OLAP and data mining

Many senior managers and executives say they like businesss intelligence systems/EIS. But majority of data warehouse users still using Excels, reporting and data analysis tools, or their own customized applications to draw data from warehouses and transform it into business reports and charts. In general, these approaches work fine static data analysis with small amount of data.

Multidimensional databases and reporting system usually generate attractive sales presentations and demonstrations. Sales information can be viewed from various dimensions such as region, product type, time and sales person. OLAP enables users to explore enterprise data from different perspectives.

OLAP servers and desktop tools support high-speed analysis of data. Many verdors provide OLAP tools including Microsoft, SAP, Oracle and Cognos. Data manipulaiton in multidimensional databases can be very fast because they store the data in structures denormalized and optimized for speed. But multidimensional databases take huge amount of time to update. Software developers are attempting to deal with the update issue through the use of partitioning.

Data is valuable asset to organizations because it enables decision making. OLAP along with data mining, when incorporated into a data warehousing products, help decision makers analyze historical data and extract hidden patterns in data. OLAP provides drill down and roll up data analysis. Data mining tools enables supervised and unsupervised learning. Multidimensional analysis requires users to interact with the database to find information in the database. Data mining tool does not require users to specify a problem to be examined. For example a data mining tool in a supermarket database can find out which products customers usually buy together. Then, supermarket can provide special offers on these products or put them in adjacent shelves to generate more sales.