Data Analytics: The Real Cash Cow

Data used by corporations is sourced from both public domains (geo data, zip codes, weather and temperature data, population census, etc.) and is collected by the organization itself or a third-party vendor collecting crucial information such as retail data, customer details, purchase timings, foot traffic, etc. When embarking on solving a problem, like low sales or frequent stock-outs, there are certain hypotheses that form the questions, and their answers are often hidden in the data. One chooses factors (sales information, date, number of items, etc.) from the data that will help answer the question but bear in mind that sometimes the unlikeliest of factors can influence the witnessed outcome. After analyzing the factors for their weightage in the outcome as well as their relevance, a mathematical model appropriate for the problem at hand is designed, which is essentially an equation where the behavior of different factors (X1, X2, X3, and so on) is measured on the outcome, Y. Other times, the probability of Y occurring or not occurring is ascertained from the performance of the factors. It goes without saying that the larger the amount of data available for the relevant factors and how far back it goes historically, plays a critical role in the accuracy of the model and makes the result more bullet-proof.