Market Overview:
The global data science platform market is expected to grow from USD 1.02 Billion in 2018 to USD 3.73 Billion by 2030, at a CAGR of 17.5% during the forecast period. The growth of the market can be attributed to the increasing demand for big data and analytics solutions, growing need for data-driven decision-making, and rising focus on innovation through R&D activities. However, lack of skilled professionals is restraining the growth of the market to some extent. On-premises deployment type is expected to hold a larger share of the global data science platform market during the forecast period.
Product Definition:
A data science platform is a software tool that enables data scientists to easily perform complex analytics and machine learning tasks on large amounts of data. Data science platforms are important because they make it easier for data scientists to do their jobs, which in turn makes it easier for companies to take advantage of the insights that can be gleaned from big data.
On-Premises:
On-premises, it's usage.
On-premises software is installed and run from the provider’s server. The software is free from any third party access. It has a high level of data security as no one but the authorized person can access the system. The information stored on on-premise systems cannot be accessed by unauthorized users or hackers.
On-Demand:
On-demand data science platform is a service that allows users to gain access to data and tools required for the development of predictive models. It also helps in deploying these models in the cloud, which further enables scalability and flexibility. The major advantage offered by on-demand platforms is that they allow users to pay according to their usage instead of purchasing an entire software package or a server with preloaded software.
Application Insights:
The marketing, sales, logistics, risk, customer support and human resources applications are expected to witness substantial growth over the forecast period. The marketing application is projected to register a CAGR of XX% in terms of revenue from 2017 to 2030 owing to the extensive use of data science platforms for managing digital campaigns across various channels such as email, social media & blog posts and online ads. The growing need for effective content marketing strategies has led major companies including Microsoft Corporation; AT&T Inc.; IBM Corporation; Salesforce.com Inc.; and Oracle Corporationto incorporate data science into their processes thus contributing towards segment growth over the forecast period.
Regional Analysis:
North America dominated the global data science platform market in 2017. The growth of this region can be attributed to the presence of a large number of vendors offering advanced solutions for data analytics and business intelligence. Moreover, increasing investments by companies in order to gain an advantage over their competitors is driving the demand for these platforms in North America. For instance, IBM Corporation has invested around USD X million from 2016 to 2018 on R&D activities related to Watson IoT Platform’s cognitive capabilities and edge computing architecture.
Asia Pacific is expected to grow at a lucrative rate during the forecast period owing to increasing adoption across various industries such as retail, telecom & IT services, healthcare & life sciences among others which are generating huge amounts of digital data every day that needs analysis and processing before making any meaningful insights available or acting upon it timely manner (i.e., real-time).
Growth Factors:
- Increasing demand for data-driven decision making: The need for data-driven decision making is increasing due to the ever-growing complexity of business problems. Organizations are looking to adopt data science platforms to enable them to make better decisions faster.
- Proliferation of big data and IoT: The growth of big data and IoT is leading to an increase in the volume and variety of data that needs to be processed. Data science platforms can help organizations manage this large volume of data more effectively and efficiently.
- Emergence of new AI applications: With the emergence of new AI applications such as machine learning, natural language processing, and predictive analytics, there is a greater demand for platforms that can support these applications effectively. Data science platforms are well suited for this purpose as they provide a comprehensive environment for developing, testing, and deploying AI applications.
Scope Of The Report
Report Attributes
Report Details
Report Title
Data Science Platform Market Research Report
By Type
On-Premises, On-Demand
By Application
Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations
By Companies
Microsoft, IBM, Google, Wolfram, Datarobot, Cloudera, Rapidminer, Domino Data Lab, Dataiku, Alteryx, Continuum Analytics, Bridgei2i Analytics, Datarpm, Rexer Analytics, Feature Labs
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
212
Number of Tables & Figures
149
Customization Available
Yes, the report can be customized as per your need.
Global Data Science Platform Market Report Segments:
The global Data Science Platform market is segmented on the basis of:
Types
On-Premises, On-Demand
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- Microsoft
- IBM
- Google
- Wolfram
- Datarobot
- Cloudera
- Rapidminer
- Domino Data Lab
- Dataiku
- Alteryx
- Continuum Analytics
- Bridgei2i Analytics
- Datarpm
- Rexer Analytics
- Feature Labs
Highlights of The Data Science Platform Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- On-Premises
- On-Demand
- By Application:
- Marketing
- Sales
- Logistics
- Risk
- Customer Support
- Human Resources
- Operations
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Data Science Platform Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
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8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
A data science platform is a software application that helps data scientists work with big data. It can include tools for managing and analyzing large datasets, as well as features to help researchers find patterns in the data.
Some of the major companies in the data science platform market are Microsoft, IBM, Google, Wolfram, Datarobot, Cloudera, Rapidminer, Domino Data Lab, Dataiku, Alteryx, Continuum Analytics, Bridgei2i Analytics, Datarpm, Rexer Analytics, Feature Labs.
The data science platform market is expected to grow at a compound annual growth rate of 17.5%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Data Science Platform Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Data Science Platform Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Data Science Platform Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Data Science Platform Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Data Science Platform Market Size & Forecast, 2020-2028
4.5.1 Data Science Platform Market Size and Y-o-Y Growth
4.5.2 Data Science Platform Market Absolute $ Opportunity
Chapter 5 Global Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Market Size Forecast by Type
5.2.1 On-Premises
5.2.2 On-Demand
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Market Size Forecast by Applications
6.2.1 Marketing
6.2.2 Sales
6.2.3 Logistics
6.2.4 Risk
6.2.5 Customer Support
6.2.6 Human Resources
6.2.7 Operations
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Data Science Platform Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Data Science Platform Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Analysis and Forecast
9.1 Introduction
9.2 North America Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Market Size Forecast by Type
9.6.1 On-Premises
9.6.2 On-Demand
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Market Size Forecast by Applications
9.10.1 Marketing
9.10.2 Sales
9.10.3 Logistics
9.10.4 Risk
9.10.5 Customer Support
9.10.6 Human Resources
9.10.7 Operations
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Analysis and Forecast
10.1 Introduction
10.2 Europe Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Market Size Forecast by Type
10.6.1 On-Premises
10.6.2 On-Demand
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Market Size Forecast by Applications
10.10.1 Marketing
10.10.2 Sales
10.10.3 Logistics
10.10.4 Risk
10.10.5 Customer Support
10.10.6 Human Resources
10.10.7 Operations
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Market Size Forecast by Type
11.6.1 On-Premises
11.6.2 On-Demand
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Market Size Forecast by Applications
11.10.1 Marketing
11.10.2 Sales
11.10.3 Logistics
11.10.4 Risk
11.10.5 Customer Support
11.10.6 Human Resources
11.10.7 Operations
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Analysis and Forecast
12.1 Introduction
12.2 Latin America Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Market Size Forecast by Type
12.6.1 On-Premises
12.6.2 On-Demand
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Market Size Forecast by Applications
12.10.1 Marketing
12.10.2 Sales
12.10.3 Logistics
12.10.4 Risk
12.10.5 Customer Support
12.10.6 Human Resources
12.10.7 Operations
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Market Size Forecast by Type
13.6.1 On-Premises
13.6.2 On-Demand
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Market Size Forecast by Applications
13.10.1 Marketing
13.10.2 Sales
13.10.3 Logistics
13.10.4 Risk
13.10.5 Customer Support
13.10.6 Human Resources
13.10.7 Operations
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Data Science Platform Market: Competitive Dashboard
14.2 Global Data Science Platform Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 Microsoft
14.3.2 IBM
14.3.3 Google
14.3.4 Wolfram
14.3.5 Datarobot
14.3.6 Cloudera
14.3.7 Rapidminer
14.3.8 Domino Data Lab
14.3.9 Dataiku
14.3.10 Alteryx
14.3.11 Continuum Analytics
14.3.12 Bridgei2i Analytics
14.3.13 Datarpm
14.3.14 Rexer Analytics
14.3.15 Feature Labs
