Key Takeaways: The 2022 Gartner Market Guide for Multipersona Data Science and Machine Learning Platforms
Gartner recently released its 2022 Market Guide for Multipersona Data Science and Machine Learning Platforms, meant to cover emerging markets currently in limbo.
Analyst house Gartner, Inc. recently released its new Market Guide for Multipersona Data Science and Machine Learning Platforms. The researcher’s Market Guide series is meant to cover new and emerging markets where software products and organizational requirements are in limbo. Gartner’s Market Guides can be a great resource for understanding how a fledgling space may line up with current and future technology needs.
According to Gartner, “A multipersona data science and machine learning (DSML) platform is a cohesive and composable portfolio of products and capabilities, offering augmented and automated support to a diversity of user types and their collaboration. The primary aim of “multipersona DSML platforms” is to create value through democratization. Increasingly, this is complemented by offering additional analytics capabilities for business intelligence, visualization and exploration.”
Multipersona data science and machine learning tools enable more people in an organization to utilize key capabilities for advanced analytics. Gartner is quick to recommend against limiting these platforms to model prototyping and development. Data and analytics leaders should put these products to use to fully support the deployment of data science and machine learning models. This “should” cover not only technical aspects but also governance, risk management, and responsible AI ethics.
Gartner highlights the following providers in the multipersona data science and machine learning market: 4Paradigm, Aera, Aible, ALEIA, Alibaba Cloud, Altair, Alteryx, Amazon Web Services (AWS), C3 AI, Dataiku, DataRobot, dotData, Einblick Analytics, Google, IBM, KNIME, MathWorks, Microsoft, Palantir, Peak, Qlik, RapidMiner, SAS, and Subex. Solutions Review editors read the report, available here, and pulled out the key takeaways.
Growth in this business software segment is growing in pace and mirrors the investments made by organizations in data science and machine learning initiatives. Largely evolving from strategy to execution, organizations that have invested in these types of technologies are now requiring a “stronger and faster return on value” according to Gartner. These organizations, as a result of their technology adoption, are less limited by the availability of scarce expert data science resources.
This market is being influenced mostly by role-based capabilities and an emphasis on either democratization or industrialization. This is a key differentiator among key vendors in this space, with some actively focused on democratization while others zone in on code-based and expert users who are responsible for complex AI systems. Still other providers are adding value on technical support for MLOps or specific topics such as governance risk management or security.
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