SummaryThe article identifies competition which exerts strong pressure on improving efficiencies, effectiveness, and to strengthen business and customer relations as prime factor for big data analysis. Competition requires data analysis to support managerial and operational decisions, enabling a higher level of competitions.
Davis (2014) also points out that Big Data builds upon well-established and widely used approaches such as (i) multivariate inferential and descriptive statistical methods, machine learning and mathematical modelling techniques for performing analyzes, and (ii) database management, data warehouses, data mining and dashboards for managing these data. According to Davis the main driver for Big Data is the ever-expanding cycle of change caused by the need to evolve in order to stay competitive and its interaction with analytics and big data technologies as means to improve competitiveness. The systematic analysis of complex data for decision making enables companies to operate more efficiently at all levels, improves their overall decision making and their ability to conduct business intelligence. In conclusion, big data analytics will eventually be completely integrated into and support the managerial functions of successful companies.
BackgroundThe three-V's of big data:
- volume (large data volumes)
- velocity (data is updated quickly and frequently)
- variety (a large number of different sources and formats)