According to The Data Warehousing Institute (TDWI), issues with data quality within US businesses alone create unnecessary expense in excess of $600 billion a year. Businesses are bought and sold, internal departments are expanded or merged, and new divisions are created with each requiring substantial investment to migrate and otherwise integrate legacy data from various historical platforms in order that information may be more readily used in new information intensive applications of today. The success of any customer relationship management, business intelligence, marketing database, or enterprise resource planning initiative is directly dependent upon the quality of the underlying data that is the result of the data migration process. Even though some checks and filters exist within existing database format migration tools, none provide an integrated and comprehensive data quality solution that can ensure data validity, accuracy, and consistency of how data has been previously recorded in a source system.

Unfortunately, more often than not, data migration projects tend to highlight a variety of data quality issues that for whatever reason were not addressed in the original points of capture nor subsequently with effective data quality processes within the original sourced systems. These discoveries of data quality inefficiencies in turn may lead to cost overruns, delayed projects, or in the worst case, abandonment of the migration initiative altogether. Common problems that may be uncovered within a data migration process include: