Data Warehousing and its Relationship to Data Mining
Data Warehousing is the process of consolidating data from multiple sources into one or more query databases. The availability of clean data in data warehouses makes data mining easier, but if you want results now, consider data mining as a data exploration and cleaning prelude to the design, care and feeding of the data warehouse.
Data Warehouses are intended to support queries and, in some cases, multidimensional analysis (on-line analytical processing, or OLAP). Performance considerations may dictate doing the data mining on a separate platform anyway.
Multidimensional Analysis (OLAP)
For example: Consider the data space indicated by the Rubik's cubes at left. Blue indicates the markets for a company's products. Red indicates the time dimension, and Yellow includes information on product sales.
Product managers can analyze the sales of one product across many time periods and markets.
Financial managers can focus on sales over time for all markets and all products.
Regional managers can focus on specific markets for all products sold in those markets.
Other planners can focus on a subset of the corporation's data, such as recent sales for a specific product in a specific market.
Strategic planners are able to analyze trends found in all dimensions of the data.
Nautilus Systems refers you to our case studies to see the results we have delivered by applying multidimensional data analysis to corporate data.
The key is the integration of multiple disciplines: traditional database management technologies, very large databases, artificial intelligence (machine learning), statistics, geographic information systems, networking, communications, and Internet/intranet technologies. The new paradigm is emerging as methodologies mature supporting the development and implementation of data warehouses and the knowledge discovery and query techniques for exploiting them.