A comprehensive approach to visualize industrial data set to meet business intelligence requirements using statistical models and big data analytics

Abstract

Traditional business intelligence solutions are slower and less efficient in comparison to Big Data Analytics. Moreover, with the technology rapidly growing and reaching more number of people every day, the amount of data getting accumulated is increasing significantly. Consequently, reformation of industrial tabular datasets that are enormously huge in size into charts and graphs that provide statistical insights into the data is an important process in order to make intelligent business decisions and understand trends and patterns. This paper introduces an approach that could be utilized to carry out the above mentioned process for answering any type of business intelligence question by performing analytical techniques like Regression, Clustering, Classification and Association. For instance, tabular datasets that contain attributes of a certain object as columns would require a statistical analysis that could measure the dependability of one or more variable on the other variables and/or the relationship between the variables, in order to study the object better. We have studied the process of constructing outcomes out of raw data and deduced a series of steps starting from the collection of data to presentation of the output of models and algorithms being applied on the data that could be executed to enhance business strategies and understand data better.