Data conversion can be defined as a process of converting data from one structural form to another to suit the requirements of the system to which it is migrated. Data conversion becomes a necessity when a firm decides to move to a different software application to maintain its business critical data.
A decision is required to be taken in this phase as to whether there is a necessity of data cleansing. Data quality of legacy systems is generally poor, rendering it unsuitable for migration considering the more stringent data requirements of the target model. For instance, consider a particular field in the target system for an entity, which is mandatory, but the same field in the legacy system can have absolutely no data at all. This renders all other related data in the legacy system to be rejected. A trade-off is thus required to be made in such cases as to whether go for data cleansing or default the field with some predefined value so that it is not rejected. In the event of defaulting it to a predefined value, we are compromising the data quality, which is usually undesirable. The decision primarily depends on the time scale of the data migration project. It should be appreciated that data cleansing is an iterative process and should be generous on time scales for any improvement in data quality as it is done by the business. Consequently, a data cleansing initiative should be addressed in the very beginning of any data conversion project and is critical in the success of any data conversion project.
Testing the converted data is an important activity of data conversion. The converted data, a result of the conversion process, based on mapping specifications should be tested for the following:
IDN offers data conversion planning, management and supplemental resources to assist your organization.
Our proven Data Conversion methodology ensures that:
Data Conversion Timetable will be setup which shall include a reasonable allowance for re-running failed jobs.