The data is not fit-for-purpose because its population is: Applying data-governance requirements, which
A dataset skewed toward young patients is not fit-for-purpose for an all-ages product because it fails to represent the intended population.
The question
A health startup plans to train a diabetes-risk model, but its available dataset comes almost entirely from patients aged 18 to 35, while the product targets adults of all ages. Applying data-governance requirements, which concern most directly undermines using this dataset as-is?
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- The organization likely lacks lawful rights to use the data, since patient records generally cannot be repurposed for model training under any circumstances.Lawful rights may need checking, but the stem's stated flaw is representativeness; patient data can be used with a valid basis, so this misidentifies the concern.
- The dataset is too large to process efficiently, since high volume increases training cost and storage without proportionally improving the model's predictions.Volume is not the issue; the sample is skewed, and size is unrelated to the representativeness gap the scenario describes.
- The data integrity is compromised because records collected from young patients are inherently more error-prone than records from older patient populations.There is no basis to claim younger patients' records are less accurate; the flaw is representativeness, not integrity.
- The data is not fit-for-purpose because its population is unrepresentative of the intended target group, threatening validity across older adults. ✓Fit-for-purpose covers whether the data represents the intended population; a young-skewed sample undermines validity for the all-ages target group.
The trap
Reaching for lawful-rights or integrity concerns when the described defect is unrepresentative data (fit-for-purpose). How to remember it
A dataset skewed toward young patients is not fit-for-purpose for an all-ages product because it fails to represent the intended population.
How many of these would you get right?
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