Businesses of all sizes and in different industries, as well as government agencies, are finding that they can realize significant benefits by implementing a data warehouse. It is generally accepted that data warehousing provides an excellent approach for transforming the vast amounts of data that exist in these organizations into useful and reliable information for getting answers to their questions and to support the decision making process. A data warehouse provides the base for the powerful data analysis techniques that are available today such as data mining and multidimensional analysis, as well as the more traditional query and reporting. Making use of these techniques along with data warehousing can result in easier access to the information you need for more informed decision making.

Organizations have vast amounts of data but have found it increasingly difficult to access it and make use of it. This is because it is in many different formats, exists on many different platforms, and resides in many different file and database structures developed by different vendors. Thus organizations have had to write and maintain perhaps hundreds of programs that are used to extract, prepare, and consolidate data for use by many different applications for analysis and reporting. Also, decision makers often want to dig deeper into the data once initial findings are made. This would typically require modification of the extract programs or development of new ones. This process is costly, inefficient, and very time consuming. Data warehousing offers a better approach.

Data warehousing implements the process to access heterogeneous data sources; clean, filter, and transform the data; and store the data in a structure that is easy to access, understand, and use. The data is then used for query, reporting, and data analysis. As such, the access, use, technology, and performance requirements are completely different from those in a transaction-oriented operational environment. The volume of data in data warehousing can be very high, particularly when considering the requirements for historical data analysis. Data analysis programs are often required to scan vast amounts of that data, which could result in a negative impact on operational applications, which are more performance sensitive. Therefore, there is a requirement to separate the two environments to minimize conflicts and degradation of performance in the operational environment. We drive ourselves hard to create real value for you and exceed your expectations.