From Qualitative to Quantitative Data Valuation in Manufacturing Companies

Hannah Stein, Lennard Holst, Volker Stich, Wolfgang Maaß

In: APMS 2021 Conference Advances in Production Management Systems. IFIP Advances in Production Management Systems (APMS-2021) Artificial Intelligence for Sustainable and Resilient Production Systems September 5-9 online France Springer 2021.


Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consist-ently and comprehensively by manufacturing companies. Currently, at-tempts 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 de-termine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to trans-fer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper con-tributes 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 de-rived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Plac-ing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence