Evolution of BI Strategies

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. 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. 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 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. published their highly-regarded special issue introductory article for business intelligence and analytics (BI & A) research in the journal MISQ in 2012, many researchers started investigating the third wave of BI & A. Several meta-studies exist that provide an overview on the status quo of the BI research field. All existing literature reviews have in common that they either focus on the first or second evolution stage of BI & A or they applied an unstructured or semi-structured search strategy, which increases the probability of an uncompleted or inadequate result set. The article at hand extends the existing body of knowledge by a structured investigation of the third wave of BI & A. We aim at drawing a comprehensive descriptive overview about research in the field of BI & A 3.0 between 2010 and 2018. Against this background, the paper at hand provides an answer to the following research question: How did information systems (IS) research address the emerging research area BI & A 3.0?

Thereby, our research contribution is fourfold. First, we develop a taxonomy to enable a rigorous classification of BI & A research results. Second, based on that taxonomy, we provide a structured and extensive overview on relevant and up-to-date big data research results within the IS discipline. Third, our results foster discussions about the predicted developments in the field, as described by Chen et al. and its differences to the observed developments in the past decade. Fourth, our analysis clearly reveals research gaps, in which no or only little research exists. We conceptualize these underrepresented research characteristics by suggesting a research agenda for future research in the field.

Source: Mathias Eggert and Jens Alberts, https://link.springer.com/article/10.1007/s40685-020-00108-y
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