Data migration
, "nearly 40 percent of data migration projects were over time, over budget, or failed entirely." As such, to achieve an effective data migration, proper planning is critical. While the specifics of a data migration plan may vary—sometimes significantly—from project to project, the computing company IBM suggests there are three main phases to most any data migration project: planning, migration, and post-migration. Each of those phases has its own steps. During planning, dependencies and requirements are analyzed, migration scenarios get developed and tested, and a project plan that incorporates the prior information is created. During the migration phase, the plan is enacted, and during post-migration, the completeness and thoroughness of the migration is validated, documented, closed out, including any necessary decommissioning of legacy systems. For applications of moderate to high complexity, these data migration phases may be repeated several times before the new system is considered to be fully validated and deployed.
Master data management
Other problems include (for example) issues with the quality of data, consistent classification and identification of data, and data-reconciliation issues. Master data management of disparate data systems requires data transformations as the data extracted from the disparate source data system is transformed and loaded into the master data management hub. To synchronize the disparate source master data, the managed master data extracted from the master data management hub is again transformed and loaded into the disparate source data system as the master data is updated. As with other Extract, Transform, Load-based data movement, these processes are expensive and inefficient to develop and to maintain which greatly reduces the return on investment for the master data management product.