Event analytics is a methodological randomization practice of exploring organizational data, with an emphasis on statistical analysis. It is used by companies committed to data-based decision making. The purpose of the analysis is to cultivate large corporate data sets and assist in the decision-making process. Event analysis is one of the most important event domains in the world. It has become an integral tool that determines the company’s growth strategy.
Event analytics starts with a data set (data set or simple data file) or usually with a database (a collection of data files containing information about people, locations, etc.). Data has become the company’s most important asset and they use their resources to find important information and insights that benefit their company directly. It describes the current state of the company by tracking key metrics and determining trends from the current data set. The purpose of this type of analysis is to determine what has happened. It provides a primary data processing method to proceed further. It also analyzes how data looks like today and identifies future behavior.
For ex bar chart location for travel companies who want to target customers based on location. This is the most important and sophisticated analysis that creates a model for predicting the occurrence or performance of a particular product by using a collection of historical and current data. It is generally an area of data of scientists and data analysts who construct predictive data models using advanced algorithms, regression analysis, time series analysis, decision trees. This becomes even more important with big data and financial firms have become the primary users, to determine events before they happen. Example: Multiple regression is used to show the relationship (or lack of relationship) between age, weight, and exercise on dietary food sales.
Event analytics prescriptive: This determines the best solution for a particular problem when a series of different solutions are presented. It also provides decision options by processing new data to improve the accuracy of predictions and decision choices. It is a combination of science and science knowledge that provides the best path for a particular path. Example: Sports stores have limited marketing budgets to target customers.
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