Data-driven Organizations: Creating Business Value with Analytics and Modern Data Management

Permanent URI for this collectionhttps://hdl.handle.net/10125/112537

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Reframing Talent Management with AI: The Organizing Vision of Skills Intelligence
(2026-01-06) Morrill, Jake; Someh, Ida Asadi; Rinta-Kahila, Tapani; Jooss, Stefan; Tona, Olgerta; Van Der Meulen, Nick
Skills intelligence is rapidly redefining talent management approaches, fueling the shift from credential-based to skills-based strategies. Drawing on organizing vision theory, this paper conceptualizes skills intelligence as an emergent socio-technical phenomenon enabled by AI. Through qualitative data analysis of the practitioner discourse, we identify the key components, functions, and affordances of skills intelligence, and argue that it has profound implications for employees, organizations, and labor markets. Our findings reveal that although industry narratives highlight the positive outcomes of efficiency and agility associated with AI-enabled skills systems, they frequently neglect critical perspectives on their unintended consequences. The paper concludes with a call to action to mobilize information systems (IS) and talent management scholars toward a critical and future-oriented approach to shaping the trajectory of this evolving technology.
Intelligent Agile: Conceptual Modeling and AI for Next-Generation Agile Software Development
(2026-01-06) Lukyanenko, Roman; Samuel, Binny; Jabbari, Araz; Wiedemann, Anna; Hertelendy, Attila; Storey, Veda; Sturm, Arnon
Although Agile is now the most popular software development methodology, agile projects continue to fail. Conceptual modeling can help address at least two major challenges in agile projects: managing requirements and communication for collaboration and coordination. At the same time, conceptual modeling has not been well received by agile teams, as traditional conceptual modeling comes with high overhead. We propose a new approach to agile that leverages artificial intelligence (AI) and conceptual modeling together. This design approach enables us to advance beyond passive conceptual modeling representations, such as static diagrams, to the new types of information systems (IS), intelligent conceptual modeling systems (ICMS), that have awareness, autonomy, adaptivity, and activity. In this way, conceptual modeling, powered by AI, facilitates requirements and communication in agile projects, resulting in what we call intelligent agile.
Data-Driven Culture: Embedding Algorithmic Technologies through Resolving Vision Conflicts
(2026-01-06) Li, Tingxuan; Gregory, Robert; Henfridsson , Ola
As algorithmic technologies become increasingly embedded in organizational routines, they hold great promise for improving organizational performance. However, realizing the potential of such systems is not straightforward. One challenge in focus is the conflict between values embedded in algorithmic systems and values of its main users. With the goal of developing a new theory on value conflicts and the creation of data-driven culture, we embarked on in-depth case research at WrapTrust, a mortgage lending firm, and their roll-out of algorithmic technologies for improving organizational performance. We identify three reciprocal leadership mechanisms (contextual calibration, paced enforcement, and empowering narrative) that enable managers to mitigate such vision conflicts. All in all, the activation of those mechanisms fosters a data-driven culture, which enables algorithmic technologies to become culturally embedded, and allows those systems, inherently intrusive or prone to suspicion, to be gradually repositioned as shared instruments for both performance enhancement and employee empowerment.
Reducing Cognitive Biases in Business Intelligence: A Framework for Objective Data Analysis
(2026-01-06) Vuorenheimo, Matthias; Hekkala, Riitta
This paper examines the ways in which cognitive biases affect judgment in business intelligence (BI) settings. Drawing on bounded rationality theory, the study explores how human cognition, BI system design, and organizational pressure interact to produce biases such as anchoring bias and confirmation bias. Qualitative data were obtained from semi-structured interviews with representatives from two logistics companies. The results of a thematic analysis revealed that users frequently rely on heuristics because of time pressure, information overload, and visual design elements. While some informal bias-mitigation strategies exist, institutional support for them is limited. This study offers a framework for understanding and reducing cognitive bias in BI use, with implications for dashboard design, user training, and decision processes.
Governing Data for Value: A Case Study on Implementing a Federated Data Governance Model in a Logistics Enterprise to Enable AI-driven Business Outcomes
(2026-01-06) Fischer, Sebastian; Pohlink, Claudia
Effective data governance (DG) is vital for analytics and AI, yet implementing it in traditional enterprises is challenging. This single-case study examines a German Mittelstand logistics firm’s multi-year DG program, covering a federated DG model, an Azure-based Data & AI platform, and literacy initiatives. We identify success factors (integrated strategy, literacy investment, clear roles including Master Data Managers and Global Data Architects) and challenges (legacy integration, cultural adaptation, master data management). DG enabled measurable AI value: route optimization saved ~€150,000 per site annually; carrier-claims automation handles ~10,000 claims/month with ~70% automation; ~150 employees gained foundational AI/Power BI skills. The journey was non-linear with setbacks. We offer actionable guidance on linking DG to AI business value and extend DG theory by modeling AI value as a reinforcing feedback loop, elevating platform governance within the domain scope, and specifying AI‑specific procedural mechanisms.
Coping Strategies for Tensions Between Digital Data and Data Practices in Data-Driven Organizations
(2026-01-06) Washik, Maryam; Hylving, Lena; Koutsikouri, Dina
This paper examines how workers cope with digital data tensions in railway operations at a safety-critical and data-intensive transport organization in Sweden. We show that the material properties of digital data and data practices create tensions in work practices, resulting in technostress under high accountability and time pressure. We identify five coping strategies: workarounds, negotiations, modifications, compromises, and abandonments. These strategies are sociomaterial; blending technical fixes, tacit expertise, and informal networks, and show how workers enact data in practice. Our findings contribute an empirical account and understanding of digital data tensions in railway operations, demonstrating that coping enhances workflows and data quality, but introduces invisible labour and new complexities. Building on these findings, we extend theoretical discussions on digital data tensions, technostress and coping, and propose conceptualizing data as dialogue, where meanings are continuously negotiated, and as a capacity-building tool, where coping generates organizational learning and resilience over time.
From Awareness to Action: Operationalizing Data Literacy in a Multinational Enterprise
(2026-01-06) Schnieders, Fabienne; Lefebvre, Hippolyte; Otto, Boris
Fostering data literacy across all organizational levels has become a strategic imperative in incumbent firms’ digital transformation efforts. While prior research clarifies the competencies that constitute data literacy, less is known about the organizational efforts required to identify and address literacy gaps. This study draws on a revelatory case of a multinational industrial enterprise to examine how the need for data literacy was recognized, how the response was structured, and how targeted initiatives were embedded into broader transformation efforts. Our findings show how organizations can progress from initial awareness to action by establishing data governance structures, aligning training with competency needs, and fostering data culture. This study contributes to data literacy research by theorizing its operationalization as both a managerial and socially embedded organizational process. It also provides actionable insights for researchers and practitioners seeking to plan, structure, and implement data literacy initiatives at scale.
Data Governance Practices for Generative AI Powered Organizational Knowledge Management Systems Using Retrieval Augmented Generation
(2026-01-06) Friedrich, Tilman; Akbari, Karl; Fürstenau, Daniel
This study examines how data governance supports the success of generative AI-based Knowledge Management Systems (KMS) using Retrieval-Augmented Generation (RAG) in large enterprises. Drawing on a multi-case study methodology, the research identifies 17 distinct data governance practices and synthesises them into a conceptual framework that theorises their contribution to KMS success. The adoption of these practices is shaped by the dynamically evolving technological affordances of generative AI and RAG, as well as the contextual challenges posed by the predominantly ingested semi-structured and unstructured textual data. While the identified practices enable value-add, they also introduce strategic trade-offs, particularly in balancing data protection and expected benefits. This study contributes to the evolving discourse on data governance by extending its scope beyond structured data and highlighting its dynamic, context-sensitive role in AI-enabled KMS.
Introduction to the Minitrack on Data-driven Organizations: Creating Business Value with Analytics and Modern Data Management
(2026-01-06) Lefebvre, Hippolyte; Mueller, Roland M.; Balkan, Sule; Dinter, Barbara

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