Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda
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Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda Mathias Eggert1
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Jens Alberts2
Received: 7 August 2019 / Accepted: 5 March 2020 The Author(s) 2020
Abstract Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda. Keywords Business intelligence Big data Data analytics Literature review Taxonomy development
& Mathias Eggert [email protected] Jens Alberts [email protected] 1
University of Applied Sciences Aachen, Eupener Str. 70, 52066 Aachen, Germany
2
ERCIS: University of Mu¨nster – European Research Center for Information Systems, Leonardo Campus 3, 48149 Muenster, Germany
123
Business Research
1 Introduction Since the beginning of data analysis in the early 1950s, researchers have been interested in developing new methods to provide insights into data using business intelligence (BI) tools that enable to produce and capture a large quantity of data (Davenport 2013; Davenport and Harris 2007). Until the early 1990s, structured data, such as numeric data in tables, dominated the area of data analysis. Techniques and corresponding research relied on data collection, extraction, and analysis capabilities (Chen et al. 2012). After this first evolution stage of data analysis research, the upcoming of unstructured data, such as video streams, music or text files, led to an exponential increase of data to be analyzed. The term big data, which describes the change of data in volume, velocity, variety, and veracity (Chen et al. 2012; Davenport 2013) was born. The new and even more changing third wave of data analytics began with new data sources, such as mobile devices and wireless connected sensors. Both enable advanced opportunities of collecting and analyzing data. Since Chen et al. (2012) published their highly-regarded special issue introductory article for bu
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