Data Warehousing Methodologies
Data warehousing methodologies vary in similarities. Though it is safe to say that there are some key features that data warehousing methodologies share, for the most part they are all relatively different. The key reason for data warehousing methodologies differences is due to software vendors. Businesses feel that their data warehousing methodologies are best suited for their customer needs and wants. This means that there are no universally accepted data warehousing methodologies. Usually data warehousing methodologies do share some similar features that are essential to the success of the data warehousing project.
Data warehousing methodologies, designing
Data warehousing methodologies are often broken up in segments. Perhaps the methodology is approached with a thought of dividing and conquering in mind. One of the pieces to the whole that often times are dealt with on its own is the designing of the data warehouse. Data warehousing methodologies involving design can be met with rapid prototyping or structured development. Rapid prototyping is mostly used in smaller project that are not expected to take very long. While structured prototyping is used when the users wish to undertake a larger more complex project which could involve multitude of data sources.
Data warehousing methodologies, development
Data warehousing methodologies must include a development stage. When the design of the data warehouse is finalized the development stage may begin, users need to devote a lot of their energy and resources into making sure that everyone is on the same page. The development stage of data warehousing methodologies needs to be coordinated and handled carefully before any testing can be done. This means that upgrades need to be made if necessary, installing the proper software and hardware, and developing a good guideline for the team. Businesses can have the option of skipping this step if they choose to outsource the project, but that would still require constant involvement and keeping up with the project and whom ever they outsourced it to.
Data warehousing methodologies, deployment
Data warehousing methodologies also include the deployment stage, once again this is another step that simply cannot be avoided, and is part of all data warehousing methodologies. Data warehousing methodologies will also focus on testing and quality assurance, but those are normally done before the system is deployed, none the less they are just as important as any steps before and after it. Deployment may prove somewhat difficult if the entire staff, including management, is not fully on board and aware of what is taking place.
Data warehousing methodologies, maintenance and upkeep
Data warehousing methodologies should have also stressed the importance of maintaining and continue running a successful data warehouse. Often times this will include upgrading both hardware and software so that the best equipment is being utilized. Your trained staff should constantly be retrained so that they know how to take full advantage of any new functions in both the hardware and software. If problems are encountered, a vendor may be contacted for technical support. Fresh data will be made available, being able to cleanse and place these into the warehouse will also be part of the upkeep and maintenance process.