Impact of Big Data Analytics on Sustainable Business Practices in India: A Comprehensive Analysis

Impact of Big Data Analytics on Sustainable Business Practices in India: A Comprehensive Analysis

Dr. Jyotirmoy Koley, WBES

Assistant Professor of Commerce, Darjeeling Government College, Darjeeling, West Bengal, India

DOI: https://doi.org/10.51584/IJRIAS.2025.100800160

Received: 30 August 2025; Accepted: 05 September 2025; Published: 01 October 2025

ABSTRACT

The rapid expansion of big data analytics has significantly altered the landscape of sustainable business practices in India. This study examines the influence of big data analytics on the triple bottom line of sustainability, encompassing the environmental, social, and economic dimensions across various sectors within the Indian context. Through a comprehensive analysis of secondary data sourced from academic journals, industry reports, and case studies, this study underscores the potential of big data analytics to enhance operational efficiency, optimize resource utilization, and foster innovation for competitive advantage. The findings reveal sector-specific applications such as improved energy management, precision agriculture, supply chain optimization, and enhanced healthcare services. Nonetheless, the study also identifies substantial challenges in the widespread adoption of big data analytics for sustainability, including technological limitations, lack of structured frameworks, data complexity, infrastructure deficiencies, and data security concerns. To fully leverage the potential of big data analytics, this study advocates a multi-stakeholder approach involving businesses, policymakers, and academia. Businesses should prioritize the integration of advanced analytics and align their initiatives with sustainable practices. Policymakers must develop supportive regulatory frameworks, provide incentives, and invest in the digital infrastructure. Academia plays a crucial role in advancing research, developing curricula, and fostering interdisciplinary collaborations. As technology evolves and data availability increases, the potential of big data analytics to drive sustainable development in India is anticipated to expand. Future research should focus on sector-specific applications, long-term impacts, and strategies to overcome barriers to adoption, particularly for small and medium-sized enterprises.

Keywords: Big Data Analytics, Sustainable Business Practices, India, Triple Bottom Line, Environmental Sustainability, Social Sustainability, Economic Sustainability, Operational Efficiency, Resource Utilization, Innovation, Competitive Advantage, Challenges, Multi-stakeholder Approach, Sector-specific Applications, Adoption Barriers, etc.

INTRODUCTION

The incorporation of big data analytics into business operations has become increasingly essential for achieving sustainability objectives, particularly in rapidly developing economies such as India. As businesses globally encounter pressure to implement environmentally conscious practices, the significance of advanced technological solutions, such as big data analytics, becomes even more pronounced. Big data analytics offers transformative potential by systematically analysing and utilizing vast quantities of information to enhance decision-making and strategize sustainable initiatives (Ali et al., 2020; Zhu & Yang, 2021). In the Indian context, the burgeoning economy and increasing technological adoption provide a fertile environment for big data analytics to significantly influence business operations. The emphasis on sustainability within Indian enterprises is not merely a trend but a strategic imperative driven by both regulatory requirements and consumer demand for environmentally responsible practices. Businesses in India are increasingly turning to big data analytics to optimize resource utilization, reduce waste, and improve overall operational efficiencies, thereby aligning their practices with global sustainability standards (Xu et al., 2024). Big data analytics aids in identifying patterns and trends that contribute to innovative solutions to sustainability challenges. For instance, by integrating such analytics, companies can better manage their supply chains, optimize energy use, and enhance product lifecycle management, leading to substantial reductions in environmental impacts (Harfouche et al., 2024). Furthermore, the capabilities of big data analytics extend to fostering data-driven cultures within organizations, promoting continuous improvement and adaptability in sustainable practices (Ramadan et al., 2020). As businesses in India continue to evolve amidst dynamic economic conditions, the strategic application of big data has emerged as a critical tool for achieving sustainable business practices. This approach not only supports environmental goals but also enhances competitive advantage by enabling firms to respond agilely to market changes. Consequently, understanding the impact of big data analytics on sustainable business practices in India can provide crucial insights into the development of strategies that meet both economic and ecological objectives, thereby contributing to broader global efforts towards sustainability (Hadi & Zeebaree, 2025; Ohlhorst, 2013).

Background of the Study

The United Nations Sustainable Development Goals (SDGs) emphasize innovative solutions for climate change, resource depletion, and social inequalities (Harfouche et al., 2024). Sustainable business practices and technology integration are vital for addressing these challenges (Harfouche et al., 2024; Pappas et al., 2018). India’s expanding economy faces significant environmental and social challenges, making it critical for sustainable development strategies (Harfouche et al. 2024). Big data analytics, as a transformative tool, drives informed decision-making across sectors (Asiri et al., 2024). Its integration enables operational efficiency and sustainability by providing data-driven insights (Ali et al. 2020; Zhu and Yang 2021). Businesses can achieve financial performance while contributing to societal goals by aligning big data analytics with sustainability (Asiri et al., 2024; Zhu & Yang, 2021).

Introduction to Big Data Analytics (BDA): Big Data Analytics encompasses the methodologies and technologies employed to examine extensive datasets to reveal concealed patterns, correlations, and insights. Its implementation can significantly enhance operational efficiency, refine decision-making processes, and stimulate innovation across various sectors.

BDA’s Role in Sustainability: Big data analytics is increasingly acknowledged as a facilitator of sustainable business practices. By enabling the integration of sustainable technology and data-driven strategies, BDA assists businesses in innovating and achieving performance enhancements in sustainable contexts (Nwobodo et al., 2024).

Performance Enhancements through BDA: In the financial sector, strategies involving Big Data Analytics (BDA) significantly influence internal processes, thereby enhancing both environmental and financial performance. By integrating big data with green management practices, businesses can achieve operational efficiency and meet sustainability objectives (Ali et al. 2020; Zhu and Yang 2021).

BDA in Supply Chains and Circular Economies: Big data capabilities exert an indirect influence on sustainable performance by enhancing supply chain management capabilities and circular economic practices. This synergy is essential for promoting sustainable value creation (Riggs et al. 2023).

Adoption Challenges in SMEs: Small and medium-sized enterprises (SMEs) encounter challenges in the adoption of big data technologies, primarily due to factors such as perceived ease of use. Nevertheless, the successful integration of these technologies can substantially enhance sustainable business performance (Asiri et al. 2024).

Sector-Specific Applications: In emerging sectors, such as sustainable energy investments, Big Data Analytics (BDA) plays a crucial role in strategic decision-making by enhancing market analysis, risk assessment, and predictive modelling. This, in turn, optimizes the impact of investments in green energy (Nwobodo et al., 2024).

Dynamics of Organizational Performance: The integration of Big Data Analytics (BDA) with organizational practices, such as green human resource management, can substantially enhance organizational performance. This underscores the significance of a data-driven culture in achieving competitive sustainability (Mahmood et al., 2022).

Global Challenges and Opportunities: While Big Data Analytics (BDA) presents significant potential, its impact is not uniform across various regions and sectors. In India, as in other emerging economies, the effective utilization of BDA for sustainability necessitates overcoming the challenges related to technology adoption and the development of data infrastructure.

This context provides a foundation for examining the potential of big data analytics to drive sustainable business practices in India, highlighting the necessity for strategic integration across various organizational functions.

Problem Statement

The problem statement addresses how big data analytics (BDA) affects organizational sustainability. India’s growing economy has shown increasing interest in BDA adoption for sustainable practices. The literature indicates that the complex impacts of BDA on economic, social, and environmental outcomes are influenced by how Indian businesses implement sustainability practices through regulatory mandates such as CSR (Singh et al., 2018). Challenges include monitoring sustainability outcomes and integrating technological innovations with organizational processes (Gangwar et al., 2022; Zhu & Yang, 2021). This study explores BDA’s role of BDA in advancing sustainable business practices among Indian enterprises by examining the adoption drivers and sustainability impacts (Gangwar et al., 2022; Mesquita et al., 2023). Indian businesses face a perceived trade-off between profitability and sustainability while needing to decouple economic growth from environmental degradation. Big data analytics can enhance sustainability through improved operational efficiency and risk mitigation (Mani et al., 2017; Mesquita et al., 2023). BDA offers transformative potential for informed decision-making towards sustainability (Nwobodo et al., 2024). This study investigates how BDA can bridge economic objectives and sustainable practices in India, contributing to better environmental, economic, and social outcomes. This study examines how data-driven insights can align corporate profitability with sustainability goals, helping Indian businesses achieve competitive advantage while meeting sustainability targets (Gopal et al., 2022; Xu et al., 2024).

LITERATURE REVIEW

Numerous scholarly articles and research papers have been authored by academics and researchers over time, examining various aspects of big data and analytics and their impact on business sustainability both in India and internationally. The most significant, pertinent, and recent articles were analyzed for this study. This information is reviewed and presented below.

Leal de Paula et al. (2024) investigated how big data analytical capabilities enhance sustainable information quality in Brazilian companies. This study shows that tangible and intangible data capabilities, particularly learning, data-driven culture, and managerial skills, are vital for producing reliable and sustainable information that meets stakeholder needs. The research highlights the integration of data sources and organizational skills to leverage big data analytics for sustainability. The findings indicate that intangible capabilities, especially managerial skills and organizational learning, have a greater impact than technical skills. This study provides insights into big data capabilities for sustainability and guidance for managers in emerging markets.

Nilashi et al. (2023) investigated challenges in assessing Sustainable Development Goals (SDGs) performance due to data quality issues like incompleteness and inaccessibility. This study emphasizes the need for reliable data for monitoring and policy formulation because many countries lack the capacity to collect comprehensive SDG indicator data. The authors explored big data analytics as a cost-effective solution to address these gaps, suggesting that advanced technologies can enhance data management. Through SWOT analysis, they highlighted that big data can mitigate data incompleteness and improve sustainable development decisions. This study advocates leveraging big data solutions to overcome data limitations and support SDG achievements.

Hammerström et al. (2019) investigated Big Data & Analytics (BD&A) impact on Corporate Social Responsibility (CSR). Companies adopt BD&A primarily to enhance operations and profitability but not CSR objectives. This study analyses energy data from automotive companies to estimate potential savings from implementing BD&A. The authors estimated that BD&A could reduce the energy consumption of vehicle production by 0.5-3% annually. These savings represent substantial reductions when applied in large-scale production. Companies can leverage these savings to justify BD&A investments, benefiting from both financial performance and CSR initiatives. The authors addressed the ethical dilemma of implementing such technologies without a legal mandate. The study concludes by highlighting the CSR potential within the Industrial Internet of Things (IIoT), while noting that external pressure may be needed for widespread adoption.

Sharma (2024) examined the role of business analytics in corporate sustainability by focusing on environmental and social impact measurements and reporting. This study emphasizes the integration of business analytics into corporate sustainability practices to achieve ESG objectives. This study addresses key topics, including sustainability metrics, regulatory pressures, stakeholder expectations, and the role of business analytics in facilitating corporate sustainability. This paper presents case studies on the successful implementation of business analytics, including Google’s carbon neutrality efforts and Unilever’s impact monitoring. This conclusion emphasizes the importance of measuring the environmental and social impact of transparency and risk management. It highlights how data-driven insights have improved sustainability in leading firms, and discusses the evolution of business analytics from an operational tool to a crucial instrument for sustainability. This study also warns about the risks faced by companies failing to adopt analytics for sustainability.

Kgakatsi et al. (2024) conducted a systematic review of Big Data’s influence on SME performance, analysing 93 research articles from 2014-2024. The findings show that Big Data adoption improves operational efficiency, revenue, and competitiveness in SMEs, despite challenges such as limited resources and technical expertise. The review reveals a predominance of quantitative methods and concludes with recommendations for SMEs to align Big Data initiatives with business objectives.

Hussien et al. (2025) investigated big data analytics’ (BDA) influence on sustainability report quality. BDA affects sustainability reporting through improved data collection, real-time analysis, stakeholder engagement, comprehensive reporting, and predictive analytics of sustainability trends. The study used quantitative analysis via questionnaires from Jordanian companies listed on the Amman Stock Exchange, analysing data using the Smart PLS software. The results showed that BDA significantly enhances sustainability report quality, enabling timely and detailed disclosures and supporting strategic planning. This study highlights the importance of BDA in improving corporate sustainability reporting and meeting stakeholders’ expectations.

Nithya et al. (2020) examine big data and analytics in India’s e-commerce sector, highlighting enterprises’ increasing reliance on data analytics for operational performance. The study noted that 90% of Internet data was generated post-2016. This review covers big data’s business value, challenges, and applications in e-commerce, including India’s market position and Internet user growth. It explores how companies such as Amazon and Flipkart use data analytics for customer segmentation and targeted marketing. A case study on Amazon demonstrated the use of big data for recommendations, supply chain optimization, and pricing. This study concludes by highlighting the advantages of big data in e-commerce, including increased revenue, enhanced sales, and improved customer understanding.

Gholami et al. (2023) analyzed big data analytics (BDA) for sustainable products and examined their evolution and contribution to product sustainability. The impact of BDA on the product sustainability indicators was evaluated using bibliometric analysis and the fuzzy TOPSIS method. This study found a growing research interest, with 870 documents published between 2012-2023, led by China and the United States, primarily in Engineering and Computer Science. BDA contributes significantly to product development, especially end-of-life management, quality, and performance. This study highlights the potential of BDA for advancing Sustainable Development Goals (SDGs) and suggests future research directions.

Alzboun (2023) investigated the analytics capabilities of supply chain sustainability in Jordan’s hospitality sector, focusing on infrastructure flexibility and management and personnel capabilities. The study gathered data from 512 hotel managers registered with the Jordan Hotels Association and used Structural Equation Modelling for testing. The results show that all three dimensions positively impact supply chain sustainability, with infrastructure flexibility having the strongest effect, followed by personnel and management capabilities. The study found that big data analytics enhances supply chain sustainability through improved inventory management, demand forecasting, and logistics. Recommendations include investing in data collection technologies, establishing analytics teams, and implementing predictive modelling. Alzboun, N. M. (2023). Big data analytics capabilities and supply chain sustainability: Evidence from the hospitality industry. This research demonstrates how big data analytics capabilities improve supply chain sustainability in Jordan’s hospitality industry.

Research Gap:

This study has several limitations. First, there is little research on big data and sustainability in emerging economies, especially India. Second, more detailed studies are required to determine how different industries are affected. Third, there are not enough long-term studies; most are short-term. Fourth, we need to understand how small- and medium-sized businesses can use big data for sustainability. Fifth, problems associated with the use of big data require further study. Finally, ethical issues and possible negative effects need to be examined. Seventh, research on how to combine big data with other new technologies for sustainability is required. Finally, we need standard methods to measure how big data affects sustainability in different situations.

Research Objectives:

The study aims to: (i) analyse the current state of Big Data Analytics (BDA) adoption for sustainability in India; (ii) identify and evaluate specific applications of BDA across the triple bottom line like Planet, People, and Profit; (iii) examine the challenges and barriers to its widespread implementation; and (iv) provide insights and recommendations for businesses, policymakers, and academia.

Scope of the Study:

This study investigates the influence of big data analytics on Indian enterprises, with a particular focus on the role of these technologies in fostering sustainable business practices within the country (Mani et al., 2017). This study examines various sectors in India, especially those that are environmentally sensitive, such as the manufacturing and energy industries. It evaluates the adoption and integration of big data to achieve sustainability objectives (Kumar et al., 2021). This study explores how big data analytics contributes to enhancing corporate sustainability by improving the environmental, economic, and social dimensions within businesses (Asiri et al., 2024; Ramadan et al., 2020). This research underscores the potential of big data analytics to facilitate improved decision-making, enhance operational efficiencies, support compliance with sustainability standards, and provide competitive advantages (Mani et al., 2017; Qaffas et al., 2022).

Limitations of the Study:

This study may encounter limitations concerning the availability and quality of data collected and analyzed from various Indian companies, which could potentially impact the accuracy and comprehensiveness of the findings (Ramadan et al., 2020). Given the focus on specific industries or sectors, the findings may not be generalizable across all sectors within India or other geographic regions without further research (Kumar et al., 2021). The differing levels of technology adoption and varying capabilities across organizations may influence the effectiveness of big data analytics, complicating the establishment of uniform impacts on sustainability (Asiri et al., 2024). The integration of big data analytics with existing systems and processes in businesses presents challenges that could limit the extent to which sustainable benefits are realized (Mangla et al., 2020).

RESEARCH METHODOLOGY

This study adopted an exploratory and descriptive methodology that exclusively utilized secondary data. The secondary data were obtained from analyses of academic journals, industry reports from organizations such as NASSCOM, McKinsey, and PwC, as well as white papers and published case studies of prominent Indian companies, including Tata, Reliance, ITC, Mahindra, Flipkart, and firms within the renewable energy sector. The research approach was both exploratory and descriptive, relying solely on secondary data. The analysis was conducted using secondary data collected from these sources. This study did not incorporate any primary data collection or quantitative analysis techniques. Instead, it relies on reviewing and synthesizing existing literature and reports to examine the impact of big data analytics on sustainable business practices in India across various sectors.

Big Data Analytics for Sustainability:

This section of the study is organized into four subsections: The Indian Context, The Indian Policy Landscape, Sector-Wise Analysis of Applications, and Specific Sector-wise Analysis. Each subsection of this paper is discussed in detail below.

The Indian Context:  Big Data Analytics (BDA) is increasingly acknowledged as a transformative mechanism for advancing sustainability across various sectors in India. The following points elucidate the role and impact of BDA in promoting sustainable development in the Indian context:

Enhancing SME Performance: Indian small and medium-sized enterprises (SMEs) stand to gain considerably from Big Data Analytics (BDA) by enhancing project performance. BDA serves as an intermediary among project success factors, including management’s emphasis on sustainability, green purchasing, and operational capabilities (Mangla et al. 2020).

Smart City Development: In the development of sustainable smart cities, Big Data Analytics (BDA) plays a crucial role in addressing significant challenges, such as the inadequacy of existing technology and frameworks, as well as the inherent complexity of big data. It is imperative to prioritize the resolution of these barriers to facilitate the growth of smart cities, thereby aiding urban planners and governmental agencies in optimizing resource utilization (Khan et al., 2022).

Agriculture Enhancement: In the agricultural sector, Big Data Analytics (BDA) facilitated by distributed and parallel computing enhances precision farming and crop monitoring. Although challenges, such as data privacy and security, persist, this advancement is crucial for sustainable farming practices (Fatima et al., 2023).

Supply Chain Optimization: Big Data Analytics (BDA) plays a crucial role in fostering sustainability within the e-commerce supply chain. The indices of economic, social, and environmental sustainability underscored the influence of BDA, with economic sustainability achieving the highest index. This suggests potential uncertainties in social and environmental dimensions (Gangwar et al., 2022).

Achieving Sustainable Competitive Advantage: The integration of Big Data Analytics (BDA) capabilities with extensive data availability significantly enhances a firm’s capacity for innovation, thereby fostering sustainable competitive advantages. Innovation capabilities directly influence the attainment of sustainability, whereas the direct impact of BDA is contingent on the level of data availability (Ramadan et al., 2020).

Corporate Sustainability and Competitive Performance: The integration of Big Data Analytics (BDA) with blockchain technologies enables firms to achieve corporate sustainability by promoting a data-driven competitive sustainability model. This integration is particularly significant in emerging economies, such as India, where it supports strategic decision-making and enhances corporate performance (Xu et al., 2024).

The aforementioned points underscore the substantial potential of Big Data Analytics (BDA) in promoting sustainability across diverse sectors in India. They offer valuable insights for stakeholders aiming to harness technological advancements to achieve long-term sustainability objectives.

The Indian Policy Landscape:  The following is a point-wise summary of the role and impact of Big Data Analytics (BDA) on sustainability within the context of Indian policy.

Mediating Role in SMEs: Big Data Analytics serves as an intermediary in improving project performance for Indian manufacturing SMEs. It contributes to areas such as project knowledge management, green purchasing, and operational capabilities, thereby supporting sustainable practices (Mangla et al. 2020).

Corporate Sustainability: The integration of Big Data Analytics (BDA) with blockchain technology substantially augments corporate sustainability. This combination has been demonstrated to enhance data-driven competitive sustainability and bolster organizational management information systems, thereby improving corporate performance (Xu et al., 2024).

Supply Chain Sustainability: Big Data Analytics plays a pivotal role in the development of a sustainability evaluation model for e-commerce supply chains in India. It facilitates the balance of economic, social, and environmental dimensions, with a particular emphasis on economic sustainability owing to the inherent uncertainties associated with social and environmental factors (Gangwar et al., 2022).

Urban management: Big Data Analytics (BDA) technologies effectively address the challenges associated with urban management and sustainability by offering solutions in areas such as smart transportation, waste management, energy consumption, and citizen engagement. These technologies contribute to ensuring urban sustainability across economic, social, and environmental dimensions (Wu et al. 2022).

Healthcare Sector: In the healthcare sector, the integration of Big Data Analytics (BDA) with Artificial Intelligence (AI) enhances the sustainability of supply chains by improving inventory management, refining demand forecasting, and mitigating environmental impacts such as energy consumption and waste production (Allahham et al. 2023).

Agriculture Enhancement: Big Data Analytics (BDA) has the potential to transform agriculture through the implementation of precision farming and crop monitoring, utilizing frameworks such as Hadoop and Spark. It contributes to sustainability by enhancing efficiency and fostering innovation in agricultural operations (Fatima et al. 2023).

Smart City Development: The implementation of Big Data Analytics (BDA) in the development of smart cities encounters several obstacles, including the absence of necessary technologies, frameworks, and platforms. Addressing these challenges is essential for optimizing resources and promoting the sustainable development of smart cities (Khan et al., 2022).

The findings underscore the transformative potential of Big Data Analytics in advancing sustainable development across various sectors in India.

Sector-Wise Analysis of Applications:

This study examines the utilization of big data analytics across various sectors in India to promote sustainability. The principal points discussed in this article are as follows.

Healthcare Sector: Big data analytics significantly enhances patient outcomes and operational efficiency in healthcare. It is employed in diagnostics, the development of personalized treatment plans, and drug discovery, thereby improving healthcare delivery.

Finance Sector: Financial analytics utilizes big data to enhance fraud detection, risk management, and financial analysis processes, thereby contributing to the development of more robust financial systems.

Agriculture Sector: Big data is utilized to promote sustainable agricultural practices through enhanced crop monitoring, yield prediction, and pest detection, thereby facilitating resource-efficient farming.

Retail Industry: Big data facilitates inventory management, customer sentiment analysis, and recommendation systems, thereby enhancing sales and improving customer satisfaction.

Manufacturing and Transportation: Enhancements in quality control, predictive maintenance, and overall operational efficiency are realized through informed decision-making grounded in big data insights.

Energy Sector: Big data analytics plays a crucial role in enhancing energy efficiency and facilitating the integration of renewable energy sources, thereby contributing to sustainable energy management.

Telecommunications: Big data enhances service delivery and customer satisfaction through advancements in network performance and predictive analytics.

Education: The use of big data facilitates the creation of personalized and efficient educational experiences tailored to meet the specific learning needs of both students and educators.

Innovation and Policy Formulation: Big data significantly influences strategic planning and policy formulation by providing comprehensive data insights across various sectors, thereby facilitating the development of more sustainable and efficient public policies.

This analysis elucidates the increasing integration of big data analytics across various industries in India, with the objective of enhancing efficiency, sustainability, and innovation (Ziegler et al., 2010).

Specific Sector-Wise Analyses:

Environmental Sustainability (Planet):

1. Energy: Big data analytics enhances the efficiency of energy management by optimizing both production and consumption systems, which may result in reduced energy wastage and improved energy sustainability (Kumar et al., 2023; Mani et al., 2017).
2. Agriculture: The application of big data analytics in agriculture facilitates the precise monitoring of crop health, soil conditions, and weather patterns, thereby enhancing productivity and sustainability through data-driven decision-making (Mani et al., 2017).
3. Manufacturing: In the manufacturing sector, big data analytics enhances project performance by facilitating environmentally sustainable purchasing and operational capabilities. It serves as a mediating factor in promoting sustainability among Indian SMEs (Mangla et al., 2020).
4. Logistics: In the domains of logistics and manufacturing, big data analytics plays a crucial role in reducing supply chain risks and enhancing operational practices, thereby promoting both economic and environmental sustainability (Khan et al., 2022; Mani et al., 2017).

Social Sustainability (People):

1. Healthcare: Big data analytics significantly enhances healthcare services by facilitating data-driven insights into patient care, resource allocation, and operational efficiency, thereby improving healthcare outcomes and accessibility (Mani et al., 2017).
2. Financial Inclusion: The incorporation of big data analytics within financial services, encompassing both banking and insurance sectors, facilitates financial inclusion by offering personalized financial services and enhancing operational efficiency (Aderemi et al., 2024).
3. Supply Chain Transparency: Big data analytics facilitates the prediction and resolution of social issues within the supply chain, thereby enhancing transparency and mitigating social risks, such as worker safety concerns and unethical practices (Mani et al., 2017).

Economic Sustainability (Profit):

1. Operational Efficiency: Big data analytics significantly enhances operational efficiency by offering insights that optimize processes, reduce costs, and improve product quality across various sectors (Olaniyi et al., 2023).
2. Risk Management: This provides tools for effective risk management by forecasting and mitigating potential threats, thereby safeguarding business operations and ensuring sustainable growth (Aderemi et al., 2024).
3. New Business Models: The advancement of novel business models through big data analytics fosters sustainable competitive advantages by promoting innovation and optimizing resource utilization (Ramadan et al., 2020).

Challenges and Barriers:

The following are the challenges and obstacles associated with the influence of Big Data Analytics on sustainable business practices in India:

Lack of Technologies for Big Data Analytics (BDA): The limited availability of advanced technologies specifically tailored for big data analytics impedes the efficient processing and analysis of large datasets, which are crucial for sustainable practices (Khan et al., 2022).

Lack of a BDA Framework: The lack of a structured framework to guide the implementation and integration of big data analytics into business processes constitutes a substantial impediment to sustainability efforts (Khan et al., 2022).

Nature of Big Data: The inherent complexity and substantial volume of big data present significant challenges in the management and extraction of meaningful insights, which are essential for the development of sustainable business strategies (Khan et al., 2022).

Low Availability of Analytics Platforms: The scarcity of analytics platforms capable of processing extensive and intricate datasets limits the ability of businesses to utilize big data for sustainable development (Khan et al., 2022).

Infrastructure Development: Numerous organizations encounter difficulties in establishing the requisite infrastructure to facilitate big data analytics, including data storage systems and high-speed Internet connectivity (Bag et al., 2020).

Quality of Information Sharing: Enhancing the quality and accuracy of information exchange among stakeholders is essential for making informed decisions regarding sustainable practices (Bag et al. 2020).

Security Challenges: Concerns regarding data privacy and protection present substantial obstacles to the implementation of big data analytics, as organizations remain wary of disclosing sensitive information (Asiri et al., 2024).

Perceived Ease of Use: The challenges associated with utilizing big data analytics tools present a significant obstacle for businesses, particularly small and medium-sized enterprises (SMEs), which may lack the necessary technical expertise to effectively implement and leverage these technologies (Asiri et al., 2024).

Need for Modern Management Practices: Organizations must adopt contemporary management practices to effectively implement and derive benefits from big data analytics-driven sustainable business models (Bag et al., 2020).

These challenges underscore the necessity for strategic planning and investment in technological infrastructure, frameworks, and education to effectively utilize big data analytics to promote sustainable business practices in India.

RECOMMENDATIONS

The recommendations regarding the impact of Big Data Analytics on sustainable business practices in India are categorized into three primary sections: business, policymakers, and academicians. These are elaborated in detail in the following subpoints.

For Businesses

1. Adopt Advanced Analytics: Enterprises in India are encouraged to utilize big data analytics to advance their sustainability initiatives, with a particular emphasis on efficient resource management and waste reduction. The adoption of data-driven decision-making processes can result in substantial enhancements in energy conservation and supply chain optimization.
2. Customer Behaviour Insights: Leverage big data to gain a comprehensive understanding of consumer behaviour and preferences, thereby facilitating the creation of more sustainable products and services that align with market demand while upholding environmental principles.
3. Performance Monitoring: The regular application of big data analytics can facilitate businesses in monitoring their sustainability performance metrics, thereby enhancing accountability and transparency regarding their environmental impact.
4. Innovation and Development: Investing in technology and innovation is essential to the development of sustainable practices, products, and services. The utilization of big data can facilitate the identification of ripe areas for innovation and aid in constructing models to predict future sustainability trends.
5. Integrating Big Data with Green Practices: Businesses should prioritize the integration of Big Data Analytics with green supply chain management and sustainability practices. This strategy not only enhances operational efficiency and environmental performance but also ensures a positive financial impact (Zhu & Yang, 2021).
6. Enhance Data-Driven Decision-Making: Organizations must develop robust data-driven decision-making capabilities to enhance competitive sustainability and improve corporate performance. Fostering a data-driven culture will facilitate the utilization of these technologies for sustainable advantages (Xu et al., 2024).
7. Adopting IoT and BDA in supply chains: The integration of Things (IoT) devices and Big Data Analytics (BDA) within the supply chain can significantly enhance transparency, security, and operational trust, which are essential for attaining sustainable manufacturing objectives (Raj et al., 2023).

For Policymakers:

1. Regulatory Frameworks: Formulate and implement regulations that promote or require the utilization of big data analytics to foster sustainable practices within organizations. Such policies should emphasize accountability and transparency in reporting sustainability metrics.
2. Support and Incentives: Offer support and financial incentives to enterprises that actively employ data analytics to attain sustainability objectives, such as tax benefits or grants for technological advancement.
3. Infrastructure Development: Enhance infrastructure developments that facilitate big data analytics, including the provision of high-speed Internet access and the promotion of digital transformation across various regions, thereby enabling businesses to incorporate sustainability into their operational frameworks.
4. Collaboration and Partnership: Promote collaboration between the public and private sectors to advance innovation in big data analytics, thereby enhancing the effectiveness of sustainable development strategies.
5. Support Data Integration Initiatives: Policymakers are encouraged to support initiatives that facilitate the integration of big data and green technologies. Such support can enhance the efficiency of smart grids and manufacturing, thereby contributing to the achievement of sustainable development goals (Ponnusamy et al., 2021; Raj et al., 2023).
6. Promote Education and Training: Educational institutions must develop and offer programs focused on big data and analytics. Such initiatives are essential to address the labour market gap and equip a workforce capable of supporting sustainable practices across various sectors (Espinosa & Armour, 2016).
7. Facilitate Collaboration: Facilitate collaboration among stakeholders, including businesses and academic institutions, to advance research and the application of big data analytics in achieving sustainability across various industries (Harfouche et al., 2024).

For Academicians:

1. Research and Development: Scholars should prioritize investigating methodologies to optimize the utilization of big data in advancing sustainable business practices. This endeavour encompasses the development of novel algorithms and models for the comprehensive analysis of sustainability data.
2. Curriculum Integration: Incorporating big data analytics and sustainability into educational curricula is essential to prepare future professionals with the competencies required to tackle environmental challenges through data-driven methodologies.
3. Interdisciplinary Collaboration: Engage in interdisciplinary collaboration to harness diverse expertise in addressing complex sustainability challenges through big data analytics. Such collaborations can promote the development of innovative solutions that incorporate multiple perspectives.
4. Dissemination of Best Practices: Conducting a comprehensive investigation and disseminating findings on the effective utilization of big data analytics in promoting sustainable practices and providing evidence-based recommendations for industry and policy implementation.
5. Research on Data Governance and Coordination: Scholars should investigate frameworks for the effective governance and coordination of big data analytics within organizations to enhance its impact on sustainability (Espinosa & Armour, 2016).
6. Cross-disciplinary Studies: Engage in interdisciplinary research to enhance the understanding of the socio-technical aspects of sustainability, as facilitated by big data analytics, and inform policymakers and businesses about best practices (Harfouche et al., 2024).
7. Evaluate BDA Impacts on Various Sectors: Conduct research to assess the effects of Big Data Analytics (BDA) across various sectors, including e-commerce, manufacturing, and energy, to gain comprehensive insights into sustainability practices (Gangwar et al., 2022).

The recommendations underscore the transformative potential of big data analytics in advancing sustainable practices among various stakeholder groups within India.

Findings of the Study:

Based on the provided text, the key findings of the study on the impact of big data analytics on sustainable business practices in India are as follows:

Big Data Analytics Enhances Sustainability Across Various Sectors in India:

• Improves the performance of small and medium enterprises (SMEs)
• Aids smart city development and urban planning
• Enhances precision farming and agricultural practices
• Optimizes supply chains, especially in e-commerce
• Fosters innovation and competitive advantages for firms
• Promotes corporate sustainability when integrated with blockchain

Sector-Specific Applications:

• Healthcare: Improves patient outcomes, diagnostics, and operational efficiency
• Finance: Enhances fraud detection, risk management, and financial analysis
• Agriculture: Enables sustainable farming through crop monitoring and yield prediction
• Retail: Improves inventory management and customer analysis
• Manufacturing: Enhances quality control and predictive maintenance
• Energy: Improves efficiency and integration of renewable sources
• Education: Enables personalized learning experiences

Environmental Sustainability Impacts:

• Optimizes energy production and consumption
• Enhances agricultural productivity and sustainability
• Reduces supply chain risks and improves operational practices in manufacturing and logistics

Social Sustainability Impacts:

• Improves healthcare services and outcomes
• Promotes financial inclusion
• Enhances supply chain transparency and mitigates social risks

Economic Sustainability Impacts:

• Increases operational efficiency across sectors
• Improves risk management capabilities
• Enables new sustainable business models and competitive advantages

Key Challenges Identified:

• Lack of advanced technologies and frameworks for big data analytics
• Complexity of managing and analysing big data
• Limited availability of analytics platforms
• Infrastructure development needs
• Data security and privacy concerns
• Difficulty in adoption, especially for SMEs
• Need for modern management practices

The study highlights the transformative potential of big data analytics in advancing sustainable business practices in India, while also noting significant challenges that need to be addressed for widespread adoption and impact.

CONCLUSION

The present study investigates the substantial influence of big data analytics on sustainable business practices in India. The findings indicate that big data analytics serves as a transformative instrument across diverse sectors, augmenting sustainability initiatives in domains such as energy management, agriculture, manufacturing, healthcare, and financial services. The integration of big data analytics has been demonstrated to enhance operational efficiency, risk management, and decision-making processes, thereby facilitating more sustainable outcomes. In the Indian context, big data analytics has shown particular efficacy in improving the performance of small and medium-sized enterprises (SMEs), supporting the development of smart cities, optimizing supply chains, and fostering innovation for competitive advantage. Nevertheless, the study also identifies several challenges in the widespread adoption and effective utilization of big data analytics for sustainability. These challenges include technological limitations, the absence of structured frameworks, data complexity, infrastructure deficiencies, and concerns regarding data security and privacy. To address these challenges and fully exploit the potential of big data analytics for sustainable business practices, a multi-stakeholder approach is imperative. Businesses should prioritize the adoption of advanced analytics, integration of data-driven decision-making, and alignment of big data initiatives with environmentally sustainable practices. Policymakers are urged to develop supportive regulatory frameworks, provide incentives, and invest in the digital infrastructure. The academic community plays a crucial role in advancing research, developing curricula, and fostering interdisciplinary collaboration. In conclusion, big data analytics offers a significant opportunity for Indian businesses to enhance their sustainability practices while maintaining competitiveness. As technology continues to evolve and data availability increases, the potential of big data analytics to drive sustainable development in India is likely to expand. Future research should concentrate on sector-specific applications, long-term impacts, and strategies to overcome barriers to adoption, particularly for SMEs.

REFERENCES

1. Aderemi, S., Nnaomah, U., Babatunde, S., Orieno, O., Olutimehin, D., & Edunjobi, T. (2024). Big Data Analytics in the Financial Services Industry: Trends, Challenges, And Future Prospects: A Review. International Journal of Science and Technology Research Archive, 6(1), 147–166. https://doi.org/10.53771/ijstra.2024.6.1.0036
2. Ali, Q., Abdullah, R., Yaacob, H., Salman, A., & Zaini, Z. (2020). Does Big Data Analytics Enhance Sustainability and Financial Performance? The Case of ASEAN Banks. The Journal of Asian Finance, Economics and Business, 7(7), 1–13. https://doi.org/10.13106/jafeb.2020.vol7.no7.001
3. Allahham, M., Ahmad, A. Y. B., Sabra, S., Hatamlah, H., Daoud, M. K., & Sharabati, A.-A. A. (2023). Big Data Analytics and AI for Green Supply Chain Integration and Sustainability in Hospitals. WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT, 19, 1218–1230. https://doi.org/10.37394/232015.2023.19.111
4. Alzboun, N. M. (2023). Big Data Analytics Capabilities and Supply Chain Sustainability: Evidence from the Hospitality Industry. Uncertain Supply Chain Management, 11, 1427-1432.
5. Asiri, A. M., Maghrabi, R. O., & Al-Somali, S. A. (2024). The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance. Sustainability, 16(8), 3209. https://doi.org/10.3390/su16083209
6. Bag, S., Wood, L., & Gupta, S. (2020). Big Data Analytics in Sustainable Humanitarian Supply Chain: Barriers and Their Interactions. Annals of Operations Research, 319(1), 721–760. https://doi.org/10.1007/s10479-020-03790-7
7. Espinosa, J. A., & Armour, F. (2016). The Big Data Analytics Gold Rush: A Research Framework for Coordination and Governance. 20, 1112–1121. https://doi.org/10.1109/hicss.2016.141
8. Fatima, S. A., Nasim, S., & Ahmed, S. (2023). Enhancing Agricultural Operations: Big Data Analytics Using Distributed and Parallel Computing. International Journal of Emerging Engineering and Technology, 2(2), 1–7. https://doi.org/10.57041/v3jj9f69
9. Gangwar, H., Mishra, R., & Kamble, S. (2022). Adoption of Big Data Analytics Practices for Sustainability Development in the E-Commercesupply Chain: A Mixed-Method Study. International Journal of Quality & Reliability Management, 40(4), 965–989. https://doi.org/10.1108/ijqrm-07-2021-0224
10. Gholami, H., Lee, J. K. Y., & Ali, A. (2023). Big Data Analytics for Sustainable Products: A State-of-the-Art Review and Analysis. Sustainability, 15(17), 12758. https://doi.org/10.3390/su151712758
11. Gopal, P. R. C., Rana, N. P., Krishna, T. V., & Ramkumar, M. (2022). Impact of Big Data Analytics on Supply Chain Performance: An Analysis of Influencing Factors. Annals of Operations Research, 333(2–3), 769–797. https://doi.org/10.1007/s10479-022-04749-6
12. Hadi, H. M., & Zeebaree, S. R. M. (2025). Data-Driven Decision Making for E-Business Success: A Review. Asian Journal of Research in Computer Science, 18(4), 209–236. https://doi.org/10.9734/ajrcos/2025/v18i4616
13. Hammerström, L., Giebe, C., & Zwerenz, D. (2019). Influence of Big Data & Analytics on Corporate Social Responsibility. Socio Economic Challenges, 3(3), 47-60. https://doi.org/10.21272/sec.3(3).47-60.2019
14. Harfouche, A., Albizri, A., Merhi, M. I., Dennehy, D., & Thatcher, J. B. (2024). Sustainable Development Through Technological Innovations and Data Analytics. Information Systems Frontiers, 26(6), 1989–1996. https://doi.org/10.1007/s10796-024-10570-2
15. Hussien, L. F., Alshaketheep, K., Al-Ahmed, H., Shajrawi, A., Alghizzawi, M., Zraqat, O., & Deeb, A. (2025). The Impact of Big Data Analytics on the Sustainability Reports Quality. Discover Sustainability, 6(1), 1-21.
16. Kgakatsi, M., Galeboe, O. P., Molelekwa, K. K., & Thango, B. A. (2024). The Impact of Big Data on SME Performance: A Systematic Review. Businesses, 4, 632–695.
17. Khan, M. A., Siddiqui, M. S., Rahmani, M. K. I., & Husain, S. (2022). Investigation of Big Data Analytics for Sustainable Smart City Development: An Emerging Country. IEEE Access, 10, 16028–16036. https://doi.org/10.1109/access.2021.3115987
18. Kumar, D., Singh, R. K., Mishra, R., & Vlachos, I. (2023). Big Data Analytics in Supply Chain Decarbonisation: A Systematic Literature Review and Future Research Directions. International Journal of Production Research, ahead-of-print(ahead-of-print), 1489–1509. https://doi.org/10.1080/00207543.2023.2179346
19. Kumar, K., Kumar, R., & Kumari, R. (2021). The State of Corporate Sustainability Reporting in India: Evidence from Environmentally Sensitive Industries. Business and Society Review, 126(4), 513–538. https://doi.org/10.1111/basr.12247
20. Leal de Paula, I. R., et al. (2024). Big Data Analytical Capabilities and Sustainable Information Quality in An Emerging Economy: Evidence from Brazil. Procedia Computer Science, 232, 2098–2107. https://doi.org/10.1016/j.procs.2024.02.030
21. Mahmood, Q.-U.-A., Ahmed, R., & Philbin, S. P. (2022). The Moderating Effect of Big Data Analytics on Green Human Resource Management and Organizational Performance. International Journal of Management Science and Engineering Management, ahead-of-print(ahead-of-print), 177–189. https://doi.org/10.1080/17509653.2022.2043197
22. Mangla, S. K., Priyadarshinee, P., Zhang, Z. (Justin), Raut, R., & Narwane, V. S. (2020). Mediating Effect of Big Data Analytics on Project Performance of Small and Medium Enterprises. Journal of Enterprise Information Management, 34(1), 168–198. https://doi.org/10.1108/jeim-12-2019-0394
23. Mani, V., Patel, P., Hazen, B., & Delgado, C. (2017). Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain. Sustainability, 9(4), 608. https://doi.org/10.3390/su9040608
24. Mesquita, L. L., Lizarelli, F. L., & Duarte, S. (2023). Big Data Analytics and Lean Practices: Impact on Sustainability Performance. Production Planning & Control, 36(3), 1–24. https://doi.org/10.1080/09537287.2023.2267512
25. Nilashi, M., Boon, O. K., Tan, G., Lin, B., & Abumalloh, R. (2023). Critical Data Challenges in Measuring the Performance of Sustainable Development Goals: Solutions and the Role of Big-Data Analytics. Harvard Data Science Review, 5(3), 1-36. https://doi.org/10.1162/99608f92.545db2cf
26. Nithya, S. M., Kusuma, M., & Murugaiah, V. (2020). Importance of Big Data and Data Analytics in E-Commerce in India. OIDA International Journal of Sustainable Development, 13(12), 33-46.
27. Nwobodo, L., Nwaimo, C., & Adegbola, M. (2024). Strategic Financial Decision-Making in Sustainable Energy Investments: Leveraging Big Data for Maximum Impact. International Journal of Management & Entrepreneurship Research, 6(6), 1982–1996. https://doi.org/10.51594/ijmer.v6i6.1238
28. Ohlhorst, F. (2013). Big Data Analytics: Turning Big Data into Big Money. Choice Reviews Online, 50(09), 50–5095. https://doi.org/10.5860/choice.50-5095
29. Olaniyi, O. O., Abalaka, A. I., & Olabanji, S. O. (2023). Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company. Journal of Scientific Research and Reports, 29(9), 64–72. https://doi.org/10.9734/jsrr/2023/v29i91785
30. Pappas, I. O., Giannakos, M. N., Mikalef, P., Lekakos, G., & Krogstie, J. (2018). Big Data and Business Analytics Ecosystems: Paving the Way Towards Digital Transformation and Sustainable Societies. Information Systems and E-Business Management, 16(3), 479–491. https://doi.org/10.1007/s10257-018-0377-z
31. Ponnusamy, V. K., Subramaniam, U., Anandan, R. K., Ramanathan, V., Madurai Elavarasan, R., Kasinathan, P., Ghosh, A., & Hossain, E. (2021). A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid. Sustainability, 13(23), 13322. https://doi.org/10.3390/su132313322
32. Qaffas, A. A., Almazmomi, N. K., Alharbi, I. M., & Ilmudeen, A. (2022). The Impact of Big Data Analytics Talent Capability on Business Intelligence Infrastructure to Achieve Firm Performance. Foresight, 25(3), 448–464. https://doi.org/10.1108/fs-01-2021-0002
33. Raj, R., Kumar, V., & Shah, B. (2023). Big Data Analytics Adaptive Prospects in Sustainable Manufacturing Supply Chain. Benchmarking: An International Journal, 31(9), 3373–3397. https://doi.org/10.1108/bij-11-2022-0690
34. Ramadan, M., Asmar, H., Salah, B., Qtaishat, L., & Shuqqo, H. (2020). Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation. Applied Sciences, 10(19), 6784. https://doi.org/10.3390/app10196784
35. Riggs, R., Roldán, J. L., Real, J. C., & Felipe, C. M. (2023). Opening the Black Box of Big Data Sustainable Value Creation: The Mediating Role of Supply Chain Management Capabilities and Circular Economy Practices. International Journal of Physical Distribution & Logistics Management, 53(7/8), 762–788. https://doi.org/10.1108/ijpdlm-03-2022-0098
36. Sharma, H. (2024). Exploring The Role of Business Analytics in Corporate Sustainability: Measuring and Reporting Environmental Social Impact. International Journal for Multipledisciplinary Research (IJFMR), 6(6), 1-17.
37. Singh, S., Pandey, V., & Holvoet, N. (2018). Bridging Sustainability and Corporate Social Responsibility: Culture of Monitoring and Evaluation of CSR Initiatives in India. Sustainability, 10(7), 2353. https://doi.org/10.3390/su10072353
38. Wu, M., Huang, Y., Sarker, M. N. I., & Yan, B. (2022). Big Data-Driven Urban Management: Potential for Urban Sustainability. Land, 11(5), 680. https://doi.org/10.3390/land11050680
39. Xu, Y., Sun, J., Sarfraz, M., Ozturk, I., & Ivascu, L. (2024). Advancing Corporate Sustainability Via Big Data Analytics, Blockchain Innovation, And Organizational Dynamics—A Cross‐Validated Predictive Approach. Business Strategy and the Environment, 34(1), 1399–1418. https://doi.org/10.1002/bse.4056
40. Zhu, X., & Yang, Y. (2021). Big Data Analytics for Improving Financial Performance and Sustainability. Journal of Systems Science and Information, 9(2), 175–191. https://doi.org/10.21078/jssi-2021-175-17
41. Ziegler, H., Jenny, M., Keim, D. A., & Gruse, T. (2010). Visual market sector analysis for financial time series data. 1, 83–90. https://doi.org/10.1109/vast.2010.5652530