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Global Data Mining Tools Market to reach USD 2428.58 million by the end of 2030.

Global Data Mining Tools Market Size Study & Forecast, by Deployment (On-Premise, Cloud), By Enterprise Type (Large Enterprises, Small & Medium Enterprises), By Application (Marketing, Supply Chain & Procurement, Intrusion Detection, Business Transaction, Others), By Industry (BFSI, Healthcare, Retail, IT & Telecom, Manufacturing, Education, Government, Others), and Regional Analysis, 2023-2030

Product Code: ICTNGT-78149059
Publish Date: 30-10-2023
Page: 200

Global Data Mining Tools Market is valued at approximately USD 900.7 million in 2022 and is anticipated to grow with a healthy growth rate of more than 13.2% over the forecast period 2023-2030. Data mining tools are software applications or platforms that facilitate the process of extracting valuable insights and patterns from large datasets. These tools utilize a variety of methods and algorithms to explore, examine, and analyze data, frequently with the aim of identifying unobserved connections, patterns, and trends that aid in prediction and decision-making. Many industries, including BFSI, healthcare, retail, IT & telecom, manufacturing, education, government, and others, regularly employ data mining techniques. The growing demand for predictive analysis, rising use of data mining tools in the banking industry, rising inclination towards data mining tools to improve organizational efficiency, and a significant increase in data volume are the most prominent factors that are propelling the market demand across the globe.

In addition, the rising emergence of cloud technology is also exhibiting a positive influence on market growth worldwide. Cloud computing is making it easier and more affordable for businesses to access data mining tools. For instance, according to the IEEE ComSoc, in 2021, the global public spending on cloud computing has reached USD 332.3 billion with a rise of nearly 23.1 % from USD 270 billion in 2020. Accordingly, the rising adoption of cloud computing is offering the market to a wider range of businesses and is driving growth in the market. Moreover, the growing awareness among enterprises to leverage the available data assets, as well as the rising incorporation of data mining and machine learning solutions presents various lucrative opportunities over the forecasting years. However, the concern associated with data privacy, security, and reliability, along with the requirement for skilled technical resources are hindering the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Data Mining Tools Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 because of the development of innovative technologies like artificial intelligence, machine learning, the Internet of Things, and more, as well as sufficient availability of supporting infrastructure. Whereas, Asia Pacific is expected to grow at the highest CAGR over the forecast years. The growing expenditure for the development of IT infrastructure, surging number of small-scale businesses, and rising commercial investments by numerous companies are significantly propelling the market demand across the region.

Major market players included in this report are:
Oracle Corporation (U.S.)
IBM Corporation (U.S.)
KNIME AG (Switzerland)
Altair Engineering Inc. (RapidMiner) (U.S.)
Orange (Ljubljana)
Rattle GUI (Togaware Pty Ltd) (Australia)
Sisense Inc. (U.S.)
Kaggle (Google LLC) (U.S.)
SAS Institute Inc. (U.S.)
Teradata Corporation (U.S.)

Recent Developments in the Market:
Ø In May 2023, WiMi Hologram Cloud Inc. announced the launch of a novel data interaction system that is developed by integrating data mining and neural network technologies. The system has the ability to provide secure and reliable information transfer through real-time interaction.
Ø In May 2023, U.S. Data Mining Group, Inc., a bitcoin mining company, announced a hosting agreement to distribute 150,000 bitcoins in collaboration with prominent companies including TeslaWatt, Sphere 3D, Marathon Digital, and others. The company provides complete industrial turnkey solutions for accounting, customer service, and management.
Ø In April 2023, One Biosciences- an Artificial intelligence and single-cell biotech analytics company declared the launch of a single cell data mining algorithm named ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities. The algorithm is used to identify treatment vulnerabilities in cancer patients.

Global Data Mining Tools Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Deployment, Enterprise Type, Application, Industry, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Deployment:

By Enterprise Type:
Large Enterprises
Small & Medium Enterprises

By Application:
Supply Chain & Procurement
Intrusion Detection
Business Transaction

By Industry:
IT & Telecom

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Million)
1.2.1. Data Mining Tools Market, by Region, 2020-2030 (USD Million)
1.2.2. Data Mining Tools Market, by Deployment, 2020-2030 (USD Million)
1.2.3. Data Mining Tools Market, by Enterprise Type, 2020-2030 (USD Million)
1.2.4. Data Mining Tools Market, by Application, 2020-2030 (USD Million)
1.2.5. Data Mining Tools Market, by Industry, 2020-2030 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Data Mining Tools Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Data Mining Tools Market Dynamics
3.1. Data Mining Tools Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Growing demand for predictive analysis Rising emergence of cloud technology
3.1.2. Market Challenges Concern associated with data privacy, security, and reliability Requirement of skilled technical resources
3.1.3. Market Opportunities Growing awareness among enterprises to leverage the available data assets Rising incorporation of data mining and machine learning solutions
Chapter 4. Global Data Mining Tools Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Data Mining Tools Market, by Deployment
5.1. Market Snapshot
5.2. Global Data Mining Tools Market by Deployment, Performance – Potential Analysis
5.3. Global Data Mining Tools Market Estimates & Forecasts by Deployment 2020-2030 (USD Million)
5.4. Data Mining Tools Market, Sub Segment Analysis
5.4.1. On-Premises
5.4.2. Cloud
Chapter 6. Global Data Mining Tools Market, by Enterprise Type
6.1. Market Snapshot
6.2. Global Data Mining Tools Market by Enterprise Type, Performance – Potential Analysis
6.3. Global Data Mining Tools Market Estimates & Forecasts by Enterprise Type 2020-2030 (USD Million)
6.4. Data Mining Tools Market, Sub Segment Analysis
6.4.1. Large Enterprises
6.4.2. Small & Medium Enterprises
Chapter 7. Global Data Mining Tools Market, by Application
7.1. Market Snapshot
7.2. Global Data Mining Tools Market by Application, Performance – Potential Analysis
7.3. Global Data Mining Tools Market Estimates & Forecasts by Application 2020-2030 (USD Million)
7.4. Data Mining Tools Market, Sub Segment Analysis
7.4.1. Marketing
7.4.2. Supply Chain & Procurement
7.4.3. Intrusion Detection
7.4.4. Business Transaction
7.4.5. Others
Chapter 8. Data Mining Tools Market, by Industry
8.1. Market Snapshot
8.2. Global Data Mining Tools Market by Industry, Performance – Potential Analysis
8.3. Global Data Mining Tools Market Estimates & Forecasts by Industry 2020-2030 (USD Million)
8.4. Data Mining Tools Market, Sub Segment Analysis
8.4.1. BFSI
8.4.2. Healthcare
8.4.3. Retail
8.4.4. IT & Telecom
8.4.5. Manufacturing
8.4.6. Education
8.4.7. Government
8.4.8. Others
Chapter 9. Global Data Mining Tools Market, Regional Analysis
9.1. Top Leading Countries
9.2. Top Emerging Countries
9.3. Data Mining Tools Market, Regional Market Snapshot
9.4. North America Data Mining Tools Market
9.4.1. U.S. Data Mining Tools Market Deployment breakdown estimates & forecasts, 2020-2030 Enterprise Type breakdown estimates & forecasts, 2020-2030 Application breakdown estimates & forecasts, 2020-2030 Industry breakdown estimates & forecasts, 2020-2030
9.4.2. Canada Data Mining Tools Market
9.5. Europe Data Mining Tools Market Snapshot
9.5.1. U.K. Data Mining Tools Market
9.5.2. Germany Data Mining Tools Market
9.5.3. France Data Mining Tools Market
9.5.4. Spain Data Mining Tools Market
9.5.5. Italy Data Mining Tools Market
9.5.6. Rest of Europe Data Mining Tools Market
9.6. Asia-Pacific Data Mining Tools Market Snapshot
9.6.1. China Data Mining Tools Market
9.6.2. India Data Mining Tools Market
9.6.3. Japan Data Mining Tools Market
9.6.4. Australia Data Mining Tools Market
9.6.5. South Korea Data Mining Tools Market
9.6.6. Rest of Asia Pacific Data Mining Tools Market
9.7. Latin America Data Mining Tools Market Snapshot
9.7.1. Brazil Data Mining Tools Market
9.7.2. Mexico Data Mining Tools Market
9.8. Middle East & Africa Data Mining Tools Market
9.8.1. Saudi Arabia Data Mining Tools Market
9.8.2. South Africa Data Mining Tools Market
9.8.3. Rest of Middle East & Africa Data Mining Tools Market

Chapter 10. Competitive Intelligence
10.1. Key Company SWOT Analysis
10.1.1. Company 1
10.1.2. Company 2
10.1.3. Company 3
10.2. Top Market Strategies
10.3. Company Profiles
10.3.1. Oracle Corporation (U.S.) Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.3.2. IBM Corporation (U.S.)
10.3.3. KNIME AG (Switzerland)
10.3.4. Altair Engineering Inc. (RapidMiner) (U.S.)
10.3.5. Orange (Ljubljana)
10.3.6. Rattle GUI (Togaware Pty Ltd) (Australia)
10.3.7. Sisense Inc. (U.S.)
10.3.8. Kaggle (Google LLC) (U.S.)
10.3.9. SAS Institute Inc. (U.S.)
10.3.10. Teradata Corporation (U.S.)
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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