Data and result in this level reflect basic facts of raw data. it tells what is happening, but don't tell the whys behind it. for example, a chart of voting geo distribution of Trump and Clinton.
In this level, we dive a bit into the data to see the "whys" behind the facts. gather other relative data, find the relationship between different data sets, use proven principle and reveals the hidden cause of facts.
When the analysis comes to this level. We have full and accurate data, both raw and cleaned data. we know the relationships between datasets. we know the causality of an event. Next, we leverage different prediction model to predict the future.
https://en.wikipedia.org/wiki/Predictive_analytics
Prediction is hard, any meaningful prediction need fully data and knowledge support.
In this level, the "People" or data analyst play the main role. data sets and results are tools. we make judgement and strategy to make a thing happen.
This kind of analysis is particularly important for a company to make a decision. which market to heading with? how to make event response strategy? sometimes, it is the most valuable result.
