Data warehousing, managing data from beyond the every day operational system structure, is a valuable, proven approach to providing business users at all levels of the organization with the information they need to make high-impact decisions. The key of data warehousing is that data is stored for business purposes or for analysis can be more efficiently used if stored outside the operational system.
There are some logical transformation procedures involved in moving data from an operational system to a data warehouse. Data in a data warehouse is de-normalized for simplicity and performance. The de-normalization of data reduces the need for joins in a SQL query. A data warehouse model incorporates a de-normalized structure mainly for performance and simplicity.
The type of architecture deemed most efficient in creating a data warehouse is considered the three-level architecture. The three-level architecture allows data stored in a data warehouse to be customized by using data mart technology that provides single subject data to a small group of people that need that specific data, thus providing customized decision support.
The dimension model of a data warehouse must be created taking into account the business needs and detailed needs of the users. The star design, the best suited schema for a data warehouse technology, relates to business needs and supports simple queries. The star schema is a data modeling technique used to map multidimensional decision support data in a relational database that facilitates advanced data analysis requirements and allows an easily constructed model for multidimensional data analysis while still preserving the relational structures on which the operational database is built. The star schema has four components including facts, dimensions, attributes and attribute hierarchies.
Data warehouses were designed to solve several business and technical problems. Data warehouses shrink the length of time it takes between when business events occur and when executives become alert. Data warehouses also provide a complete picture by combining data from multiple sources. Data warehouses further contain years of data to support trend and seasonal analysis and give users tools for looking at the data differently. Lastly, data warehouses provide freedom from IS department resource limitations.
While there are many positives to adopting a data warehousing system, there are also drawbacks. These drawbacks include large costs from setting up the system and from maintenance, source data that is produced from the internal processes of the company and excess data creation. All of these drawbacks may not have a monetary value, but they certainly impact the bottom line of a firm in the long run.
According to industry analysts, the market for data warehousing products and services in government is exploding, fueled by a growing need by government and citizens to access and analyze data for a variety of purposes. The expansion is from government agencies, large amounts of data from legacy systems, e-commerce, growing business potential from decision support systems and business intelligence systems. Today data warehousing systems and business intelligence tools enable agencies to extract valuable information from their databases and deliver more useful services to citizens and other users.
