From time-to-time various Business organizations implement new Software Application System to replace the functionality currently delivered by one or more legacy systems. Complications arise when there is an attempt to take the information currently maintained by the legacy system and transform it to fit into the new system. More often, the data structure of the legacy systems is different from the new application being implemented, and that difference is not just limited to the table names, field names or attributes or sizes. The types of databases are different and diverse, or the entity relationships definitions in the new system are not compatible with the older legacy application. To the business organizations all the data being held in the legacy system remains critical for their business functions and decision making.

To bring the legacy system data to the new application some Data Conversion must take place, where an initiative, separate or concurrent with the implementation of the new application, is undertaken to convert data from one structural form, used by the legacy application to the structural from required by the newer application.

Often in a Data Conversion process, one would tend to think that any two similar systems that maintain the same sort of data, as they are doing very similar functions should map from one to another without much trouble. But that is not really the case as:

  • In Legacy systems, historically, data integrity checks were not strictly enforced, leaving orphan data
  • Theoretical design differences exist between hierarchical and relational systems.
  • Legacy data may require some data cleansing.


Other Factors contribute to complexity of such projects are:

  • Need for a well-defined target data structure model.
  • In-depth understanding of the functionality of the source data structure.
  • Constant changes on the target model design have a knock-on effect on conversion process design.
  • Source data quality, if poor, needs to be cleansed to be successfully migrated.
  • Degree of complexity of the target model in relation to the source data model.
  • The differences in task definitions between the source and target data structures.


Therefore, it is important to have a sound, methodological approach by which organizations can undertake Data Conversion projects, which will help to confront unpleasant surprises on later stages and resolve those issues fast and effectively.

It is important to note that the methods and processes described within this document is generic in nature. Each project presents its own challenges and opportunities.