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Towards a Data Business Maturity Model for Software-intensive Embedded System Companies
LUT University,Dept. Software Engineering,Lahti,Finland.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
Chalmers University of Technology,Dept. Computer Science and Engineering,Göteborg,Sweden.
2023 (English)In: 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive companies.This multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Series
International ICE Conference on Engineering, Technology and Innovation, ISSN 2334-315X, E-ISSN 2693-8855
Keywords [en]
Data business, embedded system, data economy, data-driven engineering, multiple case study
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:mau:diva-64745DOI: 10.1109/ice/itmc58018.2023.10332290Scopus ID: 2-s2.0-85181128752ISBN: 979-8-3503-1517-2 (electronic)ISBN: 979-8-3503-1518-9 (print)OAI: oai:DiVA.org:mau-64745DiVA, id: diva2:1822676
Conference
2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Edinburgh, United Kingdom, 19-22 June 2023
Available from: 2023-12-27 Created: 2023-12-27 Last updated: 2024-02-05Bibliographically approved

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Olsson, Helena Holmström

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