Data warehouses and data marts are used in a wide range of applications. Business executives use the data in data warehouses and data marts to perform data analysis and make strategic decisions. In many firms, data warehouses are used as an integral part of a plan-execute-assess “closed-loop” feedback system for enterprise management. Data warehouses are used extensively in banking and financial services, consumer goods and retail distribution sectors, and controlled manufacturing, such as demand-based production.
Typically, the longer a data warehouse has been in use, the more it will have evolved. This evolution takes place throughout a number of phases. Initially, the data warehouse is mainly used for generating reports and answering predefined queries. Progressively, it is used to analyze, summarized, and detailed data, where the results are presented in the form of reports and charts. Later, the data, the warehouse are used for strategic purposes, performing multidimensional analysis and sophisticated slice-and-dice operations.
Finally, the data warehouse. may be employed for knowledge discovery and strategic decision-making using data mining tools. In this context, the tools for data warehousing can be categorized into access and retrieval tools, database reporting tools, data analysis tools, and data mining tools.
Types of Datawarehouse Applications —
There are three kinds of data warehouse applications. They are as follows
It supports querying basic statistical analysis and reporting using crosstabs, tables, charts, or graphs. A current trend in data warehouse information processing is to construct low-cost Web-based accessing tools that are then integrated with Web browsers.
Analytical Processing —
It supports basic OLAP operations, including Slice-and-dice, drill-down, roll-up, and pivoting. It generally operates on historical data in both summarized and detailed forms. The major strength of online analytical processing over information processing is the multidimensional data analysis of data warehouse data.
Data Mining —
It supports • knowledge discovery by finding hidden patterns and associations constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools.