A Data-Driven Decision-Making Readiness Assessment Model: The Case of a Swedish Food Manufacturer
2024 (English)In: Decision Analytics Journal, ISSN 2772-6622, Vol. 10, article id 100405Article in journal (Refereed) Published
Abstract [en]
This study proposes a model to assess data-driven decision-making (DDDM) readiness in organizations. We present the results from investigating the DDDM readiness of a Swedish organization in the food industry. We designed and developed a questionnaire to collect data about the organization’s decision-making and IT systems. We conducted eleven interviews at the case study organization: ten with various functional decision-makers and one with the IT Manager about IT systems. The interview data were then analyzed against known decision theories and state-of-the-art DDDM. Based on the interview outcomes, we analyze the data according to the assessment model and recommend changes to the organization’s readiness for data-driven decisions. The findings show that while the organization was assessed as ready in the decision-making process and decision-maker pillars, it was not ready in the data or analytics pillars. Accordingly, we recommend a set of actions, including considering integration and decision systems, further developing dashboards, increasing data and analytics resources (such as enterprise data warehouse, big data management tools, data lake environment, and data analytics algorithms), and defining key roles necessary for digitalization and DDDM (such as Data Engineer, Data Scientist, Business Intelligence Specialist, Chief Data Officer, and Data Warehouse Designer/Administrator). The contribution of this study is the DDDM readiness assessment model, accompanied by a questionnaire for determining the readiness level in organizations.
Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 10, article id 100405
Keywords [en]
Data-driven decision-making, decision theory, information technology, case study, Swedish food industry
National Category
Computer Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:ltu:diva-103896DOI: 10.1016/j.dajour.2024.100405Scopus ID: 2-s2.0-85184241410OAI: oai:DiVA.org:ltu-103896DiVA, id: diva2:1830704
Note
Validerad;2024;Nivå 1;2024-01-30 (signyg);
License full text: CC BY-NC-ND
2024-01-232024-01-232024-11-20Bibliographically approved