GM Locomotive should have provided for certain required things in order for the project to be success instead of failure. Making sure the right talent and knowledge is available is crucial to the effort and to assure the best possible outcome to the project. There are many other factors that could have affected the success or failure of GM Locomotive project. Some of these are the understanding of the data itself within the existing environment, definitions of data elements and how they are used.
Documentation of the existing system must be available, researched and understood by system analysts, as well as other project members such as the business analysts. The project must also be well organized. The systems, if more than one is affected, must be identified and made ready for the migration effort. This involves identifying the systems, current operating software, types of data storage systems, such as flat files or relational database structures.
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The objectives of the project must be well defined and supported by senior management. Creating business objectives is a first step for establishing the scope of the project. A project team must be established that will have the skills necessary to accomplish the migration. The team should be made up of a senior staff member, as suggested this individual should be the sponsor.
Technical staff should include business analysts, project planners, skilled programmers for both systems, old and new, database administrators, data diagrammers and finally system end users. Strategies Preparing the data for migration is the most critical success factor within the overall project. After forming the team and developing the migration plan and schedule, data profiling and mapping becomes the essential element toward success.
With the real possibility for data inaccuracies to exist it is important to approach data profiling in an organized fashion. One method, published in DM Review by John Shepherd considers a six-step approach for preparing data. This involves column profiling, dependency profiling and redundancy profiling.
Dependency profiling looks at data by comparing values between rows. This process uncovers relationships within tables of the database. Primary keys will be identified as well as relationships that are true in varying times or relationships. Redundancy profiling is the technique for comparing data between tables. This will lead to the identification of overlapping or identical data sets in multiple tables.
It will also help in discovering identical data types or values with differing names. Normalization of data within the context of a relational model is an absolute. Through the profiling processes redundancies are identified within data across tables and this process will eliminate data redundancy.
Model enhancement involves modification of the normalized model to increase performance or addition of new requirements. This may require some denormalization or creation of indexes.
Transformation mapping process creates one-to-one relationships between the source and target application database.
This process also provides Meta data defining the linkages and why they exist, data origination and destination information for future use in a new migration project, or troubleshooting as the application is deployed. Team Skills The project team must have knowledge of the existing system in detail.
Skills in programming, both the existing legacy system and the target system must be an integral team member. Development of front-end applications for users and training support on the new system is critical to the success of the project. Skilled project management is crucial for GM Locomotive to initial planning phases and through out the project to assure tasks are completed and reported on in a fashion that allows the team and management to follow the progress of the project.
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