The world is moving rapidly evolving digital technology user constant pressure to innovate  and enhance the operational efficiency to deliver personalized customer experience in business  Industries. The GenAI  rapidly changing  the business and human value towards business transformation using  Artificial Intelligent(AI)  and GenAI.. The Big data reshaping the  current business Industries model with all possible steps. The research identifies the  current research issues on “Business Value of Generative AI and Big Data Integration:  A Framework for  Strategic Transformation” not only enhances data-driven decision-making but also enables the automated generation of content, products, and strategies at scale.

Generative AI the capacity to produce novel content including text, code  and images from the related data patterns   and beyond the traditional automation  creativity . The big data analysis gives the way of understanding AI generated concept  to produce novel level text and information. It is more feasible  with structured and unstructured datasets. The convergence of factors such as Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance, Innovation and Competitive Advantage, Cost-Benefit Analysis, Risk Management and Security, and Scalability and Sustainability allows extracting deeper insight, accelerating innovations cycles and optimizing the information in real time mode.

 The is research study is divided into five segments, chapter 1 given the strong background of GenAI and its all possible approach in business Industries to enhance the data which are more relevant  towards the novel documents and close to human being. Chapter II describes the different current research study and gap in  among them. Chapter III gives the problem statement  and identifications of research problems which are properly stated by the researcher scholars. Chapter IV  defines  the research methodology and statistical tools which are used in this research study. And finally Chapter V and Chapter VI give the data interpretation  and analysis , summary and conclusion. This research study  presents a comprehensive framework for strategic transformation through the integration of Generative AI and Big Data. It explores how this integration can be aligned with organizational goals to drive innovation, improve decision-making, streamline operations, and ultimately deliver competitive advantage. 

The research study based on convergence of Generative Artificial Intelligent(GenAI )  and big data  represent a transformative way of controlling the business  value of any organizations or enterprises. The literature survey  gives the comprehensive research study on GenAI and its supporting factors such as Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance, Innovation and Competitive Advantage, Cost-Benefit Analysis, Risk Management and Security, and Scalability and Sustainability. Recent literature underscores the growing significance of this integration in driving strategic transformation, operational efficiency, and competitive advantage.

Bommasani et al. (2021) proposed a foundation  to describe the large scale pre trained models to extract information which are more relevant for business intelligent  and transformation. The researcher identifies the significant factors  which are more relevant  to handle complex data  and advance tools to map the data  to generate GenAI  more novel documents as per  user’s requirements. Manyika et al. (2011)  provided the new opportunity for value creations and productivity enhancements for data  driven decision making process. The researcher used the  strategic transformation to enhance the results with more accurate performance. Gartner (2023) predicts that by 2025, over 30% of outbound marketing messages from large organizations will be synthetically generated.

Accenture (2023) stated that GenAI  turning the big role in data integration  and creation of business value  to optimize the data and its significant approach towards decision making process.AI Maturity Model (McKinsey, 2022) proposed a experiment adoption to full strategic integration and realizing higher ROI, Authors like Binns et al. (2018) and Jobin et al. (2019) stress the importance of explainable AI (XAI), ethical governance, and regulatory compliance in deploying GenAI solutions. The researcher use the generative AI  and Big  Data is shaping the landscape the modern enterprises to robust  and scaling to map the large data.

Tornatzky & Fleischer (1990) provided the understanding the technological approach in environmental factors such as Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance are the high significant  and useful  to produce accurate  and novel information. Teece et al. (1997) emphasized that the GenAI capabilities to integrate , build and reconfigure internal and external competence to produce accurate and significant information to create a business value towards fast decision making process. Lal, Bechoo et.al.,(2025) proposed business model which are more relevant to produce accurate and  novel data towards decision making  using GenAI technology. The researcher used the multiple machine learning approach and  Big data  to extract  valuable information towards decision making process.

Bommasani et al., 2021) proposed the content generation such as text, images and video  which are more relevant  and valuable for decision making  and produced some similar tools. Chen et al., (2021) proposed the design and product innovation  and prototype design of business value in decision making process. Haefner et al., (2023) stated the GenAI ability to learn on massive data sets  and providing velocity and  huge amount of data. Laney( 2001) emphasized  on Big Data analytics refers to processing and analyzing extremely large datasets for business insights (McAfee & Brynjolfsson, 2012) generated  the business value using GenAI and big data analytics. The researcher used the machine learning approach and natural language processing to generate accurate and more relevant information .

Wamba et al., (2015) used the GenAI and Big data for enabling the more novel  and valuable information and text data towards decision making process including structured and unstructured data. Bose (2022) proposed an Automated decision support systems (Dwivedi et al., 2023), Real-time business model innovation (Chui et al., 2023), Healthcare: GenAI-based diagnostics using big medical datasets (Esteva et al., 2019), Finance: Fraud detection, customer profiling, and automated reporting (Tang et al., 2022), Retail: Chatbots and dynamic pricing models (Lee & Suh, 2021).

Brock & von Wangenheim (2019) proposed GenAI Tools and Big Data for fostering and prototyping customer driven design and decision making process. The researcher used the short coming data which are structured  and unstructured  that would be more valuable  and more significant towards decision making process. (Haefner et al., 2023) used the machine learning –  natural Language Generators  to deeper insight data ,  (Jarek & Mazurek, 2019) stated that data privacy  and bias training model  (Binns et al., 2018) proposed the GenAI decision  making process and its more relevant information  (Doshi-Velez & Kim, 2017) propose the GenAI model to provide  more accurate and relevant information (Strubell et al., 2019) proposed the Generative AI and Big Data  transformation opportunity  to create business value and more accurate and  predictive model  to generate information towards decision making process.

PRIMARY OBJECTIVE: The researcher stated the primary objective on “ To develop a strategic framework that demonstrates how the integration of Generative AI and Big Data can drive business value and enable enterprise-wide transformation”.

3.2 SECONDARY OBJECTIVES:

1. To study the current research issues on GenAI and Big Data approach towards more novel approach   for generative business value.
2. To identify the factors on GenAI and Big Data approach in Business Industries.
3. To collect data based on primary data approach based on questionnaire and generates inferences.
4. To assess the impact of such integration on innovation, decision-making, productivity, and customer experience.

3.3 HYPOTHESIS:

H01: There is no significant relationship between business value of Industries transformation and GenAI and Big Data.

HA1: GenAI and Big Data have a significant impact on business value of Industries transformation and novel representation of data.

RESEARCH METHODLOGY

 This research study is based on primary   and secondary data and mixed approach of research design  in quantitative and qualitative ways. The researcher used the SPSS software  and  statistical tools Chi- square   to test the hypothesis and check the variability  among results.

4.1 Primary Data

1. Survey: Structured questionnaires will be distributed to mid and senior-level managers in sectors like IT, healthcare, manufacturing, and finance.
2. Interviews: Semi-structured interviews with 15–20 industry experts, CIOs, data scientists, and digital transformation officers to gain qualitative insights.

4.2 Secondary Data

1. Academic journals, white papers, industry reports (e.g., McKinsey, Deloitte, Gartner).
2. Company case studies and press releases illustrating real-world implementations.

Apart from descriptive analysis the researcher used the  principal components analysis (PCA) to reduce the dimensional of data , reliability test to verify the internal consistency between factors of Genrative AI , ANOVA and Chi- Square statistical tools to test the hypothesis at 0.05 significant levels.

4.3 Measurement Scale: The researcher used the Likert scale to measure the quality parameters of Generative AI factors  and  its significant approach towards this research study.

Research Statement: Statistical analysis of factors on the Business Value of Generative AI and Big Data Integration: A Framework for Strategic Transformation.

Data Analysis

 The above data analysis report is the descriptive statistics of Generative AI factors which is compiled by SPSS software based on 105 datasets. The analysis report showing that Integration Architecture, Strategic Business Alignment, Governance and Ethical Compliance mean value is 2.9804, 2.9208, and 2.9020.  which are showing the minimum value from the other factors and showing the minimum variability between variables (Table 1.2).

The reliability analysis showing the internal consistency of attributes which is used to measure the reliability of a summated scale where several items are summed to form a total score.  This measure of reliability in reliability analysis focuses on the internal consistency of the set of items forming the scale. The key strategies for Generative AI and Big Data Integration: A Framework for Strategic Transformation which significantly enhance the   business  value  and played a significant role to organized a business value in organization. The reliability score 0.985 which is showing the strong internal consistency between variables (Table 1.3).

Fig.1.1: Extraction Method: Principal Component Analysis

The above data analysis report showing the Eigen Value and Eigen Vector based on principal components analysis  at 95%  class interval and 0.05 significant level. The researcher used the 10 factors  such as  Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance, Innovation and Competitive Advantage, Cost-Benefit Analysis, Risk Management and Security, and Scalability and Sustainability based on Eigen Value and Eigen Vector which are subsequently  decreasing  and make a shape like Elbow which are showing a perfect relations between dependent and independent variables(Fig 1.1). 

INFERENCE

The above data analysis report is compiled by SPSS software on Significant  factors  on “The Business Value of Generative AI and Big Data Integration: A Framework for Strategic Transformation” such as Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance, Innovation and Competitive Advantage, Cost-Benefit Analysis, Risk Management and Security, and Scalability and Sustainability at 0.05 significant levels. The probability of statistics P-Value is 0.000 which is less than 0.05 significant levels, so the null hypothesis is rejected and result is significant. At this level the researcher concluded that the factors of Generative AI is significant and have impact on strategic transformation towards this research study based on Generative AI and Big data integration (Table 1.5).

Fig.1.2: Statistical Analysis of Lower Dimensional Projection

The above data analysis is showing the  lower dimension of Generative AI factors and its components. The research used the 10 parameters  such as Data Infrastructure Readiness, Generative AI Capabilities, Integration Architecture, Strategic Business Alignment, Organizational Readiness and Culture, Governance and Ethical Compliance, Innovation and Competitive Advantage, Cost-Benefit Analysis, Risk Management and Security, and Scalability and Sustainability on  “The Business Value of Generative AI and Big Data Integration: A Framework for Strategic Transformation”.  The statistical analysis shows that data infrastructure readiness and integration architecture are more significant towards GenAI  and Big Data   driven approach to  generate more relevant and novel information towards decision making process.(Fig.1.2).

The research paper explored how the integration of Generative Artificial Intelligence (GenAI) with Big Data can create significant business value and serve as a foundation for strategic transformation across industries. It highlights that while Big Data provides vast volumes of structured and unstructured information, Generative AI brings the ability to create new content, insights, and solutions from this data—enabling organizations to move beyond traditional analytics. The integration of generated AI and big data represents the power of change in modern business strategies. In this context, how do the combination of AI generation skills, such as creating new content, simulations, and solutions, examine the enormous analytical strength of big data innovation, efficiency, and competitive advantage? Essentially, this research study  highlights a data-controlled Strategic Transformation process in which generative models use structured, unstructured data to automate complex tasks, improve personalization, and support business knowledge in real time. The most important value areas include process optimization, product innovation, customer loyalty and predictive intelligence.

1. Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. Stanford CRFM.
2. McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review.
3. Wamba, S. F., et al. (2015). Big data analytics and firm performance. Information & Management, 52(1), 1–20.
4. Teece, D. J., et al. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
5. Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
6. Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group.
7. Zhang, J., et al. (2023). The Role of Generative AI in Business Content Automation. MIS Quarterly Executive.
8. Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code. arXiv:2107.03374.
9. Haefner, N., et al. (2023). Generative Design in Product Innovation. Journal of Product Innovation Management.
10. Brock, J. K.-U., & von Wangenheim, F. (2019). Demystifying AI in Business. Business Horizons, 62(6), 765–775.
11. Bose, I. (2022). Personalized Marketing Using Big Data and AI. Journal of Business Research, 142, 175–188.
12. Chui, M., et al. (2023). The Economic Potential of Generative AI. McKinsey & Company.
13. Esteva, A., et al. (2019). A Guide to Deep Learning in Healthcare. Nature Medicine, 25, 24–29.
14. Lee, D., & Suh, A. (2021). Chatbot Adoption in E-commerce: A Big Data Perspective. Journal of Retailing and Consumer Services.
15. Dwivedi, Y. K., et al. (2023). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57.
16. Jarek, K., & Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review.
17. Lal, Bechoo et.al.,(2025). Elevating intelligent voice assistant chatbots with natural language processing, and AI technologies, Indonesian Journal of Electrical Engineering and Computer Science, Scopus – Q3, Volume 37, Issue 1, Pages 507 - 517January 2025, ISSN: 2502-4752, DOI:10.11591/ijeecs.v37.i1.pp507-517.
18. Tang, C., et al. (2022). AI in FinTech: Case Studies and Implications. AI & Society, 37, 111–124.
19. Binns, R., et al. (2018). ‘It's Reducing a Human Being to a Percentage’: Perceptions of Justice in Algorithmic Decisions. CHI.
20. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608.
21. Strubell, E., et al. (2019). Energy and Policy Considerations for Deep Learning in NLP. ACL.
22. Ghasemaghaei, M. (2019). Does data analytics use improve firm decision making quality?. Information & Management, 56(8).
23. Akter, S., et al. (2016). How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics.
24. Mariani, M., & Borghi, M. (2023). Big Data Analytics in Hospitality and Tourism. International Journal of Hospitality Management.
25. Benbya, H., et al. (2021). From reactive to proactive AI in digital transformation. MIS Quarterly Executive.
26. Bawack, R. E., et al. (2022). Generative AI: A research agenda and implications for digital business strategy. Journal of Strategic Information Systems.