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$140+ Billion Data Science Platform Market Insights, 2020-2024 - Industry Expected to Witness a CAGR of 30%
[February 18, 2020]

$140+ Billion Data Science Platform Market Insights, 2020-2024 - Industry Expected to Witness a CAGR of 30%

DUBLIN, Feb. 18, 2020 /PRNewswire/ -- The "Data Science Platform Market by Component (Platform and Services), Business Function (Marketing, Sales, Logistics, and Customer Support), Deployment Mode (On-Premises and Cloud), Organization Size, Industry Vertical, and Region - Global Forecast to 2024" report has been added to's offering.

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The global data science platform market is projected to grow from USD 37.9 billion in 2019 to USD 140.9 billion by 2024, at a CAGR of 30% during the forecast period.

The market's revenue is classified primarily into revenues from platform and services. Services' revenue is associated with training and consulting integration and deployment, and support and maintenance services. The market is also segmented on the basis of components, deployment modes, industry verticals, organization size, business functions, and regions.

Increasing focus of enterprises on ease of use methods for driving business to boost the data science platform market growth

Major growth factors for the market include the growing need to gain insights from voluminous data to gain a competitive advantage and enable data-driven decisions in enterprises.

Logistics industry vertical to grow at the highest CAGR during the forecast period

Based on business functions, the data science platform market is divided into Banking, Financial Services, and Insurance (BFSI), telecom and IT, retail and eCommerce, healthcare and life sciences, manufacturing, energy and utilities, media and entertainment, transportation and logistics, government, and others (travel and hospitality, education, and research).

The logistics industry vertical would benefit from the technological and methodological advancements of big data. It is changing drastically due to internal and external pressures from the rising costs. ML has the potential to revolutionize the logistics industry vertical through determining the most important factors for the success of a supply network, while it also has the potential to learn in the process. The companies such as Google and Altair have launched AI-powered solutions that make the development of ML projects easier and assist in building and running ML applications.

Cloud segment to hold a higher market share during the forecast period

Organizations have been gradually recognizing the importance of data science platform and have started deploying it as per their needs in various business functions, such as supply chain, customer support, and sales and marketing. The cloud-based data science platform is a cost-effective and efficient way of managing data science platform issues across organizations. The availability of easy deployment options and miniml requirements of capital and time are expected to facilitate the growth of the cloud segment among global enterprises.

Asia Pacific to grow at the highest CAGR during the forecast period

The growth in APAC can be attributed to the focus of countries such as China, India, and Japan on the enhanced management of data to enable data-based business decisions and enhance business processes. The commercialization of big data, AI, and IoT technologies and the need for further advancements to leverage these technologies to the best are expected to drive the adoption of data science platform and services in the region.

APAC constitutes major economies, such as China, India, and Japan, which are expected to register high growth in the data science platform market. The verticals such as healthcare and life sciences, manufacturing, and retail and eCommerce are expected to rapidly adopt data science platform and services in the region. Tremendous technological adoption over the last few decades and ongoing innovations by companies such as Cloudera, Zaloni, and Temenos have helped the market to grow significantly in the region.

Key Topics Covered

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights
4.1 Attractive Opportunities in the Data Science Platform Market
4.2 Market: Top 3 Business Functions
4.3 Market: By Region
4.4 Market in North America, by Business Function and Industry Vertical

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers Enterprises Focusing on Ease of Use Methods to Drive Business Growing Need to Extract In-Depth Insights from Voluminous Data to Gain Competitive Advantage
5.2.2 Restraints Stringent Government Rules and Regulations
5.2.3 Opportunities Higher Inclination of Enterprises Toward Data-Intensive Business Strategies Rise in Adoption of Advanced Technologies
5.2.4 Challenges Lack of Adequately Skilled Workforce Data Privacy, Security, and Reliability Concerns
5.3 Industry Trends
5.3.1 Value Chain Analysis
5.3.2 Use Cases Use Case: Scenario 1 Use Case: Scenario 2 Use Case: Scenario 3
5.4 Impact of Technologies on Data Science Platform
5.4.1 Machine Learning
5.4.2 Deep Learning
5.4.3 Natural Language Processing
5.5 Regulatory Implications
5.5.1 General Data Protection Regulation
5.5.2 The International Organization for Standardization 27001
5.5.3 Payment Card Industry Data Security Standard
5.5.4 Basel Committee on Banking Supervision 239 Compliance
5.5.5 California Consumer Privacy Act
5.5.6 Health Insurance Portability and Accountability Act of 1996
5.5.7 Health Information Technology for Economic and Clinical Health Act
5.5.8 Sarbanes-Oxley Act of 2002
5.5.9 Personal Data Protection Act

6 Data Science Platform Market, By Component
6.1 Introduction
6.2 Platform
6.3 Services

7 Market, By Deployment Mode
7.1 Introduction
7.2 On-Premises
7.3 Cloud

8 Data Science Platform Market, By Organization Size
8.1 Introduction
8.2 Large Enterprises
8.3 Small and Medium-Sized Enterprises

9 Market, By Business Function
9.1 Introduction
9.2 Marketing
9.3 Sales
9.4 Logistics
9.5 Finance and Accounting
9.6 Customer Support
9.7 Others

10 Market, By Industry Vertical
10.1 Introduction
10.2 Banking, Financial Services, and Insurance
10.3 Telecom and IT
10.4 Retail and Ecommerce
10.5 Healthcare and Life Sciences
10.6 Manufacturing
10.7 Energy and Utilities
10.8 Media and Entertainment
10.9 Transportation and Logistics
10.10 Government and Defense
10.11 Others

11 Data Science Platform Market, By Region
11.1 Introduction
11.2 North America
11.3 Europe
11.4 Asia-Pacific
11.5 Middle East and Africa
11.6 Latin America

12 Competitive Landscape
12.1 Overview
12.2 Competitive Leadership Mapping
12.2.1 Visionary Leaders
12.2.2 Innovators
12.2.3 Dynamic Differentiators
12.2.4 Emerging Companies

13 Company Profiles
13.1 Introduction
13.2 Microsoft
13.3 IBM
13.4 Google
13.5 SAS Institute
13.6 Altair
13.7 Cloudera
13.8 Alteryx
13.9 Databricks
13.10 Wolfram
13.11 MathWorks
13.12 RapidMiner
13.13 Anaconda
13.14 Bridgei2i
13.15 Rexer Analytics
13.16 Domino Data Lab
13.17 Dataiku
13.18 Civis Analytics
13.20 RStudio
13.21 Rapid Insight
13.22 SAP

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