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The Top Five Mistakes in a Data Migration Project # 1

The Top Five Mistakes in a Data Migration Project # 1

June 22, 2021 Roxane Morgan Comments Off

Having been involved in a number of large-scale data migration projects, InfoBluePrint has come across a number of re-occurring themes that have the potential to derail not only the data migration itself, but the entire parent programme. In a series of five articles, Bryn Davies will provide an overview of the top five mistakes that should be avoided at all costs. This is the first in the series:

Mistake #1: Underestimating the complexity, duration and associated costs of a Data Migration

Data migration is typically required because of a broader systems implementation that is being planned, and all too often we have heard stakeholders say that the data migration is the “easy part”. It thus gets relegated to a line item near the bottom of the project plan. BIG mistake. Regardless of the data volumes in question, data migration is a complex endeavour that requires dedicated, focused and structured attention starting early on in the programme. Some of this complexity comes from the fact that there are so many questions about the new system’s functionality, data structures and related transitioning from old to new, that cannot be easily answered, or answered at all, and many of these prevail late into the programme. This situation demands a well-defined migration methodology that helps to provide a common reference point for all involved, as well as a disciplined approach that is still agile enough to allow for the inevitable and frequent changes in direction. It also demands the early engagement of experienced data migration professionals, who have an appreciation for what’s to come, and can help to confidently navigate the common pitfalls and frustrations inherent to such a project. A good yardstick is to budget at least 15% to 20% of the total services costs of the parent project to the data migration. Yes, it’s that big!

Mistake #1: Underestimating the complexity, duration and associated costs of a Data Migration
Mistake #2: Not Involving Business Early Enough.
Mistake #3: Not Addressing Data Quality
Mistake #4: Delaying Because “The Target is Undefined”
Mistake #5: No Processes for Managing Dynamic Business & Data Rules

By Bryn Davies, CEO, Info.BluePrint.
Johannesburg, 22 June 2021

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