Data science is increasingly indispensable to project management, enabling evidence-based planning, execution, monitoring, and control across the project lifecycle (Project Management Institute, 2024; Singh, 2015). Integrating predictive analytics and machine learning allows teams to forecast schedule slippage, cost overruns, and emergent risks, enabling proactive intervention and smarter resource allocation (Bauskar et al., 2024; Singh, 2015). Empirical guidance and studies show that data-driven decisions improve project performance relative to intuition-based approaches (Bauskar et al., 2024). Embedding analytics within PMOs strengthens governance and benefits realization by linking leading indicators to strategic outcomes and continuous learning (Project Management Institute, 2024; Singh, 2015)This Special Issue seeks to explore and advance the intersection of data science and project management by promoting the integration of data science into practice – showcasing how techniques such as machine learning, predictive analytics, and big data can enhance project planning, execution, monitoring, and control. It fosters methodological innovation by encouraging contributions that develop or adapt data science approaches specifically for project environments, including agile, traditional, and hybrid settings. The issue highlights real-world applications and impacts through empirical studies, case analyses, and industry insights that surface the tangible benefits, challenges, and boundaries of applying data science across domains such as IT, construction, healthcare, and sustainability. It also bridges theory and practice by advancing understanding of how data-driven decision-making transforms project processes, competencies, and success criteria, while encouraging interdisciplinary collaboration by drawing together project management and data science researchers to spur cross-disciplinary dialogue and innovation. Beyond tool adoption or algorithmic detail, the Special Issue aims to articulate how data-enabled decision processes, governance mechanisms, and capabilities reconfigure project value creation.The intersection of data science and project management is emerging as a critical area of inquiry and practice, yet remains underexplored in mainstream project studies. As organizations increasingly operate in data-rich (Çelik, 2020), technology-driven environments, project professionals are being called upon to make faster, evidence-based decisions using complex and dynamic data sources (Liu et al., 2024; Walker and Lloyd-Walker, 2019). Despite this shift, the project management discipline has only recently begun to engage seriously with the tools, methods, and theoretical implications of data science.While isolated studies have begun to explore the application of predictive analytics (Almalki, 2025; Hammad et al., 2020), artificial intelligence (Mariani et al., 2023; Wang et al., 2012), and big data (Marnewick and Marnewick, 2024; Monageng et al., 2024; Olsson and Bull-Berg, 2015; Whyte et al., 2016) in projects, there is currently no cohesive body of knowledge that systematically investigates the integration of data science into project practices. Most contributions remain fragmented across disciplines (e.g. information systems, operations research, sociology, and engineering), and there is a lack of frameworks or empirical studies that examine the organizational, managerial, and methodological implications of data-driven project management (Shen et al., 2024). Research in the project community has a crucial role in addressing these knowledge needs to enable project practice to fully benefit from the transformative benefits of data analytics. It is necessary to boost the “technical” perspective that has not received enough attention in project management research. This Special Issue addresses this gap by offering a dedicated scholarly space to understand:The proposal is timely for several reasons:This SI stimulates critical rethinking of the boundaries of project management as a discipline, enabling it to evolve in response to digital transformation. It also provides novel insights and frameworks that can inform project governance, strategic alignment, performance management, and benefits realization in the age of data.While previous Special Issues in IJMPB and related journals have addressed themes such as digital transformation, project analytics, or AI in projects, none have explicitly focused on the broader ecosystem of data science as it applies to the full project life cycle.This proposal is distinct in that:By framing data science not just as a technical add-on but as a transformative force in project thinking, this Special Issue offers a novel and necessary contribution to advancing the field of project management.This Special Issue (SI) offers novelty and originality by integrating the full data science ecosystem into project management scholarship and explicitly targeting holistic, discipline-wide transformation, rather than the partial treatments of AI, analytics, or digital tools seen in prior work. It addresses clear empirical gaps and methodological needs. Despite strong interest in analytics, surveys indicate that 82% of data science teams lack formal project methodologies and only about 25% employ explicit data science frameworks, leaving no coherent methodological backbone for managing data-intensive projects. Accordingly, the SI aims to stimulate research on integrated lifecycle models that unite CRISP-DM, agile practices, governance, ethics, and real-time analytics within structured frameworks. The SI also expands the theoretical boundaries of project management by moving beyond narrow domain applications (e.g. construction forecasting or risk) to examine how data science transforms classic constructs such as stakeholder engagement, governance, sustainability, and value creation, while encouraging theorization around data ecosystems, algorithmic project roles, and data-driven decision hierarchies. To bridge disciplines, it fosters transdisciplinary dialogue between project scholars and data scientists and incorporates critical theory to reflect concerns about data justice and ethics. Finally, the SI emphasizes societal and practical value by confronting contemporary challenges – recognizing that improved sustainability and outcomes require robust data-driven governance in sectors like disaster recovery, public health, and infrastructure, even as it acknowledges risks such as misuse, bias, widening inequalities (the “data divide”), and an over reliance on easily documented facts (“what's not in the data does not exist”). By advancing well-considered, data-inclusive project standards, the SI supports the UN Sustainable Development Goals, including industry innovation (SDG 9) and institutional transparency (SDG 16).The proposed Special Issue addresses a critical gap in project management literature by integrating data science not merely as a technical tool but as a transformative force with strategic, methodological, and societal implications. Despite increasing interest in AI, predictive analytics, and big data, current studies remain fragmented, lacking a cohesive framework that connects data science to core project processes such as governance, decision-making, and benefits realization. Recent global events have underscored the urgency for real-time, data-enabled project management, yet many organizations still struggle to harness data effectively. This SI offers a platform to explore the impacts of data science on project roles, ethics, performance, and long-term value. It also responds to pressing societal challenges and aligns directly with several UN Sustainable Development Goals, including SDG 9 (Industry, Innovation and Infrastructure), SDG 16 (Peace, Justice and Strong Institutions), SDG 4 (Quality Education), and SDG 17 (Partnerships for the Goals). By encouraging cross-disciplinary engagement and emphasizing the human and ethical dimensions of data use, this SI aims to shift the boundaries of project management thinking and practice in a data-intensive world.The themes map the ways data science intersects with project management, e.g. linking decision-making, methodology, governance, competence, tools, and impact. The themes reflect practical needs, theoretical gaps, and ethical concerns, while deliberately remaining broad to encourage interdisciplinary approaches from information systems, operations research, sociology, and engineering. Together the themes support inquiry into both micro-level changes (team skills, dashboards, automation) and macro-level shifts (ecosystems, benefits realization, SDG-aligned impact), enabling contributors to advance frameworks, empirical evidence, and practice guidance that directly address contemporary challenges in data-rich project environments.Generally, for the SI to be completed, we expect approximately two years. However, once a paper will be accepted for publication, it will be made available online before entering the Special Collection.
Marnewick et al. (Thu,) studied this question.