Holistic Model of Advanced Analytics to Optimize Organizational Decision-Making

Data-Driven Decision Making in Digital Enterprises

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.

Toward an analytics-driven sustainability assessment framework for organizational systems

The Role Of Big Data Analytics In Strategic Decision-Making And Business Performance: A Systematic Literature Review

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.

Data Analytics Capabilities and Decision-Making in Construction: Global Insights and Implications for New Zealand SMEs

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.

MIDA5: A Collaborative and User-Centered Business Process Analytics Methodology

Business Process Analytics (BPA) has become increasingly important for improving organizational processes and supporting data-driven decision-making. However, existing Business Process Management and Data Analytics methodologies provide limited support for stakeholder participation, User-Centered Design (UCD), and collaborative implementation, creating barriers for organizations with limited analytical maturity. This study presents MIDA5, an initial methodological framework that integrates BPA, Data Analytics, Business Process Management, UCD, and gamification into a participative implementation methodology. Developed following a Design Science Research approach, MIDA5 comprises five phases, 14 stages, 33 activities, and 61 methodological artifacts. The framework was evaluated through a 3-month-and-12-day organizational case study conducted at a public university involving four core organizational participants, seven organizational stakeholders, and complementary organizational applications. The implementation formalized an undocumented process, integrated heterogeneous data sources, developed an analytical database and ETL workflow, and produced three interactive Power BI dashboards. The analytical solution achieved a System Usability Scale score of 84.2 and a SERVQUAL score of 4.35/5, providing initial evidence of usability, stakeholder acceptance, and organizational applicability. MIDA5 contributes an initial, collaborative, user-centered methodological framework that operationalizes BPA through structured stakeholder participation, standardized artifacts, and iterative organizational validation, while reducing methodological and technological barriers.

Evaluating Data Discovery in Decision Making: A Pathway for Organizational Performance

The proliferation of data in contemporary organizational environments has precipitated unprecedented challenges and opportunities for decision-making processes. This paper examines the multidimensional construct of data discovery as a critical antecedent to effective decision making and, by extension, organizational performance. Through a comprehensive theoretical framework, the study interrogates the mechanisms through which data discovery capabilities mediate the relationship between information availability and organizational outcomes. Employing a systematic review methodology, this research synthesizes findings from multiple disciplines including information systems, organizational behavior, strategic management, and data science to develop an integrated understanding of data discovery processes. The analysis reveals that effective data discovery is characterized by four fundamental dimensions: technical infrastructure adequacy, analytical capability maturity, organizational information culture, and governance framework sophistication. These dimensions interact synergistically to determine an organization's capacity to transform data into actionable insights that drive strategic, tactical, and operational decisions. The empirical evidence further suggests that organizations exhibiting high performance across these dimensions demonstrate superior decision quality, efficiency, and adaptability in volatile environments. The paper extends existing theoretical perspectives by introducing a process-oriented maturity model for data discovery capabilities, which delineates the developmental trajectory organizations typically navigate in cultivating sophisticated data utilization strategies.