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Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective

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Digital Transformation

Abstract

Business intelligence and data analytics projects often involve low-level, ad hoc data wrangling and programming, which increases development effort and reduces usability of the resulting analytics solutions. Conceptual modeling allows to move data analytics onto a higher level of abstraction, facilitating the implementation and use of analytics solutions. In this chapter, we provide an overview of the data analytics landscape and explain, along the (big) data analysis pipeline, how conceptual modeling methods may benefit the development and use of data analytics solutions. We review existing literature and illustrate common issues as well as solutions using examples from cooperative research projects in the domains of precision dairy farming and air traffic management. We target practitioners involved in the planning and implementation of business intelligence and analytics projects as well as researchers interested in the application of conceptual modeling to business intelligence and analytics.

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Acknowledgements

We thank Ilko Kovacic for permission to adapt his figures on enriched multidimensional models and OLAP patterns. We thank Median Hilal for feedback on the graphical presentation of analysis graphs.

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  1. Institute of Business Informatics – Data & Knowledge Engineering, Johannes Kepler University Linz, Linz, Austria

    Christoph G. Schuetz & Michael Schrefl

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  1. Christoph G. Schuetz
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Correspondence to Christoph G. Schuetz .

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  1. Technische Universität München, Garching b. München, Bayern, Germany

    Birgit Vogel-Heuser

  2. Johannes Kepler University Linz, Linz, Oberösterreich, Austria

    Manuel Wimmer

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Schuetz, C.G., Schrefl, M. (2023). Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective. In: Vogel-Heuser, B., Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65004-2_13

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