Citation

Nagarajan, Deivanayagampillai and Thangavel, Bhuvaneswari and Suppiah, Yasothei and Anbalagan, Kanchana (2026) Introduction to data-driven decision-making. In: Data-driven Decision Making and Soft Computing. CRC Press, pp. 1-21. ISBN 978-100363483-6, 978-104106297-4, 978-104106314-8

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Abstract

In today’s fast-paced and increasingly complex business environment, organizations are facing unprecedented challenges in making informed decisions. The sheer volume and velocity of data being generated from various sources, including social media, sensors, and transactional systems, have created both opportunities and obstacles for decision-makers. To stay ahead of the competition and achieve sustainable success, organizations must be able to harness the power of data to drive decision-making. Data-driven decision-making has emerged as a critical capability for organizations seeking to leverage data as a strategic asset. By combining data analytics, business acumen, and technical expertise, data-driven decision-making enables organizations to uncover hidden insights, identify new opportunities, and optimize business outcomes. This chapter provides an introduction to the fundamental concepts and principles of data-driven decision-making, and sets the stage for further exploration of advanced topics in subsequent chapters. This article explores the impact of Artificial Intelligence (AI) on the B2B sales funnel. For each stage of the funnel, we detail essential sales tasks, highlight the specific contributions AI offers, and clarify the critical roles humans fulfil. Additionally, we provide managerial insights to optimize the combined efforts of AI and human expertise within the B2B sales landscape (Paschen et al., 2020). This study examines the potential of integrating AI into organizational decision-making under uncertainty, as identified by current research. It presents a conceptual model that outlines how humans can leverage AI for decision-making in uncertain contexts while addressing the associated challenges, prerequisites, and outcomes. Although research extensively covers organizational structures, AI application choices, and knowledge management possibilities, it highlights a notable gap: the lack of clear recommendations for ethical frameworks, which are recognized as a critical foundation (Trunk et al., 2020). This research examines the challenges and opportunities of data-driven decision-making (DDDM) in a dynamic business environment. It explores the benefits of DDD, including enhanced decision quality, improved operational efficiency, and a stronger competitive edge. Additionally, this chapter identifies key challenges organizations encounter, such as data quality and governance issues, technological complexities, and the demand for skilled data professionals. To address these challenges and maximize opportunities, this chapter outlines effective strategies and best practices. These include fostering a data-driven culture, investing in robust data infrastructure and analytics capabilities, ensuring data privacy and security, and promoting collaboration between business and IT teams. The findings of this study enhance the understanding of DDD’s role in an evolving business landscape and offer valuable insights for organizations seeking to leverage data for strategic advantage (Prachi, 2023). This study employed a mixed-methods approach to examine the current practices and challenges faced by Decision-Making Operators (DMOs) at each stage of Athamena and Houhamdi’s data-driven decision-making model. The findings indicate that Canadian DMOs have been slow to adopt data-driven decision-making practices. Key issues include insufficient data on sustainability indicators, concerns about data quality, and resource constraints. The study explores both theoretical and practical implications (Novotny et al., 2024). In this study, we enhance the original conceptual framework by incorporating a newly developed process ontology, further enriching M&E terminology with stereotypes derived from the process conceptual base. This enhanced framework positively impacts the other strategy capabilities by ensuring terminological consistency and improving the testability of process and method specifications. To illustrate these improvements, we highlight excerpts of process specifications reflecting the updated approach (Becker et al., 2013). This chapter presents an approach supported by a language that facilitates the specification of the why, what, and how aspects in a localized and integrated manner for each stakeholder. The language’s simulation capabilities promote data-driven decision-making, reducing reliance on human expertise. Additionally, the approach allows less-experienced users to operate at an expert level. A real-life case study is provided to illustrate the application of the proposed approach (Kulkarni et al., 2015). This study emphasizes the need for contractual control and coordination to address information uncertainty. Contract clauses should be precise, and incentive schemes should align with information requirements. Relational governance is the most effective approach for managing information equivocality. Identifying information needs and minimizing uncertainty are essential prerequisites for organizational transformation efforts aimed at reducing information equivocality (Aben et al., 2021). This article reviews empirical research on how these shifts have transformed buyer–seller negotiations, a crucial aspect of buyer–seller interactions. Several key insights emerge from this analysis. First, these shifts have fundamentally altered buyers’ and sellers’ roles, power dynamics, aspirations, and information processing. Second, these shifts, along with these fundamental changes, have significantly impacted buyer–seller interactional processes and outcomes, including (1) shifts in buyers’ attitudes and behaviours, (2) changes in sellers’ effectiveness in engaging with buyers, and (3) transformations in buyer–seller interactional dynamics. Drawing from these insights, the authors propose a research agenda to reevaluate existing theories and develop new frameworks for understanding buyer–seller interactions. (Ahearne et al., 2022). Data are no longer merely a component of administrative and managerial tasks but a pervasive resource through which organizations understand and respond to the challenges they face. We theorize that ongoing technological advancements not only reinforce the traditional functions of data as tools for management and control but also redefine and expand their role. By transforming data into technical entities, digital technologies reshape the process of knowing and the knowledge functions data serve in socioeconomic contexts. These functions are often mediated by integrating dispersed and continuously updated data into more stable entities, which we refer to as data objects. When users, customers, products, and physical machines are rendered as data objects, they become the technical and cognitive foundations through which organizational knowledge, patterns, and practices evolve (Alaimo and Kallinikos, 2022). Technologies have the potential to profoundly influence all aspects of organizing. This article introduces the special issue, “Emerging Technologies and Organizing.” We define these technologies as “emerging” because their applications and effects remain fluid, lacking a stable set of recognizable patterns, and because they are inherently designed to evolve and adapt. To explore the relationship between emerging technologies and organizing, we draw on relational thinking from philosophy and sociology to develop a relational perspective on these technologies. Our aim is to provide organizational scholars with a new framework for integrating the growing influence of technology into their theories of key organizational processes and phenomena. By conceptualizing emerging technologies not as fixed entities but as dynamic and evolving relationships, we offer a fresh approach for understanding their role within organizational research (Bailey et al., 2022). The literature offers a diverse array of techniques for assessing and enhancing data quality. Given the complexity and variety of these techniques, recent research has focused on developing methodologies to facilitate their selection, customization, and application. This article aims to provide a systematic and comparative analysis of such methodologies. The comparison is structured across several key dimensions, including methodological phases and steps, strategies and techniques, data quality dimensions, types of data, and the categories of information systems each methodology addresses. The article concludes with a summarized overview of each methodology (Batini et al., 2009). Information asymmetry occurs when one party in a relationship possesses more or better information than the other. This concept is widely integrated into management research and serves as a fundamental assumption in leading organizational theories. However, despite its central role, no systematic review of the management literature has examined information asymmetry comprehensively. Consequently, there is no established understanding of information asymmetry as a management concept, nor a unified foundation for guiding future research (Bergh et al., 2019). The theory posits that information technology acts as a decentralizing force, while communication technology serves as a centralizing force. Analyzing a new dataset of American and European manufacturing firms, we find that advanced information technologies such as enterprise resource planning for plant managers and computer-assisted design/computer-assisted manufacturing for production workers are linked to greater autonomy and a broader span of control. In contrast, communication-enhancing technologies, such as data intranets, reduce autonomy for both workers and plant managers. Our findings are further reinforced using instrumental variables, including the distance from the enterprise resource planning’s place of origin and variations in telecommunication costs due to regulatory differences (Bloom et al., 2014). This study, the first in a two-part series, explores and seeks to answer the question “What is Smart Maintenance?” Using an empirical, inductive research approach, the authors conceptualized Smart Maintenance through focus groups and interviews with over 110 experts from more than 20 firms. By analyzing the data through the lens of multiple general theories, this study provides new insights into contemporary and future maintenance research. This chapter presents empirical observations and theoretical interpretations, culminating in the first empirically grounded definition of Smart Maintenance and its four key dimensions: data-driven decision-making, human capital resources, internal integration, and external integration. Additionally, it defines the relationships between these dimensions and formally models the concept structure. By achieving conceptual clarity on Smart Maintenance, this study offers significant theoretical and managerial contributions, serving as a valuable guide for scholars and practitioners in industrial maintenance management (Bokrantz et al., 2020). This chapter examines the impact of data-driven decision-making (DDM) on operational performance, moderated by two key dimensions of digitalization: data integration and the adoption of emerging digitization technologies. A cross-country survey of 138 Italian and U.S. auto-supplier firms, supplemented by plant visits and interviews, reveals a dynamic interplay between DDM and these digitalization factors. The findings indicate that higher levels of data integration within information systems enhance the positive effect of DDM, increasing the likelihood of cost reductions. Conversely, the introduction of multiple new digitization technologies negatively impacts DDM outcomes, particularly in terms of cost performance.

Item Type:	Book Section
Uncontrolled Keywords:	Artificial intelligence, Behavioral research, Competition
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 03:54
Last Modified:	04 Sep 2026 03:54
URII:	http://shdl.mmu.edu.my/id/eprint/16707

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