A data science platform is defined as a framework of the entire life cycle of a data science project. Market Research Future (MRFR) has published a research report about the global data science platform market that predicts growth for the market during the forecast period between 2017 and 2023. In terms of cash, the market is expected to be worth the US $ 115 bn during the forecast period.
The factors that contribute to the growth of global data science platform market include advancement of big data technologies, data explosion, demand for big data analysis to gain deeper insights into consumer buying patterns, global rapid advancement in big data technologies, growing concern of data security & protection, increase in data collection & analysis from mobile devices, and realization of importance of data science by organizations.
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The global data science platform market has been segmented on the basis of business function, deployment model, vertical and region. Based on business function, this market has been segmented into customer support, human resources, logistics, marketing, operations, risk management, sales, and others. On the basis of deployment model, the market has been segmented into on-demand deployment and on-premise deployment. As per the vertical-based segmentation, the market has been segmented into Banking, Financial Services and Insurance (BFSI), defense, energy & utilities, government, healthcare, Information Technology (IT), retail, transportation, and others.
The regional segmentation of the global data science platform market segments the market into continent-based regional markets namely North America, Europe, Asia Pacific, and the rest of the world (RoW). During the forecast period, the North American market is expected to hold the largest market share. This is because many capital-intensive industries are present across the region. Enterprises are embracing data science platform as the revered platform that helps to score a competitive edge in the marketplace. Many key players of this market are based in North America especially in the United States of America (USA), the biggest economy in this region. Other important country-specific markets in this region are Canada and Mexico.
Europe is another significant market because, after North America, Europe is the most technologically advanced region. The most important country-specific markets in this region include France, Germany, Italy, Spain, and the United Kingdom (UK). During the forecast period, the Asia Pacific region has been anticipated to emerge as the fastest growing market. Factors aiding the market growth in this region include digitalization, industrialization, and several smart city initiatives by the governments of various countries in this region. The significant country-specific markets in this region are China, India, and Japan, followed by the rest of Asia Pacific.
RoW segment covers Latin America and Middle East & Africa (MEA) region. In Latin America, the market is limited due to limited technological advancement. Economies in this region that have the potential to emerge as strong markets are Argentina and Brazil. In the MEA region, the market is small and limited. Reasons for the limited market growth in this region are lack of awareness, lack of education, lack of technological development, and political instability.
The key players in the global data science platform market include Alteryx Inc. (USA), Continuum Analytics Inc. (USA), Dataiku (France), DataRobot Inc. (USA), Domino Data Lab (USA), Google, Inc. (the USA), IBM Corporation (USA), Microsoft Corporation (USA), RapidMiner Inc. (USA), Sense Inc. (USA), and Wolfram (USA).
Latest Industry News
- RapidMiner™, the leading data science platform for analytics teams, has launched the RapidMiner Artificial Intelligence (AI) Cloud, a unified Software as a Service (SaaS) platform that has been designed to make it easy for teams to build, train, manage, and deploy predictive models in the cloud. 11 OCT 2018
- Generally associated with autonomous vehicles. deep learning, and other higher-end enterprise and scientific workloads, and gaming, Graphics processing unit (GPU) leader Nvidia, is mounting an open source end-to-end GPU acceleration platform and ecosystem directed at data analytics and machine learning, domains heretofore within the CPU realm. 10 OCT 2018
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