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Theory-driven or Process-driven Prediction?: Epistemological Challenges of Big Data Analytics
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-4250-4752
German University in Cairo (GUC).
2017 (English)In: Journal of Big Data, E-ISSN 2196-1115, Vol. 4, no 1, 19Article in journal (Refereed) Published
Abstract [en]

Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence of data processing while seemingly abstaining from being theoretically informed about the subject matter. This paper discusses how to deal with the shift from theory-driven to process-driven prediction through analyzing the BDA steps and identifying the epistemological challenges and various needs of theoretically informing BDA throughout data acquisition, preprocessing, analysis, and interpretation. We suggest a theory-driven guidance for the BDA process including acquisition, pre-processing, analytics and interpretation. That is, we propose—in association with these BDA process steps—a lightweight theory-driven approach in order to safeguard the analytics process from epistemological pitfalls. This study may serve as a guideline for researchers and practitioners to consider while conducting future big data analytics.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 4, no 1, 19
Keyword [en]
Big Data Analytics, epistemological challenges, information systems theories, predictive research
National Category
Computer Systems
Research subject
Information systems
Identifiers
URN: urn:nbn:se:ltu:diva-63813DOI: 10.1186/s40537‑017‑0079‑2OAI: oai:DiVA.org:ltu-63813DiVA: diva2:1107123
Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-06-26Bibliographically approved

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