Analytics is the process of using computational methods to discover and report influential patterns in data. The goal of analytics is to gain insight and often to affect decisions. Data is necessarily a measure of historic information so, by definition, analytics examines historic data. The ideas behind analytics are not new at all but have been represented by different terms throughout the decades, like data analysis, neural networks, pattern recognition, statistics, knowledge discovery, data mining, and now even data science
The rise of analytics in recent years is practical: As organizations collect more data and begin to summarize it, there is a natural progression toward using the data to improve estimates, forecasts, decisions, and ultimately, efficiency
Predictive analytics is the process of discovering interesting and meaningful patterns in data. It draws from several related disciplines, some of which have been used to discover patterns in data for more than 100 years, including pattern recognition, statistics, machine learning, artificial intelligence, and data
Predictive Analytics vs. Other Analytics
Predictive analytics is data-driven. Algorithms derive key characteristic of the models from the data itself rather than from assumptions made by the analyst.The induction process can include identification of variables to be included in the model, parameters that define the model, weights or coefficients in the model, or model complexity
Predictive analytics algorithms automate the process of finding the patterns from the data. Powerful induction algorithms not only discover coefficients or weights for the models, but also the very form of the models.
Predictive analytics doesn’t do anything that any analyst couldn’t accomplish with pencil and paper or a spreadsheet if given enough time; the algorithms, while powerful, have no common sense