Data Analytics as a Source of Strategic Advantage
- International Academy
- Dec 1, 2025
- 17 min read
Author: Hassan El Malki – Affiliation: Independent Researcher
Abstract
Data analytics has moved from the margins of management to the center of strategic decision-making. In many industries, firms that use data well outperform those that do not, not only by improving efficiency but also by shaping markets, customer expectations, and even regulatory debates. This article explores how data analytics can become a source of strategic advantage, rather than just an operational tool. It draws on three major theoretical lenses: Bourdieu’s theory of capital, world-systems theory, and institutional isomorphism.
The article is based on a narrative review of recent empirical and conceptual studies on big data analytics capabilities, dynamic capabilities, innovation, and competitive performance. It shows that data analytics can be understood as a form of “digital capital” that interacts with economic, cultural, and social capital inside organizations. At the global level, unequal data capabilities reproduce core–periphery structures described by world-systems theory, as firms in core economies accumulate more data, talent, and infrastructure. At the organizational field level, institutional pressures push firms to adopt similar analytics practices, creating isomorphism but also opening space for differentiation when firms combine analytics with unique resources and culture.
The findings suggest that data analytics becomes a strategic advantage when it is embedded in dynamic capabilities, supported by a data-driven culture, aligned with innovation and sustainability goals, and governed responsibly. The article concludes with practical implications for managers and researchers who want to treat data analytics as a long-term strategic asset rather than a short-term technology project.
Keywords: data analytics, competitive advantage, digital capital, dynamic capabilities, institutional isomorphism, world-systems, strategy
1. Introduction
In the last decade, managers have been told repeatedly that “data is the new oil.” Yet in practice, many organizations still struggle to turn their data into real strategic advantage. They invest in dashboards, algorithms, and cloud platforms, but they do not always see clear improvements in market performance or innovation. Some firms even report “analytics fatigue,” where more reports and metrics create confusion instead of clarity.
At the same time, a growing body of research shows that when firms develop strong data analytics capabilities, they can improve performance, innovation, sustainability, and customer experience. Studies find that big data analytics capabilities contribute to competitive advantage through dynamic capabilities, operational improvements, business model innovation, and green innovation (Mikalef et al., 2020; Wamba et al., 2017; Rizvi, 2023; Kalyar, 2024; Korayim, 2024; El Manzani, 2025; Zhang and Thurasamy, 2025). These findings suggest that data analytics can be more than a support function; it can be a core element of strategy.
However, the way we talk about data analytics in management is often narrow and technical. Many articles focus on tools, algorithms, and architectures. Less attention is given to the social, institutional, and global dimensions of data. Who has access to data and analytical skills? How do power relations shape data practices? Why do firms in the same industry often copy each other’s analytics strategies? Why do some countries and regions become “data-rich” cores while others stay in the periphery?
This article addresses these questions by combining insights from management studies with three powerful sociological and institutional theories:
Bourdieu’s theory of capital – to understand data analytics as a form of “digital capital” that interacts with economic, cultural, and social capital in organizations.
World-systems theory – to situate data analytics in a global system where core firms and countries accumulate more data resources than peripheral ones.
Institutional isomorphism – to explain why organizations in the same field often adopt similar analytics practices under coercive, normative, and mimetic pressures.
The objective is to show that data analytics is not only about technology; it is also about power, inequality, culture, and legitimacy. By using these theories, we can better explain why some firms manage to build durable strategic advantages from data, while others simply follow fashion or remain stuck at a superficial level of analytics maturity.
The article is structured as follows. The next section develops the theoretical background. Then, the method section explains the narrative literature review approach. The analysis section integrates empirical findings with the three theoretical lenses. The following section summarizes the main findings and implications for practice. The article concludes by highlighting future research directions and the conditions under which data analytics can be a sustainable source of strategic advantage.
2. Background: Theoretical Perspectives on Data Analytics and Strategy
2.1 Bourdieu, capital, and “digital capital” inside organizations
Pierre Bourdieu’s work is widely used to understand how different forms of capital (economic, cultural, social, and symbolic) shape power and inequality (Bourdieu, 1986). Economic capital refers to financial resources; cultural capital refers to knowledge, skills, and tastes; social capital refers to networks and relationships; symbolic capital refers to prestige and recognition. These forms of capital interact and can be converted into each other over time.
In the digital age, scholars have extended Bourdieu’s framework to introduce digital capital or e-capital: the skills, resources, and competencies related to digital technologies and data (Ragnedda and Ruiu, 2020; Merisalo, 2022; Verwiebe, 2024; Rodríguez-Camacho, 2024). Digital capital allows individuals and organizations to access, interpret, and act on data in ways that produce economic, cultural, and social benefits.
Within organizations, data analytics capabilities can be seen as a specific form of digital capital. They include:
Technical skills – data engineering, statistics, machine learning.
Analytical literacy – managers’ ability to ask good questions and understand results.
Data infrastructure – platforms, databases, and tools that make data accessible.
Data culture – shared norms that encourage experimentation, transparency, and evidence-based decisions.
Firms with high levels of digital capital can convert data into economic capital (profits, cost savings), cultural capital (reputation as an innovative firm), social capital (stronger relationships with partners and customers), and symbolic capital (awards, rankings, media attention). Those with low digital capital may remain dependent on external vendors, consultants, or more powerful partners.
Recent research shows that digital capital strongly affects social status and opportunities at the individual level (Ragnedda and Ruiu, 2020; Rodríguez-Camacho, 2024), and similar dynamics can be seen at the organizational level. Strong analytics teams, prestigious data scientists, and visible data-driven successes all contribute to symbolic capital, which in turn attracts more talent and partners, reinforcing the advantage.
2.2 World-systems theory: Data analytics in a global core–periphery hierarchy
World-systems theory, associated mainly with Immanuel Wallerstein, describes the global economy as a system divided into core, semi-periphery, and periphery (Wallerstein, 2004). Core regions control advanced technology, finance, and global markets; peripheral regions provide raw materials, cheap labor, or low-margin services.
In the context of data analytics, this theory is useful for understanding global inequalities in data capacity. Many of the world’s largest data centers, AI labs, and analytics platforms are located in core economies. Firms in these countries enjoy:
Easier access to high-quality infrastructure and cloud services.
Larger pools of skilled data scientists and engineers.
More capital to invest in experimentation and long-term projects.
Stronger legal and institutional frameworks that support data innovation.
By contrast, firms in peripheral regions may have limited infrastructure, constrained budgets, or weak regulatory capacity. They may depend on systems designed elsewhere, on foreign cloud providers, or on “black box” analytics products. This can lock them into subordinate positions in the global value chain.
Recent studies show that big data analytics capabilities are becoming necessary not just for competitive advantage but for basic participation in global markets (Dubey et al., 2019; Bag et al., 2020; El Manzani, 2025). Firms that cannot meet data-intensive requirements in supply chains, sustainability reporting, or customer analytics may be excluded from preferred partnerships or struggle to comply with global standards.
Thus, from a world-systems perspective, data analytics does not simply level the playing field; it can also reinforce unequal structures, unless active efforts are made to build capabilities in semi-peripheral and peripheral regions.
2.3 Institutional isomorphism: Why organizations copy each other’s analytics strategies
Institutional theory and the concept of institutional isomorphism help explain why organizations in the same field often look increasingly similar over time (DiMaggio and Powell, 1983). Isomorphism arises from three kinds of pressure:
Coercive pressures – regulations, legal requirements, and demands from powerful stakeholders.
Normative pressures – professional norms, standards, and education.
Mimetic pressures – imitation of successful peers under uncertainty.
These pressures are clearly visible in the adoption of data analytics. Firms face coercive pressures through regulations on data protection, sustainability reporting, and digital taxation. They experience normative pressures through professional associations, analytics certifications, and business school curricula. They face mimetic pressures when high-profile firms are celebrated in the media for “data-driven” success and others feel compelled to follow.
Empirical studies show that institutional pressures influence the way firms build big data analytics capabilities, especially in emerging economies (Klein, 2023; Bag et al., 2020; Dubey et al., 2019; Haider et al., 2024). Companies invest in specific tools, architectures, and certifications not only because they are efficient but also because they signal legitimacy. This may lead to convergence in practices: similar dashboards, similar key performance indicators (KPIs), and similar “best practices” across the field.
However, institutional isomorphism does not completely eliminate strategic choice. Firms can still differentiate themselves by combining standard analytics tools with unique data sources, organizational cultures, or business models. The real strategic advantage lies not in simply adopting analytics, but in how analytics is embedded in the firm’s capabilities and identity.
3. Method
This article adopts a narrative literature review and conceptual synthesis approach. Rather than conducting a systematic review with rigid inclusion criteria, it aims to integrate key insights from recent and influential studies in management, information systems, and sociology.
The selection of literature followed three main steps:
Identification of core management and IS studies on big data analytics capabilities (BDAC), dynamic capabilities, innovation, and competitive advantage. This included widely cited works and more recent empirical studies published in the last five years.
Inclusion of theoretical and empirical work on digital capital, the digital divide, and Bourdieu’s theory of capital, as well as key texts in world-systems theory and institutional isomorphism.
Integration of institutional and global perspectives on the adoption of data analytics under different pressures and inequalities.
The review focused on peer-reviewed journal articles and books, mainly in English. No primary data were collected; instead, the article synthesizes findings from diverse contexts (manufacturing, services, agribusiness, public sector, healthcare, and green innovation) to construct a conceptual model of data analytics as a source of strategic advantage.
The method is appropriate for the goals of this article, which are:
To link empirical findings on data analytics capabilities with broader theories of capital, global inequality, and institutional pressures.
To offer a conceptual framework that can guide future empirical research and managerial practice.
Limitations include potential selection bias (not all relevant studies could be included) and the interpretive nature of the synthesis. Nevertheless, by drawing on multiple recent sources and well-established theories, the article aims to provide a balanced and robust view.
4. Analysis
4.1 Data analytics capabilities and competitive performance
A large body of research shows that data analytics capabilities are strongly associated with competitive performance. Wamba et al. (2017) found that big data analytics capabilities contribute to firm performance and competitive advantage by enabling better decision-making and process optimization. Mikalef et al. (2020) showed that big data analytics capabilities improve competitive performance through dynamic and operational capabilities, indicating that analytics is most effective when it is embedded in the firm’s ability to sense, seize, and reconfigure resources.
More recent studies confirm and extend these findings in different sectors and regions. For example:
Korayim (2024) finds that organizational innovation mediates the relationship between big data utilization and competitive advantage, and that a data-driven culture and proactive technological climate strengthen this relationship.
Rizvi (2023) shows that big data analytics capabilities support competitive advantage through business model innovation, suggesting that analytics can drive strategic rather than purely operational changes.
Zhang and Thurasamy (2025) examine agribusiness firms in China and show that absorptive capacity mediates the relationship between big data analytics capabilities and competitive advantage.
Kalyar (2024) and El Manzani (2025) examine how big data analytics capabilities support green innovation and sustainable competitive advantages.
Across these studies, several recurring mechanisms emerge:
Enhanced sensing – firms use data to detect market trends, customer preferences, and competitor moves more quickly and accurately.
Improved seizing – firms leverage analytics to design better products, services, and processes, and to allocate resources more efficiently.
Faster reconfiguration – analytics support continuous improvement, experimentation, and reorganization in response to environmental change.
These mechanisms align closely with the dynamic capabilities framework. Data analytics strengthens dynamic capabilities by providing timely, granular information that supports strategic learning and adaptation (Haider et al., 2024; Elazhary, 2020).
4.2 Data analytics as digital capital in the organizational field
Using Bourdieu’s concepts, analytics capabilities can be interpreted as part of a firm’s digital capital. This digital capital interacts with other forms of capital in several ways:
Economic capital: Firms with more financial resources can invest in advanced analytics platforms, hire skilled data scientists, and run large-scale experiments. Over time, successful analytics projects generate more economic capital through cost savings, revenue growth, and new business models.
Cultural capital: Organizations with a culture that values learning, experimentation, and evidence-based decision-making are more likely to integrate analytics into strategic processes. Training programs, analytics literacy among managers, and supportive leadership build cultural capital that makes analytics meaningful.
Social capital: Partnerships with technology vendors, universities, and startups, as well as networks of analysts and managers, provide access to knowledge and tools. These relationships can improve the quality and impact of analytics projects.
Symbolic capital: Visible successes in analytics—such as awards, case studies, or rankings—contribute to the firm’s prestige and attractiveness to talent and investors.
Recent research on digital capital highlights how digital skills and resources shape social and economic outcomes (Ragnedda and Ruiu, 2020; Merisalo, 2022; Rodríguez-Camacho, 2024; Verwiebe, 2024). At the organizational level, firms with strong digital capital can “play the game” of data-intensive competition more effectively. They can participate in data-driven ecosystems, comply with demanding reporting standards, and respond to new technological waves (such as AI and machine learning) more quickly.
Importantly, digital capital is not only about technology; it is also about habitus, or the internalized ways of thinking and acting that Bourdieu describes. Firms that succeed with analytics often have managers who spontaneously ask for data, challenge assumptions, and accept that decisions should be justified with evidence. In such firms, analytics does not feel like an add-on; it is part of everyday practice.
4.3 Global inequalities and world-systems dynamics
From a world-systems perspective, data analytics capabilities are unevenly distributed across the globe. Core economies host many of the major cloud providers, AI platforms, and global digital companies. They also produce many of the theories, tools, and “best practices” that are exported to other regions.
Studies on supply chains and manufacturing show that big data analytics and AI increasingly shape how global value chains are organized (Dubey et al., 2019; Bag et al., 2020). Firms that can demonstrate strong analytics capabilities are more likely to be selected as partners and to capture higher-value activities such as design, branding, and customer analytics. Firms that lack such capabilities may remain stuck in low-margin, labor-intensive positions.
At the same time, there is also evidence of semi-peripheral upgrading. Emerging economies with strong industrial bases and growing digital infrastructure are building their own analytics capabilities, universities, and research centers. Studies from China, India, Brazil, and other countries show that local firms are using analytics to improve operations and gain regional advantage (Zhang and Thurasamy, 2025; Klein, 2023; Issa, 2021).
However, significant gaps remain. Access to high-quality data, advanced hardware, and top-level talent still favors core economies. Global data governance and platform dominance further concentrate power in the hands of a few multinational corporations. From a world-systems perspective, data analytics can thus be seen as another area where core–periphery dynamics operate, with the potential either to deepen or to challenge existing inequalities.
4.4 Institutional pressures and isomorphism in analytics adoption
Research on institutional pressures shows that firms often adopt analytics not only for efficiency but also to maintain legitimacy. Bag et al. (2020) and Klein (2023) find that coercive, normative, and mimetic pressures shape the development of big data analytics and AI capabilities in manufacturing and service firms.
Coercive pressures include regulations on data protection (such as privacy laws), sustainability reporting, and compliance standards that require data collection and analytics.
Normative pressures arise from professional standards, analytics curricula in business schools, and the expectations of industry associations that “modern” organizations should use data-driven methods.
Mimetic pressures occur when firms imitate the “best practices” of leading companies, especially under uncertainty about which strategies will succeed.
These pressures often lead to institutional isomorphism, meaning that firms adopt similar structures (such as chief data officer roles, analytics centers of excellence, or standard KPIs) and technologies (such as specific analytics platforms).
While this can raise the overall level of analytics maturity in an industry, it also creates a risk: if everyone adopts similar approaches, the potential for genuine differentiation may be reduced. Firms can end up with expensive analytics infrastructures that mainly serve to show that they are “modern” rather than to create unique value.
However, there are also examples where firms use standard tools but differentiate themselves through unique data sources, specific combinations of capabilities, or distinctive cultural and strategic orientations. For instance, some firms use analytics to drive green innovation and sustainability in ways that go beyond compliance (Kalyar, 2024; El Manzani, 2025). Others integrate analytics deeply with customer-centric strategies or platform-based business models. In these cases, the institutional pressure to adopt analytics becomes a starting point, not an endpoint, for strategic innovation.
4.5 Towards a conceptual model: When does data analytics become strategic?
Bringing these elements together, we can outline a conceptual model of data analytics as a source of strategic advantage:
Base layer: Data infrastructure and talent
Storage, processing, and integration of data across systems.
Skilled analysts, data engineers, and data-literate managers.
Digital capital formation
Development of analytics skills, tools, and culture (digital capital).
Integration with economic, cultural, social, and symbolic capital.
Dynamic capabilities
Using data to sense opportunities and threats.
Using analytics to seize opportunities through innovation and better decisions.
Reconfiguring processes, structures, and business models based on insights.
Institutional context
Navigating coercive, normative, and mimetic pressures in the organizational field.
Achieving legitimacy while preserving room for strategic differentiation.
Global position
Leveraging or overcoming core–periphery dynamics through alliances, local innovation, and capability building.
Strategic outcomes
Competitive advantage in cost, quality, speed, innovation, sustainability, or customer experience.
Long-term resilience and adaptability in a volatile environment.
In this model, data analytics becomes a true strategic advantage when all layers are aligned. Simply having data infrastructure or hiring data scientists is not enough. The organization must build digital capital, embed analytics in dynamic capabilities, respond intelligently to institutional pressures, and navigate global inequalities.
5. Findings and Implications
Based on the analysis, several key findings emerge.
5.1 Data analytics is a necessary but not sufficient condition for advantage
The literature strongly supports the idea that big data analytics capabilities are positively associated with competitive performance across industries and regions (Wamba et al., 2017; Mikalef et al., 2020; Rizvi, 2023; Korayim, 2024; Zhang and Thurasamy, 2025; El Manzani, 2025). However, the relationship is rarely direct. It is usually mediated by dynamic capabilities, innovation, absorptive capacity, or culture.
This means that data analytics is a necessary but not sufficient condition for strategic advantage. Firms must invest in complementary capabilities—such as innovation, learning, and cross-functional collaboration—to fully realize the value of analytics.
5.2 Digital capital is a powerful lens for understanding organizational differences
Thinking of data analytics as digital capital helps explain why some firms consistently get more value from data than others. It highlights the importance of internal culture, knowledge, and networks, not only technical infrastructure.
Firms with high digital capital:
Integrate analytics into everyday decision-making.
Attract and retain analytics talent.
Learn from experiments and adapt quickly.
Convert digital successes into symbolic capital that attracts partners and investors.
Firms with low digital capital may purchase similar tools but fail to generate similar outcomes because managers lack the skills or habitus to interpret and act on insights.
5.3 Global inequalities shape who benefits from data analytics
World-systems theory suggests that data analytics may reinforce global inequalities unless specific strategies are adopted. Core economies have structural advantages in infrastructure, talent, and capital. Peripheral economies risk becoming dependent on external platforms and tools.
However, semi-peripheral regions can upgrade by building local capabilities, forming strategic alliances, and developing niche expertise. Policy-makers and development agencies have a role to play in supporting digital infrastructure, education, and research, to ensure that data analytics becomes a tool for inclusive development rather than only for core dominance.
5.4 Institutional pressures create both risks and opportunities
Institutional isomorphism explains why many firms adopt similar analytics strategies. This can raise the overall level of analytics maturity, but it can also lead to conformity and shallow adoption, where firms invest mainly for legitimacy rather than true strategic impact.
The challenge for managers is to:
Satisfy institutional expectations (for compliance, transparency, and modernity).
Go beyond isomorphism by developing unique combinations of data sources, capabilities, and strategic goals.
For example, firms can use standard analytics tools but apply them in distinctive ways, such as focusing on social impact, green innovation, or personalized customer journeys.
5.5 Responsible analytics and long-term legitimacy
An emerging theme in recent literature is the importance of responsible analytics, including fairness, privacy, transparency, and accountability. If data analytics is used in opaque or discriminatory ways, it may lead to reputational damage, legal sanctions, or backlash from stakeholders.
Building long-term strategic advantage from analytics therefore also requires strong governance, ethical guidelines, and stakeholder engagement. This connects digital capital with moral and symbolic capital: firms that use data responsibly can strengthen trust and legitimacy, which are themselves sources of advantage.
6. Conclusion
Data analytics has clearly become a central element of contemporary strategy. Yet it is not a magic solution that automatically delivers advantage. This article has argued that data analytics should be understood as a form of digital capital, embedded in broader structures of power, inequality, and institutional pressure.
Using Bourdieu’s theory of capital, we see that analytics capabilities interact with economic, cultural, social, and symbolic capital inside organizations. Firms that successfully convert digital capital into other forms of capital can build durable advantages. Using world-systems theory, we recognize that data analytics is shaped by global core–periphery dynamics, influencing which firms and regions can fully benefit from data-driven competition. Using institutional isomorphism, we understand why firms often converge on similar analytics practices and how they can still differentiate themselves within these constraints.
For managers, the key message is that investing in data infrastructure is only a starting point. To turn analytics into strategic advantage, organizations must:
Build digital capital through skills, culture, and internal networks.
Embed analytics in dynamic capabilities that support sensing, seizing, and reconfiguring.
Navigate institutional pressures intelligently, balancing legitimacy with differentiation.
Acknowledge and address global inequalities, especially when operating across regions.
Commit to responsible analytics, ensuring that data practices support trust and long-term legitimacy.
For researchers, there is a need for more empirical studies that combine management, sociology, and political economy to examine how data analytics both reflects and reshapes power relations within and between organizations and countries.
Ultimately, data analytics becomes a true source of strategic advantage when it is not just a technical toolkit, but a deeply embedded capability that aligns with the firm’s values, culture, and position in the global system. When this alignment is achieved, data can indeed move from being a raw resource to being a foundation for sustainable, inclusive, and innovative competitive advantage.
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