Three-level databases are the most advanced ones. The main difference between them and the two-level ones is the presence of the reconciliation stage, which is performed through Extraction, Transformation, and Load (ETL) instruments. To understand the relevance of such kinds of instruments, we can resort to a practical example once again, and to the one we were taking advantage of some lines previously: the probability of the default model.

Imagine we are estimating such kind of model for customers clustered as large corporate, for which public forecasts, outlooks and ratings are made available by financial analyses companies like Moody's, Standard & Poor, and similar.

Since this data could be reasonably related to the probability of default of our customers, we would probably be interested in adding them to our estimation database. This can be easily done through the mean of those ETL instruments. These instruments will ensure, within the reconciliation stage, that data gathered from internal sources, such as personal data and default events data, will be properly matched with the external information we have mentioned.

Moreover, even within internal data fields only, those instruments will ensure the needed level of quality and coherence among different sources, at least within the data warehouse environment.