Global AI for Data Analytics 麻豆原创 Research Report 2024

Publication ID:

PB512240259

Publication Date:

December 11, 2024

Pages:

110

Publisher:

MRRPB5

Countries:

Global [1]

USD 2,900.00

IT Services [2]

Computing & Electronics [3]

Description:

The global market for AI for Data Analytics was valued at US$ 2977 million in the year 2023 and is projected to reach a revised size of US$ 22320 million by 2030, growing at a CAGR of 36.2% during the forecast period.
AI for Data Analytics refers to the application of artificial intelligence techniques and technologies to analyze large volumes of data, extract meaningful insights, and drive decision-making. By leveraging machine learning, deep learning, natural language processing, and other AI methods, AI for data analytics can automate the process of identifying patterns, trends, and anomalies within datasets that are too complex for traditional analysis. This helps organizations optimize operations, improve forecasting, enhance customer experiences, and gain a competitive edge by enabling data-driven decision-making with greater accuracy and efficiency.
North American market for AI for Data Analytics is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for AI for Data Analytics is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for AI for Data Analytics in Healthcare and Life Sciences is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of AI for Data Analytics include IBM, Alibaba, AWS, Baidu, Cloudera, Databricks, DataRobot, Google Cloud, Huawei, Microsoft Azure, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for AI for Data Analytics, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI for Data Analytics.
The AI for Data Analytics market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global AI for Data Analytics market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the AI for Data Analytics companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
IBM
Alibaba
AWS
Baidu
Cloudera
Databricks
DataRobot
Google Cloud
Huawei
Microsoft Azure
Oracle
Palantir
Qlik
Salesforce
SAP
SAS
Snowflake
Splunk
Tableau
Teradata
Innodata
Segment by Type
Cloud-based
Local Deployment
Segment by Application
Healthcare and Life Sciences
Retail and E-Commerce
Financial Services and Banking
Manufacturing
Others
By Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Rest of Europe
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of AI for Data Analytics company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global AI for Data Analytics 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Cloud-based
1.2.3 Local Deployment
1.3 麻豆原创 by Application
1.3.1 Global AI for Data Analytics 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Healthcare and Life Sciences
1.3.3 Retail and E-Commerce
1.3.4 Financial Services and Banking
1.3.5 Manufacturing
1.3.6 Others
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Global Growth Trends
2.1 Global AI for Data Analytics 麻豆原创 Perspective (2019-2030)
2.2 Global AI for Data Analytics Growth Trends by Region
2.2.1 Global AI for Data Analytics 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 AI for Data Analytics Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 AI for Data Analytics Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 AI for Data Analytics 麻豆原创 Dynamics
2.3.1 AI for Data Analytics Industry Trends
2.3.2 AI for Data Analytics 麻豆原创 Drivers
2.3.3 AI for Data Analytics 麻豆原创 Challenges
2.3.4 AI for Data Analytics 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top AI for Data Analytics Players by Revenue
3.1.1 Global Top AI for Data Analytics Players by Revenue (2019-2024)
3.1.2 Global AI for Data Analytics Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global AI for Data Analytics 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players Ranking by AI for Data Analytics Revenue
3.4 Global AI for Data Analytics 麻豆原创 Concentration Ratio
3.4.1 Global AI for Data Analytics 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by AI for Data Analytics Revenue in 2023
3.5 Global Key Players of AI for Data Analytics Head office and Area Served
3.6 Global Key Players of AI for Data Analytics, Product and Application
3.7 Global Key Players of AI for Data Analytics, Date of Enter into This Industry
3.8 Mergers & Acquisitions, Expansion Plans
4 AI for Data Analytics Breakdown Data by Type
4.1 Global AI for Data Analytics Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global AI for Data Analytics Forecasted 麻豆原创 Size by Type (2025-2030)
5 AI for Data Analytics Breakdown Data by Application
5.1 Global AI for Data Analytics Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global AI for Data Analytics Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America AI for Data Analytics 麻豆原创 Size (2019-2030)
6.2 North America AI for Data Analytics 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America AI for Data Analytics 麻豆原创 Size by Country (2019-2024)
6.4 North America AI for Data Analytics 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe AI for Data Analytics 麻豆原创 Size (2019-2030)
7.2 Europe AI for Data Analytics 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe AI for Data Analytics 麻豆原创 Size by Country (2019-2024)
7.4 Europe AI for Data Analytics 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific AI for Data Analytics 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific AI for Data Analytics 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
8.3 Asia-Pacific AI for Data Analytics 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific AI for Data Analytics 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America AI for Data Analytics 麻豆原创 Size (2019-2030)
9.2 Latin America AI for Data Analytics 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America AI for Data Analytics 麻豆原创 Size by Country (2019-2024)
9.4 Latin America AI for Data Analytics 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa AI for Data Analytics 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa AI for Data Analytics 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa AI for Data Analytics 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa AI for Data Analytics 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Details
11.1.2 IBM Business Overview
11.1.3 IBM AI for Data Analytics Introduction
11.1.4 IBM Revenue in AI for Data Analytics Business (2019-2024)
11.1.5 IBM Recent Development
11.2 Alibaba
11.2.1 Alibaba Company Details
11.2.2 Alibaba Business Overview
11.2.3 Alibaba AI for Data Analytics Introduction
11.2.4 Alibaba Revenue in AI for Data Analytics Business (2019-2024)
11.2.5 Alibaba Recent Development
11.3 AWS
11.3.1 AWS Company Details
11.3.2 AWS Business Overview
11.3.3 AWS AI for Data Analytics Introduction
11.3.4 AWS Revenue in AI for Data Analytics Business (2019-2024)
11.3.5 AWS Recent Development
11.4 Baidu
11.4.1 Baidu Company Details
11.4.2 Baidu Business Overview
11.4.3 Baidu AI for Data Analytics Introduction
11.4.4 Baidu Revenue in AI for Data Analytics Business (2019-2024)
11.4.5 Baidu Recent Development
11.5 Cloudera
11.5.1 Cloudera Company Details
11.5.2 Cloudera Business Overview
11.5.3 Cloudera AI for Data Analytics Introduction
11.5.4 Cloudera Revenue in AI for Data Analytics Business (2019-2024)
11.5.5 Cloudera Recent Development
11.6 Databricks
11.6.1 Databricks Company Details
11.6.2 Databricks Business Overview
11.6.3 Databricks AI for Data Analytics Introduction
11.6.4 Databricks Revenue in AI for Data Analytics Business (2019-2024)
11.6.5 Databricks Recent Development
11.7 DataRobot
11.7.1 DataRobot Company Details
11.7.2 DataRobot Business Overview
11.7.3 DataRobot AI for Data Analytics Introduction
11.7.4 DataRobot Revenue in AI for Data Analytics Business (2019-2024)
11.7.5 DataRobot Recent Development
11.8 Google Cloud
11.8.1 Google Cloud Company Details
11.8.2 Google Cloud Business Overview
11.8.3 Google Cloud AI for Data Analytics Introduction
11.8.4 Google Cloud Revenue in AI for Data Analytics Business (2019-2024)
11.8.5 Google Cloud Recent Development
11.9 Huawei
11.9.1 Huawei Company Details
11.9.2 Huawei Business Overview
11.9.3 Huawei AI for Data Analytics Introduction
11.9.4 Huawei Revenue in AI for Data Analytics Business (2019-2024)
11.9.5 Huawei Recent Development
11.10 Microsoft Azure
11.10.1 Microsoft Azure Company Details
11.10.2 Microsoft Azure Business Overview
11.10.3 Microsoft Azure AI for Data Analytics Introduction
11.10.4 Microsoft Azure Revenue in AI for Data Analytics Business (2019-2024)
11.10.5 Microsoft Azure Recent Development
11.11 Oracle
11.11.1 Oracle Company Details
11.11.2 Oracle Business Overview
11.11.3 Oracle AI for Data Analytics Introduction
11.11.4 Oracle Revenue in AI for Data Analytics Business (2019-2024)
11.11.5 Oracle Recent Development
11.12 Palantir
11.12.1 Palantir Company Details
11.12.2 Palantir Business Overview
11.12.3 Palantir AI for Data Analytics Introduction
11.12.4 Palantir Revenue in AI for Data Analytics Business (2019-2024)
11.12.5 Palantir Recent Development
11.13 Qlik
11.13.1 Qlik Company Details
11.13.2 Qlik Business Overview
11.13.3 Qlik AI for Data Analytics Introduction
11.13.4 Qlik Revenue in AI for Data Analytics Business (2019-2024)
11.13.5 Qlik Recent Development
11.14 Salesforce
11.14.1 Salesforce Company Details
11.14.2 Salesforce Business Overview
11.14.3 Salesforce AI for Data Analytics Introduction
11.14.4 Salesforce Revenue in AI for Data Analytics Business (2019-2024)
11.14.5 Salesforce Recent Development
11.15 SAP
11.15.1 SAP Company Details
11.15.2 SAP Business Overview
11.15.3 SAP AI for Data Analytics Introduction
11.15.4 SAP Revenue in AI for Data Analytics Business (2019-2024)
11.15.5 SAP Recent Development
11.16 SAS
11.16.1 SAS Company Details
11.16.2 SAS Business Overview
11.16.3 SAS AI for Data Analytics Introduction
11.16.4 SAS Revenue in AI for Data Analytics Business (2019-2024)
11.16.5 SAS Recent Development
11.17 Snowflake
11.17.1 Snowflake Company Details
11.17.2 Snowflake Business Overview
11.17.3 Snowflake AI for Data Analytics Introduction
11.17.4 Snowflake Revenue in AI for Data Analytics Business (2019-2024)
11.17.5 Snowflake Recent Development
11.18 Splunk
11.18.1 Splunk Company Details
11.18.2 Splunk Business Overview
11.18.3 Splunk AI for Data Analytics Introduction
11.18.4 Splunk Revenue in AI for Data Analytics Business (2019-2024)
11.18.5 Splunk Recent Development
11.19 Tableau
11.19.1 Tableau Company Details
11.19.2 Tableau Business Overview
11.19.3 Tableau AI for Data Analytics Introduction
11.19.4 Tableau Revenue in AI for Data Analytics Business (2019-2024)
11.19.5 Tableau Recent Development
11.20 Teradata
11.20.1 Teradata Company Details
11.20.2 Teradata Business Overview
11.20.3 Teradata AI for Data Analytics Introduction
11.20.4 Teradata Revenue in AI for Data Analytics Business (2019-2024)
11.20.5 Teradata Recent Development
11.21 Innodata
11.21.1 Innodata Company Details
11.21.2 Innodata Business Overview
11.21.3 Innodata AI for Data Analytics Introduction
11.21.4 Innodata Revenue in AI for Data Analytics Business (2019-2024)
11.21.5 Innodata Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.1.1 Research Programs/Design
13.1.1.2 麻豆原创 Size Estimation
13.1.1.3 麻豆原创 Breakdown and Data Triangulation
13.1.2 Data Source
13.1.2.1 Secondary Sources
13.1.2.2 Primary Sources
13.2 Author Details
13.3 Disclaimer

Companies Mentioned:

IBM
Alibaba
AWS
Baidu
Cloudera
Databricks
DataRobot
Google Cloud
Huawei
Microsoft Azure
Oracle
Palantir
Qlik
Salesforce
SAP
SAS
Snowflake
Splunk
Tableau
Teradata
Innodata

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