisfaction were good, its scores for vision were lower, primarily due to performance and scalability challenges for emerging use cases using very large datasets (which are, however, partially addressed by its new Hadoop\/Spark integration).\u201d<\/p>

MathWorks<\/h2>
MathWorks is a privately held corporation headquartered in Natick, MA. \u201cIts two major products are Matlab and Simulink, but only Matlab met the inclusion criteria for this Magic Quadrant,\u201d Gartner says. \u201cA new entrant to the Magic Quadrant, MathWorks is a Challenger. Its Ability to Execute is driven by its prominent presence in data-science-related domains, but it still has to demonstrate its vision by producing a significantly easier-to-use platform that can address the concerns of corporate data scientists, especially for customer-facing use cases like marketing, sales and CRM.\u201d<\/p>

Quest<\/h2>
Quest is headquartered in Aliso Viejo, CA. \u201cAs a result of the sale of Dell Software to Francisco Partners and Elliott Management, completed in November 2016, Quest now sells the Statistica Analytics Platform. Quest is a Challenger in this year's Magic Quadrant, whereas Dell was a Leader in last year's. This shift is largely due to the second change in ownership for Statistica in three years and to the lack of some product improvements central to native cloud and some Spark capabilities. The platform's large customer base and all-around strength on-premises across the production, business exploration and advanced prototyping use cases merit a position as a strong Challenger.\u201d<\/p>

The Visionaries<\/h2>
\u201cVisionaries are typically smaller vendors or newer entrants which embody trends that are shaping, or will shape, the market,\u201d Gartner explains. \u201cThere may, however, be concerns about these vendors' ability to keep executing effectively and to scale as they grow. They might also be hampered by a lack of awareness of them in the market, and therefore by insufficient momentum. Visionaries have a strong vision and a roadmap for achieving it. They are innovative in their approach to their platform offerings and provide strong functionality for the capabilities they address. Typically, however, there are gaps in the breadth and completeness of their capabilities.\u201d<\/p>

Alpine Data<\/h2>
Alpine Data is based in San Francisco, CA. \u201cIt offers a citizen data science platform, Chorus, with a focus on enabling collaboration between business analysts and front-line operational users in building and running analytic workflows,\u201d Gartner says. \u201cChorus does not require users to move their data from where it resides, be that within a traditional database or Apache Hadoop. This is important for industries in which organizations are unable to move their data between servers for security reasons. As last year, Alpine is in the Visionaries quadrant. It has limited visibility in this market, despite its continued growth in enterprise data science environments.\u201d<\/p>

Dataiku<\/h2>
Dataiku is headquartered in New York City and has a main office in Paris, France. \u201cIt has chosen to take a very ambitious path with its data science platform, Data Science Studio (DSS),\u201d Gartner says. Dataiku is a new entrant to the Magic Quadrant. Its placement as a Visionary is due to the innovative nature of the DSS, especially its openness and ability to cater to different skill levels, which enables better collaboration. Dataiku's Ability to Execute suffers from limited user adoption and deficiencies in its data access and exploration capabilities.\u201d<\/p>

Domino Data Lab<\/h2>
Domino Data Lab is headquartered in San Francisco, CA. \u201cIt offers the Domino Data Science Platform, with a focus on openness, collaboration and reproducibility of models,\u201d Gartner says. \u201cFounded in 2013, Domino has gained significant attention and momentum in only a few years. A new entrant to the Magic Quadrant, Domino is a Visionary due to its support for a wide range of open-source technologies and offer of freedom of choice to data scientists. Its innovation scores are among the highest of any vendor in this Magic Quadrant. To raise its Ability to Execute score, Domino will need to improve its data access and data preparation capabilities, and devote resources to strengthening its UI and functionality for the business exploration use case.\u201d<\/p>

H2O.ai<\/h2>
H2O.ai is based in Mountain View, CA. \u201cIt offers an open-source data science platform, H2O, with a focus on fast execution of cutting-edge machine-learning capabilities,\u201d according to Gartner. \u201cThis evaluation focuses on the following products and versions, which were generally available at the end of July 2016: H2O Flow, Steam and Sparkling Water. The company has recently launched deep-learning capabilities in the form of Deep Water. A new entrant to the Magic Quadrant, H2O.ai is a Visionary because of its solid range of highly scalable machine-learning implementations. It is one of the machine-learning partners most frequently mentioned by companies such as IBM and Intel.\u201d<\/p>

Microsoft Corp.<\/h2>
"Microsoft has bundled its data science tools into its cloud-based Microsoft Cortana Intelligence Suite and Microsoft R Server. This evaluation concentrates on the Azure Machine Learning platform, part of the Cortana Intelligence Suite, which includes many additional components, such as Azure Data Factory, Azure Stream Analytics and Power BI. During the past three years, Microsoft has undertaken a remarkable revamp in the context of machine learning. It entered the market with a very limited product offering and remains a Visionary for its market-leading data science cloud solution.\u201d<\/p>

The Niche Players<\/h2>
\u201cNiche Players demonstrate strength in a particular industry, or pair well with a specific technology stack,\u201d Gartner explains. \u201cSome Niche Players demonstrate a degree of vision, which suggests they might become Visionaries, but they are struggling to make this vision compelling. They may also be struggling to develop a track record of continual innovation. Other Niche Players have the opportunity to become Challengers if they continue to develop their products with a view to improving their overall execution.\u201d<\/p>

FICO<\/h2>
FICO is based in San Jose, CA. \u201cIts Decision Management Suite (DMS) offers analytic tools, including Model Builder, Optimization Modeler and Decision Modeler,\u201d Gartner explains. \u201cFICO is again positioned as a Niche Player. It is an especially strong choice for organizations in the financial services sector and for those that depend on scorecard modeling. FICO's placement stems largely from its need to catch up with, and innovate at the level of, many other vendors, which results in a low overall score for Completeness of Vision. Its Ability to Execute score is dragged down by low market traction in other vertical areas and limited machine-learning capabilities. FICO needs to deliver on its many product roadmap promises in the near future.\u201d<\/p>

SAP<\/h2>
SAP is based in Waldorf, Germany. \u201cIt has rebranded its data science platform, which is now called SAP BusinessObjects Predictive Analytics (BOPA),\u201d Gartner says. \u201cIn addition, SAP has a broad array of other analytics offerings, including the SAP BusinessObjects Business Intelligence platform and SAP BusinessObjects Lumira for self-service data discovery, which are not included in this evaluation. A fall in SAP's Ability to Execute score has caused it to drop slightly, from near the bottom of the Challengers quadrant to near the top of the Niche Players quadrant. SAP is lagging behind in terms of Spark integration, open-source support, Python and notebook integration, and cloud deployment.\u201d<\/p>

Teradata<\/h2>
Teradata is headquartered in Dayton, OH. \u201cIt offers a data science platform called Aster Analytics, which has three layers: analytic engines, prebuilt analytic functions, and the Aster AppCenter for analysis and connectivity to external business intelligence (BI) tools,\u201d Gartner says. \u201cAster Analytics can be shipped as software only, as an appliance, or as a service in the cloud on AWS or a Teradata-managed cloud. Configuration is available on the platform's own massively parallel processing shared-nothing database or directly on Hadoop. The most popular use of Aster Analytics is for customer analytics in its various forms. Teradata is a new entrant to the Magic Quadrant. It is a Niche Vendor, largely due to its low level of adoption and lack of broad usability and applicability.\u201d<\/p>

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The Leaders<\/h2>
\u201cLeaders have a strong presence and significant mind share in the market,\u201d according to Gartner. \u201cResources skilled in their tools are readily available. Leaders demonstrate strength in depth and breadth across a full development and implementation process. While maintaining a broad and long-established customer base, Leaders are also nimble in responding to rapidly changing market conditions, driven by the overwhelming interest in data science across all industries and domains. Leaders are in the strongest position to in\u0192luence the market's growth and direction. They address all industries, geographies, data domains and use cases. This gives them the advantage of a clear understanding and strategy for the data science market.\u201d<\/p>

IBM<\/h2>
IBM is based in Armonk, NY. \u201cAs a large enterprise software vendor, IBM offers a wide array of analytics solutions,\u201d Gartner says. \u201cFor this Magic Quadrant, we evaluated SPSS Modeler and, to a lesser extent, SPSS Statistics. IBM is again a Leader. Its position on the Ability to Execute axis has dropped slightly, as its attention is split between SPSS Modeler and its new data science platform, IBM Data Science Experience (DSx). DSx is not evaluated in this Magic Quadrant, but it does strengthen IBM's position on the Completeness of Vision axis.\u201d<\/p>

KNIME<\/h2>
KNIME ("Konstanz Information Miner") is based in Zurich, Switzerland. \u201cIts open-source KNIME Analytics Platform is a fully functional and scalable platform for advanced and expert data scientists,\u201d Gartner says. \u201cIts commercial offering provides extended value-added proprietary tools, service layers and support. KNIME's platform is used in a number of industries. Its strongest presence is in manufacturing and life sciences, but it also has a strong presence in the financial services, education, government and retail sectors. KNIME remains a Leader and a popular choice for data science needs. Its strong Ability to Execute is attributable to solid interactions with customers via its sales team, its responsiveness and its community support.\u201d<\/p>

RapidMiner<\/h2>
RapidMiner is based in Boston, MA. \u201cIt offers its RapidMiner GUI-based data science platform for the full spectrum of data scientists, including beginners,\u201d Gartner explains. \u201cIt also offers access to the core open-source code for expert data scientists who prefer to program. RapidMiner's data science studio is available both as a free edition and as a commercial edition, which offers additional functionality for working on larger datasets and connecting to more data sources. RapidMiner is again a Leader, owing to its market presence, the volume of client inquiries that Gartner receives about it, its user community, and its well-rounded product that addresses most data science use cases well.\u201d<\/p>

SAS<\/h2>
SAS is based in Cary, NC. \u201cIt provides a vast array of software products for analytics and data science,\u201d Gartner says. \u201cThis evaluation covers SAS Enterprise Miner (EM) and the line of SAS products with names starting with the word \u2018Visual,\u2019 such as Visual Statistics and Visual Data Mining and Machine Learning, which we refer to collectively as the Visual Analytics suite (VAS). SAS's focus is now interactive modeling in its VAS, but it continues to support its traditional programmatic approach (for Base SAS). SAS's delivery of these capabilities has enabled it to retain a strong position in the Leaders quadrant.\u201d<\/p>

The Challengers<\/h2>
\u201cChallengers have an established presence, credibility, viability and robust product capabilities,\u201d according to Gartner. \u201cThey may not, however, demonstrate thought leadership and innovation to the same degree as Leaders. There are two main types of Challenger: 1. Long-established data science 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 in\u0192luential. If they simply continue doing what they have been doing, their growth and market presence may decrease. 2. Vendors well-established in adjacent markets that are entering the data science market with solutions that can reasonably be considered by most customers.\u201d<\/p>

Alteryx<\/h2>
Alteryx is based in Irvine, CA. \u201cIt offers a data science platform geared toward citizen data scientists,\u201d says Gartner. \u201cThe platform's self-service data preparation capabilities and advanced analytics enable business users to blend data from internal and external sources and then to analyze it using predictive, prescriptive tools, using the same UI in a single work\u0192low. Integration with open-source R enables expert data scientists to extend functionality by creating and running custom R scripts. Alteryx also offers a cloud-based analytics gallery for collaboration, sharing and version control of work\u0192lows. This year, Alteryx is at the bottom right of the Challengers quadrant. Its move out of the Visionaries quadrant is due partly to solid customer growth, which has resulted in a higher score for Ability to Execute.\u201d<\/p>

Angoss<\/h2>
Angoss is based in Toronto, Ontario, Canada. \u201cIt provides a suite of visual-based advanced data mining and predictive analytics tools for expert data scientists and citizen data scientists. It also offers prescriptive analysis and optimization capabilities that can be used to solve complex decision-making problems. Although its overall scores from reference customers for product satisfaction were good, its scores for vision were lower, primarily due to performance and scalability challenges for emerging use cases using very large datasets (which are, however, partially addressed by its new Hadoop\/Spark integration).\u201d<\/p>

MathWorks<\/h2>
MathWorks is a privately held corporation headquartered in Natick, MA. \u201cIts two major products are Matlab and Simulink, but only Matlab met the inclusion criteria for this Magic Quadrant,\u201d Gartner says. \u201cA new entrant to the Magic Quadrant, MathWorks is a Challenger. Its Ability to Execute is driven by its prominent presence in data-science-related domains, but it still has to demonstrate its vision by producing a significantly easier-to-use platform that can address the concerns of corporate data scientists, especially for customer-facing use cases like marketing, sales and CRM.\u201d<\/p>

Quest<\/h2>
Quest is headquartered in Aliso Viejo, CA. \u201cAs a result of the sale of Dell Software to Francisco Partners and Elliott Management, completed in November 2016, Quest now sells the Statistica Analytics Platform. Quest is a Challenger in this year's Magic Quadrant, whereas Dell was a Leader in last year's. This shift is largely due to the second change in ownership for Statistica in three years and to the lack of some product improvements central to native cloud and some Spark capabilities. The platform's large customer base and all-around strength on-premises across the production, business exploration and advanced prototyping use cases merit a position as a strong Challenger.\u201d<\/p>

The Visionaries<\/h2>
\u201cVisionaries are typically smaller vendors or newer entrants which embody trends that are shaping, or will shape, the market,\u201d Gartner explains. \u201cThere may, however, be concerns about these vendors' ability to keep executing effectively and to scale as they grow. They might also be hampered by a lack of awareness of them in the market, and therefore by insufficient momentum. Visionaries have a strong vision and a roadmap for achieving it. They are innovative in their approach to their platform offerings and provide strong functionality for the capabilities they address. Typically, however, there are gaps in the breadth and completeness of their capabilities.\u201d<\/p>

Alpine Data<\/h2>
Alpine Data is based in San Francisco, CA. \u201cIt offers a citizen data science platform, Chorus, with a focus on enabling collaboration between business analysts and front-line operational users in building and running analytic workflows,\u201d Gartner says. \u201cChorus does not require users to move their data from where it resides, be that within a traditional database or Apache Hadoop. This is important for industries in which organizations are unable to move their data between servers for security reasons. As last year, Alpine is in the Visionaries quadrant. It has limited visibility in this market, despite its continued growth in enterprise data science environments.\u201d<\/p>

Dataiku<\/h2>
Dataiku is headquartered in New York City and has a main office in Paris, France. \u201cIt has chosen to take a very ambitious path with its data science platform, Data Science Studio (DSS),\u201d Gartner says. Dataiku is a new entrant to the Magic Quadrant. Its placement as a Visionary is due to the innovative nature of the DSS, especially its openness and ability to cater to different skill levels, which enables better collaboration. Dataiku's Ability to Execute suffers from limited user adoption and deficiencies in its data access and exploration capabilities.\u201d<\/p>

Domino Data Lab<\/h2>
Domino Data Lab is headquartered in San Francisco, CA. \u201cIt offers the Domino Data Science Platform, with a focus on openness, collaboration and reproducibility of models,\u201d Gartner says. \u201cFounded in 2013, Domino has gained significant attention and momentum in only a few years. A new entrant to the Magic Quadrant, Domino is a Visionary due to its support for a wide range of open-source technologies and offer of freedom of choice to data scientists. Its innovation scores are among the highest of any vendor in this Magic Quadrant. To raise its Ability to Execute score, Domino will need to improve its data access and data preparation capabilities, and devote resources to strengthening its UI and functionality for the business exploration use case.\u201d<\/p>

H2O.ai<\/h2>
H2O.ai is based in Mountain View, CA. \u201cIt offers an open-source data science platform, H2O, with a focus on fast execution of cutting-edge machine-learning capabilities,\u201d according to Gartner. \u201cThis evaluation focuses on the following products and versions, which were generally available at the end of July 2016: H2O Flow, Steam and Sparkling Water. The company has recently launched deep-learning capabilities in the form of Deep Water. A new entrant to the Magic Quadrant, H2O.ai is a Visionary because of its solid range of highly scalable machine-learning implementations. It is one of the machine-learning partners most frequently mentioned by companies such as IBM and Intel.\u201d<\/p>

Microsoft Corp.<\/h2>
"Microsoft has bundled its data science tools into its cloud-based Microsoft Cortana Intelligence Suite and Microsoft R Server. This evaluation concentrates on the Azure Machine Learning platform, part of the Cortana Intelligence Suite, which includes many additional components, such as Azure Data Factory, Azure Stream Analytics and Power BI. During the past three years, Microsoft has undertaken a remarkable revamp in the context of machine learning. It entered the market with a very limited product offering and remains a Visionary for its market-leading data science cloud solution.\u201d<\/p>

The Niche Players<\/h2>
\u201cNiche Players demonstrate strength in a particular industry, or pair well with a specific technology stack,\u201d Gartner explains. \u201cSome Niche Players demonstrate a degree of vision, which suggests they might become Visionaries, but they are struggling to make this vision compelling. They may also be struggling to develop a track record of continual innovation. Other Niche Players have the opportunity to become Challengers if they continue to develop their products with a view to improving their overall execution.\u201d<\/p>

FICO<\/h2>
FICO is based in San Jose, CA. \u201cIts Decision Management Suite (DMS) offers analytic tools, including Model Builder, Optimization Modeler and Decision Modeler,\u201d Gartner explains. \u201cFICO is again positioned as a Niche Player. It is an especially strong choice for organizations in the financial services sector and for those that depend on scorecard modeling. FICO's placement stems largely from its need to catch up with, and innovate at the level of, many other vendors, which results in a low overall score for Completeness of Vision. Its Ability to Execute score is dragged down by low market traction in other vertical areas and limited machine-learning capabilities. FICO needs to deliver on its many product roadmap promises in the near future.\u201d<\/p>

SAP<\/h2>
SAP is based in Waldorf, Germany. \u201cIt has rebranded its data science platform, which is now called SAP BusinessObjects Predictive Analytics (BOPA),\u201d Gartner says. \u201cIn addition, SAP has a broad array of other analytics offerings, including the SAP BusinessObjects Business Intelligence platform and SAP BusinessObjects Lumira for self-service data discovery, which are not included in this evaluation. A fall in SAP's Ability to Execute score has caused it to drop slightly, from near the bottom of the Challengers quadrant to near the top of the Niche Players quadrant. SAP is lagging behind in terms of Spark integration, open-source support, Python and notebook integration, and cloud deployment.\u201d<\/p>

Teradata<\/h2>
Teradata is headquartered in Dayton, OH. \u201cIt offers a data science platform called Aster Analytics, which has three layers: analytic engines, prebuilt analytic functions, and the Aster AppCenter for analysis and connectivity to external business intelligence (BI) tools,\u201d Gartner says. \u201cAster Analytics can be shipped as software only, as an appliance, or as a service in the cloud on AWS or a Teradata-managed cloud. Configuration is available on the platform's own massively parallel processing shared-nothing database or directly on Hadoop. The most popular use of Aster Analytics is for customer analytics in its various forms. Teradata is a new entrant to the Magic Quadrant. It is a Niche Vendor, largely due to its low level of adoption and lack of broad usability and applicability.\u201d<\/p>

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16 top data science platform providers

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