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:
- Inconsistent Data . . . data from multiple business units, systems, or vendors can most often lead to discovery of inconsistent data and values that may in fact conflict with one another
- Lack of Data Conformity . . . information previously stored in non-standard formats which may include free floating text fields or items stored within fields for which they were not originally intended
- Inaccurate Data . . . data that is no longer correct based which may include a change in household or corporate location, marital status, or other lifestyle or purchase histories
- Incomplete Data . . . data that is missing some critical component which may include suite or apartment modifiers, intra company delivery information, area codes, or items pertaining to purchase history
- Duplication . . . multiple occurrences of the same information for historical customer transactions or based upon a lack of previous customer management systems being integrated with prospective customer marketing database systems
- Data Integrity . . . incomplete or non-existent data linkage disabling the ability to create a single customer view and/or aggregate views of transactional history by individuals, households, corporate families, or purchase patterns
A data migration initiative on its own will not serve to address the various data quality problems that may previously exist or be discovered during the process particularly when faced with aggregating data from multiple source systems such as in a business merger/acquisition. With the intensity and "instant gratification" requirements of today’s data-intensive platforms and operating environments, a substandard approach to data migration will only serve to highlight these erroneous items and create a formula for substandard results in the destination application(s). The success of any data migration project must have data quality processes at its foundation in order to effect consistency, conformity, and accuracy so that data integrity can be established to ensure the success of not only the initial migration but ongoing data warehousing activities.
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