The growing complexity of the business environment forces companies to be able to make decisions rapidly and effectively. This requires knowing how to manage internal processes and making sure that data support decisions. The strategic use of data not only supports cost reduction and increased efficiency but also allows us to reveal new opportunities by facilitating the emergence of hidden or unknown paths. For example, the analysis of hundreds of demographic and health variables may help predict the risk associated with hospital admission (Valentini, 2017) or prevent injuries in professional footballers (Davenport, 2006). Broadly speaking, the fields of possible applications of big data (BD) and business analytics (BA) are practically immeasurable. Today’s available technologies allow us to analyse vast quantities of data coming from many different sources. For example, the emergence of the internet of things has opened the unexpected possibility of gathering data from every object connected to the internet. The value of this amount of data lies in the possibility of analysing and learning from it by relating different data to produce new knowledge. Some scholars suggest that ‘big data is possibly the most significant “tech” disruption in business and academic ecosystems since the meteoric rise of the internet and the digital economy’ (Agarwal & Dhar, 2014, p. 443). At the same time, companies in both the private and public sectors run the risk of being overwhelmed by data (Schniederjans et al., 2014), missing the above-mentioned opportunities. For these reasons, BD and BA have started to attract the interest of business and management scholars with regard to their implications for the business environment. This chapter focuses on their role and provides an overview of how modern technologies may foster their use for a wider variety of organisations and how they will impact the possibility of being more competitive and profitable.

Big Data and Business Analytics: Definitions and Implications in the Business Environment

Marisa Agostini;
2021-01-01

Abstract

The growing complexity of the business environment forces companies to be able to make decisions rapidly and effectively. This requires knowing how to manage internal processes and making sure that data support decisions. The strategic use of data not only supports cost reduction and increased efficiency but also allows us to reveal new opportunities by facilitating the emergence of hidden or unknown paths. For example, the analysis of hundreds of demographic and health variables may help predict the risk associated with hospital admission (Valentini, 2017) or prevent injuries in professional footballers (Davenport, 2006). Broadly speaking, the fields of possible applications of big data (BD) and business analytics (BA) are practically immeasurable. Today’s available technologies allow us to analyse vast quantities of data coming from many different sources. For example, the emergence of the internet of things has opened the unexpected possibility of gathering data from every object connected to the internet. The value of this amount of data lies in the possibility of analysing and learning from it by relating different data to produce new knowledge. Some scholars suggest that ‘big data is possibly the most significant “tech” disruption in business and academic ecosystems since the meteoric rise of the internet and the digital economy’ (Agarwal & Dhar, 2014, p. 443). At the same time, companies in both the private and public sectors run the risk of being overwhelmed by data (Schniederjans et al., 2014), missing the above-mentioned opportunities. For these reasons, BD and BA have started to attract the interest of business and management scholars with regard to their implications for the business environment. This chapter focuses on their role and provides an overview of how modern technologies may foster their use for a wider variety of organisations and how they will impact the possibility of being more competitive and profitable.
Intellectual Capital, Smart Technologies and Digitalization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3745372
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