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Predictive Analysis

Predictive analysis is the process of using historical data to make predictions about the future.

It is used in almost every industry, including financial, manufacturing and commerce. For example, data analytics has been used frequently by marketing managers who have engaged with big data from multiple apps and websites, online clicks and other ways to predict customer behavior. The vast, unstructured data that is collected through data mining is then delivered and packaged in an interactive format using dashboards to signify profits, conversion rates, averages and percentages.

The goal of data, then, is to search for patterns and find relationships between variables using inputs from human beings. If one assumption and hypothesis is valid, additional testing on the data continues. Data analytics, based on past events, is descriptive and does not predict the impact of a change in any variable.

A natural segue of data analytics is to predict using the collected data of what might happen. One differentiating factor is the elaborate methodologies, technologies, and infrastructure involved in predictive analysis. The predictions are based on historical data and where human interaction controls data query, pattern validation, and testing assumptions.

Of course, assumptions drawn from past experiences presupposes the future will follow the same patterns. “What if” assumptions based on the understanding of the past are used in this analysis, which is limited by the volume, time and cost constraints of human data analysts.

The insights gained by these predictions are extremely useful to marketers to predict campaign effectiveness, decision-making on collateral, geographic markets and targeted demographics. One of the most famous application scope is big data analytics, which aims to unveil patterns, subtle correlations, and trends to empower decision-making processes.

Machine learning is an extension of the concepts around predictive analytics, with one key difference – AI is able to make assumptions, test and learn autonomously.

Every modern business sector can gain maximum potential from predictive analytics. The key ingredient being the possession of sufficiently large, varied, detailed, and reliable historical and “intelligent” data in order to optimize their processes. This can be applied to fraud detection, processes optimization, costs reductions, market trends anticipation, and the discovery and innovation of new business opportunities.