Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modelling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation, the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Analysis is focused on understanding the past; what happened. Analytics focuses on why it happened and what will happen next.
Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, Decision Tree, Logistic Regression, linear to multiple regression analysis, Classification to do predictive modelling. It also includes Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis.