Decisions based on data are crucial for the successful operation of modern companies. The fundamental part of decision making and knowledge creation is the business intelligence process. The effectivity of business intelligence tools depends on many factors. One factor of major importance is data quality. From the perspective of business intelligence data quality is related to multiple dimensions including those connected to the understanding of data. The aim of this paper is to improve the data understanding process in the existing typical business intelligence architecture by adding specific knowledge layers. An explicit data knowledge layer should be connected to the existing metadata layer. Data governance principles suggest setting up an ownership structure in data processes which also allows access to tacit knowledge. The practical value of the inclusion of the suggested knowledge layers in the existing business intelligence architecture is confirmed via a real business case study from the banking sector. The selected case study reflects the manner in which the current metamodel contributes to the big data phenomenon by improving its value element within the context of collaborative decision making in big organizations by using quality data that stems from tacit knowledge, and via a synergetic functionality of business intelligence and knowledge management.
Business intelligence, Data knowledge, Data quality, Tacit knowledge, Explicit knowledge, Metamodel.
Martin ZELENKA, Athanasios PODARAS, "Increasing the Effectivity of Business Intelligence Tools via Amplified Data Knowledge", Studies in Informatics and Control, ISSN 1220-1766, vol. 30(2), pp. 67-77, 2021. https://doi.org/10.24846/v30i2y202106
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).