Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).
Based on that definition of Business Intelligence, we can say that Predictive Analytics actually falls under the umbrella of BI. How else could there be a decision support system without considering future plans and forecasts?
Instead of comparing Predictive Analytics with BI, it makes more sense to differentiate it with Descriptive Analytics (what traditional BI tools offer).
According to The Institute of Business Forecasting and Planning (“IBF”), “It is important to understand that all levels of analytics provide value whether it is descriptive or predictive, and all are used in different applications.”
Below are excerpts from IBF’s blog:
Descriptive Analytics: Data, data, data
The easiest way to define it is the process of gathering and interpreting data to describe what has occurred.
Descriptive analytics takes the raw data and, through data aggregation or data mining, provides valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why.
Predictive Analytics: The Future
Predictive analytics, no longer asks what happened, but why it happened, and what could happen in the future.
It brings together a number of data mining methodologies, forecasting methods, predictive models and analytical techniques to analyze current data, assess risk and opportunities, and capture relationships and make predictions about the future. At this stage you are no longer just asking what happened, but why it happened, and what could happen in the future.