2022· International Journal of Commerce, Finance and Digital Economy· Vol 5, pp. 1-14· 0 citations
TL;DR
The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning, and predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions.
Abstract
The rapid advancement of digital technologies has enabled organizations to generate and analyze vast amounts of data, making Data-Driven Decision Making (DDDM) a critical component of modern digital enterprises. DDDM leverages data analytics, business intelligence, machine learning, and predictive modeling to support evidence-based decision-making, improving operational efficiency, customer satisfaction, innovation, and competitive advantage. Organizations integrate data from enterprise systems, customer platforms, IoT devices, cloud environments, and digital transactions to gain actionable business insights and optimize strategic decisions. Despite its benefits, DDDM implementation faces challenges such as data quality issues, privacy concerns, system integration complexities, and organizational resistance. Successful adoption requires robust data governance, advanced analytical infrastructure, and a data-driven organizational culture aligned with business objectives. This study examines the role of DDDM in digital enterprises by exploring key technologies, implementation strategies, and analytical frameworks that support effective decision-making. A conceptual methodology is proposed to illustrate the transformation of organizational data into actionable business intelligence. The study also presents a quantitative evaluation of decision effectiveness across operational efficiency, customer satisfaction, revenue growth, and risk management. The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning. Furthermore, predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions. The study concludes that DDDM is a key driver of sustainable growth and innovation, with emerging technologies such as artificial intelligence and autonomous analytics expected to further transform enterprise decision-making.
Big Data Analytics (BDA) has evolved from a predominantly technical batch function into a socio-technical capability integrating cloud-native platforms, stream processing, Lakehouse architecture, machine learning operations (MLOps), visualization, governance, and managerial judgment. This paper proposes an integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence. The framework comprises seven interconnected stages: data sources, ingestion and integration, storage and platform, processing, analytics and artificial intelligence, visualization and interpretation, and decision, action, and learning. Governance, human oversight, organizational readiness, task characteristics, and continuous feedback influence all stages. Key implementation requirements include data quality, interoperability, security, privacy, scalability, cost, explainability, bias, skills, and sustainability. The proposed configurable reference architecture links technical integration, task–analytics fit, governance assurance, human judgment, and organizational readiness with decision quality and organizational outcomes. Organizational size and maturity, sectoral risk, decision criticality, technological context, and regulatory environment are defined as boundary conditions for future empirical validation.
W. Alma'aitah, Fatima N. Al-Aswadi, Addy Quraan et al.· Computers· 0 citations
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
S. Rahman· International Journal of Art...· 0 citations
Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Amelia Contesa, Ilzi Adrolis, Wenni Syafitri et al.· Business System & Innova...· 0 citations
The rapid advancement of digital technologies has encouraged organizations to adopt data-driven approaches in human resource management. This study aims to examine the utilization of People Analytics and Big Data in improving the efficiency of human resource (HR) decision-making in modern organizations. The research employs a library research method by reviewing books, peer-reviewed journal articles, conference papers, and other relevant scientific publications. Data were analyzed using content analysis to identify recurring themes, relationships, and significant findings. The results indicate that the integration of People Analytics and Big Data enhances recruitment, performance evaluation, workforce planning, talent management, employee engagement, and retention through more accurate, objective, and evidence-based decision-making. The findings are supported by the Resource-Based View, Human Capital Theory, and Evidence-Based Management, which emphasize the strategic value of analytical capabilities and human capital. Despite challenges related to data quality, organizational readiness, and privacy concerns, the study concludes that integrating People Analytics and Big Data significantly improves HR decision-making efficiency and strengthens organizational competitiveness in the digital era.
Ananias Barreto· International Journal of Eco...· 0 citations
Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector.
James O. B. Rotimi, Upuli Rasanjani Kaluarachchi Kaluarachchillage· Buildings· 0 citations
Digital transformation has become a central component of modern commerce, fundamentally changing how organizations create value, conduct business activities, interact with customers, and compete in domestic and international markets. The rapid development of cloud computing, artificial intelligence, big data analytics, Internet of Things (IoT), blockchain, mobile technologies, digital platforms, and enterprise information systems has created new opportunities for businesses to improve operational efficiency and organizational performance. Digital transformation, however, extends beyond the adoption of individual technologies. It involves the strategic integration of technology with business processes, organizational structures, human capabilities, and customer-oriented strategies.
This article examines the relationship between digital transformation and business performance, with particular emphasis on the role of technology in modern commerce. The study adopts a conceptual and descriptive research approach based on secondary sources, including academic literature, institutional reports, books, and industry studies. The article analyzes the influence of digital technologies on operational efficiency, financial performance, customer experience, innovation, decision-making, supply chain management, marketing, human resource management, and competitive advantage. It also identifies major barriers to digital transformation, including financial constraints, cybersecurity threats, data privacy concerns, employee resistance, skill shortages, technological complexity, and organizational culture. The study argues that successful digital transformation requires a strategic approach in which technology is integrated with organizational capabilities and business objectives. The article concludes that digital transformation can significantly improve business performance when supported by effective leadership, digital skills, appropriate infrastructure, data-driven decision-making, organizational agility, and continuous innovation.
Keywords: Digital Transformation, Business Performance, Digital Technology, Modern Commerce, Business Innovation, Information Technology, Digitalization, Competitive Advantage, E-Commerce, Organizational Performance
Saddam Hussain· International Journal of Cre...· 0 citations
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