research topics in business analytics

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Home » 500+ Business Research Topics

500+ Business Research Topics

Business research is an essential component of any successful organization, as it allows companies to make informed decisions based on data-driven insights. Whether it’s market research to identify new opportunities, or analyzing internal processes to improve efficiency, there are a vast array of business research topics that companies can explore. With the constantly evolving business landscape, it’s critical for organizations to stay up-to-date with the latest research trends and best practices to remain competitive. In this post, we’ll explore some of the most compelling business research topics that are currently being studied, providing insights and actionable recommendations for businesses of all sizes.

Business Research Topics

Business Research Topics are as follows:

• The impact of social media on consumer behavior
• Strategies for enhancing customer satisfaction in the service industry
• The effectiveness of mobile marketing campaigns
• Exploring the factors influencing employee turnover
• The role of leadership in organizational culture
• Investigating the relationship between corporate social responsibility and financial performance
• Assessing the impact of employee engagement on organizational performance
• The challenges and opportunities of global supply chain management
• Analyzing the effectiveness of e-commerce platforms
• Investigating the impact of organizational culture on employee motivation
• The role of corporate governance in ensuring ethical business practices
• Examining the impact of digital marketing on brand equity
• Strategies for managing diversity and inclusion in the workplace
• Exploring the effects of employee empowerment on job satisfaction
• The role of innovation in business growth
• Analyzing the impact of mergers and acquisitions on company performance
• Investigating the impact of workplace design on employee productivity
• The challenges and opportunities of international business expansion
• Strategies for managing talent in the knowledge economy
• The role of artificial intelligence in transforming business operations
• Examining the impact of customer loyalty programs on retention and revenue
• Investigating the relationship between corporate social responsibility and brand reputation
• The role of emotional intelligence in effective leadership
• The impact of digital transformation on small and medium-sized enterprises
• Analyzing the effectiveness of green marketing strategies
• The role of entrepreneurship in economic development
• Investigating the impact of employee training and development on organizational performance
• The challenges and opportunities of omnichannel retailing
• Examining the impact of organizational change on employee morale and productivity
• The role of corporate social responsibility in attracting and retaining millennial talent
• Analyzing the impact of employee motivation on organizational culture
• Investigating the impact of workplace diversity on team performance
• The challenges and opportunities of blockchain technology in business operations
• Strategies for managing cross-functional teams
• The role of big data analytics in business decision-making
• Examining the impact of corporate social responsibility on customer loyalty
• Investigating the relationship between corporate social responsibility and employee engagement
• The impact of social media marketing on customer engagement and brand loyalty.
• The effectiveness of AI in improving customer service and satisfaction.
• The role of entrepreneurship in economic development and job creation.
• The impact of the gig economy on the labor market.
• The effects of corporate social responsibility on company profitability.
• The role of data analytics in predicting consumer behavior and market trends.
• The effects of globalization on the competitiveness of small businesses.
• The impact of e-commerce on traditional brick-and-mortar retail.
• The role of emotional intelligence in leadership and team management.
• The effects of workplace diversity on employee productivity and satisfaction.
• The role of corporate culture in employee retention and satisfaction.
• The impact of employee training and development on company performance.
• The effectiveness of performance-based pay structures on employee motivation.
• The impact of sustainability practices on company reputation and profitability.
• The effects of artificial intelligence on job displacement and the future of work.
• The role of innovation in the growth and success of small businesses.
• The impact of government regulations on business operations and profitability.
• The effects of organizational structure on company performance and efficiency.
• The role of emotional labor in service industries.
• The impact of employee empowerment on job satisfaction and retention.
• The effects of workplace flexibility on employee productivity and well-being.
• The role of emotional intelligence in negotiation and conflict resolution.
• The impact of branding on consumer behavior and purchase decisions.
• The effects of customer experience on brand loyalty and advocacy.
• The role of storytelling in marketing and advertising.
• The impact of consumer psychology on pricing strategies and sales.
• The effects of influencer marketing on consumer behavior and brand loyalty.
• The role of trust in online transactions and e-commerce.
• The impact of product design on consumer perception and purchasing decisions.
• The effects of customer satisfaction on company profitability and growth.
• The role of social entrepreneurship in addressing societal problems and creating value.
• The impact of corporate governance on company performance and stakeholder relations.
• The effects of workplace harassment on employee well-being and company culture.
• The role of strategic planning in the success of small businesses.
• The impact of technology on supply chain management and logistics.
• The effects of customer segmentation on marketing strategies and sales.
• The role of corporate philanthropy in building brand reputation and loyalty.
• The impact of intellectual property protection on innovation and creativity.
• The effects of trade policies on international business operations and profitability.
• The role of strategic partnerships in business growth and expansion.
• The impact of digital transformation on organizational structure and operations.
• The effects of leadership styles on employee motivation and performance.
• The role of corporate social activism in shaping public opinion and brand reputation.
• The impact of mergers and acquisitions on company performance and stakeholder value.
• The effects of workplace automation on job displacement and re-skilling.
• The role of cross-cultural communication in international business operations.
• The impact of workplace stress on employee health and productivity.
• The effects of customer reviews and ratings on online sales and reputation.
• The role of competitive intelligence in market research and strategy development.
• The impact of brand identity on consumer trust and loyalty.
• The impact of organizational structure on innovation and creativity
• Analyzing the effectiveness of virtual teams in global organizations
• The role of corporate social responsibility in crisis management
• The challenges and opportunities of online marketplaces
• Strategies for managing cultural diversity in multinational corporations
• The impact of employer branding on employee retention
• Investigating the impact of corporate social responsibility on investor behavior
• The role of technology in enhancing customer experience
• Analyzing the impact of social responsibility initiatives on customer satisfaction
• Investigating the impact of supply chain disruptions on business performance
• The role of business ethics in organizational decision-making
• The challenges and opportunities of artificial intelligence in customer service
• Strategies for managing employee burnout and stress in the workplace.
• Impact of social media on consumer behavior and its implications for businesses.
• The impact of corporate social responsibility on company performance.
• An analysis of the relationship between employee satisfaction and customer loyalty.
• The effect of advertising on consumer behavior.
• A study on the effectiveness of social media marketing in building brand image.
• The impact of technological innovations on business strategy and operations.
• The relationship between leadership style and employee motivation.
• A study of the effects of corporate culture on employee engagement.
• An analysis of the factors influencing consumer buying behavior.
• The effectiveness of training and development programs in enhancing employee performance.
• The impact of global economic factors on business decision-making.
• The role of organizational communication in achieving business goals.
• The relationship between customer satisfaction and business success.
• A study of the challenges and opportunities in international business.
• The effectiveness of supply chain management in improving business performance.
• An analysis of the factors influencing customer loyalty in the hospitality industry.
• The impact of employee turnover on organizational performance.
• A study of the impact of corporate governance on company performance.
• The role of innovation in business growth and success.
• An analysis of the relationship between marketing and sales performance.
• The effect of organizational structure on employee behavior.
• A study of the impact of cultural differences on business negotiations.
• The effectiveness of pricing strategies in increasing sales revenue.
• The impact of customer service on customer loyalty.
• A study of the role of human resource management in business success.
• The impact of e-commerce on traditional brick-and-mortar businesses.
• An analysis of the relationship between employee empowerment and job satisfaction.
• The effectiveness of customer relationship management in building brand loyalty.
• The role of business ethics in corporate decision-making.
• A study of the impact of digital marketing on consumer behavior.
• The effect of organizational culture on employee turnover.
• An analysis of the factors influencing employee engagement in the workplace.
• The impact of social media on business communication and marketing.
• A study of the relationship between customer service and customer loyalty in the airline industry.
• The role of diversity and inclusion in business success.
• The effectiveness of performance management systems in improving employee performance.
• The impact of corporate social responsibility on employee engagement.
• A study of the factors influencing business expansion into new markets.
• The role of brand identity in customer loyalty and retention.
• The effectiveness of change management strategies in organizational change.
• The impact of organizational structure on organizational performance.
• A study of the impact of technology on the future of work.
• The relationship between innovation and competitive advantage in the marketplace.
• The effect of employee training on organizational performance.
• An analysis of the impact of online reviews on consumer behavior.
• The role of leadership in shaping organizational culture.
• The effectiveness of talent management strategies in retaining top talent.
• The impact of globalization on small and medium-sized enterprises.
• A study of the relationship between corporate social responsibility and brand reputation.
• The effectiveness of employee retention strategies in reducing turnover rates.
• The role of emotional intelligence in leadership and employee engagement.
• The impact of digital marketing on customer behavior
• The role of organizational culture in employee engagement and retention
• The effects of employee training and development on organizational performance
• The relationship between corporate social responsibility and financial performance
• The impact of globalization on business strategy
• The importance of supply chain management in achieving competitive advantage
• The role of innovation in business growth and sustainability
• The impact of e-commerce on traditional retail businesses
• The role of leadership in managing change in organizations
• The effects of workplace diversity on organizational performance
• The impact of social media on brand image and reputation
• The relationship between employee motivation and productivity
• The role of organizational structure in promoting innovation
• The effects of customer service on customer loyalty
• The impact of globalization on small businesses
• The role of corporate governance in preventing unethical behavior
• The effects of technology on job design and work organization
• The relationship between employee satisfaction and turnover
• The impact of mergers and acquisitions on organizational culture
• The effects of employee benefits on job satisfaction
• The impact of cultural differences on international business negotiations
• The role of strategic planning in organizational success
• The effects of organizational change on employee stress and burnout
• The impact of business ethics on customer trust and loyalty
• The role of human resource management in achieving competitive advantage
• The effects of outsourcing on organizational performance
• The impact of diversity and inclusion on team performance
• The role of corporate social responsibility in brand differentiation
• The effects of leadership style on organizational culture
• The Impact of Digital Marketing on Brand Equity: A Study of E-commerce Businesses
• Investigating the Relationship between Employee Engagement and Organizational Performance
• Analyzing the Effects of Corporate Social Responsibility on Customer Loyalty and Firm Performance
• An Empirical Study of the Factors Affecting Entrepreneurial Success in the Technology Sector
• The Influence of Organizational Culture on Employee Motivation and Job Satisfaction: A Case Study of a Service Industry
• Investigating the Impact of Organizational Change on Employee Resistance: A Comparative Study of Two Organizations
• An Exploration of the Impact of Artificial Intelligence on Supply Chain Management
• Examining the Relationship between Leadership Styles and Employee Creativity in Innovative Organizations
• Investigating the Effectiveness of Performance Appraisal Systems in Improving Employee Performance
• Analyzing the Role of Emotional Intelligence in Leadership Effectiveness: A Study of Senior Managers
• The Impact of Transformational Leadership on Employee Motivation and Job Satisfaction in the Healthcare Sector
• Evaluating the Effectiveness of Talent Management Strategies in Enhancing Organizational Performance
• A Study of the Effects of Customer Relationship Management on Customer Retention and Loyalty
• Investigating the Impact of Corporate Governance on Firm Performance: Evidence from Emerging Markets
• The Relationship between Intellectual Capital and Firm Performance: A Case Study of Technology Firms
• Analyzing the Effectiveness of Diversity Management in Improving Organizational Performance
• The Impact of Internationalization on the Performance of Small and Medium-sized Enterprises: A Comparative Study of Developed and Developing Countries
• Examining the Relationship between Corporate Social Responsibility and Financial Performance: A Study of Listed Firms
• Investigating the Influence of Entrepreneurial Orientation on Firm Performance in Emerging Markets
• Analyzing the Impact of E-commerce on Traditional Retail Business Models: A Study of Brick-and-Mortar Stores
• The Effect of Corporate Reputation on Customer Loyalty and Firm Performance: A Study of the Banking Sector
• Investigating the Factors Affecting Consumer Adoption of Mobile Payment Systems
• The Role of Corporate Social Responsibility in Attracting and Retaining Millennial Employees
• Analyzing the Impact of Social Media Marketing on Brand Awareness and Consumer Purchase Intentions
• A Study of the Effects of Employee Training and Development on Job Performance
• Investigating the Relationship between Corporate Culture and Employee Turnover: A Study of Multinational Companies
• The Impact of Business Process Reengineering on Organizational Performance: A Study of Service Industries
• An Empirical Study of the Factors Affecting Internationalization Strategies of Small and Medium-sized Enterprises
• The Effect of Strategic Human Resource Management on Firm Performance: A Study of Manufacturing Firms
• Investigating the Influence of Leadership on Organizational Culture: A Comparative Study of Two Organizations
• The Impact of Technology Adoption on Organizational Productivity: A Study of the Healthcare Sector
• Analyzing the Effects of Brand Personality on Consumer Purchase Intentions: A Study of Luxury Brands
• The Relationship between Corporate Social Responsibility and Customer Perceptions of Product Quality: A Study of the Food and Beverage Industry
• Investigating the Effectiveness of Performance Management Systems in Improving Employee Performance: A Study of a Public Sector Organization
• The Impact of Business Ethics on Firm Performance: A Study of the Banking Industry
• Examining the Relationship between Employee Engagement and Customer Satisfaction in the Service Industry
• Investigating the Influence of Entrepreneurial Networking on Firm Performance: A Study of Small and Medium-sized Enterprises
• The Effect of Corporate Social Responsibility on Employee Retention: A Study of High-tech Firms
• The impact of workplace communication on employee engagement
• The role of customer feedback in improving service quality
• The effects of employee empowerment on job satisfaction
• The impact of innovation on customer satisfaction
• The role of knowledge management in organizational learning
• The effects of product innovation on market share
• The impact of business location on customer behavior
• The role of financial management in business success
• The effects of corporate social responsibility on employee engagement
• The impact of cultural intelligence on cross-cultural communication
• The role of social media in crisis management
• The effects of corporate branding on customer loyalty
• The impact of globalization on consumer behavior
• The role of emotional intelligence in leadership effectiveness
• The effects of employee involvement in decision-making on job satisfaction
• The impact of business strategy on market share
• The role of corporate culture in promoting ethical behavior
• The effects of corporate social responsibility on investor behavior
• The impact of sustainability on brand image and reputation
• The role of corporate social responsibility in reducing carbon emissions.
• The effectiveness of loyalty programs on customer retention
• The benefits of remote work for employee productivity
• The impact of environmental sustainability on consumer purchasing decisions
• The role of brand identity in consumer loyalty
• The relationship between employee satisfaction and customer satisfaction
• The impact of e-commerce on traditional brick-and-mortar stores
• The effectiveness of online advertising on consumer behavior
• The impact of leadership styles on employee motivation
• The role of corporate social responsibility in brand perception
• The impact of workplace diversity on organizational performance
• The effectiveness of gamification in employee training programs
• The impact of pricing strategies on consumer behavior
• The effectiveness of mobile marketing on consumer engagement
• The impact of emotional intelligence on leadership effectiveness
• The role of customer service in consumer loyalty
• The impact of technology on supply chain management
• The effectiveness of employee training programs on job performance
• The impact of culture on consumer behavior
• The effectiveness of performance appraisal systems on employee motivation
• The impact of social responsibility on organizational performance
• The role of innovation in business success
• The impact of ethical leadership on organizational culture
• The effectiveness of cross-functional teams in project management
• The impact of government regulations on business operations
• The role of strategic planning in business growth
• The impact of emotional intelligence on team dynamics
• The effectiveness of supply chain management on customer satisfaction
• The impact of workplace culture on employee satisfaction
• The role of employee engagement in organizational success
• The impact of globalization on organizational culture
• The effectiveness of virtual teams in project management
• The impact of employee turnover on organizational performance
• The role of corporate social responsibility in talent acquisition
• The impact of technology on employee training and development
• The effectiveness of knowledge management on organizational learning
• The impact of organizational structure on employee motivation
• The role of innovation in organizational change
• The impact of cultural intelligence on global business operations
• The effectiveness of marketing strategies on brand perception
• The impact of change management on organizational culture
• The role of leadership in organizational transformation
• The impact of employee empowerment on job satisfaction
• The effectiveness of project management methodologies on project success
• The impact of workplace communication on team performance
• The role of emotional intelligence in conflict resolution
• The impact of employee motivation on job performance
• The effectiveness of diversity and inclusion initiatives on organizational performance.
• The impact of social media on consumer behavior and buying decisions
• The role of diversity and inclusion in corporate culture and its effects on employee retention and productivity
• The effectiveness of remote work policies on job satisfaction and work-life balance
• The impact of customer experience on brand loyalty and revenue growth
• The effects of environmental sustainability practices on corporate reputation and financial performance
• The role of corporate social responsibility in consumer purchasing decisions
• The effectiveness of leadership styles on team performance and productivity
• The effects of employee motivation on job performance and turnover
• The impact of technology on supply chain management and logistics efficiency
• The role of emotional intelligence in effective leadership and team dynamics
• The impact of artificial intelligence and automation on job displacement and workforce trends
• The effects of brand image on consumer perception and purchasing decisions
• The role of corporate culture in promoting innovation and creativity
• The impact of e-commerce on traditional brick-and-mortar retail businesses
• The effects of corporate governance on financial reporting and transparency
• The effectiveness of performance-based compensation on employee motivation and productivity
• The impact of online reviews and ratings on consumer trust and brand reputation
• The effects of workplace diversity on innovation and creativity
• The impact of mobile technology on marketing strategies and consumer behavior
• The role of emotional intelligence in customer service and satisfaction
• The effects of corporate reputation on financial performance and stakeholder trust
• The impact of artificial intelligence on customer service and support
• The role of organizational culture in promoting ethical behavior and decision-making
• The effects of corporate social responsibility on employee engagement and satisfaction
• The impact of employee turnover on organizational performance and profitability
• The role of customer satisfaction in promoting brand loyalty and advocacy
• The effects of workplace flexibility on employee morale and productivity
• The impact of employee wellness programs on absenteeism and healthcare costs
• The role of data analytics in business decision-making and strategy formulation
• The effects of brand personality on consumer behavior and perception
• The impact of social media marketing on brand awareness and customer engagement
• The role of organizational justice in promoting employee satisfaction and retention
• The effects of corporate branding on employee motivation and loyalty
• The impact of online advertising on consumer behavior and purchasing decisions
• The role of corporate entrepreneurship in promoting innovation and growth
• The effects of cultural intelligence on cross-cultural communication and business success
• The impact of workplace diversity on customer satisfaction and loyalty
• The role of ethical leadership in promoting employee trust and commitment
• The effects of job stress on employee health and well-being
• The impact of supply chain disruptions on business operations and financial performance
• The role of organizational learning in promoting continuous improvement and innovation
• The effects of employee engagement on customer satisfaction and loyalty
• The impact of brand extensions on brand equity and consumer behavior
• The role of strategic alliances in promoting business growth and competitiveness
• The effects of corporate transparency on stakeholder trust and loyalty
• The impact of digital transformation on business models and competitiveness
• The role of business ethics in promoting corporate social responsibility and sustainability
• The effects of employee empowerment on job satisfaction and organizational performance.
• The role of corporate governance in mitigating unethical behavior in multinational corporations.
• The effects of cultural diversity on team performance in multinational corporations.
• The impact of corporate social responsibility on consumer loyalty and brand reputation.
• The relationship between organizational culture and employee engagement in service industries.
• The impact of globalization on the competitiveness of small and medium enterprises (SMEs).
• The effectiveness of performance-based pay systems on employee motivation and productivity.
• The relationship between innovation and corporate performance in the pharmaceutical industry.
• The impact of digital marketing on the traditional marketing mix.
• The role of emotional intelligence in leadership effectiveness in cross-cultural teams.
• The relationship between corporate social responsibility and financial performance in the banking sector.
• The impact of diversity management on employee satisfaction and retention in multinational corporations.
• The relationship between leadership style and organizational culture in family-owned businesses.
• The impact of e-commerce on supply chain management.
• The effectiveness of training and development programs on employee performance in the retail sector.
• The impact of global economic trends on strategic decision-making in multinational corporations.
• The relationship between ethical leadership and employee job satisfaction in the healthcare industry.
• The impact of employee empowerment on organizational performance in the manufacturing sector.
• The relationship between corporate social responsibility and employee well-being in the hospitality industry.
• The impact of artificial intelligence on customer service in the banking industry.
• The relationship between emotional intelligence and employee creativity in the technology industry.
• The impact of big data analytics on customer relationship management in the telecommunications industry.
• The relationship between organizational culture and innovation in the automotive industry.
• The impact of internationalization on the performance of SMEs in emerging markets.
• The effectiveness of performance appraisal systems on employee motivation and retention in the public sector.
• The relationship between diversity management and innovation in the pharmaceutical industry.
• The impact of social entrepreneurship on economic development in developing countries.
• The relationship between transformational leadership and organizational change in the energy sector.
• The impact of online customer reviews on brand reputation in the hospitality industry.
• The effectiveness of leadership development programs on employee engagement in the finance industry.
• The relationship between corporate social responsibility and employee turnover in the retail sector.
• The impact of artificial intelligence on the recruitment and selection process in the technology industry.
• The relationship between organizational culture and employee creativity in the fashion industry.
• The impact of digital transformation on business models in the insurance industry.
• The relationship between employee engagement and customer satisfaction in the service industry.
• The impact of mergers and acquisitions on organizational culture and employee morale.
• The effectiveness of knowledge management systems on organizational performance in the consulting industry.
• The impact of social media marketing on brand loyalty in the food and beverage industry.
• The relationship between emotional intelligence and customer satisfaction in the airline industry.
• The impact of blockchain technology on supply chain management in the logistics industry.
• The relationship between corporate social responsibility and employee engagement in the technology industry.
• The impact of digitalization on talent management practices in the hospitality industry.
• The effectiveness of reward and recognition programs on employee motivation in the manufacturing industry.
• The impact of industry 4.0 on organizational structure and culture in the aerospace industry.
• The relationship between leadership style and team performance in the construction industry.
• The impact of artificial intelligence on financial forecasting and decision-making in the banking sector.
• The relationship between corporate social responsibility and customer loyalty in the automotive industry.
• The impact of virtual teams on employee communication and collaboration in the pharmaceutical industry.
• The impact of remote work on employee productivity and job satisfaction
• The effects of social media marketing on customer engagement and brand loyalty
• The role of artificial intelligence in streamlining supply chain management
• The effectiveness of employee training and development programs on organizational performance
• The impact of diversity and inclusion initiatives on organizational culture and employee satisfaction
• The role of corporate social responsibility in enhancing brand reputation and customer loyalty
• The effects of e-commerce on small businesses and local economies
• The impact of big data analytics on marketing strategies and customer insights
• The effects of employee empowerment on organizational innovation and performance
• The impact of globalization on the hospitality industry
• The effects of corporate governance on organizational performance and financial outcomes
• The role of customer satisfaction in driving business growth and profitability
• The impact of artificial intelligence on financial forecasting and risk management
• The effects of corporate culture on employee engagement and retention
• The role of green marketing in promoting environmental sustainability and brand reputation
• The impact of digital transformation on the retail industry
• The effects of employee motivation on job performance and organizational productivity
• The role of customer experience in enhancing brand loyalty and advocacy
• The impact of international trade agreements on global business practices
• The effects of artificial intelligence on customer service and support
• The role of organizational communication in facilitating teamwork and collaboration
• The impact of corporate social responsibility on employee motivation and retention
• The effects of global economic instability on business decision-making
• The role of leadership styles in organizational change management
• The impact of social media influencers on consumer behavior and purchasing decisions
• The effects of employee well-being on organizational productivity and profitability
• The role of innovation in driving business growth and competitive advantage
• The impact of digital marketing on consumer behavior and brand perception
• The role of strategic planning in organizational success and sustainability
• The impact of e-commerce on consumer privacy and data security
• The effects of corporate reputation on customer acquisition and retention
• The role of diversity and inclusion in organizational creativity and innovation
• The impact of artificial intelligence on customer relationship management
• The effects of customer feedback on product development and innovation
• The role of employee job satisfaction in reducing turnover and absenteeism
• The impact of global competition on business strategy and innovation
• The effects of corporate branding on customer loyalty and advocacy
• The role of digital transformation in enhancing organizational agility and responsiveness
• The effects of employee empowerment on customer satisfaction and loyalty
• The role of entrepreneurial leadership in driving business innovation and growth
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• Business intelligence and Visualization

Top Business Intelligence Research Topics to Choose from in 2024

Home Blog Business intelligence and Visualization Top Business Intelligence Research Topics to Choose from in 2024

In 2024, Business Intelligence ( BI ) is a rapidly evolving field focusing on data collection, analysis, and interpretation to enhance decision-making in organizations. To contribute meaningfully and stay at the forefront of industry advancements, selecting a compelling research topic is vital. This article explores prominent research subjects within BI for 2024. Each topic offers a comprehensive overview, emphasizing its significance, potential investigation inquiries, and exploration possibilities. While not exhaustive, these areas represent the most relevant and promising directions in BI research. You can gain expertise from international experts in Tableau, BI, TIBCO, and Data Visualization through Business Intelligence and Visualization training .

Top Business Intelligence Research Topics

These are excellent Topics for Business research in the field of business intelligence. Here is a brief overview of each topic:

• A literature review of business intelligence - Parameters, models, and implications: This topic involves conducting a comprehensive review of existing literature on business intelligence, including its various parameters, models, and implications. It aims to provide a holistic understanding of the field and identify gaps or areas for further research.
• Bridging the gap between theory and practice for business intelligence models:  This topic focuses on examining the challenges and opportunities in applying business intelligence models in real-world settings. It explores ways to bridge the gap between theoretical concepts and practical implementation, considering factors such as organizational context, data availability, and user acceptance.
• The impact of business intelligence in network security systems:  This topic investigates the role of business intelligence in enhancing network security systems. It examines how BI techniques and technologies can be applied to detect and prevent cybersecurity threats, improve incident response, and ensure data protection within organizational networks.
• A historical perspective of business intelligence, current practice, and future developments:  This topic involves studying the historical evolution of business intelligence, examining its current practices, and forecasting future developments. It explores the growth and advancements in BI over time, along with emerging trends and potential future directions for the field.
• Content-Based Data Masking Strategy in Business Intelligence Platform for Built-in Framework: This topic focuses on developing a content-based data masking strategy for business intelligence platforms. It explores techniques to protect sensitive data while maintaining its usefulness for analysis and reporting, considering factors such as data masking algorithms, data classification, and access control mechanisms.
• Research on Knowledge Extraction Using Data Mining for Business Operations:  This topic explores the application of data mining techniques for knowledge extraction in business operations. It investigates how data mining algorithms can be utilized to discover hidden patterns, insights, and actionable knowledge from large datasets, aiding in decision-making and improving operational efficiency.
• The efficiency of online data storage for businesses and areas for development: This topic assesses the efficiency and effectiveness of online data storage solutions for businesses. It examines the benefits and challenges associated with cloud-based storage, data backup, and disaster recovery, along with identifying areas for improvement and potential future enhancements.
• The impact of business intelligence on marketing with emphasis on cooperative learning:  This topic investigates the influence of business intelligence on marketing strategies, with a specific emphasis on the concept of cooperative learning. It explores how BI can facilitate collaboration and knowledge sharing among marketing teams, leading to more effective marketing campaigns and improved customer targeting.
• An analysis of Agile analytics as an extension of rapidly growing business intelligence systems - applications and barriers:  This topic examines the concept of Agile analytics and its role as an extension of traditional business intelligence systems. It investigates the applications, benefits, and potential barriers associated with implementing Agile analytics methodologies in organizations, considering factors such as data agility, user collaboration, and adaptive decision-making.

These research topics offer a diverse range of avenues to explore within the field of business intelligence, providing opportunities to contribute to knowledge, theory, and practical applications. Researchers can choose the topic that aligns with their interests, expertise, and the current gaps or challenges in the industry.

How to Write a Perfect Research Paper?

Writing a perfect Business Intelligence research paper requires careful planning, organization, and attention to detail. Here is a step-by-step guide to help you write an excellent research paper:

Understand the Business Intelligence Thesis: Begin by thoroughly reading and understanding the requirements and guidelines provided by your instructor or institution. Clarify any doubts or questions before proceeding.

• Ø   Choose a topic: Select a research topic that is interesting, relevant, and has sufficient available resources for investigation. Refine your topic to make it focused and specific.
• Conduct preliminary research: Before diving into writing, conduct preliminary research to familiarize yourself with the existing literature, theories, and findings related to your topic. This will help you develop a strong theoretical foundation for your research paper.
• Develop a thesis statement: Craft a clear and concise thesis statement that outlines the main argument or objective of your research. The thesis statement should guide your entire paper and provide a roadmap for the reader.
• Create an outline: Organize your thoughts and main points by creating a detailed outline for your research paper. This will help you structure your paper logically and ensure a coherent flow of ideas.
• Gather and evaluate sources: Collect relevant sources, such as academic journals, books, reputable websites, and other scholarly materials. Evaluate the credibility, reliability, and relevance of each source to ensure that you use reliable information in your research paper.
• Write the introduction: Start your paper with an engaging introduction that captures the reader's attention and provides background information on your topic. Clearly state your research objectives and the significance of your study.
• Develop the literature review: Provide a comprehensive review of the existing literature on your topic. Summarize and critically analyse relevant studies, theories, and frameworks. Identify gaps or limitations in the literature that your research aims to address.
• Methodology: Provide an overview of the research approach employed, encompassing the research design, methods for data collection, sample size determination, and the techniques used for data analysis. Justify your choices and explain how they align with your research objectives.
• Present your findings: Present your research findings in a clear, organized, and logical manner. Use appropriate tables, charts, or graphs to illustrate data and support your arguments. Interpret the results and discuss their implications.
• Discussion and conclusion: Analyze and interpret your findings in the context of your research objectives. Discuss the implications, limitations, and potential areas for future research. Summarize your main points and restate your thesis in the conclusion.
• Revise and edit: Review your research paper for clarity, coherence, grammar, and punctuation errors. Revise and refine your content, ensuring that your arguments are well-supported, and your writing is concise and precise.
• Proofread: Carefully proofread your paper to catch any spelling or typographical errors. Check formatting, citations, and references to ensure accuracy and consistency.
• Seek feedback: Before finalizing your research paper, seek feedback from your peers, mentors, or professors. Incorporate their suggestions and make necessary revisions to enhance the quality of your paper.
• Finalize and submit: Make the final adjustments and formatting changes, double-check all references, and ensure that your research paper meets the required guidelines. Submit your paper within the given deadline.
• Writing a perfect research paper takes time, effort, and attention to detail. By adhering to these steps and adopting a systematic approach, it is possible to generate a research paper of exceptional quality that effectively communicates your findings and makes a significant contribution to your field of study.

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Why Business Intelligence is Important in 2024?

Business intelligence (BI) is increasingly recognized for its significance as organizations endeavour to make well-informed decisions in an intricate and fiercely competitive business environment. In the year 2024, BI holds immense value due to the following reasons: The prominence of data-driven decision-making: In the present era of digitization, enterprises possess an abundance of data resources.

• Data-driven decision-making: In the digital age, BI helps businesses analyse vast data, gain actionable insights, and make informed decisions based on evidence and trends, reducing reliance on intuition.
• Competitive advantage: BI provides organizations a competitive edge by extracting valuable insights from data, enabling quick responses to market trends, customer preferences, and emerging opportunities, optimizing operations, and capitalizing on market shifts.
• Customer insights and personalization: BI enables organizations to gain a deeper understanding of their customers by analysing their behaviour, preferences, and feedback. Utilizing this information enables the customization of marketing campaigns, enhancement of customer experiences, and optimization of product offerings. 
• Forecasting and predictive analytics: Business Intelligence (BI) employs predictive modelling and forecasting by analyzing historical data and patterns to anticipate future trends and outcomes. This enables organizations to make proactive decisions, allocate resources effectively, and mitigate risks based on accurate predictions of market demand and customer behaviour.
• Data governance and compliance: With the increasing focus on data privacy and security, BI tools play a vital role in ensuring data governance and compliance with regulatory requirements. They manage data access, monitor data quality, and enforce data protection measures. 

By leveraging BI effectively, businesses can stay agile, adapt to changing market conditions, and drive sustainable growth in a data-centric world. You can go through this well-designed course to learn more about KnowledgeHut Business Intelligence and Visualization training .

In conclusion, writing a perfect research paper requires meticulous planning, organization, and attention to detail. By adopting a systematic approach and adhering to the provided guidelines, you will be able to create a research paper of outstanding quality that effectively communicates your findings and makes a valuable contribution to your field of study.

Throughout the research paper writing process, it is crucial to have a clear understanding of the assignment and choose a relevant and engaging topic. Conducting preliminary research helps in developing a strong theoretical foundation and crafting a focused thesis statement. Creating a detailed outline ensures a logical structure and coherent flow of ideas in the paper.

The gathering and evaluation of credible sources are essential for supporting your arguments and providing a comprehensive literature review. Careful consideration of research methodology, data collection methods, and analysis techniques helps in ensuring the validity and reliability of your findings.

The presentation of your findings should be clear, organized, and supported by appropriate visuals.  The goal of business intelligence is to transform raw data into actionable insights that can drive strategic and operational decisions. Power BI is the most trending tool these days and we do not want to stay behind in the race to get ahead in knowing about BI tools, so check out this amazing Power BI course which will help you upskill yourself and learn a lot more about Business Intelligence.

Frequently Asked Questions (FAQs)

BI research explores data techniques and tools for informed decision-making in organizations, covering data analytics, visualization, mining, machine learning, and predictive modeling to boost business performance.

Three major types of business intelligence:

• Descriptive BI: Analyses historical data to gain insights into past events within the organization.
• Predictive BI: Uses statistical models to forecast future trends and outcomes based on historical data.
• Prescriptive BI: Recommends actions or strategies by going beyond descriptive and predictive analytics.

The four fundamental concepts of business intelligence are data collection from diverse sources, data analysis using statistical techniques and machine learning, data visualization with charts and graphs for easy comprehension, and data-driven decision-making to support organizational performance and achieve business objectives.

Gauri Guglani

Gauri Guglani works as a Data Analyst at Deloitte Consulting. She has done her major in Information Technology and holds great interest in the field of data science. She owns her technical skills as well as managerial skills and also is great at communicating. Since her undergraduate, Gauri has developed a profound interest in writing content and sharing her knowledge through the manual means of blog/article writing. She loves writing on topics affiliated with Statistics, Python Libraries, Machine Learning, Natural Language processes, and many more.

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Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr Wilson Chen

Dr  Bern Conlon

Dr  Nam Ho-Nguyen

Dr  Pablo Montero-Manso

Dr  Stephen Tierney

Dr  Chao Wang

Dr Qin Fang

Dr  Simon Loria

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

• Date: 2 Jun 2017 at 11am
• Speaker: Professor Wray Buntine, Master of Data Science, Monash University

Optimisation and equilibrium problems in engineering

• Date: 26 May 2017 at 11am
• Speaker: Prof Steven Gabriel, Department of Mechanical Engineering, University of Maryland

Exact subsampling MCMC

• Date: 12 May 2017 at 11am
• Speaker: Dr Matias Quiroz, UNSW Business School, University of New South Wales

Effects of taxes and safety net pensions on life-cycle labor supply, savings and human capital: The case of Australia

• Date: 21 Apr 2017 at 11am
• Speaker: Dr Fedor Iskhakov, College of Business and Economics, Australian National University

Trial-offer markets with social influence: The impact of different ranking policies

• Date: 18 Apr 2017 at 11am
• Venue: Rm 5040, Abercrombie Building (H70)
• Speaker: Dr Gerardo Berbeglia, Melbourne Business School, University of Melbourne

Conditionally optimal weights and forward-looking approaches to combining forecasts

• Date: 7 Apr 2017 at 11am
• Speaker: Dr Andrey Vasnev, Discipline of Business Analytics, The University of Sydney

A flexible generalised hyberbolic option pricing model and its special cases

• Date: 31 Mar 2017 at 11am
• Speaker: Dr Simon Kwok, School of Economics, The University of Sydney

Scheduling with variable processing times: Complexity results and approximation algorithms

• Date: 24 Mar 2017 at 11:00am
• Speaker: Associate Professor Daniel Oron, Discipline of Business Analytics, The University of Sydney

Modelling insurance losses using contaminated generalised beta type-2 distribution

• Date: 17 Mar 2017 at 11am
• Speaker: Dr Boris Choy, Discipline of Business Analytics, The University of Sydney

How (not) to get what you ask for: Survey mode effects on self-reported substance use

• Date: 24 Feb 2017 at 11am
• Speaker: Dr Bin Peng, School of Mathematics, University of Technology Sydney

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211 Business Topics For Research Paper [Updated]

Are you looking for intriguing business topics to explore in your research paper? Whether you’re a student delving into the world of business studies or a seasoned professional seeking fresh insights, selecting the right topic is crucial. In this blog, we’ll walk you through a diverse array of business topics for research paper. From management strategies to emerging trends like sustainability and digital transformation, there’s something for everyone. Let’s dive in!

What Are The Characteristics of Business Research Topics?

Table of Contents

Business research topics possess several key characteristics that distinguish them from other types of research topics. These characteristics include:

• Relevance: Business research topics should address current issues, trends, and challenges facing the business world. They should be of interest to academics, practitioners, and policymakers alike.
• Practicality: Business research topics should have real-world applicability and relevance to industry practices. They should offer insights that can be implemented to improve organizational performance, decision-making, and strategy.
• Interdisciplinary Nature: Business research often draws from multiple disciplines such as economics, management, marketing, finance, and psychology. Topics should be interdisciplinary in nature, incorporating insights from various fields to provide comprehensive analysis.
• Data-Driven: Business research relies heavily on empirical evidence and data analysis. Topics should lend themselves to quantitative, qualitative, or mixed-method research approaches, depending on the research question and objectives.
• Innovation and Creativity: Business research topics should encourage innovative thinking and creative problem-solving. They should explore emerging trends, disruptive technologies, and novel approaches to address business challenges.
• Ethical Considerations: Ethical considerations are paramount in business research. Topics should adhere to ethical principles and guidelines, ensuring the protection of participants’ rights and the integrity of research findings.
• Global Perspective: Business research topics should consider the global context and implications of business decisions and practices. They should explore cross-cultural differences, international markets, and global trends shaping the business landscape.
• Impact: Business research topics should have the potential to generate meaningful insights and contribute to the advancement of knowledge in the field. They should address pressing issues and offer practical solutions that can drive positive change in organizations and society.

By embodying these characteristics, business research topics can effectively address the complexities and challenges of the modern business environment, providing valuable insights for academic scholarship and practical application.

211 Business Topics For Research Paper

• The Impact of Leadership Styles on Employee Motivation
• Strategies for Managing Multicultural Teams Effectively
• The Role of Emotional Intelligence in Leadership Success
• Marketing Strategies for Small Businesses on a Limited Budget
• The Influence of Social Media Marketing on Consumer Behavior
• Brand Loyalty: Factors Influencing Consumer Purchase Decisions
• Ethical Considerations in Advertising Practices
• Financial Risk Management in Multinational Corporations
• Corporate Governance and Financial Performance
• The Role of Financial Derivatives in Hedging Market Risks
• Success Factors for Startups in Competitive Markets
• Innovation and Entrepreneurship: Key Drivers of Economic Growth
• Challenges and Opportunities in Scaling a Business Globally
• Ethical Dilemmas in Business Decision-Making
• Corporate Social Responsibility Practices and Brand Image
• Balancing Profit Motives with Social and Environmental Concerns
• The Business Case for Sustainability Initiatives
• Renewable Energy Adoption in Businesses
• Circular Economy Models and Business Sustainability
• The Impact of Digital Technologies on Traditional Business Models
• E-Commerce Trends and Consumer Preferences
• Cybersecurity Challenges in E-Commerce Transactions
• The Benefits of Diversity in the Workplace
• Strategies for Promoting Gender Equality in Leadership Roles
• Addressing Unconscious Bias in Recruitment Processes
• The Impact of Remote Work on Employee Productivity
• Flexible Work Arrangements and Work-Life Balance
• The Role of Corporate Culture in Employee Engagement
• Talent Management Strategies for Attracting and Retaining Top Talent
• Performance Appraisal Systems: Best Practices and Challenges
• Workplace Diversity and Inclusion Initiatives
• Employee Training and Development Programs
• Change Management Strategies for Organizational Transformation
• Crisis Management and Business Continuity Planning
• Supply Chain Resilience: Lessons Learned from Disruptions
• Sustainable Sourcing Practices in Supply Chain Management
• Inventory Management Strategies for Reducing Costs
• Logistics Optimization for Efficient Operations
• The Impact of Globalization on Supply Chain Networks
• Strategic Alliances and Collaborative Partnerships in Business
• Mergers and Acquisitions: Drivers and Challenges
• Corporate Restructuring Strategies for Turnaround Success
• The Role of Corporate Social Responsibility in Building Customer Trust
• Reputation Management in the Digital Age
• Crisis Communication Strategies for Managing Reputational Risks
• Customer Relationship Management: Strategies for Enhancing Customer Loyalty
• Personalization Techniques in Marketing and Customer Service
• Omnichannel Retailing: Integrating Online and Offline Channels
• The Future of Brick-and-Mortar Retail in the Digital Era
• Pricing Strategies for Maximizing Profitability
• Revenue Management Techniques in Hospitality Industry
• Brand Extension Strategies and Brand Equity
• Customer Experience Management: Best Practices and Trends
• The Impact of Artificial Intelligence on Business Operations
• Machine Learning Applications in Marketing and Sales
• Automation and Robotics in Manufacturing Processes
• Blockchain Technology: Opportunities and Challenges for Businesses
• Augmented Reality and Virtual Reality in Marketing
• Data Privacy and Security Concerns in the Digital Age
• The Role of Big Data Analytics in Business Decision-Making
• Predictive Analytics for Sales Forecasting and Demand Planning
• Customer Segmentation Techniques for Targeted Marketing
• The Influence of Cultural Factors on Consumer Behavior
• Cross-Cultural Marketing Strategies for Global Brands
• International Market Entry Strategies: Modes of Entry and Risks
• Exporting vs. Foreign Direct Investment: Pros and Cons
• Market Entry Strategies for Emerging Markets
• The Impact of Political and Economic Factors on International Business
• Foreign Exchange Risk Management Strategies
• Cultural Intelligence and Global Leadership Effectiveness
• The Role of Multinational Corporations in Economic Development
• Corporate Governance Practices in Different Countries
• Comparative Analysis of Business Laws and Regulations
• Intellectual Property Rights Protection in Global Business
• The Influence of Cultural Differences on Negotiation Styles
• Cross-Border Mergers and Acquisitions: Legal and Cultural Challenges
• International Trade Agreements and Their Impact on Businesses
• The Role of Non-Governmental Organizations in Sustainable Development
• Corporate Philanthropy and Social Impact Investing
• Microfinance and Economic Empowerment of Women
• Entrepreneurship Ecosystems and Innovation Hubs
• Government Policies and Support for Small Businesses
• Venture Capital Financing and Startup Growth
• Crowdfunding Platforms: Opportunities for Entrepreneurs
• Social Entrepreneurship: Business Models for Social Change
• Innovation Clusters and Regional Economic Development
• Angel Investors and Their Role in Startup Funding
• Technology Incubators: Nurturing Startup Innovation
• Intellectual Property Rights Protection for Startup Innovations
• Business Model Innovation: Disrupting Traditional Industries
• The Impact of Climate Change on Business Operations
• Green Technologies and Sustainable Business Practices
• Carbon Footprint Reduction Strategies for Businesses
• Environmental Management Systems and Certification
• Corporate Reporting on Environmental Performance
• Circular Economy Business Models: Closing the Loop
• Sustainable Supply Chain Management Practices
• The Role of Renewable Energy in Achieving Carbon Neutrality
• Smart Cities and Sustainable Urban Development
• Green Building Technologies and Sustainable Construction
• The Influence of Cultural Factors on Entrepreneurship
• Gender Differences in Entrepreneurial Intentions and Success
• Social Capital and Networking for Entrepreneurial Ventures
• Family Business Succession Planning and Governance
• Corporate Entrepreneurship: Fostering Innovation Within Organizations
• Franchising: Opportunities and Challenges for Entrepreneurs
• Online Platforms and the Gig Economy
• Digital Nomads: Remote Work and Entrepreneurship
• The Sharing Economy: Business Models and Regulation
• Blockchain Applications in Supply Chain Traceability
• Cryptocurrency Adoption in Business Transactions
• Initial Coin Offerings (ICOs) and Tokenization of Assets
• Decentralized Finance (DeFi) and Its Implications for Traditional Banking
• Smart Contracts and Their Potential in Business Operations
• Privacy-Preserving Technologies in Data Sharing
• Cryptocurrency Exchanges: Regulation and Security Issues
• Central Bank Digital Currencies (CBDCs) and Monetary Policy
• The Impact of Artificial Intelligence on Financial Services
• Robo-Advisors and Algorithmic Trading in Wealth Management
• Fintech Startups and Disruption in Traditional Banking
• Peer-to-Peer Lending Platforms: Opportunities and Risks
• Digital Identity Management Systems and Security
• Regulatory Challenges in Fintech Innovation
• Financial Inclusion and Access to Banking Services
• Green Finance: Sustainable Investment Strategies
• Socially Responsible Investing and ESG Criteria
• Impact Investing: Financing Social and Environmental Projects
• Microfinance Institutions and Poverty Alleviation
• Financial Literacy Programs and Consumer Empowerment
• Behavioral Finance: Understanding Investor Behavior
• Risk Management Strategies for Financial Institutions
• Corporate Fraud Detection and Prevention Measures
• Financial Market Volatility and Risk Hedging Strategies
• The Role of Central Banks in Monetary Policy Implementation
• Financial Stability and Systemic Risk Management
• Corporate Governance Practices in Banking Sector
• Credit Risk Assessment Models and Default Prediction
• Asset Allocation Strategies for Portfolio Diversification
• Real Estate Investment Strategies for Wealth Accumulation
• Commercial Property Valuation Methods
• Real Estate Crowdfunding Platforms: Opportunities for Investors
• Property Management Best Practices for Rental Properties
• Real Estate Development and Urban Planning
• Mortgage Market Trends and Homeownership Rates
• Affordable Housing Initiatives and Government Policies
• The Impact of Interest Rates on Real Estate Investments
• Sustainable Architecture and Green Building Design
• Real Estate Investment Trusts (REITs) and Tax Implications
• The Influence of Demographic Trends on Housing Demand
• Residential Property Flipping Strategies and Risks
• Health and Wellness Tourism: Trends and Opportunities
• Medical Tourism Destinations and Quality of Care
• Wellness Retreats and Spa Resorts: Business Models
• The Impact of Technology on Healthcare Delivery
• Telemedicine and Remote Patient Monitoring
• Healthcare Data Security and Privacy Regulations
• Healthcare Financing Models: Insurance vs. Out-of-Pocket
• Value-Based Healthcare Delivery and Payment Models
• Healthcare Workforce Challenges and Solutions
• Healthcare Infrastructure Development in Emerging Markets
• The Role of Artificial Intelligence in Healthcare Diagnosis
• Precision Medicine: Personalized Treatment Approaches
• Pharmaceutical Industry Trends and Drug Development
• Biotechnology Innovations in Healthcare Solutions
• Mental Health Awareness and Support Services
• Telehealth Adoption and Patient Engagement
• Chronic Disease Management Programs and Prevention
• Health Information Exchange Platforms: Interoperability Challenges
• Patient-Centered Care Models and Outcomes
• The Influence of Healthcare Policies on Access to Care
• Human Resource Management in the Hospitality Industry
• Employee Training and Development in Tourism Sector
• Quality Service Delivery in the Hotel Industry
• Revenue Management Strategies for Hospitality Businesses
• Destination Marketing and Tourism Promotion Campaigns
• Sustainable Tourism Practices and Eco-Friendly Resorts
• Technology Integration in Travel and Tourism Services
• Cultural Heritage Tourism and Conservation Efforts
• Adventure Tourism: Risks and Safety Measures
• The Role of Online Travel Agencies in Tourism Distribution
• Sustainable Transportation Solutions for Tourism
• Food and Beverage Management in Hospitality Operations
• Wellness Tourism: Trends and Market Segmentation
• Airbnb and Short-Term Rental Market Dynamics
• Wellness Retreats and Spas: Market Positioning Strategies
• Community-Based Tourism Development Initiatives
• Luxury Travel Market: Trends and Consumer Preferences
• Aviation Industry Trends and Airline Marketing Strategies
• Sustainable Event Management Practices
• Convention and Exhibition Tourism: Economic Impact
• Destination Management Organizations and Tourism Planning
• Customer Relationship Management in the Tourism Sector
• Online Reputation Management for Hospitality Businesses
• Accessibility and Inclusivity in Tourism Infrastructure
• Cultural Tourism: Heritage Preservation and Promotion
• Agritourism: Farm-to-Table Experiences and Trends
• The Impact of Climate Change on Tourism Destinations
• Wildlife Tourism: Conservation and Responsible Practices
• Wellness Tourism in Developing Countries: Challenges and Opportunities
• The Role of Tour Operators in Sustainable Tourism Development
• Virtual Reality Applications in Tourism Marketing
• The Rise of Medical Tourism: Market Growth and Challenges
• Responsible Travel and Ethical Tourism Practices
• Event Marketing Strategies for Business Success
• Sponsorship and Partnership Opportunities in Event Management
• Technology Integration in Event Planning and Execution
• Event Risk Management and Contingency Planning
• Corporate Event Planning: Trends and Best Practices
• Trade Show Marketing Strategies for Exhibitors
• Sports Event Management : From Planning to Execution
• Sustainable Event Certification Programs and Standards

How To Prepare Research Paper?

Preparing a research paper involves several key steps, from selecting a topic to writing and formatting the final document. Here’s a comprehensive guide on how to prepare a research paper:

• Select a Topic: Choose a topic that interests you and aligns with the requirements of your assignment or research objectives. Consider the scope of the topic, its relevance, and the availability of resources for conducting research.
• Conduct Background Research: Read up on books and studies that talk about the same things you want to research. This will help you see what people already know, find out where there are still things we don’t know, and make your research questions or ideas better.
• Develop a Research Question or Thesis Statement: Formulate a clear and focused research question or thesis statement that guides your study. Your research question should be specific, relevant, and capable of being answered through empirical investigation.
• Create an Outline: Organize your ideas and research findings into a logical structure by creating an outline for your research paper. Outline the introduction, literature review, methodology, results, discussion, and conclusion sections, along with any subheadings or subsections.
• Write the Introduction: Begin your research paper with an interesting introduction. Share some basic info about your topic, explain why your study is important, and clearly state what you’ll be focusing on in your research. The introduction should also outline the structure of the paper.
• Review the Literature: Conduct a comprehensive review of relevant literature to provide context for your study, support your arguments, and identify gaps in existing research. Summarize key findings, theories, and methodologies from previous studies in your literature review.
• Describe the Methodology: Clearly explain the research design, methods, and procedures used to collect and analyze data. Include details on the population/sample, data collection instruments, data analysis techniques, and any ethical considerations.
• Present the Results: Report the findings of your study in a clear and concise manner. Use tables, graphs, or charts to present quantitative data, and provide descriptive analysis for qualitative data. Ensure that your results are relevant to your research question or thesis statement.
• Discuss the Implications: Interpret the results of your study and discuss their implications that are for theory, practice, or policy. Analyze the strengths and limitations of your research, address any unexpected findings, and propose recommendations for future research or action.
• Write the Conclusion: Summarize the key findings and contributions of your study in the conclusion section. Restate your research question or thesis statement, review the main points that you have discussed in the paper, and highlight the significance of your research in advancing knowledge in the field.
• Revise and Edit: Review your research paper for clarity, coherence, and accuracy. Ensure that your arguments are well-supported by evidence, your writing is concise and precise, and your paper follows the appropriate style and formatting guidelines.
• Cite Sources: Acknowledge the contributions of other scholars by properly citing their work in your research paper. Use a consistent citation style (e.g., APA, MLA, Chicago) and include a reference list or bibliography at the end of your paper.
• Proofread: Carefully proofread your research paper to correct any spelling, grammar, or punctuation errors. Pay attention to formatting details such as margins, font size, and line spacing to ensure consistency throughout the document.
• Get Feedback: Seek feedback from peers, instructors, or mentors to improve the quality of your research paper. Consider their suggestions for revision and make appropriate changes to strengthen your arguments and clarify your writing.
• Finalize the Paper: Make any final revisions or edits based on feedback and proofreading, and then finalize your research paper for submission. Double-check all formatting requirements and ensure that your paper is properly formatted and ready for submission.

Final Thoughts

Researching business topics offers a unique opportunity to delve into the complexities of the modern economy and explore innovative solutions to real-world challenges.

Whether you’re passionate about leadership, marketing, finance, entrepreneurship, or corporate social responsibility, there’s a wealth of knowledge waiting to be discovered. So roll up your sleeves, sharpen your analytical skills, and get ready to make your mark in the world of business research! I hope you find the best and most relevant answer to business topics for research paper. 

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses , and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

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37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Business analytics approach to artificial intelligence

Melva inés gómez-caicedo.

1 Economic Sciences, The Liberators University, Bogotá, Colombia

Mercedes Gaitán-Angulo

2 Business School, Konrad Lorenz University Foundation, Bogotá, Colombia

Jorge Bacca-Acosta

Carlos yesid briñez torres.

3 Faculty of Mathematics and Engineering, Pilot University of Colombia, Bogotá, Colombia

Jenny Cubillos Díaz

4 Economics and Management, University Corporation of Meta, Villavicencio, Colombia

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Artificial Intelligence has become an essential element for strengthening the business fabric. The advances obtained in recent years as a result of the incorporation of technology for the improvement of productive activities and the positioning of companies in the markets are remarkable. Hence, the purpose of this paper is to analyze the origin, evolution and development of business analytics (BA) and its relationship with Artificial Intelligence (AI); from the conceptualization, evolution and identification of the main characteristics and research areas of AI and BA, as well as research conducted and published in journals indexed in Scopus between 2002 and 2022. The aim is to define the incidence of BA in business activities and analyze scientific activity and advances of BA to define new research horizons in this field. For this purpose, a bibliometric and documentary analysis is applied, allowing to highlight the findings that provide recognition and comparison of the results. This will facilitate the understanding of the current dynamics, its importance for organizations, and its impact in the face of the new challenges generated by the requirements of world trade.

Introduction

The term business analytics is relatively new. Initially, it was presented as a concept related to economics and the way of managing resources. Business analytics can be defined as the process of collecting data, processing this data and gain insights from the data (Gupta, 2021 ). Business analytics is also defined as the science of discovering insights from data to support timely decision-making (Delen and Ram, 2018 ).

In the 1950s when time and motion studies began to be used in production processes, and the 1960s when computers were used for automatic data processing (Delen and Ram, 2018 ), analytics was used for the use of mathematical models to provide a solution to the problem. Analytics is used for the use of mathematical models to provide solutions to problems identified in organizations (Sharma, 2016 ).

In this way, Business analytics uses statistical and mathematical models to respond to problems or needs in organizations. Simon ( 2017 ) states that analytics is the process of using raw data to obtain clues and improve the understanding of a topic or phenomenon.

Frequently in the literature, it is also clarified that Business analytics cannot be considered as report generation, because the latter is limited to the visualization of the behavior of one or several variables, while the former gives answers to questions of interest to an organization around its business (Simon, 2017 ).

Camm et al. ( 2020 ) consider that business analytics is “the scientific process of transforming data into clues for making better decisions” (p. 5).

For Muñoz-Hernandez et al. ( 2016 ) business analytics allows the efficient use of all the information available to organizations, to obtain a competitive advantage. Other authors such as Evans and Lindner ( 2012 ) consider that it is fundamental for decision making.

Thus, Zumstein et al. ( 2022 ) in their research highlight the increase in the level of maturity, benefits, challenges and development of companies and economies from the use of artificial intelligence.

Likewise, authors such as Shi et al. ( 2022 ), indicate that a large amount of data and the limits of digital products drive organizations to use business analytics (BA) to increase customer engagement. Within their main findings, they indicate that BA culture does not directly improve performance on its own but must be integrated with existing organizational strengths. In addition, the development of the literature on this subject shows that BA techniques moderate the relationship between customers and innovation since it allows companies to be better informed when data is available online.

This has led several companies to choose to have in their structure an area dedicated exclusively to business analytics and the relationship between research and data analysis processes, which contribute to the improvement of productive conditions (Acito and Khatri, 2014 ).

Silva et al. ( 2021 ) indicate that the term “Industry 4.0” has emerged to characterize various adoptions of Information and Communication Technologies (ICT) in production processes. Hence, business analytics (BA) is considered as a technological advancement that facilitates decision making (Namvar et al., 2021 ). Thus, business analytics facilitates data analysis and relates it to the use of emerging technologies, which enable the transformation of decision-making dynamics in organizations (Seufert and Schiefer, 2005 ; Ward et al., 2014 ).

Resource management starts from the premise that technology and data analytics are at the service of operational efficiency, which allows organizations to understand their information and use this analysis to identify problems and decisions.

During the last decades, the dynamics of markets and companies have generated conditions that strengthen productive activity, facilitating the development of processes that tend to strengthen productive activity. Hence, several elements have been identified that, when used efficiently, promote growth and competitiveness, such as resource management based on the premise that technology and data analytics are at the service of operational efficiency, which allows organizations to understand their own information and use this analysis to identify problems and decisions.

In that regard, business analytics uses statistical analysis, predictive modeling, data mining and other techniques to employ information and develop a competitive advantage in its favor (Evans and Lindner, 2012 ; Medina, 2012 ).

The link between business analytics and artificial intelligence can be seen from different perspectives. One the one hand, artificial intelligence is considered to be one of the three pillars of business analytics together with visualization and statistical modeling (Raghupathi and Raghupathi, 2021 ). In particular, the machine learning subset of artificial intelligence is the most common component of this pillar of business analytics. Machine learning provides the techniques and methods to gain insights from business data. On the other hand, artificial intelligence is also considered to be the evolution of traditional analytics and the era of artificial intelligence is called analytics 4.0 in the context of business analytics (Davenport, 2018 ). Moreover, other authors suggest that business analytics is often supported by artificial intelligence to transform data into information (Schmitt, 2022 ).

Hence, the objective of this research is to analyze the evolution and development of artificial intelligence and its relationship with Business Analytics, based on its conceptualization, evolution, identification of its main characteristics, research areas and the recognition of publications indexed in Scopus between 2002 and 2022. In this sense, in the first part of this document a systematic and historical review of business analytics is made, in the second part the main publications associated with this concept from 2001 to 2018 are presented, the most cited authors, the countries that are most interested in the subject, and finally, how research networks have been created from its relationship with Artificial Intelligence.

Historical analysis of business analytics

To understand the origin of business analytics it is important to understand how statistics and mathematics have been used throughout history as a support for the development of competitive intelligence in organizations.

During the industrial revolution, statistics began to be used in standardization and manufacturing processes as a control tool. Additionally, the focus of organizations begins to be the minimization of waste and therefore the optimization of production costs, becoming a trend and consolidating as the quality movement (Quality Movement) (Sharma, 2016 ). From this movement emerged several years later practices such as Six Sigma and Toyota's just-in-time manufacturing methodology.

Years later, when the United States participated in World War I, quality and standardization become fundamental aspects of production processes because ammunition had to be compatible with weapons from different manufacturers and countries. At this time, organizations begin to invest in Total Quality Management training and statistical measurement processes (van Kemenade and Hardjono, 2018 ), allowing the emergence of various techniques such as control charts, histograms, Pareto and scatter diagrams (Sadeghi Moghadam et al., 2021 ).

Toward the decade of the 1920s, the quality control method is known as Statistical Process Control also emerged as a mechanism to control production processes seeking their best standardization and with the minimum possible waste (Zan et al., 2019 ).

Likewise, historical data began to be used for climate prediction. In 1950 the first numerical weather prediction was developed, performed on the ENIAC computer by a group of meteorologists and mathematicians. In 1956 engineer Bill Fair and mathematician Earl Isaac found Fair Isaac Corporation (FICO) as a company to use data intelligently for the development of competitive intelligence (Sharma, 2016 ). Two years later they launch their credit risk and scoring system for investments in the United States.

In 1958, in the IBM Journal of Research and Development, an article written by Luhn is published where one of the initial references to the term Business Intelligence is made. This article proposes the construction of an intelligent system that uses data processing mechanisms to perform auto-summarization and auto-coding of documents to provide different information profiles according to the organization's lines of action (Luhn, 1958 ).

During the 1960s most companies began to use centralized systems for inventory control and in the 1970s guidelines were developed to facilitate materials planning. It should be noted that, during this period, data collection was done annually, and the available data came from manual processes through interviews and questionnaires from which mathematical models could be built to solve optimization problems with constraints. In this way, those problems that could not be solved with linear and non-linear models were addressed through simulations (Delen and Ram, 2018 ).

In the 1970s, Rule-Based Enterprise Systems also emerged with the promise that the knowledge of an expert in a specific domain could be represented as a set of rules that could be processed by a machine and could be used to solve queries as an expert would (Simões et al., 2020 ).

Subsequently, in the 1980s, Enterprise Resource Planning (ERP) or Enterprise Resource Planning (ERP) systems emerge and become the first data collection and storage systems for organizations to provide support in areas such as planning, sales, manufacturing, distribution and costs (Sharma, 2016 ).

Thus, the emergence of relational database systems enabled the capture, storage and organization of data to avoid duplication. At this time, the amount of data being stored was larger and one of the major challenges was to maintain data integrity and consistency.

This is how the concept of enterprise data warehouses or Enterprise Data Warehouse (EDW) emerged as unified data storage systems for organizations. These systems were also upgraded so that they could respond to various changes in data effectively to display information in real time l which gave rise to real-time data warehouse systems (Delen and Ram, 2018 ).

In this regard, EDWs facilitated the collection of data from different sources which was subsequently used to extract knowledge and information of interest to organizations and this gave rise to the term Business Intelligence (BI) in the first decade of the 2000s, initially focusing on the analysis of data collected by organizations to know the progress of the organization.

The 1990s also saw the emergence of executive information systems, i.e., decision support systems, which displayed information using graphs and charts to facilitate decision making (Delen and Ram, 2018 ).

Sharma ( 2016 ) states that between the years 1990 and 2000 organizations began to see the need to use the data obtained to be able to generate predictive analytics, through descriptive, inferential, differential and associative statistical techniques. Hence, the amount of data produced by users or consumers through devices and interaction with social networks and other digital media led to the emergence of a new term: big data, which refers to techniques and procedures to analyze large amounts of unstructured data and the emergence of methods such as “Deep learning.”

It is important to note that the term business analytics has been associated with other terms such as Business Intelligence and Supply Chain Management that have been in the research spotlight for some years. However, the studies derived from each of the terms differ in the analysis and results obtained. For example, Supply Chain Management was one of the central topics in business research during the decade from 2000 to 2005. However, this term was transformed into Business Intelligence, because conducting research focused solely on the Supply Chain left aside other types of information.

Business Intelligence was born as a response to the lack of information from organizations for the analysis of existing dynamics. In addition, the decrease in the costs of data storage services has increased the volume of data that organizations keep and this has allowed the growth of Business Intelligence as a fundamental area for organizations. It has been estimated that the storage volume to be reached by 2020 will be 40 zettabytes (1,021 bytes or 1 sextillion bytes) (Sharma, 2016 ).

Today, some researchers are still being conducted that ensure that the predictive value of Business Intelligence interferes with the natural dynamics of information coming from organizations. Therefore, business analytics generates new information from company data without creating new data, but rather by analyzing existing data.

Based on this premise, business analytics studies indicate that there is a close relationship between the use of data analysis and the performance of an organization in terms of revenue, competitiveness, profitability and shareholder return. This means that entities with better performance are those in which the use of data analysis is an extra component compared to their competitors and this gives them a greater probability of strengthening their competitiveness (Davenport and Harris, 2007 ; Evans and Lindner, 2012 ).

The results suggest a statistically significant relationship between organizations' competencies have analytics on performance and the effect of business process-oriented information systems. The results provide a better understanding of the areas where the impact of business analytics may be the strongest (Trkman et al., 2010 ).

Related works

Previous works that have attempted to synthesize research in the area of business intelligence can be found in the literature. For example, Gimenez et al. ( 2015 ) conducted a systematic review of 22 articles on the applications of business analytics in the supply chain. The authors report the main challenges and trends in the area. Similarly, Mishra et al. ( 2018 ) conducted a bibliometric analysis on the topics of Big Data and Supply Chain Management between 2006 and 2016, analyzing 280 publications in the 20 most important journals in the area.

However, these two references only allow us to appreciate the research is done in business analytics (BA) in its relationship with Supply Chain. Recently, Yin and Fernandez ( 2020 ) conducted a systematic literature review (40 articles in the area of BA) to present a common definition, its applications, research methods and its relationship with Business Intelligence (BI). The authors include a bibliometric review focused on the evolution of publications between 2000 and 2018, as well as the journals where the topic is most published and the most relevant authors. However, the bibliometric review is focused only on articles that have received a certain number of citations, which means that the results do not fully reflect the overall BI landscape.

Sahoo ( 2021 ) conducted a bibliometric review of 89 articles focusing on the terms Big Data and BA as they relate to the topic of “manufacturing.” In this article, the authors identify several areas of future work and research challenges. However, although the reported results are valuable to the scientific community, the bibliometric review is focused solely on the area of manufacturing.

Similarly, Silva et al. ( 2021 ) conducted a systematic literature review of 169 articles to identify the relationship between business analytics and Industry 4.0. From this literature review, the authors conclude that there are still many open questions surrounding its application.

The research was conducted by Dahish ( 2021 ), who conducted a literature review of 57 articles on Business Intelligence and social networks; and authors such as Purnomo et al. ( 2021 ) who reviewed Scopus databases on the same subject in the period between 1975 and 2020 stand out. The research was focused on identifying relevant topics in the area.

Ting-Peng and Yu-Hsi ( 2018 ) conducted a bibliometric literature review with a broader focus on articles indexed in Web of Science on the topics of Big Data and Business Intelligence during the years 1990 and 2017. One of the main findings emerged much earlier than the concept of Big Data, publications have grown faster and are associated with algorithmic and computational topics, while the area of Business Intelligence is more associated with management, data analytics and predictive analytics topics.

Zumstein and Kotewski ( 2020 ) indicate in their research that digital commerce is growing in most countries, it is a medium used by new and established online retailers to promote their different products, digital and customer services are considered essential to increase business success. Within the results of this study we found, that digital analytics is important to study and monitor digital business. By analyzing different success factors, this technique contributes to online stores having customer, service and data orientation generating high conversion rates and revenues. Finally, successful omnichannel marketers use various digital marketing channels, such as search engine optimization and advertising, marketing.

Chiang et al. ( 2018 ) highlight in their research the importance of the correct accumulation of data for analysis, decision making and strategic planning in organizations, based on the design and application of different analytical techniques since it is concluded that data analysis without generating value offers no contribution to organizations, regardless of whether the data is big or small.

Methodology

Bibliometrics emerged in the field of library and information science, allowing statistical and quantitative analysis of scholarly outputs, including descriptive statistics, networks on keywords, texts, citations, authors, institutions and their connections. This methodology allows establishing the frequency, connectedness, centrality, author and text group, publication trends, knowledge base, citation pattern, author network, reader usage, impact and importance of a topic or article (Huang et al., 2017 ).

For there to be conceptual clarity, scientific research must be associated with an exhaustive review of the area of knowledge to be worked on to be clear about the possible areas for the development of the research and its difficulty. For this, there is the bibliometric review, a quantitative analysis technique that uses mathematical and statistical methods to know the main characteristics of the topic under consideration (Ejdys, 2016 ; Sarkar and Searcy, 2016 ).

This article will carry out a historical review of artificial intelligence that will provide the basis for developing a descriptive bibliometric analysis that allows us to synthesize and understand the evolution of business analytics in certain fields of knowledge. For this analysis, publications published between 2002 and 2022 will be reviewed, in the Scopus bibliographic database, in the indexed academic literature that addresses specific topics directly related to the subject.

The first stage shows the route used to carry out the bibliometric study was:

(TITLE-ABS-KEY (“business analytics”) OR TITLE-ABS-KEY (“business analytics”) OR TITLE-ABS-KEY (“analyse des affaires”) OR TITLE-ABS-KEY (“análise de negócios”) OR TITLE-ABS-KEY (“analisi aziendale”)) AND (EXCLUDE (PREFNAMEAUID, “Undefined#Undefined”)) AND (LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “bk”)) AND (EXCLUDE (PUBYEAR, 2023))).

The search filters consisted of the terms “business analytics,” which could be located in the title, abstract and keywords; the search was conducted in English, Spanish, French, Italian, Portuguese and French; it was limited to conference papers, articles, book chapters, reviews and books and was limited to the period from 2002 to 2022. The search yielded 1,605 documents.

The purpose of the second stage was to analyze the information using the Bibliometrix package of R. This was done to visually organize the information downloaded from Scopus and to obtain schemas that would feed the research carried out.

Finally, in stage 3, the analysis of the descriptive results was carried out, where the information obtained was condensed, relating the evolution of artificial intelligence together with business analytics and the results obtained.

Figure 1 shows how the articles included in this bibliometric review were selected.

PRISMA. Source: Own elaboration using Prisma Statement 2020. http://www.prisma-statement.org/ . Source: Page et al., 2021 .

Descriptive results

From the results of the search, it can be noted that business analytics has been a topic of interest to researchers since 2002. It is possible to make this inference since the information searches do not yield results from before that year.

In addition, it was possible to identify that the number of research projects is increasing and has reached 210 publications in 2021 alone (see Figure 2 ; Table 1 ).

Annual scientific production. Source: Own elaboration using Bibliometrix.

Production per year.

Source: Own elaboration using Bibliometrix.

Moreover, not only the annual output indicates the evolution of the term, other factors such as the average number of citations of articles per year indicate the value of the academic output and its usefulness in another research (see Figure 3 ).

Average number of article citations per year. Source: Own elaboration using Bibliometrix.

Among the findings of the search, it is possible to point out that the articles produced in 2002 are those that have been most cited by other researchers. Likewise, it can be observed that in 2004 there was a non-significant number of citations, while from 2005 onwards they increase, with two peaks of higher citations between 2010 and 2014. This tendency may be due to the changes and topics published in those years (see Figure 6 ).

Graph of three fields: Authors, subjects, and journal names. Source: Own elaboration using Bibliometrix.

It was also possible to identify the authors who have published the most papers on the subject: Shanks with more than 20 papers associated with business analytics, followed by Duan with ~10 papers and Cao, Marjanovic, and Sharma with eight papers each (see Figure 4 ; Table 2 ).

Most relevant authors. Source: Own elaboration using Bibliometrix.

Production by authors.

In addition, the results allow us to analyze the number of publications made by the authors according to the year of publication (see Figure 5 ), which shows that there are authors such as Shanks G., De Oliveira MPV, Na Na, who maintain their production levels between 2010 and 2022, with some insignificant variations per year. The graph shows the number of publications, with the larger the circle indicating the author and the year, the higher the author's level of production. For example, Bekmamedova had a high volume of publications in 2012, but in 2013 it decreased and in the following years there were no publications. Also, authors such as Sharda R., Daily S. Doster B. Ryan J. and Lewis C. started research on the subject in 2013 and have maintained the publication trend until 2022.

Production of the main authors by year. Source: Own elaboration using Bibliometrix.

One of the reasons why authors such as Shanks have remained significant authors in business analytics is due to the consistent level of publications per year (see Figure 5 ). It is also important to note that the relationship that exists between authors, the topics to be covered and the journals that publish these topics are the central dynamic that ensures the success of business analytics as a major Research Topic (see Figure 6 ).

For example, Shanks, the author with the most publications (see Figure 4 ), has researched topics related to business analytics and predictive analytics, publishing in journals such as Resource-based View. On the other hand, Duan and Cao, the second and third authors respectively, have worked on business analytics and Big Data and have published in journals such as Communications of the association for information systems and Communications in computer and information science.

Regarding the dynamics of the publication sources, the results showed similar indicators to the period in which the topic had the highest number of publications. For example, in 2010, publications began to increase ( Figure 2 ) and the main publication sources also began to increase the level of publications on business analytics. The source with the highest growth is AMCIS 2017—Americas conference on information systems: A tradition of innovation. The second fastest growing source is ACM International Conference Proceeding Series (see Figure 7 ). However, most of the sources show a similar development and between 2010 and 2012 the growth was much more significant.

Growth of publication sources. Source: Own elaboration using Bibliometrix.

The relationship between sources and countries of the publication provides clues about the dynamics of researchers. For example, the United States is the country with the highest number of publications with ~2,400 publications. This contrasts significantly with the second country, Australia, which has ~400 publications (see Figure 8 ; Table 3 ). The difference is significant and exposes the importance of the United States for business analytics.

Countries of publication. Source: Own elaboration using Bibliometrix.

Most cited countries.

It is also significant to note that Brazil is the only Latin American country to appear in the top 20 ranking for business analytics publications.

However, most of the publications produced by the countries are international collaborations. Figure 9 shows how the dynamics of co-authored publications are generated. In green are the publications of Multiple Country Publications and in orange are the publications of the Single Country Publications type. It is possible to point out that collaborative publications have a greater impact and significantly position the country in the publication rankings.

Correspondence between author and country. Source: Own elaboration using Bibliometrix.

It is also important to note that partnerships between countries are vital to accounting for the research development of business analytics. The results show that the United States is the country with the highest number of collaborations. Within its relationships are countries such as France, Spain, Italy, Germany, China, Brazil, India, Portugal, Norway, Sweden and Finland, which are the countries in dark blue (see Figure 10 ). The strongest connection between countries is between the United States and Australia with a combined frequency of 21 citations. Countries such as New Zealand, South Africa, Egypt, Saudi Arabia, Nigeria, Colombia, Chile and Canada are countries with a medium number of publications on business analytics and mostly have partnerships with the United States and Australia. Finally, the countries in gray are those with no publications in this area.

Map of collaboration between countries. Source: Own elaboration using Bibliometrix.

Within these country associations, there are common themes that help to relate and understand what the topics of interest are according to the country, the keywords and the journal of publication. For example, the United States has worked on business analytics related to Big Data, business value, predictive analytics, business intelligence, data mining, social media, predictive analytics, among others, with the journal Information Technology having the largest number of publications (see Figure 11 ).

Three-Fields Plot Keyword Chart. Source: Own elaboration using Bibliometrix.

On the other hand, Australia remains the second country in the ranking of publications and works on topics such as big data, social media, knowledge management, decision making, support systems, among others similar to those researched in the United States, hence the strong relationship that exists between the countries concerning publishing partnerships.

Thus, according to the topics that are most related to business analytics, it can be observed that data mining, decision marketing, information systems, Big Data and competitive intelligence are the topics that are most related and with which business analytics has been most researched (see Figure 12 ).

Keywords. Source: Own elaboration using Bibliometrix.

The search results indicate that there are co-citation networks between authors that determine the alliances that exist for business analytics research. For example, in Figure 13 it can be seen that there are three co-citation networks (indicated in blue, red and green) which are distinguished from each other by the connections established.

Co-citation networks. Source: Own elaboration using Bibliometrix.

One of the largest networks is identified with red color, it is composed of authors such as Davenport, Watson, Shanks, Lavalle, Simon, Yin, among others, which are shown related to the citation lines between them. The second significant network is identified with blue and is composed of authors such as Wang, Zhan, Yang, Wu, Han, Lee, Kim among others, it is an extensive network, however, the composition of the figure in terms of size and position it can be established that it has less impact than the red network. Finally, there is the green network, which is smaller, but is immersed between the red and blue networks, and is composed by authors such as Chen, Cohen, Anderson, Manykil, Bose, among others.

Business Intelligence is a tool that allows organizations to take advantage of all the information of competitive advantages in the market and decision making, as by combining its analysis with emerging technologies, it allows information to be obtained and projects possible situations that may arise in the development of the productive activity. The results of this bibliometric analysis show that there is an increasing interest in the field because the number of publications is growing every year. This result confirms the findings of previous studies that reported an exponential growth in the number of publications in this field (Yin and Fernandez, 2020 ). However, business analytics is still an emerging field (Raghupathi and Raghupathi, 2021 ) and further research is needed to uncover its affordances and benefits for a timely deciation to improve the conditions in which resources are used. Hence, its relationship with business analytics facilitates the generation-making support in companies.

With the results obtained throughout this research, it was possible to establish the influence of AI and BA on productive development. It should be noted that the trend of growth in the number of research and publications to be generated will increase and contribute significantly to the improvement of the business and competitive fabric of economies.

The development of AI and BA research in the United States stands out as reported in previous studies in the field of business analytics (Yin and Fernandez, 2020 ), followed by Australia, Germany, India, the United Kingdom, and Canada. However, unlike previous studies in the field, in this paper we identified that India is another country that is publishing research in the field of business analytics and is currently in the second position in the most productive countries in this field.

The topics that commonly appear connected with the term business analytics are: data mining, decision marketing, information systems, big data and competitive intelligence. This result shows that other field such as big data and artificial intelligence are relevant for the implementation of business analytics approaches in companies around the world. In that regard, the support of different fields is important to overcome some challenges that business analytics face today such as the need to collect data from multiple sources and process them effectively and in real-time so that the results can be used for making decisions and void the lag between data collection and data analysis (Raghupathi and Raghupathi, 2021 ).

Another topic that appeared connected with business analytics was descriptive analytics, which is one of the three types of analytics often reported in the literature. The other two types of analytics (predictive and prescriptive analytics) did not appeared frequently in this bibliometric analysis. A possible interpretation of this result might be that research on descriptive analytics has captured the attention of researchers in the first era of business analytics. However, further research is needed to investigate the use of predictive and prescriptive analytics for decision-making processes at companies. Moreover, recent research suggest a new type of business analytics: discovery analytics. This later type of analytics might be considered the next step in business analytics and is focused on supporting the discovery of new markets, products and strategies (Raghupathi and Raghupathi, 2021 ).

The results also show that there are three co-citation networks on this topic, one of the largest networks is composed of authors such as Davenport, Watson, Shanks, Lavalle, Simon, Yin, the second of authors such as Wang, Zhan, Yang, Wu, Han, Lee, Kim among others, and the third of authors such as Chen, Cohen, Anderson, Manykil, Bose, among others.

Conclusions

This paper presents an overview of the research landscape in business analytics. We found that business analytics is an emerging field that has attracted the attention of many researchers around the world and the number of publications is increasing year by year. To further develop this field and increase the impact of this field in the industry, there is a need of a synergy between scholars and companies to identify the best practices in business analytics that are effective for companies.

Future research directions in the field of business analytics include the investigation of the impact of predictive, prescriptive and discovery analytics through case studies to uncover the affordances and benefits of these types of analytics for the timely decision-making of companies around the world. Moreover, it is important to continue working in this line of knowledge as we have seen the benefits of this topic, we can continue to deepen in topics such as digital analytics, due to the importance for companies to study and improve their campaigns, user experience, search engine marketing and the achievement of digital business objectives.

Data availability statement

Author contributions.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Business analytics refers to the statistical methods and computing technologies for processing, mining and visualizing data to uncover patterns, relationships and insights that enable better business decision-making.

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

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Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making. Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results. 1 An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance , human resources , supply chain , marketing , sales  or information technology , plus all industries, including healthcare , financial services and consumer goods .

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes?  Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season is projected to have warmer temperatures. Predictive modeling 2 also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

• Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.
• Data mining or KDD : Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets and is a significant component of big data analytics. The growing importance of big data makes data mining a critical component of any modern business by assisting companies in transforming their raw data into useful knowledge.
• Data warehousing : A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources, including apps, Internet of Things (IoT) devices, social media and spreadsheets into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI) and machine learning (ML). A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot.
• Data visualization : The representation of data by using graphics such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easier to understand, being especially helpful for nontechnical staff to understand analytics concepts, and helping show patterns in multiple data points. Data visualization can also help with idea generation, idea illustration or visual discovery.
• Forecasting : This tool takes historical data and current market conditions and then makes predictions as to how much revenue an organization can expect to bring in over the next few months or years. Forecasts are adjusted as new information becomes available. When companies embrace data and analytics with well-established planning and forecasting best practices, they enhance strategic decision-making and can be rewarded with more accurate plans and more timely forecasts.
• Machine learning algorithms : A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks, most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Machine learning algorithms enable machine learning to learn, delivering the power to analyze data, identify trends and predict issues before they occur.
• Reporting : Business analytics runs on the fuel of data to help organizations make informed decisions. Enterprise-grade reporting software can extract information from various applications used by an enterprise, analyze the data and generate reports.
• Statistical analysis : Statistical analysis enables an organization to extract actionable insights from its data. Advanced statistical analysis procedures help ensure high accuracy and quality decision-making. The analytics lifecycle includes data preparation and management to analysis and reporting.
• Text analysis : Identifies textual patterns and trends within unstructured data by using machine learning, statistics and linguistics. By transforming the data into a more structured format through text mining and text analysis , more quantitative insights can be found.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience , by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists  either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL) , which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

• Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.
• Outdated data values: Certain data sets, including customer information, might need editing due to customers leaving, product lines being discontinued or other historical data that is no longer relevant.
• Missing data: Companies might have changed how they collect data or the data they collect, which means historic entries might be missing data that is crucial to future business analysis. Companies in this situation might need to invest in either manual data entry or identify ways to use algorithms  or machine learning  to predict what the correct data should be.
• Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it might need to merge the data so it’s all in one place. While the foundation of any business analytics approach is first-party data (data the company has collected from stakeholders and owns), they might want to append third-party data (data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing . Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI ) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs) .

Business analytics programs can now quickly take huge amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders.

Data visualization best practices include understanding which visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. An organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making.

• Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management , integrating data across functions, and improving supply chain analytics and demand forecasting.
• Planning analytics: An integrated business planning approach that combines spreadsheets and database technologies to make effective business decisions about topics such as demand and lead generation, optimization of operating costs, and technology requirements based on solid metrics. Many organizations have historically used tools including Microsoft Excel for business planning, but some are transitioning to tools such as IBM Planning Analytics .
• Integrated sales and marketing planning: Most organizations have historical data about their lead generation, sales conversions and customer retention success rates. Organizations looking to create more accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data are using business analytics to allocate resources based on performance or changing demand to meet business objectives.
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they might need to ensure they have the right workforce with the right analytical skills. This is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.

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Learn more about business analytics by reading these blogs and articles. 

IBM Planning Analytics has helped support organizations across not only the office of finance but all departments in their organization.

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

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1 Business intelligence versus business analytics  (link resides outside ibm.com), Harvard Business School. 2  How predictive analytics can boost product development  (link resides outside ibm.com), McKinsey, August 16, 2018.

Top 10 Analytics And Business Intelligence Trends For 2024

Business Intelligence Trends 2024

1) Artificial Intelligence

2) data security.

3) Data Discovery

4) D&A Sustainability

5) Data Sharing

6) continuous intelligence, 7) data literacy, 8) natural language processing (nlp).

9) Predictive & Prescriptive Analytics Tools

10) Embedded Analytics

Over the past decade, business intelligence has been revolutionized. Data exploded and became big. And just like that, we all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards . The rise of self-service analytics democratized the data product chain. Suddenly, advanced analytics wasn’t just for analysts.

2023 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2024. But the BI landscape is evolving, and the future of business intelligence is playing now, with emerging trends to watch. In 2024, BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific needs?

Businesses are no longer wondering if visualizations improve analyses but what is the best way to tell each data story, especially with the help of modern BI dashboard software . 2024 will be the year of data security and discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and AI. We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2024!

Let’s Discuss The Top Trends In Business Intelligence

We will start analyzing what is new in business intelligence with AI. This trend is wildly being covered by Gartner in their latest Strategic Technology Trends report, combining AI with engineering and hyper-automation and concentrating on the level of security in which AI risks developing vulnerable points of attack.

Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix, or the Master Control Program of Tron), AI is not yet on the verge of destroying us, despite the legit warnings of some reputed scientists and tech-entrepreneurs.

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management, while increments in security measures must be taken into account. The fact is that it is and will affect our lives, whether we like it or not.

It is expected that in the coming year, AI will evolve into a more responsible and scalable technology as organizations will require a lot more from AI-based systems. According to Gartner’s Data and Analytics research for 2021, with COVID-19 completely changing the business landscape, historical data will no longer be the main driver of AI-based technologies. In change, these solutions will need to work with smaller datasets and more adaptive machine learning while also being compliant with new privacy regulations. This concept is known as ethical AI, and it aims to ensure that organizations use AI systems in a way that will not break the law. To this day, many organizations have faced legal issues for illegally collecting user data. The Facebook and Cambridge Analytica scandal is a perfect example of that. 

In that sense, implementing systems and models to ensure the correct use of AI-related technologies will become even more important in the coming years. In fact, the US government recently released a blueprint for the “AI Bill of Rights” , presenting 5 principles that should guide the design, use, and deployment of automated systems “to protect the American public in the age of artificial intelligence”.

In response to this increasing need for AI accountability, Gartner presents  AI TRiSM as one of the concepts that will help organizations ensure “AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection”. This cross-functional framework needs to be implemented from the earliest stages of system design and involve people from compliance, legal, IT, and analytics for a successful approach. By 2026, businesses that apply this framework to their AI models are expected to be 50% more successful in adoption, business goals, and user acceptance. 

It can’t be denied that AI is still a topic of concern even today. The number of AI-based applications has become so big that many IT professionals don’t even know how to use or interpret them. This leaves the doors open for breaches and financial losses that can significantly impact companies and customers alike. As a response, terms such as explainable AI (XAI) will be at the center of the conversation during 2024. XAI is an emerging field that aims to apply specific processes and methods to allow humans to understand the results and outputs created by machine learning and AI algorithms. The end goal of this field is to ensure trust and transparency with these systems to give humans control over them. 

AI-based business analytics

When it comes to analytics, businesses are evolving from static, passive reports of things that have already happened to proactive analytics with dashboards that help them see what is happening at every second and give alerts when something is not how it should be. Solutions such as an AI algorithm based on the most advanced neural networks provide high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be immediately registered, and the system will notify the user.

Another feature that AI has on offer in BI solutions is the upscaled insights capability. It basically fully analyzes your dataset automatically without needing effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, revenue) that the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

Time gain is also present in the form of AI assistants. Tools have started to develop AI features that enable users to communicate with the software in plain language - the user types a question or request, and the AI generates the best possible answer. If this is something you are interested in, then keep reading because we will dive into it in more detail later in the post with the natural language processing trend.

The demand for real-time online data analysis software is increasing, and the arrival of the IoT (Internet of Things) also brings a countless amount of data, promoting statistical analysis and management at the top of the priority list. However, businesses today want to go further, and Adaptive AI might be the answer. As stated by Gartner, Adaptive AI systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise”. What makes these systems so interesting for companies today is the fact that they can learn from behavioral patterns and adjust to real-world changes, making it easier to make fast and improved decisions. 

In that same realm, Generative AI is another technology that has revolutionized the industry in 2023 and will continue to do so in 2024.  It basically enables AI systems to generate text, images, audio, and other types of content based on human-generated input. A famous example of Generative AI is ChatGTP. In 2023, the tool revolutionized the industry with its ability to generate well-written texts based on a short input. However, as with many AI-related innovations, ChatGTP was quickly scrutinized because it could generate biases, copyright infringement, fake news, and more if not used ethically. 

From a business perspective, using technologies like Adaptive and Generative AI has facilitated several processes, including data collection, cleaning, and analysis, which can be automated and tailored to the company’s needs. Risk management is another area in which these technologies thrive. Businesses can use Generative AI to predict any kind of fraud or attack, as well as generate risk simulations and test strategies in an imaginary scenario. 

Overall, we cannot deny the value of AI and how it has continued to develop over the years. That said, it is fundamental for regulators and decision-makers to ensure ethical and secure measures are being imposed when implementing these systems. It all comes back to security, and we will discuss it in more detail in our next trend.  

As you saw with our extensive AI trend, data and information security have been on everyone’s lips in 2023, and they will continue to buzz the world in 2024. The implementation of privacy regulations such as the GDPR ( General Data Protection Regulation ) in the EU, the CCPA ( California Consumer Privacy Act ) in the USA, and the LGPD ( General Personal Data Protection Law ) in Brazil have set building blocks for data security and management of customers personal information.

Moreover, the recent overturn by the European Court of Justice of the legal framework called Data Privacy Shield hasn't made software companies' lives much easier. The Shield was a legal framework that enabled them to transfer data from the EU to the USA, but with recent legal developments causing the invalidation of the process, companies that have their headquarters in the US don't have the right to transfer any of the EU data subjects.

Actually, a similar situation happened in 2015 when the EU and the USA had no legally valid agreements on this matter for a while. Many US-based (software) providers argue that they use European servers, and there is no data transfer to the US at all. However, from a legal perspective, even this solution is questionable, as, in theory, the US judiciary could force US-based businesses to reveal even data from EU-based servers. In essence, the information that is located in the EU needs to stay in the EU. In practice, that means that EU-based businesses that use in the current situation, US-based software vendors that store any kind of data for them are taking hazards as they operate in a legal grey area. For companies such as datapine , this doesn't represent a big issue since the registration, business, and servers are located in the EU. 

Taking all this into account, businesses have been forced to invest in security to stay compliant with the new regulations and also to protect themselves from cybercrime. In fact, global spending on cybersecurity products is expected to reach $1.75 trillion in the next 5 years. This is not a surprise to the experts as, during 2020 and the beginning of COVID-19, companies of all sizes were forced to mutate from physical to digital, and, to accelerate the transformation, they relied on online services, leaving a gap for cybercriminals to attack. According to the 2023 KPMG CEO Outlook Pulse survey , cybersecurity is among the top 10 “risks to growth” topics for CEOs in the coming years. Even more concerning is that 27% of the surveyed CEOs admit to being unprepared for a potential attack, which increased compared to 24% in the previous year. 

This might change now that company boards recognize cybersecurity as an overall business risk more than an IT-related issue. According to Gartner's Cybersecurity Predictions for 2023-2024, by 2026, 70% of boards will include one member with cybersecurity expertise. 

Amongst the measures organizations are taking in the coming years, we will see an increase in adopting the Zero Trust framework. Zero Trust doesn’t describe a specific technology but an approach in which businesses remove the “implicit trust” from all computing infrastructure by verifying every stage of digital interaction from devices to users, regardless of location. This means every user who wants to interact with the company's systems needs to be validated and verified. According to Gartner, by 2026, 10% of large enterprises will have a “comprehensible, mature and measurable” Zero Trust program in place, compared to the less than 1% that have one today. However, almost half of them might fail as a Zero Trust approach requires full organizational involvement and connection to business goals to succeed. 

The concern in cybersecurity also presents a challenge for SaaS BI tools as they need to ensure they offer a secure product that clients will trust with their sensitive data. Like any other cloud BI solution, online business intelligence software is also subjected to security risks. Some of them include processing data quickly to provide real-time insights that might be subjected to regulatory compliance, vulnerabilities when moving data from user’s systems to the BI tool’s cloud, or when the tool provides access to data from multiple devices that may be unsafe and exposed to attacks. To prevent any of this from happening, BI software needs to have a clear focus on security. 

One of the latest trends in business intelligence to help SaaS BI solutions stay safe is cybersecurity mesh architecture. Cybersecurity mesh is a composable and scalable security control that protects digital assets that reside in applications, in the cloud, IoT, and others. It seeks to establish a defined security perimeter around a person or a specific point with a more modular approach, enabling users to securely access data from their smartphones. One of Gartner's cybersecurity predictions for 2021-2022 stated that by the end of 2024, organizations adopting cybersecurity mesh architecture will reduce the financial impact of security incidents by around 90% . Since data breaches have been regularly in the news, buzzing industries, and average users, the demand for security products and services is understandable.

With these security threats increasing, businesses must adopt an organizational approach to protect their data. That is why data governance will remain one of the hottest topics related to security in 2024. This concept refers to a set of processes, policies, and roles that ensure appropriate valuation, creation, consumption, and control of business data at a strategic, tactical, and operational level. It establishes roles and responsibilities regarding who can manipulate the data, in which situation, and with what tools and methods to ensure a secure and efficient data management process. 

In the past years, due to tighter regulations, such as GDPR, organizations were obligated to ensure a secure environment for sensitive data, enhancing the need for stronger governance processes. As we mentioned earlier, companies of all sizes are exposed to attacks and breaches, leaving massive amounts of sensitive information from customers, suppliers, employees, and more exposed to misuse. In that sense, implementing a well-crafted governance plan will help organizations comply with government regulations while setting the perfect environment to use quality data and achieve their goals. 

In today's highly competitive business environment, where data collection keeps growing every second, data governance becomes a mandatory practice. A well-implemented governance framework not only assists organizations in staying compliant but also in minimizing risks, reducing costs, improving communication from an internal and external point of view, and achieving strategic goals, among other things. 

3) Data Discovery/Visualization

Data discovery using visuals has opened the analytical doors to a wider audience, and it is expected to keep growing in the coming years. As stated by a survey conducted by the Business Application Research Center, data discovery was already listed in the top 6 business intelligence trends by the importance hierarchy for 2023 and is expected to keep growing in 2024. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

*Source: Business Application Research Center (click image to enlarge)*

Essentially, data discovery is the process of collecting data from various internal and external sources and using advanced visual analytics tools to consolidate all the information. This allows businesses to engage every relevant stakeholder with the information by empowering them to intuitively analyze and manipulate it and extract actionable insights. To achieve this, businesses of all sizes turn to modern solutions such as business intelligence tools that offer data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with big amounts of data efficiently and intuitively.

An essential element to consider is that data discovery tools depend upon a process, and the generated findings will bring business value. It requires understanding the relationship between data through data preparation, visual analysis, and guided advanced analytics. “The high demand for data discovery solutions reflects a huge shift in the BI world towards increased data usage and the extraction of insights,” the Research Center emphasizes. Using online data visualization tools to perform those actions is an invaluable resource for producing relevant insights and creating a sustainable decision-making process. That being said, business users require software that is:

• Easy to use
• Agile and flexible
• Reduces time to insight
• Allows easy handling of a high volume and variety of data

Discovering trends in business operations that you didn’t even know existed or enabling immediate actions when a business anomaly occurs have become invaluable tools in effectively managing businesses of all sizes.

Data visualization has evolved into a state-of-the-art solution to present and interact with numerous graphics on a single screen, whether it's focused on developing sales charts or comprehensive interactive reports. The point is that data discovery is a process that enables decision-makers to reveal insights, and by using visualizations, teams have the chance to spot trends and major outliers within minutes.

In 2024, the dashboard will continue to be a major visual communication tool that will enhance collaboration between teams by being the analytical hub of a project. But more than just a visualization tool, KPI dashboards will take their interactivity features to the next level with technologies such as AI-based alarms and real-time data. Since humans process visual information better, the data discovery trend will be one of the most important BI trends in 2024.

4) D&A Sustainability

Moving on with our list of the new trends in business intelligence, we have data and analytics (D&A) sustainability. The topic, also mentioned in Gartner’s 2023 Data and Analytics Trends, is one of the most important ones we will discuss in this post, as climate change remains a global concern for the next years. 

In recent years, businesses started diving into sustainability mostly as a marketing tactic to brand themselves as “conscious”. As the topic becomes increasingly important, with new regulations forcing organizations to report on their ESG initiatives , decision-makers have realized that sustainability also represents a big way to reduce operating costs and increase overall profitability and efficiency. That is where D&A sustainability comes into the picture. 

Now that businesses of all sizes and across industries have realized the hidden potential of sustainability, we will start to see many using data and analytics as a way to boost their strategies and make the most out of their efforts. By tracking important metrics like energy consumption, gas emissions, labor rights, supply chain performance, and others, organizations can extract valuable insights to guide their sustainability journey. 

In 2024 and beyond, we can expect organizations to use D&A sustainability to anticipate changes in demand and adjust their resource purchases and usage to be more financially intelligent. However, we will also see other factors coming into play besides just purely resource-related data. Production levels, sales volume, employee headcounts, and even weather data will help paint a more accurate picture to facilitate real-time decision-making. 

We can also expect to see different tools emerge to help track sustainability data from a past, present, and future perspective, providing a big competitive advantage for companies that manage to adopt it correctly. That being said, ensuring all employees and relevant stakeholders are involved in the process is also necessary. Implementing training instances to engage employees with the process is a good way to start. 

Linking ESG initiatives to business outcomes is not an easy task. As of today, sustainability analytics is valuable for three main reasons: the first one is to stay compliant with the law, the second one is to track the performance of ESG goals, and the third one is to uncover new opportunities to keep integrating sustainability into operations. Organizational leaders must take charge to ensure all these aspects are covered and supported with the best tools and technologies. 

It is no secret that sustainability has transitioned from a buzzword to a mandatory practice in the business world. It is a growing trend that we will see everywhere in 2024 and many more years to come. 

Data and analytics have become a business’s most valuable competitive asset. Making informed decisions based on accurate insights can skyrocket success to a whole new level. That being said, analyzing data and extracting insights is not enough. Especially considering how accessible it has become to extract and manage valuable business data. To really extract the maximum potential out of your analytical journey, it is necessary to ensure full organizational adoption through powerful data sharing practices, which leads us to our next trend. 

Gartner already identified data sharing as one of the top 10 data and analytics trends for 2023. Stating that businesses that implement efficient data sharing processes with internal and external stakeholders will outperform their competitors on most business value metrics. 

While the importance of data sharing might seem obvious to some, it presents a challenge for most organizations as, for decades, it was the norm to say, “don’t share data unless…”. The issue is that in today’s context, where most businesses are undergoing digital transformations, not sharing data can be detrimental, as everyone across the company needs to be united to connect analytics to general business goals. In that sense, Gartner advises organizations to switch their mindset to “must share data unless..”. Doing so will enable more robust data and analytics strategies, empowering stakeholders to make agile and informed decisions. 

Changing the mindset might not be easy, and organizations that don’t take it seriously might fail in the process. Gartner suggests establishing trust-based mechanisms to ensure decision-makers trust the data they collect and use to inform their strategies. This way, they will feel confident in using it, sharing it, and re-sharing it with those who might need it. This can be easily done by tracking data quality metrics and implementing catalogs to compile all the information related to the trustworthiness of the data. 

When discussing data sharing, the term " self-service BI ” quickly pops up because those solutions do not require an IT team to access, interpret, and understand all the data. These online BI tools make sharing easier by generating automated reports that can be scheduled at specific times and to specific people. For instance, they enable you to set up business intelligence alerts and share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices, which enhances the decision-making and problem-solving processes critical for today's ever-changing environment. This is especially necessary now that the pandemic has forced businesses to shift to a home office dynamic in which collaboration needs to be supported by the right tools more than ever. 

Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions . However, data sharing does not only occur around the exchange or updates of some documents. It has to track the progress of meetings, calls, e-mail exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept. 

In fact, it is expected that, in 2024, data sharing will move further from just sharing insights and will start from earlier stages. Starting from data exploration and spreading across the entire analytical workflow for a more efficient decision-making process that includes every stakeholder, regardless of location. This last point is especially important when considering the growing security concerns many businesses face today. Implementing a collaborative BI approach enables every stakeholder and data user to be accountable for the decisions he or she makes, ensuring a more secure workflow. 

In response to all these changes, data analytics and BI providers are prioritizing collaboration for 2024, introducing multiple capabilities that connect users at every stage of their work and with a level of interactivity that breaks the barriers between data and analytics and the different business functions. A recent survey shows that 75% of executives say their business functions are competing rather than collaborating. This presents a major challenge, especially for companies still undergoing a digital transformation due to the pandemic. By implementing a collaborative approach supported by the right tools and processes, developers and average business users are expected to work together under the same analytics umbrella, enabling more united communication and a productive work environment. Let’s see how it will be developed in the business intelligence trends topics of 2024.

Next, in our list of trends in data analytics, we will talk about continuous intelligence (CI). Gartner defines the concept as a “design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events”. 

It basically describes the use of tools and processes to facilitate the integration of real-time analytics into business operations with the help of augmented analytics. Traditionally, the analytical process has relied on predefined metrics that are tracked on specified schedules. CI is a machine-driven approach that automates the extraction of data insights no matter how many data sources or massive volumes of data need to be handled, providing businesses with a continuous and frictionless flow of real-time insights. 

The concept was born out of a necessity for an integrated analytical approach to keep up with modern organizations' demands in the digital revolution. Having data silos and decentralized analytical processes can only lead to a waste of resources and valuable time. With continuous intelligence, organizations can go further from analyzing static metrics that must be constantly updated to being able to identify trends, growth opportunities, and anomalies that might remain hidden otherwise. 

So, it is clear that all CI applications have real-time analysis at their core. However, historical data also plays a pivotal role in the process. For example, you might be a manufacturing company analyzing machine performance in real time and realize, in just seconds, that a specific part of the machine is about to fail, which can help you implement corrective measures immediately. Complementing the live data you just got, you can use historical data to understand how many times this same machine has failed in the past few weeks, months, or even years. Allowing you to get a 360-degree view and make the most efficient decisions.  

CI tools are expected to offer automated, real-time data ingestion, simplify and unify data collection, management, and analysis, and use advanced in-memory technology to store and manage historical information. Combining these solutions will give businesses the power to optimize their day-to-day operations by spending less time shifting through massive amounts of data and more time focusing on what really matters. Plus, they can significantly accelerate the time to action in any business scenario thanks to various features, like dynamic alerting and event triggering powered by AI and ML algorithms. 

In 2024 and the years ahead, we can expect more and more organizations to adopt CI technologies to make smarter decisions live and with less manual work. CI offers a shift from traditional BI processes based on curated historical data to AI-driven augmented analytics with real-time insights that allow for efficient and agile responses to unexpected events. 

As data becomes the foundation of strategic decisions for businesses of all sizes, understanding and using this data as a collaborative tool that everyone in the organization can use becomes critical for success. That said, data literacy will be one of the relevant data analytics trends to look out for in 2024. 

Data literacy is defined as the ability to understand, read, write, and communicate data in a specific context. This means understanding the techniques and methods used to analyze the data as well as the tools and technologies implemented. According to Gartner , poor data literacy is listed as the second-biggest roadblock to the success of the CDO’s office, and it adds that by 2024, data literacy will become essential in driving business value. 

Even with the rise of self-service tools that are accessible to everyone, data literacy continues to be the foundation of a successful data-driven culture. Business leaders are responsible for providing the needed training and tools to the entire organization to empower everyone to work with data and analytics. To achieve a successful data literacy process, a careful assessment of the skills of employees and managers needs to be made in order to identify weak spots and gaps. Gartner recommends starting by identifying fluent data users that can serve as “mediators” for non-skilled groups as well as identifying communication barriers where data is failing its purpose. With all this knowledge in hand, the creation of targeted training instances will become an easier task. 

In the long run, with the proper training and the right tools, users from all levels of knowledge will be able to perform advanced analysis and use data as their main language. With technologies such as predictive analytics becoming accessible for regular users, data science will no longer need to be performed by experts- shifting these professionals to focus on other advanced tasks such as Machine Learning or MLOps. In fact, according to Gartner, it is expected that by 2025, the shortage of data scientists will no longer be an obstacle to businesses adopting advanced technological processes. That said, data literacy will be one of the most important business intelligence market trends in the coming year.

Natural Language Processing (NLP) is one of the recent trends in business intelligence that is revolutionizing how companies approach their analytical processes. Considered amongst the most powerful branches of AI, NLP enables computers and machines to understand, learn from, and interpret human language in a spoken or written form, and it can be divided into two subsets: natural language understanding (NLU) and natural language generation (NLG). NLU focuses on understanding the meaning behind text and speech, while NLG focuses on text generation based on specific data input. 

The growth of this trend has been such in the past years that its $3 billion worldwide market revenue from 2017 is expected to be almost 14 times larger by 2025, reaching $43 billion, according to research by Statista. This is not surprising as language-processing applications are already present in our daily lives in the shape of car navigation systems, smart voice assistants like Siri or Alexa, autocomplete text features on our phones, and translation apps, just to name a few. 

Considering all of that, it is not surprising that businesses have begun to adopt this technology to manage the large amounts of unstructured text data they gather from different sources such as emails, social media, or surveys. As a response, multiple BI software providers offer their users language insight features. There are two major use cases for which language processing is becoming increasingly popular in the BI industry. Let’s look at them in more detail below: 

BI data assistant: Similar to the chatbots we see on multiple websites today, a data assistant is integrated into BI software to answer any analytical questions that a user might have. All you need to do is write a question in human language, and the assistant will provide you with the answer. As the technology matured in the past years, AI-based assistants went from simply showing search results for users to analyze to being able to filter and organize the data to generate analytical insights as an answer. This development has also helped democratize data as non-technical users can simply type a question, and the software will automatically show them an answer without needing complicated calculations or analysis. 

Sentiment analysis : Also known as opinion mining, it is the process of analyzing text data to identify the emotional tone behind it. Businesses often use it to analyze comments on social media, emails, blog posts, webchats, and more and define if the tone of what is being said is negative, positive, or neutral. Through this, organizations can extract useful insights regarding product development and brand positioning, as well as understand pain points to improve the customer experience on different touch points. 

NPL is one of the business intelligence emerging trends we will see developing in multiple areas over the coming years. BI software that exploits this capability with a self-service approach will gain a competitive advantage by allowing users to conduct efficient analysis without the need for any calculations. We will definitely be watching how this technology develops in 2024.

9) Predictive & Prescriptive Analytics Tools

Business analytics of tomorrow is focused on the future and tries to answer the question: what will happen? How can we make it happen? Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among BI professionals, especially since big data is becoming the main focus of analytics processes being leveraged by big enterprises and small and medium-sized businesses.

Predictive analytics is the practice of extracting information from existing data sets to forecast future probabilities. It’s an extension of data mining that refers only to past data. Predictive analytics includes estimated future data and, therefore, always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessments. Applied to business, predictive analytics is used to analyze current data and historical facts to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities. In contrast, bankers use it to generate a credit score – the number generated by a predictive model that incorporates all the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

Predictive analytics must also become accessible for everyone, and in 2024, we will witness even more relevance that will cater to that notion. Self-service analytical possibilities are becoming a criterion for BI vendors and companies alike; both can profit from it and bring more value to their businesses. The predictive models, in practice, use mathematical models, in other words, forecast engines, to predict future happenings. Users simply select past data points, and the software automatically calculates predictions based on historical and current data, as shown in the example:

**click to enlarge**

Among different predictive analytics methods, two are quite popular among data scientists: artificial neural networks (ANN) and autoregressive integrated moving averages (ARIMA).

In artificial neural networks, data is processed in a similar way as in biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it, and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with a considerable volume, number of variables, or diversity of the data. 

ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be considered when making predictions. Each part of ARIMA takes care of different sides of model creation – the autoregressive part (AR) tries to estimate the current value by considering the previous one. Any difference between predicted data and real value is used by the moving average (MA) part. We can check if these values are normal, random, and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predict new anomalies, or help to discover underlying patterns not visible by the bare eye. ARIMA techniques are complex, and concluding the results may not be as straightforward as for more basic statistical analysis approaches. However, once the basic principles are grasped, the ARIMA provides a powerful predictive analysis tool.

Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps are taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be to adjust the decisions before they are actually made. This greatly improves decision-making, as future outcomes are considered in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory, and supply chain design to deliver what your customers want in the most optimized way, and these are some of the emerging trends in business intelligence 2024 that we will hear more about.

When data analytics occurs within a user’s natural workflow, embedded analytics is the name of the game. Businesses have recognized the potential of embedding various BI components, such as dashboards or reports, into their own application, thus improving their decision-making processes and increasing productivity. Formerly strangled by spreadsheets, companies have realized how utilizing embedded dashboards enables them to provide higher value within their own applications. In fact, according to Allied Market research, the embedded analytics market is projected to reach $77.52 BN by 2026, with a CAGR of 13.6% from 2017 to 2023 , and this is one of the business analytics topics we will hear even more in 2024.

Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations. In 2024, we will see even more companies adopting it. Departments and company owners seek professional solutions to present their data without building their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting they can offer consumers.

More than just embedding a dashboard or BI features in an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By allowing clients and employees to manipulate the data in a well-known environment, you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends on this list. 

Business Wire recently published a report called “Global Embedded Analytics Market (2021 to 2026) - Growth, Trends, COVID-19 Impact, and Forecasts,” in which they mention that “organizations are deploying embedded analytics solutions to realize significant gains in revenue growth, marketplace expansion, and competitive advantage.” They also add that embedding analytics will grow significantly in the healthcare industry in the coming years. Considering the massive amounts of data that hospitals collect, which got even bigger with COVID-19 and telemedicine interactions, healthcare businesses “switch from paying for service volume toward service value”. By using powerful healthcare analytics software that can be embedded, hospital managers can extract valuable insights that will help them optimize processes from a clinical, operational, and financial point of view. 

This is one of the trends in business analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexity.

What Are The Analytics & Business Intelligence Trends For 2024?

We’ve summed up in this article what the near future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2024:

• Artificial Intelligence
• Data Security
• Data Discovery/Visualization
• D&A Sustainability 
• Data Sharing 
• Continuous Intelligence 
• Data Literacy
• Natural Language Processing
• Predictive And Prescriptive Analytics Tools
• Embedded Analytics

Become Data-driven In 2024!

Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2024 will be an exciting year of looking past all the hype and moving towards extracting the maximum value from state-of-the-art online business intelligence software . We hope you enjoyed this overview, and stay tuned for more business intelligence industry trends!

If you’re ready to start your business intelligence journey, or keep up with the 2024 trends, trying our software for a 14-day trial will do the trick! And it’s completely free!