While all three branches of Analytics – Descriptive, Prescriptive and Predictive Analytics have significant roles in the field of Data Analytics and Business Intelligence, it is Predictive Analytics, or the power to make future predictions and forecasts with analytics, that gets more attention from enterprises.
What and How
Predictive Analytics, which uses statistical algorithms and relies on data assets, is an optimal combination of Data Modeling and mining, Artificial Intelligence and Machine learning used to make sales and business forecasts and predict future events.
Businesses can use this specialized branch of analytics to power their business success and improve performance. However, it is important to mention there are several predictive models and the choice of a platform depends on the requirements and expectations.
Where and Why
Widely used in all industries and verticals, it helps to:
- Predict customer behavior, patterns and improve retention
- Optimize marketing campaigns and sales,
- Plan inventory and product placement
- Improve operations
- Detect fraud and reduce risks
- Lower costs
Predictive analytics has a significant impact in business applications. By a careful and timely analysis of historical data, companies can predict the success or failure of a product or offering in the market.
According to a recent IDG report, the most common challenges in business today are, “unconnected data sources, poor understanding of customer behavior, and poor analysis of sales potential.” All of these, however, can be overcome if the foundation of data is strong.
The foundation for predictive analytics is data assets and if a business relies on unconnected, fragmented data silos for its predictions, it might get skewed results. Unclean data can lead to more than just wrong analytics, it can lead to absolute disaster and failure of a business.
Predictive analytics aims to go above and beyond the realm of historical data to provide a near-accurate prediction of what might/will happen in future.