Predictive analytics helps to connect data to effective action by drawing reliable conclusions based on historical data and statistical models. Predictive analytics functions effectively in customer churn prediction, marketing campaign growth, credit scoring, and revenue forecasting.

Predictive analysis is all about making use of huge volumes of data for getting insights about trends and anticipating the consequences. Usually, data is collected into a dark lake which contains information in a raw state like structured (tabular form), semi-structured (XML format), or unstructured (social network comments). However, it is mandatory to understand the differences and implement the right tool.

Even though predictive analysis completely relies on big data, statistics is still used to test and validate the assumptions. This has a specific hypothesis about consumers’ behavior and conditions that indicate fraud. Statistically, these are put to the test and decisions are made based on numbers, not on hunches.

The very nature of a predictive model is that it may lose accuracy over time. Reality is not static, and neither is data. A model may be valid for a certain period only. It’s a good practice to revise the models periodically and test with new data making sure that they haven’t lost their significance. Key performance indicators should be used to know what the model should predict and how. Collected data that is relevant is transformed to fit the model’s framework. Once this is completed, data scientists split data into two frames, one for building model and other for testing the accuracy of its predictions.

A survey conducted by Deloitte on analytics advantage shows that analytics promote better decision making (49 percent), ensuring key strategic initiatives (16 percent), and fostering a better relationship with customers and business partners (10 percent). Predictive analytics also helps businesses achieve competitive advantage (68 percent), find new revenue opportunities (55 percent), and increase profitability (52 percent). Among different predictive analytics methods, ANN (artificial neural networks) and ARIMA (autoregressive integrated moving average) are two of the most effective approaches. Implementing predictive analytics is highly important for any businesses today; however, using the appropriate approach, understanding the right requirements, and implementing the solution in relevance is the key to a successful business.