Data Warehousing & Data Mining
The term “data mining” is often confused with OLAP. In OLAP, the user can carry out the what-if analysis, make forecasts based on the historical data, run weekly or monthly reports and obtain the results within seconds. OLAP analysis, while hints at, say, customer behavior, generally does not provide the benchmarks to segment the customers. Data mining provides these benchmarks. Data mining is data driven and not user driven as in OLAP. The current generation of data mining systems enables a user to launch a search process without actually knowing the answer to the query beforehand.
Moreover, data mining is the iterative process of discovering actionable and meaningful patterns, profiles and trends that can enable our clients to be more competitive, more productive and more efficient. In some cases, the implementation of data mining applications has unearthed millions of dollars in savings and new revenue opportunities. Byte Magazine reported that some companies have reaped returns on investment of as much as 1,000 times their initial investment on a single project. It is used to compare the general features of target class data objects with the general features of objects from one or a set of contrasting classes. The contrasting classes is specified by a user and the corresponding data objects retrieved through database queries. Data integration is used to combine data from multiple sources into a coherent data store, as in a data warehouse. These source may include multiple database, data cubes or flat files. Due to wide availability of huge amounts and the imminent need for tuning such data into useful information and