In addition, there is autonomy of processing at the spoke and no necessary redundancy of data at the spokes. And there is a rich amount of history at the hub. In the early days, when there was a nascent data warehouse, classic data normalization was the basis for design. As independent data marts emerged, star joins and snowflake structures became the norm for design. Once the warehouse and its architectural components are built, the next part involves exploiting the warehouse.
Data mining is the next logical step in completing the circle of effective decision support.
With data mining, you can discover important business patterns, examine relationships between obscure and otherwise unnoticed variables, and measure long-term trends. In short, data mining fulfills many of the expectations of data warehousing. The answer is that data mining can be done with no data warehouse or data marts at all. But just because data mining can be done does not mean that data mining can be done effectively. The answer is that data warehousing is absolutely essential for effective data mining.