The power of analytics grows when we move from batch analytics to real-time analytics and then to predictive analytics, but as always, the problem you are trying to resolve will always dictate the best method. 

Batch analytics processes historical data to a job and after a period of time presents the data results. It's commonly used when we're doing reporting or some kind of BI analysis. So we have years of data in your data warehouse or in logs, in spreadsheets and we want to get reporting to correlate this data to try to find interesting patterns like potential sales, potential profits we can get, or potential insights from research data.

This is quite different from real-time analytics where we need to get answers now. You and your application might not be able to wait hours or days to get an answer because if you lose time there can be serious consequences, like for example, fast reaction over security alerts from Intrusion detection systems, or reactions to an ad campaign.

And we also have the last type which is predictive analytics, which takes historical data as an input, it then learns from the history, and then leaves us predictions for future behaviors. This is a common case for machine learning like spam detection where based on past behavior we identify malicious messages, predicting and avoiding spam messages.

Lectures

Course Intro - Data Analytics - Data Types - When to Use Analytics - Data Analytic Process