Descriptive Data Analysis (DDA):
This analysis is performed to reduce data volumes and their complexity and represent, summarize and explain universal, bi- and multi-variance distribution of characteristics, attributes and variables with statistical indexes.
Confirmatory Data Analysis (CDA):
The confirmatory data is implemented in three directions:
- Apply the results obtained on a sample to the whole dataset
- Proof of hypotheses (e.g. customers with an instant access savings account and a personal equity plan)
- Checking the cause-effect relations
With the CDA the procedure is top-down, because it is based on a hypothesis which has to be proved. A tool used very often with the CDA is OLAP (online analytical processing).
Exploratory Data Ananlysis (EDA):
The exploratory data analysis or exploratory statistics is part of statistics. This explores only the data where only little is known about their relations. Many EDA techniques are used in data mining. The EDA is also used for the recognition of unknown patterns in data records or making forecasts.