Data Science Platform Market Trends, Industry Size With Domino Data Lab, Wolfram, DataRobot, Alteryx, Dataiku, Alpine Data, And More

Data science is moving from the edge profoundly to the business. Moreover, data science platform eases and simplifies the utilization of applications and application program interfaces to operationalize results. Data science platform gives real time data spilling and further developing data pipelines, which will support huge data into various classes, for example, significant and quick information.

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Data Science Platform

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The Global Data Science Platform Market accounted for USD 20.2 billion in 2017, and is projected to grow at a CAGR of 39.7% the forecast period of 2018 to 2025. Market Analysis for The data science platform market report contains data for historic years 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025 (Current Year Statistic Will Be Provided in Report). Some of the major players operating in the global data science platform market are

  • Google, Inc.
  • Domino Data Lab
  • IBM Corporation
  • Datarobot, Inc.
  • Microsoft Corporation
  • Wolfram
  • Continuum Analytics, Inc.
  • Dataiku
  • Bridgei2i Analytics
  • Feature Labs
  • Tableau
  • VMware
  • New Relic
  • Alation
  • Tera Data
  • SAP

Others are Alpine Data Labs, Datarpm, Rexer Analytics, Civis Analytics, Sense, Inc., Alteryx, Inc., Rapidminer, Inc., Snowflake, MeritDirect, Cazena, CBIG Consulting, Loggly, Clairvoyant, Arcadia, Experfy, Datatorrent, Jethro, SiSense, Thoughtworks, MuSigma, Cogito, Datameer. The global data science platform market is fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market in order to sustain in long run.

A huge rise in data volume is the main driving force for the data science platform. This development of Data volume will keep on evolving in the blink of an eye.

  • Data science platform helps in facilitating a wide state of relationship over data analysts, business investigators, data architects and engineers in various fields.
  • Data science platform enable the associations to get ready information, to manufacture models and operationalize investigation.

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Market Segmentation: Global Data Science Platform Market

  • The global data science platform market is segmented based on
    • Business function division,
    • Deployment model,
    • End-user application and
    • Geographical segments.
  • Based on Business Function division, the global data science platform market is segmented into
    • Marketing,
    • Sales,
    • Logistics,
    • Risk,
    • Customer support,
    • Human resources, and
  • Based on deployment model, the global data science platform market is segmented into
    • On-premises,
    • On-demand.
  • Based on the end-user application, the global data science platform market is segmented into following sectors
    • Banking, financial services, and insurance (BFSI),
    • Healthcare and life sciences,
    • Information technology and telecom,
    • Retail and consumer goods,
    • Media and entertainment,
    • Manufacturing,
    • Transportation and logistics,
    • Energy and utilities,
    • Government and defense, and others.
  • Based on geography, the global industrial agitation market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific and Middle East & Africa.
  • Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2025, Asia-Pacific is estimated to dominate the market.

Market Drivers and Restraints:

  • Global rapid advancement in big data technologies.
  • Need for ease of use technological methods to drive business effectiveness.
  • Growing demand for public cloud and adoption of artificial intelligence.
  • The evolution of internet of things (IoT).
  • Platform independency.
  • Huge Investment costs.
  • Data Explosion.

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