Data Migration Effort Estimation: Practical Techniques from Richard Trapp of Northfield Consulting Group
Thursday, February 9 in
Data Migration Techniques How do you cope with the uncertainty around data quality on data migration projects, particularly with budgeting and forecasting effort?
Please note, this post was first featured on our sister site Data Quality Pro in : How do you estimate impact of poor data quality on a data migration project?
In an excellent discussion on the IAIDQ LinkedIn forum Richard Trapp attempted to answer this very question posed by John Platten, long time expert panelist on Data Quality Pro and Data Migration Pro.
John Platten of Vivamex opened out the discussion for opinions from others on...
...what to do if you take on a migration lead role and it becomes clear after the contract is signed that the data is too poor, the budget too small...and you realise that the outcome is going to be compromised...
John, I overcome the hurdle you mention by employing a very comprehensive parametric estimating model. All scope assumptions are clearly presented and approved prior to work starting. When/if assumptions turn out to not be true (number of objects, complexity, environmental constraints, etc.), the impacts to progress are documented and communicated (Earned Value Analysis) and decisions presented to the client to either reduce scope to meet current budget, or to add budget to meet the change in scope.
Intrigued, I asked Richard to expand and he kindly answered several questions that clarified his earlier comments with a detailed explanation.
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