A prodigious CAGR of 29% is predicted for the sector by 2033. According to FMI, the revenue share of the data science platform market is anticipated to increase from US$ 106.74 billion in 2023 to US$ 1,362.09 billion by 2033.
The growing application of machine learning in these systems satisfies demands in model construction, scalability, and deployment. Data management and data science are being advanced by artificial intelligence and machine learning. Additionally, it is anticipated that the market share is likely to increase due to the advancement of Big Data technology and the significance of data collection.
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Data science platforms are being used by organizations including banks and financial institutions, healthcare providers, retail and e-commerce businesses, and even government and public organizations. Different organizations have come a long way to gather insights, make better decisions, and improve customer experiences.
Businesses are making significant investments in data science platforms to enhance consumer experiences, develop cutting-edge goods and services, and gain a competitive edge in the market. Companies are also investing in data science platforms to generate individualized customer experiences, understand customer behavior and trends, and increase customer loyalty.
To improve the companies' end-to-end data management and evaluation process, the players included new technology in their product offerings. For instance
The telecommunications sector is growing due to the active application of data science and machine learning. Telecom businesses utilize extensive communication networks and infrastructures to run with the full data flow. One of the most useful solutions is using data science tools to analyze and handle this data.
| Attributes | Details |
|---|---|
| Data Science Platform Market CAGR (2023 to 2033) | 29% |
| Data Science Platform Market Size (2023) | US$ 106.74 billion |
| Data Science Platform Market Size (2033) | US$ 1,362.09 billion |
When compared to the 25.2% CAGR recorded between 2016 and 2022, the data science platform business is predicted to expand at a 29.0% CAGR between 2023 and 2033. The average growth of the market is expected to be around 1.29x between 2022 and 2023.
| Year | Market Value |
|---|---|
| 2016 | US$ 17.95 billion |
| 2016 | US$ 64.14 billion |
| 2022 | US$ 82.74 billion |
Short-term Growth (2023 to 2026): Due to the adoption of cloud-based products and services, as well as the targeting of developing and untapped areas for data science platforms, the market is predicted to rise rapidly.
Medium-term Growth (2027 to 2029): During this period, it is projected that the development of Big Data technology would be promising for the market.
Long-term Growth (2030 to 2033): Data science is no longer an optional expense for businesses undergoing digital transformation. Many businesses are being assessed to be using a data-driven approach in their operational environment, which encourages global demand.
The companies have publicly said that they want to regularly undertake model-driven campaigns in a number of their functional areas, including sales, operations, manufacturing, and human resources.
Principal Consultant
The pace of technological advancement has been hastened by more research and development spending. As a result, as there are more businesses, there is an increasing need for technology that boosts production and efficiency.
For corporate growth, modern data handling methods and solutions are crucial, and there is a high need for data science platforms, which makes it easier to train, create, scale, and deploy ML models.
There is a growing flow of data in both organized and unstructured formats as a result of the Internet of Things (IoT), social media, and multimedia. Data generated by humans and machines is expanding 10x faster than data generated by businesses.
Advanced analytics techniques are used by organizations, such as machine learning, streaming analytics, and predictive analytics. To create a machine learning model, technical proficiency and analytical thinking skills are required.
Many end users lack the workforce with the necessary technical knowledge and abilities, which impedes the market's expansion. Significant barriers to the industry's expansion include a lack of technology dependability, data security, and privacy issues, strict government rules and regulations, and high investment needs.
This technology's users need to upgrade their platforms frequently to keep up with new technologies and data resources, which is another issue limiting the market's expansion. The proliferation of data, a lack of analytical skills, and a lack of domain expertise are a few barriers in this sector.
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Cloud-based deployment provides real-time data transfer that helps in improving services and business operations. Due to this, the cloud segment currently holds the leading data science platform market share and is anticipated to expand at the leading CAGR during the projection period.
The SAS Institute debuted its "SAS Viya" all-in-one platform on the Microsoft Azure market in September 2022. By enabling the cloud-based deployment of their product, they broadened their business model.
Over the projected period, the large enterprise is anticipated to lead the revenue share. The availability of a high percentage of IT budgets is expected to enhance the need for data science platforms in large enterprises.
The category of small and medium-sized businesses is anticipated to experience a high CAGR over the forecast period due to the growing adoption of digital platforms. SMEs are making investments in this platform to improve their customer support functions, which can aid in business growth.
The data science platform sector in the United States was estimated to be worth US$ 2.59 billion in 2018 and is anticipated to rise to US$ 71.87 billion by 2033. The market is expected to see a CAGR of 24.8% by 2033.
This rise is attributable to the rising importance of data-driven decision-making in the sector. The rising number of data science specialists and the rising requirement for data-driven decision-making among companies leads to high demand for data science platforms.
The expanding need for automated machine learning and the availability of open-source and cloud-based platforms are all contributing factors to the market's expansion.
The launch of a cloud-based data science platform was announced by the technology corporation Oracle in February 2020. The new platform's capabilities include shared projects, team security policies, audibility, reproducibility, and model catalogs.
| Attributes | Statistics |
|---|---|
| United Kingdom Market Value 2033 | US$ 4.66 billion |
| United Kingdom Market Value 2023 | US$ 1.64 billion |
| United Kingdom Market CAGR (2023-2033) | 11% |
The need for effective data storage and management solutions, as well as the rising demand for advanced data analytics and AI-driven solutions, are predicted to fuel this expansion. The expansion is also aided by the rise of IoT technologies and the increased use of smart devices.
Through the introduction of several programs, like the Digital Economy Network, which aims to assist businesses in maximizing the potential of data analytics, the United Kingdom government is also supporting the usage of data science platforms.
| Country | China |
|---|---|
| Market Value (2022) | US$ 4.82 billion |
| Market Value (2033) | US$ 78.65 billion |
| Market CAGR (2023 to 2033) | 28.9% |
| Country | India |
|---|---|
| Market Value (2022) | US$ 1.27 billion |
| Market Value (2033) | US$ 10.26 billion |
| Market CAGR (2023 to 2033) | 20.9% |
| Country | Japan |
|---|---|
| Market Value (2022) | US$ 9.2 billion |
| Market Value (2033) | US$ 22.85 billion |
| Market CAGR (2023 to 2033) | 8.6% |
This expansion is ascribed to the speedy uptake of data science platforms and technologies by businesses across. The market's expansion is being further fueled by the rising need for big data and predictive analytics technology. The government's attempts to encourage business adoption of AI are also anticipated to accelerate industry expansion.
Initiatives in the Indian Market
In terms of AI development and application, Japan is one of the top nations in the world. To help organizations quickly and effectively implement AI solutions and capitalize on the technology, firms like IBM and Microsoft have introduced platforms for AI-as-a-Service.
To improve data accessibility, Japan is also making use of open data platforms. Businesses can now access, analyze, and use data from a variety of sources thanks to platforms that have been launched by companies like Fujitsu and NTT Data.
Startups Provide a Modern Approach to Target a Large Market Share
Due to the rising need for data-driven insights and analytics, startups in the market for data science platforms are also expanding. To extract insights from data, they are using cutting-edge technologies like machine learning and artificial intelligence.
They are also implementing advanced analytics solutions to power the decision-making process. Startups are also concentrating on enhancing the consumer experience by offering sophisticated data-driven solutions. This is helping the data science platform sector develop even further.
Recent Developments
| IBM | Data scientists can work on a variety of data science projects using an integrated set of tools provided by IBM's complete platform, IBM Watson Studio. Data scientists can work together and examine data from diverse sources using IBM's platform. The platform from IBM offers data scientists a wide range of resources and services to assist them in creating predictive models, visualizing data, and working together with coworkers. |
|---|---|
| Microsoft | The Azure platform from Microsoft offers a broad range of cloud-based services designed with data scientists in mind. For the benefit of data scientists, Azure offers access to a variety of potent tools and services, such as machine learning and artificial intelligence. To aid data scientists in delving further into their data, Azure also gives users access to a variety of big data and analytics capabilities, including HDInsight and Power BI. |
| Amazon | For data scientists, Amazon has created a complete platform known as Amazon Web Services (AWS). Data scientists can leverage a range of cloud-based services from AWS to analyze, display, and create models. To assist data scientists to obtain a great understanding of their data, AWS also gives them access to a variety of big data and analytics services, including Amazon Athena and Amazon Redshift. |
| A complete platform for data scientists called Google Cloud Platform has been created by Google (GCP). Data scientists can access a range of cloud-based services through GCP, including Google BigQuery, Google Data Studio, and Google Cloud ML, to analyze, visualize, and create models. To help data scientists develop a deeper understanding of their data, GCP also gives them access to a variety of big data and analytics technologies, including Google BigQuery and Google Cloud ML. |
A CAGR of 29% is estimated throughout 2033.
From 2016 to 2022, the market expanded at a 25.2% CAGR.
The market is predicted to grow by 1.29x between 2022 and 2023.
Increasing demand for modern data handling systems will drive market growth.
By 2033, the market is expected to reach a CAGR of 28.9%.
1. Executive Summary | Data Science Platform Market
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Component
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Component, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Component, 2023 to 2033
5.3.1. Platform
5.3.2. Services
5.4. Y-o-Y Growth Trend Analysis By Component, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Component, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Deployment Mode
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Deployment Mode, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Deployment Mode, 2023 to 2033
6.3.1. Cloud
6.3.2. On-premises
6.4. Y-o-Y Growth Trend Analysis By Deployment Mode, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Deployment Mode, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Enterprise Size
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Enterprise Size , 2018 to 2022
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Enterprise Size , 2023 to 2033
7.3.1. Small and Medium-Sized Enterprises
7.3.2. Large Enterprises
7.4. Y-o-Y Growth Trend Analysis By Enterprise Size , 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Enterprise Size , 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Application
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Million) Analysis By Application, 2018 to 2022
8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Application, 2023 to 2033
8.3.1. Marketing & Sales
8.3.2. Logistics
8.3.3. Finance and Accounting
8.3.4. Customer Support
8.4. Y-o-Y Growth Trend Analysis By Application, 2018 to 2022
8.5. Absolute $ Opportunity Analysis By Application, 2023 to 2033
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By End-User
9.1. Introduction / Key Findings
9.2. Historical Market Size Value (US$ Million) Analysis By End-User, 2018 to 2022
9.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By End-User, 2023 to 2033
9.3.1. BFSI
9.3.2. IT and Telecom
9.3.3. Transportation
9.3.4. Healthcare
9.3.5. Government and Defence
9.3.6. Energy and Utilities
9.3.7. Retail and E-Commerce
9.4. Y-o-Y Growth Trend Analysis By End-User, 2018 to 2022
9.5. Absolute $ Opportunity Analysis By End-User, 2023 to 2033
10. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
10.1. Introduction
10.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
10.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
10.3.1. North America
10.3.2. Latin America
10.3.3. Europe
10.3.4. South Asia
10.3.5. East Asia
10.3.6. Oceania
10.3.7. MEA
10.4. Market Attractiveness Analysis By Region
11. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. The USA
11.2.1.2. Canada
11.2.2. By Component
11.2.3. By Deployment Mode
11.2.4. By Enterprise Size
11.2.5. By Application
11.2.6. By End-User
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Component
11.3.3. By Deployment Mode
11.3.4. By Enterprise Size
11.3.5. By Application
11.3.6. By End-User
11.4. Key Takeaways
12. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. Brazil
12.2.1.2. Mexico
12.2.1.3. Rest of Latin America
12.2.2. By Component
12.2.3. By Deployment Mode
12.2.4. By Enterprise Size
12.2.5. By Application
12.2.6. By End-User
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Component
12.3.3. By Deployment Mode
12.3.4. By Enterprise Size
12.3.5. By Application
12.3.6. By End-User
12.4. Key Takeaways
13. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. Germany
13.2.1.2. United Kingdom
13.2.1.3. France
13.2.1.4. Spain
13.2.1.5. Italy
13.2.1.6. Rest of Europe
13.2.2. By Component
13.2.3. By Deployment Mode
13.2.4. By Enterprise Size
13.2.5. By Application
13.2.6. By End-User
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Component
13.3.3. By Deployment Mode
13.3.4. By Enterprise Size
13.3.5. By Application
13.3.6. By End-User
13.4. Key Takeaways
14. South Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. India
14.2.1.2. Malaysia
14.2.1.3. Singapore
14.2.1.4. Thailand
14.2.1.5. Rest of South Asia
14.2.2. By Component
14.2.3. By Deployment Mode
14.2.4. By Enterprise Size
14.2.5. By Application
14.2.6. By End-User
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Component
14.3.3. By Deployment Mode
14.3.4. By Enterprise Size
14.3.5. By Application
14.3.6. By End-User
14.4. Key Takeaways
15. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
15.2.1. By Country
15.2.1.1. China
15.2.1.2. Japan
15.2.1.3. South Korea
15.2.2. By Component
15.2.3. By Deployment Mode
15.2.4. By Enterprise Size
15.2.5. By Application
15.2.6. By End-User
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Component
15.3.3. By Deployment Mode
15.3.4. By Enterprise Size
15.3.5. By Application
15.3.6. By End-User
15.4. Key Takeaways
16. Oceania Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
16.2.1. By Country
16.2.1.1. Australia
16.2.1.2. New Zealand
16.2.2. By Component
16.2.3. By Deployment Mode
16.2.4. By Enterprise Size
16.2.5. By Application
16.2.6. By End-User
16.3. Market Attractiveness Analysis
16.3.1. By Country
16.3.2. By Component
16.3.3. By Deployment Mode
16.3.4. By Enterprise Size
16.3.5. By Application
16.3.6. By End-User
16.4. Key Takeaways
17. MEA Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
17.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
17.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
17.2.1. By Country
17.2.1.1. GCC Countries
17.2.1.2. South Africa
17.2.1.3. Israel
17.2.1.4. Rest of MEA
17.2.2. By Component
17.2.3. By Deployment Mode
17.2.4. By Enterprise Size
17.2.5. By Application
17.2.6. By End-User
17.3. Market Attractiveness Analysis
17.3.1. By Country
17.3.2. By Component
17.3.3. By Deployment Mode
17.3.4. By Enterprise Size
17.3.5. By Application
17.3.6. By End-User
17.4. Key Takeaways
18. Key Countries Market Analysis
18.1. USA
18.1.1. Pricing Analysis
18.1.2. Market Share Analysis, 2022
18.1.2.1. By Component
18.1.2.2. By Deployment Mode
18.1.2.3. By Enterprise Size
18.1.2.4. By Application
18.1.2.5. By End-User
18.2. Canada
18.2.1. Pricing Analysis
18.2.2. Market Share Analysis, 2022
18.2.2.1. By Component
18.2.2.2. By Deployment Mode
18.2.2.3. By Enterprise Size
18.2.2.4. By Application
18.2.2.5. By End-User
18.3. Brazil
18.3.1. Pricing Analysis
18.3.2. Market Share Analysis, 2022
18.3.2.1. By Component
18.3.2.2. By Deployment Mode
18.3.2.3. By Enterprise Size
18.3.2.4. By Application
18.3.2.5. By End-User
18.4. Mexico
18.4.1. Pricing Analysis
18.4.2. Market Share Analysis, 2022
18.4.2.1. By Component
18.4.2.2. By Deployment Mode
18.4.2.3. By Enterprise Size
18.4.2.4. By Application
18.4.2.5. By End-User
18.5. Germany
18.5.1. Pricing Analysis
18.5.2. Market Share Analysis, 2022
18.5.2.1. By Component
18.5.2.2. By Deployment Mode
18.5.2.3. By Enterprise Size
18.5.2.4. By Application
18.5.2.5. By End-User
18.6. United Kingdom
18.6.1. Pricing Analysis
18.6.2. Market Share Analysis, 2022
18.6.2.1. By Component
18.6.2.2. By Deployment Mode
18.6.2.3. By Enterprise Size
18.6.2.4. By Application
18.6.2.5. By End-User
18.7. France
18.7.1. Pricing Analysis
18.7.2. Market Share Analysis, 2022
18.7.2.1. By Component
18.7.2.2. By Deployment Mode
18.7.2.3. By Enterprise Size
18.7.2.4. By Application
18.7.2.5. By End-User
18.8. Spain
18.8.1. Pricing Analysis
18.8.2. Market Share Analysis, 2022
18.8.2.1. By Component
18.8.2.2. By Deployment Mode
18.8.2.3. By Enterprise Size
18.8.2.4. By Application
18.8.2.5. By End-User
18.9. Italy
18.9.1. Pricing Analysis
18.9.2. Market Share Analysis, 2022
18.9.2.1. By Component
18.9.2.2. By Deployment Mode
18.9.2.3. By Enterprise Size
18.9.2.4. By Application
18.9.2.5. By End-User
18.10. India
18.10.1. Pricing Analysis
18.10.2. Market Share Analysis, 2022
18.10.2.1. By Component
18.10.2.2. By Deployment Mode
18.10.2.3. By Enterprise Size
18.10.2.4. By Application
18.10.2.5. By End-User
18.11. Malaysia
18.11.1. Pricing Analysis
18.11.2. Market Share Analysis, 2022
18.11.2.1. By Component
18.11.2.2. By Deployment Mode
18.11.2.3. By Enterprise Size
18.11.2.4. By Application
18.11.2.5. By End-User
18.12. Singapore
18.12.1. Pricing Analysis
18.12.2. Market Share Analysis, 2022
18.12.2.1. By Component
18.12.2.2. By Deployment Mode
18.12.2.3. By Enterprise Size
18.12.2.4. By Application
18.12.2.5. By End-User
18.13. Thailand
18.13.1. Pricing Analysis
18.13.2. Market Share Analysis, 2022
18.13.2.1. By Component
18.13.2.2. By Deployment Mode
18.13.2.3. By Enterprise Size
18.13.2.4. By Application
18.13.2.5. By End-User
18.14. China
18.14.1. Pricing Analysis
18.14.2. Market Share Analysis, 2022
18.14.2.1. By Component
18.14.2.2. By Deployment Mode
18.14.2.3. By Enterprise Size
18.14.2.4. By Application
18.14.2.5. By End-User
18.15. Japan
18.15.1. Pricing Analysis
18.15.2. Market Share Analysis, 2022
18.15.2.1. By Component
18.15.2.2. By Deployment Mode
18.15.2.3. By Enterprise Size
18.15.2.4. By Application
18.15.2.5. By End-User
18.16. South Korea
18.16.1. Pricing Analysis
18.16.2. Market Share Analysis, 2022
18.16.2.1. By Component
18.16.2.2. By Deployment Mode
18.16.2.3. By Enterprise Size
18.16.2.4. By Application
18.16.2.5. By End-User
18.17. Australia
18.17.1. Pricing Analysis
18.17.2. Market Share Analysis, 2022
18.17.2.1. By Component
18.17.2.2. By Deployment Mode
18.17.2.3. By Enterprise Size
18.17.2.4. By Application
18.17.2.5. By End-User
18.18. New Zealand
18.18.1. Pricing Analysis
18.18.2. Market Share Analysis, 2022
18.18.2.1. By Component
18.18.2.2. By Deployment Mode
18.18.2.3. By Enterprise Size
18.18.2.4. By Application
18.18.2.5. By End-User
18.19. GCC Countries
18.19.1. Pricing Analysis
18.19.2. Market Share Analysis, 2022
18.19.2.1. By Component
18.19.2.2. By Deployment Mode
18.19.2.3. By Enterprise Size
18.19.2.4. By Application
18.19.2.5. By End-User
18.20. South Africa
18.20.1. Pricing Analysis
18.20.2. Market Share Analysis, 2022
18.20.2.1. By Component
18.20.2.2. By Deployment Mode
18.20.2.3. By Enterprise Size
18.20.2.4. By Application
18.20.2.5. By End-User
18.21. Israel
18.21.1. Pricing Analysis
18.21.2. Market Share Analysis, 2022
18.21.2.1. By Component
18.21.2.2. By Deployment Mode
18.21.2.3. By Enterprise Size
18.21.2.4. By Application
18.21.2.5. By End-User
19. Market Structure Analysis
19.1. Competition Dashboard
19.2. Competition Benchmarking
19.3. Market Share Analysis of Top Players
19.3.1. By Regional
19.3.2. By Component
19.3.3. By Deployment Mode
19.3.4. By Enterprise Size
19.3.5. By Application
19.3.6. By End-User
20. Competition Analysis
20.1. Competition Deep Dive
20.1.1. Google, Inc
20.1.1.1. Overview
20.1.1.2. Product Portfolio
20.1.1.3. Profitability by Market Segments
20.1.1.4. Sales Footprint
20.1.1.5. Strategy Overview
20.1.1.5.1. Marketing Strategy
20.1.2. Alteryx, Inc.
20.1.2.1. Overview
20.1.2.2. Product Portfolio
20.1.2.3. Profitability by Market Segments
20.1.2.4. Sales Footprint
20.1.2.5. Strategy Overview
20.1.2.5.1. Marketing Strategy
20.1.3. Microsoft Corporation
20.1.3.1. Overview
20.1.3.2. Product Portfolio
20.1.3.3. Profitability by Market Segments
20.1.3.4. Sales Footprint
20.1.3.5. Strategy Overview
20.1.3.5.1. Marketing Strategy
20.1.4. IBM Corporation
20.1.4.1. Overview
20.1.4.2. Product Portfolio
20.1.4.3. Profitability by Market Segments
20.1.4.4. Sales Footprint
20.1.4.5. Strategy Overview
20.1.4.5.1. Marketing Strategy
20.1.5. SAS Institute, Inc.
20.1.5.1. Overview
20.1.5.2. Product Portfolio
20.1.5.3. Profitability by Market Segments
20.1.5.4. Sales Footprint
20.1.5.5. Strategy Overview
20.1.5.5.1. Marketing Strategy
20.1.6. Cloudera, Inc.
20.1.6.1. Overview
20.1.6.2. Product Portfolio
20.1.6.3. Profitability by Market Segments
20.1.6.4. Sales Footprint
20.1.6.5. Strategy Overview
20.1.6.5.1. Marketing Strategy
20.1.7. Dataiku SAS
20.1.7.1. Overview
20.1.7.2. Product Portfolio
20.1.7.3. Profitability by Market Segments
20.1.7.4. Sales Footprint
20.1.7.5. Strategy Overview
20.1.7.5.1. Marketing Strategy
20.1.8. RapidMiner, Inc
20.1.8.1. Overview
20.1.8.2. Product Portfolio
20.1.8.3. Profitability by Market Segments
20.1.8.4. Sales Footprint
20.1.8.5. Strategy Overview
20.1.8.5.1. Marketing Strategy
20.1.9. Wolfram Research
20.1.9.1. Overview
20.1.9.2. Product Portfolio
20.1.9.3. Profitability by Market Segments
20.1.9.4. Sales Footprint
20.1.9.5. Strategy Overview
20.1.9.5.1. Marketing Strategy
20.1.10. Teradata Corporation
20.1.10.1. Overview
20.1.10.2. Product Portfolio
20.1.10.3. Profitability by Market Segments
20.1.10.4. Sales Footprint
20.1.10.5. Strategy Overview
20.1.10.5.1. Marketing Strategy
20.1.11. WNS Global Services Pvt. Ltd.
20.1.11.1. Overview
20.1.11.2. Product Portfolio
20.1.11.3. Profitability by Market Segments
20.1.11.4. Sales Footprint
20.1.11.5. Strategy Overview
20.1.11.5.1. Marketing Strategy
20.1.12. H2O.ai
20.1.12.1. Overview
20.1.12.2. Product Portfolio
20.1.12.3. Profitability by Market Segments
20.1.12.4. Sales Footprint
20.1.12.5. Strategy Overview
20.1.12.5.1. Marketing Strategy
20.1.13. TIBCO Software Inc.
20.1.13.1. Overview
20.1.13.2. Product Portfolio
20.1.13.3. Profitability by Market Segments
20.1.13.4. Sales Footprint
20.1.13.5. Strategy Overview
20.1.13.5.1. Marketing Strategy
20.1.14. Oracle
20.1.14.1. Overview
20.1.14.2. Product Portfolio
20.1.14.3. Profitability by Market Segments
20.1.14.4. Sales Footprint
20.1.14.5. Strategy Overview
20.1.14.5.1. Marketing Strategy
21. Assumptions & Acronyms Used
22. Research Methodology
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