Data mapping is a process of data organisation used in data warehousing. It is a basic and one of the first steps of data integration. In this process various different unsorted data models are linked to each other by using certain preset methods to categories the data into a defined set.
Certain sets of standards are followed for the process of data linking. These are set based on domain values of data models in question. There are various data mapping tools and different models that can be used in this process, depending on the needs of the client and the specific case. However, there are certain practices which can be followed for faster and more accurate results.
Set your rules in advance
Before everything it is important to document your map, heuristics and set some standing business rules which will be the guiding step for the process of development of every map. These preset rules should contain the well developed utilisation cases for every map, identification of applications which are to use the said map, as well as necessary documentation to instruct in detail how these mapping rules have been created and how they are to be deployed in workflow. This will help you create a clear set of rules for every map, and thus, reduce your redundancy of your in the process.
Create a testing programme
It is a smart idea to create a program and a process which can be used to check the validity and functionality of the data map. For it to give a thorough analysis this test program needs to cover the entire development process of the said map as well as any other associated tools which have been used in the entire process of creating the map, from its development to the end-user acceptance, its testing and the approval too. A test programme will help you get a basic idea of how you can expect it to work and where you can rectify to get better results so that the final product comes out unhindered by any flaws.
Maintain on a regular basis
In order to have a smoothly running system you need to formulate and implement a regular maintenance system. Owing to the regular processes and their unique challenged you might need to make changes to the source as well as the target code sets for a map. From time to time you need to update any such modification, remove any outdated and discontinued rules from source or target data sets and implement all the version changes, at the major ones, from either end of the said map. Without regular maintenance and updates your system will become stagnant and the data maps will be irrelevant and incorrect.
A little planning and effort go a long way when it comes to data mapping. These practices will help you get the best results from data mapping without any inaccuracies and errors. A good data map will streamline and optimise your processes more effectively.
