The market for Data Science and Machine Learning is growing and promising

The market for Data Science and Machine Learning is growing and promising

As part of its Magic Quadrant 2021 (the first was carried out in 2014), published on March 1, 2021, Gartner has released a detailed analysis of the solutions proposed by 20 players related to Data Science and Machine Learning. This report should help many organizations that have adopted the “data culture” and are using artificial intelligence in which they expect rationalization, structuration and important and significant benefits.

What is the Gartner « Magic Quadrant » ?

The Gartner “Magic Quadrant” is the result of detailed research and analysis of the market and players in many areas of technology. Gartner, which is the author of this approach, uses a clear, simple and effective visual to provide companies in search of such products and services the means to choose their suppliers and providers. This visual distinguishes four blocks or groups of players selected each year :

  • The “Leaders”: players combining an excellent vision of the market with a good ability to execute;
  • The “Challengers“: players with a solid capacity for execution but suffering from an imperfect view of the market;
  • The “Visionaries“: players with an excellent vision of the market but not yet performing well enough to be leaders;
  • The “Niche players”: players who focus on certain specific scope of activities (algorithms, databases, machine learning, etc.) and / or some verticals (pharma, aeronautics, etc.) in the market with a capacity for execution and a vision of the market that is lagging behind their competitors.

The criteria used by Gartner to position the various players on the “Magic Quadrant” essentially relate to the functionalities of the products and services offered, the quality and efficiency of customer support, the go-to-market and the business model, the geographical implementation and local partnerships, the customer assessments and experience, the information and communication, he ability to take into account specific customer requests.

What market are we talking about ?

The Data Science and Machine Learning market (DSML) is experiencing a very strong growth driven by the proliferation of data from all fields of activities. Gartner defines a Data Science and Machine Learning (DSML) platform « as a core product and a portfolio of products, components, libraries and frameworks that are cohesively integrated (including owners, partners and open source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers, and machine learning (ML) specialists.”

The DSML platform offers the following features:

  • Provision of a mix of basic and advanced functionalities essential to the creation of DSML solutions (mainly predictive and prescriptive models);
  • Integration of these solutions into business processes, surrounding infrastructure, products and applications;
  • Supports the sustainable consumption of information derived from the platform and provides functionality to quantify and track the value of data science projects;
  • Assistance and support to data science professionals (data scientist, etc.);
  • Support for data science lifecycle tasks such as support.

Overview of the 20 players reviewed in 2021

Gartner has evaluated 20 platform providers around the world. Not surprisingly, the major groups in the field fall into the category of “leaders” and “visionaries” (see graph opposite). Detailed analysis of the “performance” of each player is available in the Gartner report

Two main insights from this study

This report brings to us two important insights :

A solid and promising market

Despite the Covid19 pandemic crisis, and unlike several other markets (transport, tourism, etc.), the DSML market has shown strong resilience and has been able to continue to grow and innovate. The overall turnover of DSML platform software increased by 17.5% in 2019 (turnover of 4 billion USD) against 24.3% in 2018 (3.4 billion USD) to represent the second segment at the fastest growing analytics and BI software market. Its share of the global analytics and BI market increased from 15.1% in 2018 to 16.1% in 2019. It is mainly the small and young suppliers in this market that are fueling hyper-growth.

Innovation in the DSML market is accelerating

Innovation is at the heart of the DSML market. It will be growing in almost every area, especially the following:

  • The DSML platform evolves strongly over time: platforms composed of several components have become the norm as suppliers develop their own components or partner with other suppliers to expand their offerings.
  • Focus on open source: all DSML platforms use and integrate more and more, to varying degrees, free software (open source).
  • DSML Platform Consistency: The increase in what Gartner calls “componentization” and the incorporation of more open source software creates more potential for fragmented and sometimes inappropriate solutions. The need to access multiple components and platforms for complete and robust capabilities must be weighed against the opportunity to access all functionality transparently and consistently. As offers embrace a heterogeneous environment, cohesion becomes more and more important. As offerings expand to provide more capacity and keep pace with emerging technologies, it is crucial that they support the ability not only to manage multiple components, but also to easily and seamlessly access them at any time. from the platform.
  • Model and Data Repositories: There is a trend to provide a means of tracking and sharing data and analytical artifacts generated as part of the model building and deployment process. This is essential for the deduplication of effort, governance and corporate scalability of data science initiatives.”
  • More “collaborative”: as in other areas of decision support, users who access DSML platforms want to be able to work more and more in collaborative mode because they want to exchange and share in real time throughout of the data science lifecycle.