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Data migration projects play a key role in so many business initiatives, from mergers and acquisitions to system upgrades, that their very ubiquity can seduce you into thinking that they are straightforward undertakings. But the truth is migration projects are anything but straightforward or simple. Rather, they are complex and risky activities that can consume inordinate amounts of time and money and still not succeed. When they overrun or fail – which according to research studies they do more than 80% of the time – the overarching business initiative can fall behind schedule or fail as well. To pull off a successful data migration, on time and on budget, it is imperative to understand the pitfalls you’ll face along the way and how to overcome them with proven migration-appropriate project methodologies, toolsets and techniques.



Issue One: where’s the migration expertise?

The first issue you’ll face is insufficient data migration expertise. Data migrations are specialized exercises and quite different from the application development projects familiar to most IT professionals. You may have some internal people who have been involved in a migration, but it’s rare for a company to possess a comprehensive internal store of data analysis and data modeling expertise and established data migration practices. Yet you are going to have to find and leverage such specialized skill sets in order to proceed with any level of confidence. Equally important, you will have to find a way to get your business users involved in the migration process to ensure its ultimate success – i.e.: trust in and effective usage of the new system.     

Issue Two: what’s going on in the source data?

Another immediate issue is a lack of understanding of the source data and systems. This is understandable as the people who originally developed the legacy systems and created the interfaces to get to the data are often no longer available and the documentation is frequently outdated or incomplete. Moreover, the data can easily be of poor quality and, if you are migrating data from multiple systems, you will need to resolve numerous redundancies and inconsistencies. Hence you are going to have to find a way to thoroughly understand the systems, the data and all the interfaces in order to get past square one.

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