Global Data Mining and Modeling Market Status and Forecast 2021-2027

Table of Contents

1 Product Introduction and Overview

• 1.1 Product Definition
• 1.2 Product Specification
• 1.3 Global Market Overview
  • 1.3.1 Global Data Mining and Modeling Market Status and Forecast (2016-2027)
  • 1.3.2 Global Data Mining and Modeling Sales Value CAGR by Region
• 1.4 Market Drivers, Inhibitors
  • 1.4.1 Market Drivers
  • 1.4.2 Market Inhibitors
  • 1.4.3 COVID-19 Impact Analysis

2 Global Data Mining and Modeling Supply by Company

• 2.1 Global Data Mining and Modeling Sales Value by Company
• 2.2 Data Mining and Modeling Sales Area of Main Manufacturers
• 2.3 Trend of Concentration Rate

3 Global and Regional Data Mining and Modeling Market Status by Type

• 3.1 Data Mining and Modeling Type Introduction
  • 3.1.1 Cloud-based
  • 3.1.2 On-premises
• 3.2 Global Data Mining and Modeling Market by Type
• 3.3 North America: by Type
• 3.4 Europe: by Type
• 3.5 Asia Pacific: by Type
• 3.6 Central & South America: by Type
• 3.7 Middle East & Africa: by Type

4 Global and Regional Data Mining and Modeling Market Status by Application

• 4.1 Data Mining and Modeling Segment by Application
  • 4.1.1 Large Enterprises
  • 4.1.2 Small and Medium-sized Enterprises (SMEs)
• 4.2 Global Data Mining and Modeling Market by Application
• 4.3 North America: by Application
• 4.4 Europe: by Application
• 4.5 Asia Pacific: by Application
• 4.6 Central & South America: by Application
• 4.7 Middle East & Africa: by Application

5 Global Data Mining and Modeling Market Status by Region

• 5.1 Global Data Mining and Modeling Market by Region
• 5.2 North America Data Mining and Modeling Market Status
• 5.3 Europe Data Mining and Modeling Market Status
• 5.4 Asia Pacific Data Mining and Modeling Market Status
• 5.5 Central & South America Data Mining and Modeling Market Status
• 5.6 Middle East & Africa Data Mining and Modeling Market Status

6 North America Data Mining and Modeling Market Status

• 6.1 North America Data Mining and Modeling Market by Country
• 6.2 United States
• 6.3 Canada
• 6.4 Mexico

7 Europe Data Mining and Modeling Market Status

• 7.1 Europe Data Mining and Modeling Market by Country
• 7.2 Germany
• 7.3 France
• 7.4 UK
• 7.5 Italy
• 7.6 Russia
• 7.7 Spain

8 Asia Pacific Data Mining and Modeling Market Status

• 8.1 Asia Pacific Data Mining and Modeling Market by Country
• 8.2 China
• 8.3 Japan
• 8.4 Korea
• 8.5 Southeast Asia
• 8.6 India
• 8.7 Australasia

9 Central & South America Data Mining and Modeling Market Status

• 9.1 Central & South America Data Mining and Modeling Market by Country
• 9.2 Brazil
• 9.3 Argentina
• 9.4 Colombia

10 Middle East & Africa Data Mining and Modeling Market Status

• 10.1 Middle East & Africa Data Mining and Modeling Market by Country
• 10.2 Iran
• 10.3 Israel
• 10.4 Turkey
• 10.5 South Africa
• 10.8 Saudi Arabia

11 Major Downstream Customers Analysis

• 11.1 Customer One Analysis
• 11.2 Customer Two Analysis
• 11.3 Customer Three Analysis
• 11.4 Customer Four Analysis

12 Global Data Mining and Modeling Market Forecast by Type and by Application

• 12.1 Global Data Mining and Modeling Sales Value Forecast (2022-2027)
• 12.2 Global Data Mining and Modeling Forecast by Type
• 12.3 Global Data Mining and Modeling Forecast by Application

13 Global Data Mining and Modeling Market Forecast by Region/Country

• 13.1 Global Data Mining and Modeling Market Forecast by Region (2022-2027)
• 13.2 North America Market Forecast
• 13.3 Europe Market Forecast
• 13.4 Asia Pacific Market Forecast
• 13.5 Central & South America Market Forecast
• 13.6 Middle East & Africa Market Forecast

14 Key Participants Company Information

• 14.1 Symbrium
  • 14.1.1 Company Information
  • 14.1.2 Data Mining and Modeling Product Introduction
  • 14.1.3 Symbrium Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.1.4 SWOT Analysis
• 14.2 Semantic Web Company
  • 14.2.1 Company Information
  • 14.2.2 Data Mining and Modeling Product Introduction
  • 14.2.3 Semantic Web Company Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.2.4 SWOT Analysis
• 14.3 Saturam
  • 14.3.1 Company Information
  • 14.3.2 Data Mining and Modeling Product Introduction
  • 14.3.3 Saturam Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.3.4 SWOT Analysis
• 14.4 SAS
  • 14.4.1 Company Information
  • 14.4.2 Data Mining and Modeling Product Introduction
  • 14.4.3 SAS Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.4.4 SWOT Analysis
• 14.5 Salford Systems
  • 14.5.1 Company Information
  • 14.5.2 Data Mining and Modeling Product Introduction
  • 14.5.3 Salford Systems Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.5.4 SWOT Analysis
• 14.6 RapidMiner
  • 14.6.1 Company Information
  • 14.6.2 Data Mining and Modeling Product Introduction
  • 14.6.3 RapidMiner Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.6.4 SWOT Analysis
• 14.7 Optymyze
  • 14.7.1 Company Information
  • 14.7.2 Data Mining and Modeling Product Introduction
  • 14.7.3 Optymyze Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.7.4 SWOT Analysis
• 14.8 Mozenda
  • 14.8.1 Company Information
  • 14.8.2 Data Mining and Modeling Product Introduction
  • 14.8.3 Mozenda Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.8.4 SWOT Analysis
• 14.9 Megaputer Intelligence
  • 14.9.1 Company Information
  • 14.9.2 Data Mining and Modeling Product Introduction
  • 14.9.3 Megaputer Intelligence Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.9.4 SWOT Analysis
• 14.10 Lexalytics
  • 14.10.1 Company Information
  • 14.10.2 Data Mining and Modeling Product Introduction
  • 14.10.3 Lexalytics Data Mining and Modeling Sales Value, Gross Margin and Global Share (2019-2021)
  • 14.10.4 SWOT Analysis
• 14.11 IBM
• 14.12 GMDH
• 14.13 Expert System
• 14.14 Coheris
• 14.15 Apteco

15 Conclusion

16 Methodology