Panel question

Panel question

I will be sitting on a data management panel during career week for an ivy league school in the northeast. Here is one of the questions that was submitted in advance of the session:

Data quality issues are unfathomably complex, what advice do you have for student and alumni who want to seize at least one opportunity to eliminate a single root cause to prevent potentially thousands of future errors?  

My initial thought would be to first understand what makes a data quality issue a data quality issue. For example, would timing issues qualify? How about scoping issues, such as one department’s calculation for a particular measure being different than another’s.  I would anticipate much of the discussion would lead us back to proactively creating conceptual and logical data models. After all, I see the world through a data modeler’s eyes – data modeling increases understanding of the data as well as refine business requirements and manage expectations, reducing future data quality issues.

How would you answer this question?


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