Data integration is a major part of critical business initiatives, such as data warehousing, CRM, e-Business, ERP, system migrations, and mergers and acquisitions. These enterprise data management projects represent some of the highest risk business initiatives, with as many as 88% failing to deliver on time and within budget1. A primary reason for this extraordinary failure rate is the lack of a thorough understanding of source data early in the project.
Innovative Systems research shows that too often organizations undertaking a data migration project rely on inaccurate metadata and out-of-date documentation, resulting in design specifications based on flawed assumptions about the source data. Because the assumptions used in creating the mapping specs are flawed, the project fails in the building (extracting, cleansing, matching, householding, and loading) or testing phase. Organizations are then forced to go back to the data profiling and design phase, adjust the assumptions, and repeat the entire process. The effects on the project can be catastrophic, often resulting in runaway projects, or even total project failure.
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