How To Manage Meta Data Within a Data Warehouse
Many large companies are now implementing data warehouses. They may use them to analyze specific areas such as customer service or financial issues. Many companies will create multiple goals that they will work towards by using data warehouses.
While this approach has worked well for some companies, this tactic is beginning to show a number of notable weaknesses. Both data and meta data are used with numerous data warehouses, and many of the technicians who manage these warehouses are trying to figure how to process and organize the meta data.
One of the main problems with contemporary data warehouse management strategies is that information changes rapidly. Because of this, it is difficult to be consistent when managing data warehouses. For example, what will a data warehouse manager do when the developer of an application decides to change definitions within the application? The impact that can be created by this and other issues has caused many organizations to rethink their data warehouse management strategies. Many of them are not placing an emphasis on the management of meta data. Being able to process meta data across a wide variety of different warehouses will make things easier.
One tool that can allow data warehouse managers to deal with meta data is called a repository. By using a repository, the meta data can be coordinated among different warehouses. By doing this, all the members of the organization would be able to share data structures and data definitions. The repository could act as a platform that would be capable of handling information from a number of different sources. This information could include programs from companies like Microsoft, IBM, or Oracle. In addition to this, the repository could also handle a wide variety of different tools.
One of the best advantages of using a repository is the consistency that will exist within the system. It will create a standard that can be understood among a number of different departments. If a new definition is created for a data mart implementation, a repository can support the change. A number of different departments would be able to share this information. In a nutshell, the transfer of information will become easier. It is important for the repository to function well over the lifecycle of the data warehouse. In order for this to occur, it is important to make sure the database and information is documented. A legacy model can help you in this area, and it can assist you in building a powerful data warehouse.
A repository can help data warehouse managers in a number of different ways. It can help you in the development phase, and it can also help lower the cost of maintenance. There are a number of things you will want to add to the repository to make it operate smoothly. One of the things you will want to add is a database management system. Using an industry standards tends to be better than a DBMS that was created by a vendor. You will have a number of advanced tools that can assist your in managing your database. In addition to this, it can also help you in the reporting process.
When you use an industry standard database management system, you can place an emphasis on the repository. If you decide to use a repository, it is important to make sure it is based on an entity/relationship structure. When you use an industry standard, it will be easier for your to customize information. There are a number of meta data extensions that should be supported by your repository. Some of these are adding an entity type, modifying an entity type, or modifying relationship characteristics. It will also be helpful if the vendor works with the Microsoft Open Information Model. Another thing that you can combine with a repository is API or application programming interface.
When an API is used, it will be easier for a company to custom build a meta data management system. The meta data will be separated from other tools, and this will make it easier to modify. Even if the data changes, there is no need to change the tools. By using these tools and strategies, a company can produce a data warehouse that is much more powerful and efficient.
