Business analytics

What is it about?

To sustain competition in current business environment, managers have to be more decisive to offer high-quality goods/services at cost-effective rates. In addition to the routine projections and trend analyses, they require the knowledge of advanced research techniques to make quick and accurate decisions. Data in its raw form is usually useless, and the driving force behind any data-driven organization is insights and conclusions drawn from the data, which can suggest a new course of action. In order to draw insights and reach conclusions, managers need analytical tools and techniques to interpret data from various sources and use the results for better decision-making. Further, business analytical tools also help the researchers and academicians in better theory development. Many researchers have claimed that the availability of business analytics has played a great role in converting organizations into high-performance work systems. Companies using these techniques in their decision-making process are in a better position to compete and sustain competitive advantage by minimizing risk, investing in accurate innovations, and above all providing a better picture of what is practically viable and non-viable.The significance of the analytical needs can be judged from the fact that a significant proportion of high-performance companies have high analytical skills among their personnel. And companies employing data analytical methods and techniques in their decision-making process are in a better position to compete and sustain competitive advantage. Among the various statistical techniques, structural equation models (SEMs), including confirmatory factor analysis, help in both theory building and predictive analysis, and their roles have become more crucial with the advent of big data. Carrying out predictive modelling on large data sets has the potential to generate fresh insights for business practitioners and drive new theories for management researchers. Addressing this need, our efforts in this context are to fill the extant gap and help managers and entrepreneurs in knowledge-based decision-making.

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Why is it important?

The significance of the analytical needs can be judged from the fact that a significant proportion of high-performance companies have high analytical skills among their personnel. And companies employing data analytical methods and techniques in their decision-making process are in a better position to compete and sustain competitive advantage. Among the various statistical techniques, structural equation models (SEMs), including confirmatory factor analysis, help in both theory building and predictive analysis, and their roles have become more crucial with the advent of big data. Carrying out predictive modelling on large data sets has the potential to generate fresh insights for business practitioners and drive new theories for management researchers. Addressing this need, our efforts in this context are to fill the extant gap and help managers and entrepreneurs in knowledge-based decision-making.

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