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Global Big Data Analytics in Education Market to reach USD 48.91 billion by the end of 2029.

Global Big Data Analytics in Education Market Size study & Forecast, by Component (Software and Services), by Deployment Model (On-premise and Cloud), by Application (Behavior Detection, Skill Assessment, Course Recommendation, Student Attrition Rate Detection, and Others), by Sector (K-12, Preschool, and Higher Education) and Regional Analysis, 2022-2029

Product Code: ICTEITS-16888200
Publish Date: 17-10-2022
Page: 200

Global Big Data Analytics in Education Market is valued at approximately USD 15.66 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 15.3% over the forecast period 2022-2029. Big Data Analytics plays important role in Education sector. Data analysis is utilized in Education sector for taking data driven decisions. Big data analytics has applications in different aspects of Education management including development of customized education programs, skill assessment, behavior detection, tracking of academic performance, and designing of precise grading system for students. The growing expansion of big data analytics industry and rising internet penetration as well as strategic initiatives from leading market players are key factors accelerating the market growth.

According to Statista – in 2021, the global big data market was valued at USD 64 billion, and the market is projected to grow to USD 103 billion by 2027. Moreover, increasing internet penetration worldwide is another key factor driving market growth. For instance, as per Statista – in 2020 the number of internet users in India was estimated at 749 million, and the number of users is projected to grow to 1500 million by 2040. Also, increasing adoption of Data-driven decision-making (DDDM) and growing emergence of EdTech platforms would create lucrative growth prospectus for the market over the forecast period. However, high initial investment requirements coupled with lack of awareness & dearth of Skilled Resources hinder the market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Big Data Analytics in Education Market study include Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America is the leading region in terms of market share owing to presence of leading market players and increasing utilization of big data analytics solutions in the region. Whereas, Asia Pacific is expected to grow significantly during the forecast period, owing to factors such as rising penetration of EdTech startups as well as increasing internet penetration in the region.

Major market players included in this report are:
Alteryx Inc.
Blackboard Inc.
Fintellix Solutions pvt. ltd.
LatentView Analytics
International Business Machines Corporation
Microsoft Corporation
Oracle Corporation
SAS Institute Inc.
Tableau Software
TIBCO Software Inc.

Recent Developments in the Market:
 In February 2029, Microsoft Corporation acquired DataSense data analytics platform for schools from San Francisco based edtech company BrightBytes. This new acquisition would enable Microsoft to develop data analytics tools for Educational Institutions.

 In May 2022, SchoolMint launched SchoolMint Insights, an enrolment-centric analytics platform. SchoolMint Insights combines data generated throughout the entire enrolment process. This platform is designed to offer enrolment trends and insights to US K-12 education organizations.

Global Big Data Analytics in Education Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Component, Deployment Model, Application, Sector, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
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 Component
By Deployment Model
By Application
Behavior Detection
Skill Assessment
Course Recommendation
Student Attrition Rate Detection
By Sector
Higher Education
By Region:
North America
Asia Pacific
South Korea
Latin America
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Big Data Analytics in Education Market, by Region, 2019-2029 (USD Billion)
1.2.2. Big Data Analytics in Education Market, by Component, 2019-2029 (USD Billion)
1.2.3. Big Data Analytics in Education Market, by Deployment Model, 2019-2029 (USD Billion)
1.2.4. Big Data Analytics in Education Market, by Application, 2019-2029 (USD Billion)
1.2.5. Big Data Analytics in Education Market, by Sector, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Big Data Analytics in Education Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Big Data Analytics in Education Market Dynamics
3.1. Big Data Analytics in Education Market Impact Analysis (2019-2029)
3.1.1. Market Drivers Growing expansion of big data analytics industry Rising internet penetration Strategic initiatives from leading market players
3.1.2. Market Challenges High initial investment requirement Lack of Awareness and dearth of Skilled Resources
3.1.3. Market Opportunities Increasing adoption of Data-driven decision-making (DDDM) Growing emergence of EdTech platforms
Chapter 4. Global Big Data Analytics in Education 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. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Investment Adoption Model
4.5. Analyst Recommendation & Conclusion
4.6. Top investment opportunity
4.7. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Big Data Analytics in Education Market, by Component
6.1. Market Snapshot
6.2. Global Big Data Analytics in Education Market by Component, Performance – Potential Analysis
6.3. Global Big Data Analytics in Education Market Estimates & Forecasts by Component, 2019-2029 (USD Billion)
6.4. Big Data Analytics in Education Market, Sub Segment Analysis
6.4.1. Software
6.4.2. Services
Chapter 7. Global Big Data Analytics in Education Market, by Deployment Model
7.1. Market Snapshot
7.2. Global Big Data Analytics in Education Market by Deployment Model, Performance – Potential Analysis
7.3. Global Big Data Analytics in Education Market Estimates & Forecasts by Deployment Model, 2019-2029 (USD Billion)
7.4. Big Data Analytics in Education Market, Sub Segment Analysis
7.4.1. On-premises
7.4.2. Cloud
Chapter 8. Global Big Data Analytics in Education Market, by Application
8.1. Market Snapshot
8.2. Global Big Data Analytics in Education Market by Application, Performance – Potential Analysis
8.3. Global Big Data Analytics in Education Market Estimates & Forecasts by Application, 2019-2029 (USD Billion)
8.4. Big Data Analytics in Education Market, Sub Segment Analysis
8.4.1. Behavior Detection
8.4.2. Skill Assessment
8.4.3. Course Recommendation
8.4.4. Student Attrition Rate Detection
8.4.5. Others
Chapter 9. Global Big Data Analytics in Education Market, by Sector
9.1. Market Snapshot
9.2. Global Big Data Analytics in Education Market by Sector, Performance – Potential Analysis
9.3. Global Big Data Analytics in Education Market Estimates & Forecasts by Sector, 2019-2029 (USD Billion)
9.4. Big Data Analytics in Education Market, Sub Segment Analysis
9.4.1. K-12
9.4.2. Preschool
9.4.3. Higher Education
Chapter 10. Global Big Data Analytics in Education Market, Regional Analysis
10.1. Big Data Analytics in Education Market, Regional Market Snapshot
10.2. North America Big Data Analytics in Education Market
10.2.1. U.S. Big Data Analytics in Education Market Component breakdown estimates & forecasts, 2019-2029 Deployment Model breakdown estimates & forecasts, 2019-2029 Application breakdown estimates & forecasts, 2019-2029 Sector breakdown estimates & forecasts, 2019-2029
10.2.2. Canada Big Data Analytics in Education Market
10.3. Europe Big Data Analytics in Education Market Snapshot
10.3.1. U.K. Big Data Analytics in Education Market
10.3.2. Germany Big Data Analytics in Education Market
10.3.3. France Big Data Analytics in Education Market
10.3.4. Spain Big Data Analytics in Education Market
10.3.5. Italy Big Data Analytics in Education Market
10.3.6. Rest of Europe Big Data Analytics in Education Market
10.4. Asia-Pacific Big Data Analytics in Education Market Snapshot
10.4.1. China Big Data Analytics in Education Market
10.4.2. India Big Data Analytics in Education Market
10.4.3. Japan Big Data Analytics in Education Market
10.4.4. Australia Big Data Analytics in Education Market
10.4.5. South Korea Big Data Analytics in Education Market
10.4.6. Rest of Asia Pacific Big Data Analytics in Education Market
10.5. Latin America Big Data Analytics in Education Market Snapshot
10.5.1. Brazil Big Data Analytics in Education Market
10.5.2. Mexico Big Data Analytics in Education Market
10.6. Rest of The World Big Data Analytics in Education Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Alteryx Inc. Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
11.2.2. Blackboard Inc.
11.2.3. Fintellix Solutions pvt. ltd.
11.2.4. Latent View Analytics
11.2.5. International Business Machines Corporation
11.2.6. Microsoft Corporation
11.2.7. Oracle Corporation
11.2.8. SAP SE
11.2.9. SAS Institute Inc.
11.2.10. Tableau Software
11.2.11. TIBCO Software Inc.
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. 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.
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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:
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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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