Business Intelligence in Tourism

Wolfram Höpken () and Matthias Fuchs ()
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Wolfram Höpken: Ravensburg-Weingarten University of Applied Sciences
Matthias Fuchs: Mid-Sweden University

Chapter 21 in Handbook of e-Tourism, 2022, pp 497-527 from Springer

Abstract: Abstract Business intelligence encompasses all activities dealing with collecting, storing/managing, and analyzing business-relevant data with the objective of generating knowledge as input to decision support. Business intelligence is often used as an umbrella term for data warehousing, reporting and OLAP (online analytical processing), MIS/DSS, and data mining, respectively. If we count all topics listed above, it is obvious that business intelligence has quite a long history also in the tourism domain. As early examples in tourism, we can identify the DINAMO system introduced by American Airlines already in 1988 or TourMIS in 1998. The widespread use of ICT, especially the uptake of the Internet and social media, led to an increase of available data on customers, competitors, and the whole market in all major business domains, including tourism. More powerful hardware and sophisticated methods to store and analyze such data turned business intelligence into one of the fastest-growing technologies and most challenging areas in the last decade. This chapter gives an overview on the topic of business intelligence and all technical components of a BI architecture (i.e., information extraction and transformation, data warehousing, and different mechanisms and tools to access and analyze data, like reporting or OLAP tools, dashboards, or data mining toolsets). Moreover, the chapter looks at the history of BI in tourism and presents and discusses typical application scenarios in tourism. Finally, we look at current trends and latest developments in the area of business intelligence and their expected implications for the tourism domain.

Keywords: Business intelligence; Data warehousing; Data analysis; Online analytical processing; Management information systems; Decision support systems (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-48652-5_3

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