Data Driven Decision Making in Global Organisations: A Review of Analytics, Managerial Capabilities, and Competitive Advantage

Abstract

Data driven decision making (DDDM) has emerged as a critical strategic capability for global organisations operating in data intensive and highly competitive environments. Advances in big data analytics, artificial intelligence, and digital platforms have fundamentally altered how organisations generate insights, allocate resources, and sustain competitive advantage. This review article systematically synthesises prior research published in peer reviewed journals and authoritative industry sources to examine how analytics capabilities and managerial capabilities jointly influence organisational performance and competitiveness. Drawing on 45–50 studies sourced from Google Scholar and ScienceDirect indexed literature, the paper integrates findings across information systems, operations management, strategic management, and international business. The review identifies dominant themes, analytical approaches, capability dimensions, performance outcomes, and unresolved challenges. An integrative conceptual framework is proposed to explain how data resources, analytics maturity, and managerial cognition interact to create sustained competitive advantage in global organisations. The study contributes by consolidating fragmented literature and offering clear implications for managers and future research.

Introduction

The text provides a comprehensive review of data-driven decision making (DDDM) in global organisations, emphasizing the integration of analytics capabilities, managerial skills, and organisational culture to achieve competitive advantage.

Key points include:

1. Rise of DDDM:

• Organisations now generate massive volumes of structured and unstructured data from operations, customers, supply chains, and external environments.

• DDDM relies on systematic data collection, advanced analytics, and evidence-based reasoning rather than intuition, improving strategic and operational effectiveness.

• In global contexts, DDDM helps manage complexity arising from geographic dispersion, cultural differences, institutional diversity, and regulatory heterogeneity.

2. Conceptual Foundations:

• Early systems focused on descriptive analytics; modern approaches include predictive and prescriptive analytics.

• Theoretical perspectives include:

  • Resource-Based View (RBV): Analytics capabilities are valuable, rare, and inimitable resources.

  • Dynamic Capabilities: Organisations sense, seize, and reconfigure resources via analytics-enabled learning.

  • Sociotechnical Systems Theory: Decision outcomes depend on the interplay between technology, people, and structures.

3. Analytics Capabilities:

• Core components: data infrastructure, integration, analytical tools, and skilled personnel.

• Maturity progresses from descriptive → diagnostic → predictive → prescriptive analytics.

• Global organisations face challenges in cross-border data integration, quality management, cybersecurity, and compliance.

• Advanced analytics improves forecasting, supply chain optimization, risk management, and customer personalization.

4. Managerial and Organisational Capabilities:

• Analytics alone is insufficient; managers must interpret, question, and contextualize insights.

• Data literacy, analytical reasoning, and leadership commitment are crucial.

• A data-driven culture promotes evidence-based decision-making, experimentation, and cross-functional collaboration.

• Governance, ethics, and accountability structures ensure responsible data use.

5. Competitive Advantage:

• DDDM improves operational efficiency, innovation, financial performance, and strategic responsiveness.

• Competitive benefits depend on industry dynamics, organisational maturity, and strategy.

• Analytics supports global coordination and integration across dispersed units.

6. Challenges and Ethics:

• Issues include data silos, poor data quality, algorithmic bias, and resistance to change.

• Ethical concerns involve privacy, transparency, and accountability.

• Mitigation requires governance frameworks and human oversight.

7. Integrative Framework:

• Combines data resources → analytics capabilities → managerial cognition → competitive outcomes.

• Suggests future research on longitudinal effects, cross-cultural differences, and emerging technologies like generative AI and quantum analytics.

8. Managerial Implications:

• Invest equally in analytics tools and managerial capabilities.

• Foster leadership commitment and embed data-driven principles in culture and governance.

• Implement robust data governance and ethical frameworks to mitigate risks and maintain trust.

In essence: Effective DDDM in global organisations requires balanced integration of technology, human expertise, and organisational culture, transforming data into actionable insights that create sustainable competitive advantage.

Conclusion

This review demonstrates that data-driven decision making is a multifaceted organisational capability that extends beyond analytics technology to encompass managerial skills, organisational culture, and strategic alignment. Drawing on 45–50 studies, the paper shows that global organisations that effectively integrate analytics capabilities with managerial capabilities are better positioned to achieve sustained competitive advantage. By synthesising fragmented literature, this study contributes a holistic understanding of how data, analytics, and managerial cognition jointly shape organisational outcomes. The proposed integrative framework provides a foundation for future empirical research, particularly in international and cross-cultural contexts. For practitioners, the findings underscore the importance of aligning analytics investments with managerial development and ethical governance to fully realise the value of data-driven decision making.

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Copyright

Copyright © 2026 Varshitha Suresh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.