What is the Data Warehouse Lifecycle?

The data warehouse lifecycle refers to the constantly changing process that data and archived data go through, in terms of computer storage, from the time the data is created and stored to the time where it is no longer needed and is archived in a company’s records. The data warehouse lifecycle system involves the creation of an efficient system for storing and retrieving computer data. Time has shown that simply dumping data into vast computer storage does not work. Instead, it is best to create a storage system, test it, and alter it as needed to fit the ever-changing data needs of the company.

Virtual warehouses are a similar concept as the older, paper card catalogs once found in most libraries. Each book had a card, arranged in alphabetical order, which allowed visitors to find the book they were looking for. As the library’s collection expanded, more cards had to be added and changes were made to better arrange the cards and accommodate their growing size. This same principle is used as the data warehouse lifecycle starts over again and more data must be added to an existing virtual storage system.

First in the data warehouse lifecycle comes the need for the data itself. This desire to easily access data creates a demand on the company to store that data in a way that allows for quick access regardless of whether the data is new or old. Thus the design stage comes first. This is a crude stage of development since designers often have only a vague idea of what types and how much data the system must handle.

Next come the prototype and testing phases. A working model of some or all of the data is created and tested by a limited group. When problems arise, they report to the programmers who fix the bug and update the prototype model. Once the model is ready for larger testing, it enters the operational stage and everyone can use the new data warehouse.

Finally, the programmers work to fix any kinks that appear once the software is operational. Even if a person programmed a perfect data warehouse system, she would still need to update it as the data warehouse lifecycle expands and changes. Changes are constantly made to the system to ensure it functions properly and is as easy to use as possible.