Abstract
AbstractBusiness 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.KeywordsBusiness intelligenceBusiness analyticsData analyticsConceptual modeling
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