However, it does not provide more fundamental characteristics or draw a clear border around what is or should be in a data warehouse. To help make these distinctions, it is helpful to use concepts from the classical definition of data warehousing. They do not have the same atomic entity focus as OLTP systems. That is, if two different source systems store conflicting data about entities, or attributes of an entity, the differences need to be resolved during the process of transforming the source data and loading it into the data warehouse. One of the key characteristics distinguishing warehouses from operational environments is the currency of the data.
Operational systems require real-time views of the data.
Data warehouse applications generally deal with longer term, historical data. They can also provide access to a greater volume of more detailed information, as required, over the longer time period. The content of OLTP systems are, by their nature, continuously changing. Inserts, deletes, and updates form the basis of a large volume of business transactions that result in a very volatile set of data. By contrast, data warehouses are static.