%0 Conference Proceedings
%T From Qualitative to Quantitative Data Valuation in Manufacturing Companies
%+ Saarland University [Saarbrücken]
%+ Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI)
%+ Institute for Industrial Management [RWTH Aachen University] (FIR e.V. an der RWTH Aachen)
%A Stein, Hannah
%A Holst, Lennard
%A Stich, Volker
%A Maass, Wolfgang
%Z Part 3: Engineering of Smart-Product-Service-Systems of the Future
%< avec comité de lecture
%@ 978-3-030-85901-5
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Nantes, France
%Y Alexandre Dolgui
%Y Alain Bernard
%Y David Lemoine
%Y Gregor von Cieminski
%Y David Romero
%I Springer International Publishing
%3 Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
%V AICT-631
%N Part II
%P 172-180
%8 2021-09-05
%D 2021
%R 10.1007/978-3-030-85902-2_19
%K Data value
%K Data valuation framework
%K Industry 4.0
%K Intangible assets
%K Case study research
%Z Computer Science [cs]Conference papers
%X Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-04117655/document
%2 https://inria.hal.science/hal-04117655/file/520755_1_En_19_Chapter.pdf
%L hal-04117655
%U https://inria.hal.science/hal-04117655
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-631