Conceptualizing Analytics: A Conceptual-Modeling Perspective on Grasping Complex Information
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Business intelligence (BI) and data analytics encourages fact-based and rational decision-making. Yet, many small and medium-sized enterprises refrain from adopting BI technology because of the perceived costs and complexity of BI technology. Furthermore, analytical queries are hard to grasp and difficult to understand for many decision makers that are non-experts in BI technology or non-statisticians. This talk, based on experience from joint research projects between industry and academia, explores the role of conceptual modeling in data analytics. Conceptual-modeling approaches can help to overcome the obstacles that inhibit the proliferation of BI technology: Reference models facilitate the implementation of BI solutions, analysis graphs conceptualize the analytical process, thereby facilitating the use of data analytics for non-expert BI users and non-statisticians. Hetero-homogeneous models allow for a more comprehensive representation of complex, heterogeneous business situations, and semantic technologies foster an understanding of business situations beyond the numbers.