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?