2.1 BDA and Sustainability

The relationship between BDA and sustainability is grounded in a set of organizational and technological theories that help explain the impacts, challenges, and opportunities arising from the adoption of these technologies across different sectors. The Triple Bottom Line Theory (Elkington 1997) forms a foundational framework by emphasizing the interdependence of economic, social, and environmental performance, a critical lens for assessing BDA's contributions to sustainability. The Stakeholder Theory (Freeman 1984) complements this by highlighting the role of diverse interest groups whose influence and engagement are amplified through data-driven transparency and accountability. In turn, Organizational Paradox Theory (Hahn et al. 2015) provides insights into the tensions between competing goals, such as profitability versus environmental impact, that often arise when implementing sustainable technologies.

The Information Systems Success Model (DeLone and McLean 2003) offers a lens to evaluate the effectiveness of BDA systems by considering dimensions like information quality, system use, and user satisfaction. Lastly, Digital Transformation Theory (Vial 2019) helps explain how digital technologies like BDA are embedded into organizational capabilities to foster innovation and long-term strategic alignment with sustainability objectives. These theoretical foundations provide conceptual support for interpreting the findings of this SLR and for developing a robust understanding of BDA's role in advancing sustainability. Building on these theoretical foundations, the next sections explore how BDA has been applied to sustainability challenges across sectors and contexts.

The worldwide popularization of digital media and technological advances produces a constant overload of data. And, although this spread of information is beneficial for the dissemination of knowledge, it also causes challenges in data management and analysis. BDA has its origins in the early 2000s (Mucci and Stryker 2024), with the aim of systematically processing and analyzing these large data sets, using advanced machine learning and data mining techniques (Mucci and Stryker 2024), allowing them to be transformed into productive and usable information and supporting data-based decision making (Maltby 2011; Mucci and Stryker 2024), by private and public managers. IBM defines four data analysis methods used in BDA: predictive analysis, statistical analysis, what-if analysis, and processing of diverse data sets (Mucci and Stryker 2024).

In the sustainability axis, BDA has been used in several sectors: in the promotion of smart cities (Al-Badi and Khan 2022; Allam and Dhunny 2019; Bibri 2018), in supply chain management (Kamble et al. 2020; Papadopoulos et al. 2017) and in agribusiness (Kamble et al. 2020; Saiz-Rubio and Rovira-Más 2020), for example. In the corporate scenario, Tamym et al. (2023) emphasize the BDA's ability to promote competitive advantage, as it helps sustainable organizations to enhance their businesses and achieve the SDGs of the UN 2030 Agenda. More than that, its convergence with sustainability is necessary to achieve economic growth (Ma et al. 2024).

In the corporate sector, the value of sustainability has gained more attention in recent years (Shaik et al. 2024). When BDA is well enhanced by business intelligence, it allows companies to effectively meet ecological demands and achieve superior economic, social, and environmental performance (Cheng et al. 2023). Furthermore, according to the authors, the business vision of the relationship between intelligence and BDA capacity highlights its positive impact on sustainability performance. Its use enhances businesses of different sizes and sectors (Tamym et al. 2023); small companies, for example, can collect and analyze a large amount of data about their economic and environmental performance using big data, which helps them identify areas of improvement and maximize their sustainability initiatives (Shaik et al. 2024).

However, while companies will actively adopt green external integration, internal integration will experience difficulties and will be driven mainly by social pressure (Mahdiraji, Sadabadi, and Altuntaş 2022; Mahdiraji, Yaftiyan, et al. 2022). After all, the long-term viability of small businesses is increasingly recognized as dependent on environmental and economic sustainability (Shaik et al. 2024). In this way, relationships between internal members, stakeholders, and also intellectual property rights are fundamental to driving innovation aimed at sustainability (Wang et al. 2023). Therefore, it is essential to integrate internal resources and the external environment in this process (Feng and Sheng 2023a).

Small businesses are more likely to achieve high levels of stakeholder satisfaction when they prioritize sustainable activities and use BDA to monitor and improve their environmental and economic performance (Shaik et al. 2024). As a result, big data technology is recognized as the main driver in building smart networks (Tu et al. 2017). Therefore, to promote sustainable business, it is urgent to emphasize various approaches that improve the integration of the green supply chain and promote the interaction between multiple influencing factors (Feng and Sheng 2023a). As an example, Kamble et al. (2020) identified that a data-driven agri-food supply chain seeks to achieve social and environmental objectives, in addition to sustainable economic results, and Taddei et al. (2024) corroborate by pointing out that a chain requires return flows capable of capturing additional value and involving different stakeholders.

Corroborating the subject, Saiz-Rubio and Rovira-Más (2020) state that this is the ideal time to move toward modern and sustainable agriculture, capable of demonstrating the full potential of data-driven management to face the challenges of food production in the 21st century. To this end, managers can use BDA, for example, to identify equipment with high energy consumption and implement measures to increase energy efficiency, while simultaneously minimizing waste generation based on energy reference parameters (Ma et al. 2024). Thus, off-road equipment manufacturers play a key role in this movement, especially if agricultural robots are considered the next generation of smarter agricultural machines (Saiz-Rubio and Rovira-Más 2020).

Furthermore, the big data component, driven by the IoT and popular in smart cities, raises concerns that need to be addressed to ensure the increased popularity and adoption rate of this emerging concept (Allam and Dhunny 2019). The potential of big data analysis lies in enabling smart cities to use their informational landscape, integrating and combining related ecosystems to improve processes, projects, and services in accordance with the vision of environmental sustainability (Bibri 2018). This big data technology acts as a facilitator of predictive variables in the context of sustainable development objectives (Al-Badi and Khan 2022) and its applications will play an essential role in understanding the main environmental characteristics of smart and sustainable cities (Bibri 2018).

2.2 Sustainability Tensions

Organizations face internal conflicts in social, environmental, and economic dimensions, needing to balance apparently contradictory objectives, such as profitability, social responsibility, and environmental protection (Hahn et al. 2015). To facilitate decision-making, Elkington (1997) created the Triple Bottom Line model, which aims to map economic, social, and environmental issues as an initial concept of the integrative vision. In the emerging integrative view on corporate sustainability, companies need to accept these tensions and simultaneously balance different aspects of sustainability (Hahn et al. 2015).

In particular, in developing countries, coping with sustainability tensions requires solutions that balance technical functionality and business model relevance. Levänen et al. (2022) emphasize that aligning technical innovations with locally appropriate business models, especially in circular economy contexts, is essential for managing the competing demands of economic, social, and environmental sustainability. This perspective highlights the importance of context-specific strategies and organizational adaptability in addressing complex sustainability challenges.

As a result, tensions in sustainability are multifaceted and complex, resulting from the integration of individual and corporate decisions within a broader organizational and systemic context (Hahn et al. 2015). The study by Ozanne, Ozanne, and Trujillo (2016) and Ozanne, Phipps, et al. (2016) indicates that public policies play a fundamental role in highlighting tensions in organizations that seek the triple bottom line. These tensions may involve issues related to the level of analysis, the necessary changes, and the specific context of each company (Floyd et al. 2024).

Understanding and managing sustainability tensions is essential to effectively promote corporate sustainability and align business practices with its fundamental principles (Hahn et al. 2015). In this sense, it is essential to map the tensions that organizations face and the success rate of the strategies adopted (Joseph et al. 2020). To this end, Hahn et al. (2015) proposed an integrative framework which relates different levels between which tensions can occur, outlining strategies for their resolution.

This balancing strategy allows organizations to reduce salient tensions and stimulate innovation opportunities that include reaching new markets, developing new products, and adopting new payment methods, among others (Ozanne, Ozanne, and Trujillo 2016; Ozanne, Phipps, et al. 2016). After all, these tensions can impact the entire value chain of companies, from suppliers to consumers, influencing production, distribution, and consumption practices in a sustainable way (Hahn et al. 2015). Thus, the ability to deal with the challenges of companies' competitiveness in the market is changing strategy and practices, as all sectors must be able to find a balance between technological challenges, sustainability demands, and digitalization opportunities (Floyd et al. 2024).

Despite the growing interest in the intersection of BDA and sustainability, several research gaps remain. First, certain sectors, such as healthcare, local government, and retail, are still underexplored in terms of BDA applications. Second, the literature shows a clear dominance of studies conducted in developed countries, especially in Europe and North America, with limited attention to emerging economies, where challenges related to infrastructure, regulation, and organizational culture may differ. Third, there is a methodological gap, as most of the selected studies adopt cross-sectional and quantitative approaches, with little longitudinal or qualitative research that explores long-term dynamics or stakeholder perspectives. Addressing these gaps offers a rich opportunity for advancing the theoretical and practical understanding of how BDA can contribute to sustainable development.

4.1 Characterization of the Profile of the Journals Researched

The final sample of the 26 journals analyzed in this study is found in Tables 3 and 4. The relevance of the research is highlighted, of which 65% were published in the last four years.

As shown in Table 3, approximately 70% of the publications in this SLR are concentrated in two journals. The most significant of them is in the area of technology, which is not surprising, considering the fact that the theme of this study emerges from the area. Despite this, the aforementioned journal publishes, above all, research in the technological area and its interconnection with social impacts.

According to Table 4, the current nature of the research is evident, considering that most of the publications occurred from 2019 onwards. Of the publications from the last year ended (2023), 66.6% of them were published in the journal Business Strategy and the Environment. Also noteworthy is the journal Technological Forecasting & Social Change, which contains all the 2019 publications from this sample. Regarding the demographic application of the studies, there is a significantly heterogeneous sample, with emphasis on Europe (34, 62%), India and the United States (15.38%). Some research did not use any specific theory as a basis. Of the total, 38.46% adopted one of the following theories: Organizational Learning; Resource-Based View (RBV); Stakeholder Theory; Dynamic capabilities; Green innovation.

4.2 How to Use BDA to Improve Sustainability

Table 5 presents the different uses of BDA, indicated in each of the 26 articles in the sample analyzed in this research.

After individually tabulating the surveys, it was possible to identify that the industrial (34.62%) and energy (19.23%) sectors make up the largest sample of surveys. Also, most BDA practices or theories focused on the supply chain (26.92%); renewable energy/energy sector (23.08%); and in the circular economy (15.38%). Sustainable cities and/or urban mobility themes were also the subject of research (11.54%). As seen in Table 5, some articles address multiple uses of BDA, especially those that adopted the review methodology (A3, A4, A6, A13) and bibliometric analysis (A25). Article 6 is a special volume focused on finding effective ways to achieve sustainable development, for this reason it addressed different ways of using BDA in sustainability. Article 13 used BDA in practice, when analyzing articles from the magazine Business Strategy and the Environment, similarly article 25 carries out a bibliometric analysis of academic works that researched social business. The focus of the article was technological intervention, including BDA, in sustainable social businesses.

The main organizational applications of BDA are: selecting socially and environmentally correct suppliers raised through the integration of BDA and supply chain management, optimizing logistics processes and reducing costs (AlNuaimi et al. 2021; Bag et al. 2023; Dubey et al. 2017; Gupta et al. 2019); mitigate impacts generated by COVID-19 on various supply chain activities (Belhadi et al. 2021); adding value to stakeholders and increasing corporate image (Bag et al. 2023; Zhou et al. 2016); continuous improvement within the scope of the circular economy (Gupta et al. 2019; Kristoffersen et al. 2021; Luoma et al. 2022; Schöggl, Rusch, et al. 2023; Schöggl, Stumpf, and Baumgartner 2023); making cities more sustainable and improving urban mobility (Del Vecchio et al. 2019; Soni et al. 2021; Zhu et al. 2022); improving industrial or urban energy efficiency (Chalvatzis et al. 2019; Schuelke-Leech et al. 2015; Tu et al. 2017; Zhou et al. 2016); and reducing costs in implementing sustainability in the energy sector (Teng et al. 2021).

What seems to be a consensus is that BDA is substantially useful in enhancing assertive decision-making (AlNuaimi et al. 2021; Bag et al. 2023; Cappa et al. 2021; Feng and Sheng 2023a; Kristoffersen et al. 2021; Soni et al. 2021; Teng et al. 2021; Wang et al. 2023; Zhou et al. 2016) and identify sustainable patterns and trends (Feng and Sheng 2023a; Zhou et al. 2016) minimizing the possibility of implementing failed strategies. Ultimately, research has shown that the integration of BDA in public and private organizations promotes advances in social well-being and sustainable development (Bag et al. 2023; Cappa et al. 2021; Wang et al. 2023).

Additionally, some research has focused on digitalization strategies and tools integrated into the BDA as a way of promoting sustainability (Ardito 2023). Schöggl, Rusch, et al. (2023) and Schöggl, Stumpf, and Baumgartner (2023) identified BDA as the second most used digital technology in the circular economy strategy in Austrian industries. Zhou et al. (2016) recognize benefits of using BDA also for public management, by promoting energy efficiency, managing energy demand and consumption in public buildings, for example. Urban planning and intelligent promotion of urban mobility were also observed in research (Del Vecchio et al. 2019; Zhu et al. 2022), favoring sustainable urban development (Cappa et al. 2021). At the business level, the main focus was on the supply chain, on monitoring and selecting sustainable suppliers and on the positive impacts that these measures can generate for stakeholders (AlNuaimi et al. 2021; Bag et al. 2023; Gupta et al. 2019; Feng and Sheng 2023a).

Although the multiple studies relating to energy efficiency from the use of BDA, Schuelke-Leech et al. (2015) highlight that it is unlikely that the public service will fully adopt BDA for the energy sector, considering that energy utilities are strongly concerned with financial results, which makes conversations about data with this type of company difficult. Finally, it is worth highlighting the demographic and cultural context of the research, carried out mostly in countries with consolidated economies, but not free from challenges and barriers raised to the adoption and practice of BDA.

Beyond the descriptive mapping of BDA applications by sector, it was possible to identify recurring strategic patterns across the analyzed studies. In terms of sectoral readiness, industries such as manufacturing, energy, and supply chains have shown the most advanced integration of BDA for sustainability. These sectors tend to benefit from clear data flows, operational processes that are easily digitized, and a regulatory environment that encourages sustainable practices.

Regarding common barriers, three key challenges emerged: (1) lack of technical infrastructure and data governance; (2) shortage of qualified professionals with BDA and sustainability expertise; and (3) ethical concerns related to data use and transparency, including risks of greenwashing or privacy violations. These barriers were especially salient in sectors that rely heavily on stakeholder engagement or operate under strict compliance conditions.

On the other hand, several enabling factors were identified. These include institutional support (e.g., regulatory incentives, public policies), organizational culture oriented toward innovation, the presence of cross-functional teams, and the existence of clear sustainability goals aligned with BDA initiatives. Organizations that managed to integrate these enablers demonstrated greater success in using data strategically to support sustainable development objectives.

By highlighting these patterns, the study provides a more nuanced understanding of the dynamics surrounding the adoption of BDA in sustainability efforts. These insights can guide managers in anticipating potential bottlenecks and leveraging facilitators to design more effective, data-driven sustainability strategies. Table 6 summarizes the key sectoral patterns, common barriers, and enabling factors for the adoption of BDA in sustainability initiatives across the reviewed studies.

As shown in Table 6, manufacturing, energy, and supply chain contexts have demonstrated the highest readiness for BDA-driven sustainability efforts, leveraging established data infrastructures and clear operational processes. However, organizations frequently encounter obstacles such as insufficient data governance frameworks, a shortage of professionals skilled in both analytics and sustainability, and ethical concerns around data transparency. Conversely, institutions that benefit from supportive public policies, foster an innovationoriented culture, and deploy crossfunctional teams with explicit sustainability targets are better positioned to overcome these challenges and achieve strategic outcomes—ranging from enhanced operational efficiency to stronger stakeholder trust. Together, these insights offer a concise roadmap for practitioners aiming to align BDA investments with SDGs.

4.3 Sustainability Tensions That Emerged

Technological challenges were the most frequent in the article, such as data storage capacity (Cappa et al. 2021; Tu et al. 2017; Zhou et al. 2016), data security, and data management. Cybercrimes (Bag et al. 2023; Cappa et al. 2021; Tu et al. 2017). Parallel to this, data visualization technologies and the insights they can generate are also among the challenges observed (Tu et al. 2017; Zhou et al. 2016).

Other findings suggest that managing green internal integration is another organizational challenge and one that will require strategic alignment and internal awareness. What the autores Feng and Sheng (2023a) called training environmental officials. Table 7 presents the mapping of tensions found in each of the 26 sample articles analyzed in this research, and supported by Hahn et al. (2015).

Table 7 shows the mapping of tensions in the studies that make up this SLR, according to Hahn et al. (2015). Tensions are subdivided into Normative (15.38%), Descriptive (11.54%) and Instrumental (73.08%) levels. This means that the vast majority of studies value the use of sustainability management tools and techniques (instrumental), to the detriment of defining policies (normative) or evaluating sustainable impacts (descriptive). While the Individual (0.00%), Business (69.23%) and Systemic (30.77%) levels show a massive behavior of studies by business practices or strategies. Significantly also, collectivist practices, starting from public policies, gain prominence (systemic). Therefore, there was no collection at the individual level.

In the Economic (30.79%), Social (0.00%), and Environmental (42.31%) dimensions, presented by John Elkington (Elkington 1997), through the Triple Bottom Line, we highlight that seven studies used the policy multidimensional. The use of the three dimensions concomitantly occurred in 15.38% of the studies, while the joint use of the economic and environmental, economic and social, and social and environmental dimensions represents 3.84% each. This indicates a homogeneous distribution between the multidimensional aspect (26.90%) and the economic and environmental dimensions, indicating that companies spend greater efforts on the economic-sustainable dimension. Furthermore, in the context of the studies, the massive majority (84.62%) presented a temporal concern, evaluating the impacts of BDA and sustainability over the years, in the short, medium, and long term. Only 15.38% of studies evaluate these impacts geographically (Spatial).

The predominance of systemic-level tensions observed in the reviewed studies may be explained by the institutional and regulatory complexity surrounding sustainability practices. Most studies focus on policy environments, inter-organizational networks, and macro-level sustainability challenges, which naturally emphasize systemic factors over individual behaviors. This pattern also reflects a collective perspective on sustainability, where the role of government policies, stakeholder coalitions, and market regulations is more salient than individual decision-making. Additionally, the lack of individual-level analysis may stem from methodological limitations, as many studies adopt cross-sectional or organizational-level designs, with limited exploration of individual actors such as employees, managers, or consumers. These findings suggest an opportunity for future research to investigate the micro-level dynamics of sustainability-related tensions, especially the role of personal values, leadership styles, and ethical dilemmas in implementing data-driven sustainability initiatives.

The theoretical frameworks presented in the literature review provide valuable lenses for interpreting the findings of this study. For example, the Triple Bottom Line theory (Elkington 1997) is reflected in the distribution of sustainability practices across environmental, economic, and, less frequently, social dimensions. Many of the reviewed studies emphasized economic efficiency and environmental impact, often neglecting the social dimension, thereby confirming the inherent tension in balancing all three pillars simultaneously.

Stakeholder Theory (Freeman 1984) also underpins the emphasis on reputation, transparency, and stakeholder engagement as key outcomes of BDA adoption. This is particularly evident in studies addressing sustainable supply chains, where BDA is used to enhance visibility and accountability across networks.

The RBV is supported by findings that highlight the role of BDA capabilities, such as advanced analytics, real-time monitoring, and data integration—as strategic resources that can lead to competitive advantage and innovation. Organizations with strong digital infrastructure and qualified professionals tend to extract more value from BDA, aligning with RBV principles.

Finally, Organizational Paradox Theory (Hahn et al. 2015) is particularly relevant in explaining how companies manage the tensions between short-term profitability and long-term sustainability goals. These paradoxes are evident in decisions involving trade-offs between efficiency, ethics, and stakeholder expectations. By interpreting the results through these theoretical lenses, this study deepens the understanding of how BDA enables, constrains, or transforms organizational responses to sustainability challenges.

4.4 Managerial Implications of the Findings

Studies suggest preventing greenwashing and the reproduction of false information through the transparency gained with a robust BDA system (Bag et al. 2023). Increased business trust from stakeholders and customers themselves (Bag et al. 2023; Kristoffersen et al. 2021), improving organizational performance (AlNuaimi et al. 2021; Mehmood et al. 2023). Luoma et al. (2022, 13) also support the importance of data analysis in promoting a more effective circular economy: “The more ambitious the circularity objectives, the more vital the role of data will become.”

And not only in the external image of the organization that BDA positively implies, but also in the internal credibility, especially of employees when they believe in the transparency and sharing of data and organizational knowledge (Wang et al. 2023). Furthermore, the implications provide credibility for business and improve practices for exploring organizational knowledge.

Beyond the thematic synthesis, this review reveals notable sectoral and geographic distinctions in the adoption of BDA for sustainability.

Sectorally, industries such as manufacturing, agriculture, and energy stand out for their advanced integration of BDA into sustainability practices. These sectors benefit from structured data environments, clearer performance metrics, and regulatory pressures, factors that support more robust analytics implementation. In contrast, sectors like healthcare and public administration appear less frequently in the literature, despite their potential relevance to sustainability, especially in the social dimension. This suggests a gap in the diffusion of BDA across sectors and calls for further empirical studies focused on underexplored domains.

Geographically, the vast majority of studies are concentrated in high-income, developed countries (e.g., United States, United Kingdom, Germany, China), where digital infrastructure and institutional maturity enable broader use of analytics tools. Emerging economies, such as those in Latin America, Southeast Asia, and Africa, are significantly underrepresented. This disparity reflects not only differences in technological capacity but also in data governance, regulatory frameworks, and organizational culture. Moreover, sustainability challenges in these regions may be more pressing (e.g., deforestation, urban inequality), making the use of BDA especially critical.

These findings underscore the importance of contextualized strategies for BDA adoption, which consider the specific economic, institutional, and cultural characteristics of each region and sector. Addressing this diversity could lead to more equitable and effective implementation of sustainability analytics worldwide.

While common patterns emerge in the adoption of BDA across sectors, the literature also reveals conflicting results regarding its sustainability impacts and strategic value. For instance, some studies (e.g., Gupta et al. 2022; Feng and Sheng 2023a) emphasize that BDA adoption directly enhances environmental performance through real-time monitoring and optimization. However, other research (e.g., Mahdiraji, Sadabadi, and Altuntaş 2022; Mahdiraji, Yaftiyan, et al. 2022; Ozanne, Ozanne, and Trujillo 2016; Ozanne, Phipps, et al. 2016) highlights tensions where data initiatives may generate short-term efficiency gains but compromise long-term social equity or ethical transparency. Additionally, while digital capabilities are seen as key enablers in some regions, cultural or regulatory barriers in emerging economies often negate these benefits. These inconsistencies underscore the need for context-sensitive interpretations and raise important questions about when and under what conditions BDA serves as a driver, or a barrier, to sustainable development.

In addition to the geographic imbalance, the literature points to sector-specific barriers and enablers for BDA implementation in sustainability strategies.

For instance, in the agriculture sector, enablers include IoT infrastructure, satellite monitoring, and digitalized supply chains, whereas barriers relate to low data literacy and limited internet access in rural areas (e.g., Kamilaris et al. 2017).

In energy and manufacturing, common facilitators include strong regulatory incentives and embedded performance analytics, while barriers include cybersecurity risks and high system integration costs (Gupta et al. 2019; Feng and Sheng 2023a).

Conversely, in public administration and healthcare, where BDA has been less explored, barriers include institutional inertia, lack of qualified personnel, and complex privacy regulations (Ozanne, Ozanne, and Trujillo 2016; Ozanne, Phipps, et al. 2016).

These findings suggest that sector-specific strategies are crucial to overcoming structural obstacles and promoting the effective use of data in sustainability-oriented decision-making.

While the reviewed studies generally highlight the potential of BDA to support sustainability, conflicting findings also emerged regarding its effectiveness across different sectors and contexts. For example, in the manufacturing and energy industries, BDA was frequently associated with improved efficiency and measurable environmental gains due to mature data infrastructures and strong regulatory frameworks (e.g., Gupta et al. 2019; Feng and Sheng 2023a). In contrast, sectors such as healthcare and public administration reported limited success, often due to fragmented data systems, privacy concerns, and institutional inertia (Ozanne, Ozanne, and Trujillo 2016; Ozanne, Phipps, et al. 2016; Mahdiraji, Sadabadi, and Altuntaş 2022; Mahdiraji, Yaftiyan, et al. 2022). These contradictions suggest that sectoral readiness and governance capacity play a pivotal role in determining the success of BDA-driven sustainability initiatives. Among the barriers identified, the lack of data governance structures and the shortage of professionals skilled in both analytics and sustainability emerged as the most recurrent and consequential. However, the severity of these challenges varies by region: in emerging economies, structural limitations such as digital infrastructure and regulatory maturity tend to exacerbate these barriers, whereas in developed contexts, ethical issues and stakeholder resistance appear more prominent. This divergence underscores the importance of context-sensitive strategies for BDA implementation, reinforcing the need for tailored approaches that consider both sectoral dynamics and institutional conditions.

4.5 Practical and Theoretical Contributions

The literature reviewed highlights the growing use of advanced technologies, specifically BDA, in the management of private entities and in the formulation of public policies, all within the framework of sustainability. The constant accumulation of data, driven by recent technological advancements, necessitates precise data analysis not only to generate valuable insights but also to avoid ineffective or inefficient corporate sustainability strategies. One study (Kumar et al. 2021) exemplified the practical use of BDA by analyzing the database of a periodical research subject, focusing on current and future studies related to business strategy and environmental management.

Although the research is recent, the findings emphasize the need for improvements in information technology infrastructure, particularly in data storage, distribution capacity, and the training of IT professionals specialized in data mining and analysis. Government entities could play a role in promoting such training programs (Wang et al. 2023). Beyond technical training, there is also a critical need for ethical commitment among BDA professionals, given their ability to generate valuable insights when mining and analyzing vast datasets (Bag et al. 2023). Additionally, BDA significantly contributes to green innovation (Song et al. 2019), organizational innovation, and competitive advantage, benefiting not only large corporations (Kristoffersen et al. 2021) but also small and medium-sized enterprises (Mehmood et al. 2023).

Data security concerns were also a predominant issue in the studies analyzed (Bag et al. 2023; Cappa et al. 2021; Tu et al. 2017; Zhou et al. 2016). On the national level, Brazil's General Data Protection Law (LGPD), enacted in 2018, regulates privacy and the use of personal data. However, the law still faces challenges, including the need for further training and integrated systems (Federal Senate 2023).

Moreover, the research highlights a Eurocentric bias, with no studies in the sample addressing Latin American countries. Many studies also noted the challenge of generalizing results across different regions, indicating a significant research gap. This presents an opportunity for future research to explore and address these gaps, offering valuable insights that could enhance the practical application of BDA in diverse geographical and cultural contexts.

Based on the analysis of the 26 selected articles, it was possible to identify recurring patterns regarding how BDA is applied to promote sustainability, which factors enable or hinder its adoption, and what strategic outcomes are commonly achieved. To consolidate this evidence and offer a useful tool for researchers and practitioners, we propose the integrative model presented in Figure 3. This framework synthesizes the main sectoral contexts in which BDA is used, the enablers and barriers influencing its implementation, and the strategic outcomes resulting from its application.

Figure 3 presents a conceptual framework that integrates the multiple dimensions observed in the SLR. The sectoral contexts represent the domains where BDA has been most frequently applied, such as agriculture, energy, smart cities, healthcare, and supply chains. The enablers include elements such as technological infrastructure, professional qualification, data ethics, and institutional support. The barriers reflect the technical, organizational, and cultural challenges faced by organizations. Finally, the strategic outcomes demonstrate the positive impacts of using BDA, such as improved operational efficiency, enhanced reputation, sustainable innovation, and stronger alignment with the SDGs. This model aims to support both academic understanding and practical implementation of BDA in sustainability initiatives.

Based on the gaps identified in the literature and the patterns revealed through this review, several opportunities emerge for future research. First, it is crucial to conduct empirical studies in emerging economies, where infrastructure, regulatory frameworks, and organizational maturity around sustainability and data analytics differ significantly from those in developed countries. Second, there is a need for comparative transnational research to explore how cultural, institutional, and economic contexts influence the adoption of BDA for sustainability.

Third, longitudinal studies could offer valuable insights into the evolution and impact of BDA implementation over time. Finally, future research should delve into sector-specific barriers, particularly in underexplored domains such as healthcare, public administration, and retail, where the dynamics of sustainability and data integration may present unique challenges and opportunities.

From a theoretical perspective, our findings align with the Information Systems Success Model (DeLone and McLean 2003), particularly in how data quality, system integration, and user capabilities influence organizational outcomes. The results also reflect insights from Digital Transformation theory, which emphasizes the interplay between technological enablers, strategic alignment, and organizational readiness (Vial 2019).

Furthermore, the mapping of sustainability tensions resonates with Organizational Paradox Theory, especially the dynamic between instrumental and normative goals (Hahn et al. 2015). By synthesizing these frameworks, our integrative model contributes to a more holistic understanding of how BDA supports or constrains sustainable transitions in complex organizational environments.