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Global Data Science Platform Market Research Report 2021-2026: Increased Digitalization and Emerging Technologies, Such as Big Data, Ml, Analytics, Iot, and Ai, to Drive the Market Growth

·6-min read
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Author of The Report Expects The Global Data Science Platform Market

Author of The Report Expects The Global Data Science Platform Market
Author of The Report Expects The Global Data Science Platform Market

Dublin, April 22, 2022 (GLOBE NEWSWIRE) -- The "Data Science Platform Market with COVID-19 Impact Analysis by Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical & Region - Global Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.

The Data Science Platform market size is projected to grow from USD 95.3 billion in 2021 to USD 322.9 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 27.7% during the forecast period.

The Data Science Platform industry is driven by Astonishing growth of big data, however, Rising in adoption of cloud-based solutions, Rising application of the data science platform in various industries and Growing need to extract in-depth insights from voluminous data to gain competitive advantage.

Based on Component, the service segment is expected to grow at a higher CAGR during the forecast period
The service segment of the Data Science Platform market is further segmented into professional services (consulting, support and maintenance, and deployment and integration) and managed services. This section discusses each service subsegment's market size and growth rate based on type (for selected subsegments) and region.

Based on deployment mode, on-premises segment is segmented to account for a larger market size during the forecast period

Most enterprises mostly in heavily regulated industry verticals, such as BFSI, healthcare and life sciences, and manufacturing, opt for the on-premises deployment model of the data science platform. Large enterprises with sufficient IT resources are expected to opt for the on-premises deployment model.

On-premises is the most reliable deployment mode, which an enterprise can rely on for the high level of control and security. Enterprises need to purchase the license or a copy to deploy the cloud-based platform. If an organization uses on-premises storage, they might also need to have IT staff to maintain and manage servers.

Based on business function, Finance and Accounting segment to grow at a higher CAGR during the forecast period

Financial services firms and banks, for example, use financial data science: Forward-thinking banks and FinTech's improve customer service by evaluating transactional and behavioral data using various data science methods. Data science is already being used by some of the world's largest banks to acquire insights from previous customer purchases, engagements, and accounts that are most relevant to them.

Investing items, insurance coverage, bank accounts, and mortgages are now the most common notices they receive. Data science is also providing insights into how well a product sells or to whom it sells, allowing financial services organizations and banks to build consumer products, policies, and investment instruments that will sell well in the future.

Based on organization size, large enterprise segment to account for a larger market size during the forecast period Most Large enterprises considered in the report are organizations with an employee size of more than or equal to 1,000.

The adoption of the data science platform among large enterprises is high due to the ever-increasing adoption of the cloud, and the trend is expected to continue during the forecast period. Large enterprises accumulate huge chunks of data that can be attributed to the widespread client base. In large enterprises, data plays a major role in evaluating the overall performance of organizations.

Large enterprises are leveraging the data science platform coming from various sources, for instance, social media feeds or sensors and cameras, each record needs to be processed in a way that preserves its relation to other data and sequence in time.

APAC is expected to grow at a higher CAGR during the forecast period

Asia Pacific (APAC) has continually presented lucrative market opportunities for Data Science Platform Solutions and service providers with a notable increase in Data Science Platform across its developed and emerging countries., Japan, China, and India have displayed ample growth opportunities in the Data Science Platform market.

Owing to a rapidly proliferating technology-backed economical structure, APAC is expected to emerge as the fastest-growing region in Data Science Platform software and services demand during the forecast period.

Premium Insights

  • Increased Digitalization and Emerging Technologies, Such as Big Data, Ml, Analytics, Iot, and Ai, to Drive the Market Growth

  • BFSI Vertical to Account for the Largest Market Share During the Forecast Period

  • North America to Account for the Largest Market Share in 2021

  • Marketing Business Function and BFSI Industry Vertical to Account for the Largest Shares in the Data Science Platform Market in 2021

Market Dynamics

Drivers

  • Astonishing Growth of Big Data

  • Rising Adoption of Cloud-Based Solutions

  • Rising Application of the Data Science Platform in Various Industries

  • Growing Need to Extract In-Depth Insights from Voluminous Data to Gain Competitive Advantage

Restraints

  • Lack of Clarity on Business Problem

  • Stringent Government Rules and Regulations

Opportunities

  • Higher Inclination of Enterprises Toward Data-Intensive Business Strategies

  • Rising Adoption of Advanced Technologies

Challenges

  • Lack of Adequately Skilled Workforce

  • Data Privacy, Security, and Reliability Concerns

Use Cases

  • Banking, Financial Services and Insurance: Use Case 1: Cba Uses H2O Ai Cloud Capabilities to Generate Better Customer and Community Outcomes at a Greater Pace and Scale

  • Telecommunication and Information Technology: Use Case 2: At&T Uses H2O.Ai to Address a Broad Range of Use Cases, from Marketing and Sales to Network Availability and Maintenance

  • Retail and Consumer Goods: Use Case 3: Azure Databricks Offers a Unified Data Analytics Platform for Reckitt to Improve Cost Optimization

  • Government and Defense: Use Case 4: Project Odyssey Uses Sas Software for Data Integration and Intelligent Data Mining of Large Sets of Ballistics and Crime Information Data

  • Media and Entertainment: Use Case 5: Comcast Uses H2O Ai Cloud to Enhance Customer Experience

  • Manufacturing: Use Case 6: Engie Digital Uses Amazon Sagemaker for Predictive Maintenance at Power Plants

  • Healthcare and Life Sciences

  • Use Case 7: Department of Nursing Uses Tibco Data Science Software to Strengthen the Training Process

  • Transport and Logistics: Use Case 8: Transport for London Uses Rapidminer to Aid the Performance of the Road Network

  • Energy and Utilities: Use Case 9: Blue River Analytics Uses Tibco Data Science to Save Time, Reduce Costs, and Increase Productivity

Regulatory Implications

  • General Data Protection Regulation

  • Health Insurance Portability and Accountability Act

  • Payment Card Industry Data Security Standard

  • Sarbanes-Oxley Act of 2002

  • Soc 2 Type Ii Compliance

  • Iso/Iec 27001

  • The Gramm-Leach-Bliley Act

Company Profiles

Major Players

  • IBM

  • Google

  • Microsoft

  • Mathworks

  • Sas

  • Cloudera

  • Teradata

  • Tibco

  • Aws

  • Alteryx

  • Rapidminer

  • Databricks

  • Snowflake

  • H2O.Ai

  • Anaconda

  • Altair

Smes/Start-Ups

  • Sap

  • Domino Data Lab

  • Dataiku

  • Datarobot

  • Apheris

  • Comet

  • Databand

  • Dotdata

  • Explorium

  • Noogata

  • Tecton

  • Spell

  • Arrikto

  • Iterative

For more information about this report visit https://www.researchandmarkets.com/r/hpn29f

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