6 research outputs found
A theory for conceptual modelling of evolving application domains
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proper.pdf (Publisher’s version ) (Open Access)Doctoral thesis University of Nijmegen
Promotores : E. Falkenberg, T. van der Weide en A. ter Hofstede
cum laudeX, 260 p
Companion Proceedings of the 16th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling and the 13th Enterprise Design and Engineering Working Conference: BES, DTE, FACETE, Tools & Demos, Forum, EDEN Doctoral Consortium
No full textTowards a reference ontology for a data valuation business capability
No full textDespite its recognition as primary asset, enterprises struggle to determine data value due to fragmented and impractical approaches. This paper develops a reference ontology for Data Valuation Business Capabilities (DVBC) leveraging the systematic approach for building ontologies, ArchiMate and integrating scientific insights with ex-ante expert interview validation. Comprising twelve groupings and 66 components, anchored in established ontologies and assessed against (non)-functional requirements, the ontology shapes the fragmented data valuation landscape into a structuring frame for enterprises. While advancing value modelling in information systems research, the ontology faces limitations like detailed process modelling deficiency, ex-post validation potential, and modelling language boundaries
Data valuation as a business capability: from research to practice
No full textIn our data-centric society, the imperative to determine the value of data has risen. Therefore, this paper presents a taxonomy for a data valuation business capability. Utilizing an initial taxonomy version, which originated from a systematic literature review, this paper validates and extends the taxonomy, culminating in four layers, twelve dimensions, and 59 characteristics. The taxonomy validation was accomplished by conducting semi-structured expert interviews with eleven subject matter experts, followed by a cluster analysis of the interviews, leading to a taxonomy heatmap including practical extensions. This paper's implications are manifold. Firstly, the taxonomy promotes a common understanding of data valuation within an enterprise. Secondly, the taxonomy aids in categorizing, assessing, and optimizing data valuation endeavors. Thirdly, it lays the groundwork for potential data valuation standards and toolkits. Lastly, it strengthens theoretical assumptions by grounding them in practical insights and offers an interdisciplinary research agenda following the taxonomy dimensions and characteristics
The OntoREA© Accounting and Finance Model: Inclusion of Future Uncertainty
No full textPart 2: Modeling and OntologiesInternational audienceThe OntoREA© accounting and finance model [1] indicates already in its name a fundamental distinction, i.e. the distinction between the accounting related backward looking perspective into the past and the finance related forward looking perspective into the future. Accordingly, in accounting current economic events are recorded and persisted and in finance future related commitments are addressed. Concerning the completeness of accounting and finance concepts there is an asymmetry in the OntoREA© model. The accounting concepts are completely covered, whereas in the coverage of the forward looking finance perspective one main deficiency exists: The uncertainty surrounding the forward looking perspective is not specified.In this article the problem of the missing uncertainty representation in the OntoREA© accounting and finance model is explicitly addressed. The novel approach consists in directly linking uncertainty to commitments. By conceptualizing uncertainty according to the stochastic concepts that underlie the option pricing [2–4] and the intertemporal equilibrium pricing theory [5], the missing representation is solved. Furthermore, the stochastic concepts have a precise ontological meaning [6, 7]. Hence, the extension of the current model with the proposed uncertainty representation gives a well-founded stochastic model of the accounting and finance domain
A decision-support model for data product valuation in the energy sector: A multi-criteria perspective
No full textDetermining the value of data products remains a challenge for enterprises and academia, despite the growing recognition of data as a strategic asset across their business operations. This complexity arises from varying definitions of data value, diverse stakeholder perspectives, and the interdisciplinarity of data valuation. To address these challenges, this study develops a multi-criteria evaluation model based on the MACBETH approach to help Galp Energia, a Portuguese energy company, assess the value of data products in its renewables division. The developed model incorporates seven criteria across an enterprise architecture’s business, data, and application/technology layer, providing a comprehensive assessment of five data products. The study contributes to the literature by proposing a tailorable data valuation approach that may be applicable to other industries. Beyond quantifying the data product value, the resulting model serves as a managerial tool to support data-driven decision-making. The model is constructed using a robust approach and overcomes the limitations of existing models, such as oversimplification and practical implementation challenges. Additionally, it fosters interdisciplinary collaboration between research and industry. Future research directions include using the model as a foundation for developing modular data valuation frameworks, exploring its application across sectors, and integrating cross-sector benchmarks
