*Research Scholar, CMJ University, Shillong, Meghalaya-793 003
**Lecturer,College of Computer Science & Information Systems, Jazan University, Jazan, KSA
Online published on 26 June, 2013.
Every type of system may replace or enhance the functionality currently delivered by legacy systems to new system, regardless of the type of project/application; some data conversion may take place. Difficulties arise when we take the information currently maintained by the legacy system and transform it to fit into the new system. We refer to this process as data migration. Data migration is a common element among most system implementations. It can be performed once, as with a legacy system redesign, or may be an ongoing process as in storage of historical data in the form of a data warehouse. Some legacy system migrations require ongoing data conversion if the incoming data requires continuous cleansing. It should be that any two systems that maintain the same sort of data must be doing very similar things and, therefore, should map from one to another with ease. Legacy systems have historically proven to be far too lenient with respect to enforcing integrity at the atomic level of data. Another common problem has to do with the theoretical design differences between hierarchical and relational systems. In data migration one method apply in twice (i.e. automated and manual). This paper explores the steps to migrate date in form of manual, i.e. process of data migration without the help of any special tool those made for data migration. Manual data cleaning is commonly performed in migration to improve data quality, eliminate redundant or obsolete information, and match the requirements of the new system in correct and efficient form.
Legacy data, legacy system, data cleansing, data migration, source structure, target structure, field mapping