The Data Science Platform Market report provides a unique tool for evaluating the Market, highlighting opportunities, and supporting strategic and tactical decision-making. This report recognizes that in this rapidly-evolving and competitive environment, up-to-date marketing information is essential to monitor performance and make critical decisions for growth and profitability. It provides information on trends and developments, and focuses on markets capacities and on the changing structure of the Data Science Platform.
The global data science platform market (henceforth, referred to as the market studied) was valued at USD 31.05 billion in 2019, and it is expected to reach USD 230.80 billion by 2025, registering a CAGR of 39.7 % during the forecast period, 2020-2025.
The report presents the market competitive landscape and a corresponding detailed analysis of the major vendor/key players in the market. Top Companies in the Global Data Science Platform Market: Google, Inc., Microsoft Corporation, IBM Corporation, Cloudera, Inc., Dataiku SAS, RapidMiner, Inc, Wolfram Research, SAS Institute, Inc., H2O.ai, TIBCO Software Inc., Domino Data Lab, Inc., Anaconda Inc, Alteryx Inc., Teradata Corporation, WNS Global Services Pvt. Ltd., KNIME.com AG, BRIDGEi2i Analytics Solutions Pvt Ltd, and others.
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– IT managers who support a large team of data scientists in an enterprise setting are tasked with data governance and providing the infrastructure and tools that data scientists need. The proliferation of data science tools and applications available provides opportunities along with challenges.
– Data science encompasses many job titles across different industries and organizations, starting from analytics officer, to actuary, to research scientist. But regardless of all title posts, all the roles are united in unlocking strategic insights from data, for which business demand is stronger than ever before. IBM estimated that the need for data scientists would soar 28% by 2020. The increased usage of large amounts of structured and unstructured data in the various end-user industry is boosting the adoption of big data. For instance, according to Seagate Technology PLC, the global volume of data is expected to increase to 47 zettabytes and 163 zettabytes in 2020 and 2025, respectively, from 12 zettabytes in 2015.
– Specifically, a data science platform supports all four stages of the data science production line: data preparation, model development, DevOps, and business delivery. It relies on transparent data access, consistent metadata, strong enterprise governance, automated machine learning, and model building, operationalized model management, and tools that measure and improve its impact on business.
Healthcare Sector to Dominate the Market over the Forecast Period:
– Health care is a segment where individual pieces of data provide life-or-death importance, and many organizations fail to aggregate data adequately to gain insights into broader care processes. Drawing conclusions and making decisions based on data and efficiently using medical knowledge to improve safety and quality is impossible without a comprehensive data science strategy.
– The data science platform provides various medical research communities that can broadly share, integrate, and analyze historical, patient-level data from academic and industry phase III clinical trials. Such a rich data set is a part of data science and undoubtedly will help the pharmaceutical research and development segment.
– Further, players are offering new platforms that are cloud-agnostic and can be deployed as a single-tenant Platform on AWS, GCP, Azure, or Private Cloud. In June 2020, Aigenpulse introduced a new data intelligence platform designed to expedite drug discovery and development. Aigenpulse platform harnesses the latest artificial intelligence (AI) and machine learning tools to deliver advanced analytics to underpin scientific decision making.
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Industry Latest Development:
In June 2020 – IBM Corporation announced the general availability of its IBM Cloud Pak for Data V3.0 platform. The platform has grown exponentially from being a collection of IBM data services to a robust end-to-end data and AI solution. It provides a cohesive ecosystem to accelerate data estate modernization and drives AI adoption.
In February 2020 – Oracle announced the launch of the new Oracle Cloud Infrastructure Data Science Service, a native service on Oracle Cloud Infrastructure (OCI) that is designed to let teams of data scientists collaborate on the development, deployment, and maintenance of machine learning models. As Oracle grows the footprint of its “second generation” cloud, the new service aims to leapfrog the services other public cloud vendors offer for data scientists and the problems that come with typical data scientist workflows.
What are the market factors that are explained in the report?
-Key Strategic Developments: The study also includes the key strategic developments of the market, comprising R&D, new product launch, M&A, agreements, collaborations, partnerships, joint ventures, and regional growth of the leading competitors operating in the market on a global and regional scale.
-Key Market Features: The report evaluated key market features, including revenue, price, capacity, capacity utilization rate, gross, production, production rate, consumption, import/export, supply/demand, cost, market share, CAGR, and gross margin. In addition, the study offers a comprehensive study of the key market dynamics and their latest trends, along with pertinent market segments and sub-segments.
-Analytical Tools: The Global Data Science Platform Market report includes the accurately studied and assessed data of the key industry players and their scope in the market by means of a number of analytical tools. The analytical tools such as Porter’s five forces analysis, SWOT analysis, feasibility study, and investment return analysis have been used to analyze the growth of the key players operating in the market.
Customization of the Report: This report can be customized as per your needs for additional data up to 3 companies or countries or 40 analyst hours.
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