Predictive analytics is the practice of applying mathematical models to large amounts of data to identify patterns of previous behavior and to predict future outcomes.
The combination of data mining, machine learning and statistical algorithms provides the “predictive” element, allowing predictive analytics tools to go beyond simple correlation. In business, predictive analytics has a wide variety of uses, including:
- Reducing risk in insurance and financial services
- Detecting credit card fraud
- Allowing more accurate predictions of supply and demand
- Identifying threats and issues affecting computer networks
More and more software tools are incorporating predictive analytics, making it much more accessible to users in organizations of any size. Predictive analytics provides a real opportunity to “lift all boats,” offering practical value to users who are not trained in data science or advanced analytics. This trend is often referred to as “data democratization,” the concept of making data available across an organization so that anyone can use it to make better decisions.
Below, we’ll look at why predictive analytics is valuable, how it relates to other technologies like machine learning and data mining, the role of models, and some tips on getting started.