Every privacy framework — DPDPA, GDPR, and most sectoral regulations — names data minimization as a core principle. Almost every product team, asked to actually cut a data field, pushes back that they might need it someday. Both positions are reasonable, which is exactly why this needs a real process, not a mandate.
Collecting more data than needed feels low-cost at the point of collection and high-cost only much…
For every data field collected, require an explicit, specific purpose — not “might be…
Product and growth teams often want broad behavioral data collection to support future…
Collecting more data than needed feels low-cost at the point of collection and high-cost only much later — as breach exposure, as compliance scope, as a line item in a DPIA that’s hard to justify. That asymmetry is exactly why minimization doesn’t happen by default: the cost of over-collection is deferred and diffuse, while the cost of under-collection (a missing field blocking a future feature) feels immediate and concrete to the product team making the call.
Product and growth teams often want broad behavioral data collection to support future personalization or analytics use cases that aren’t yet defined. The honest tension here doesn’t resolve by fiat in either direction — it resolves by requiring that “future use case” be specific enough to evaluate, and by using aggregated or anonymized data wherever the actual analytical question doesn’t require individual-level identifiers.
The most durable version of this is a lightweight review gate at the design-spec stage (similar to the DPIA screening question discussed in our privacy-by-design article) asking specifically: what’s the minimum data this feature needs, and have we set a retention period for it? Teams that treat this as a two-minute design question answer it far more consistently than teams that treat it as a separate compliance review bolted on afterward.
It requires more deliberate design, not necessarily less data overall — the key is distinguishing data genuinely needed for a defined model or feature from data collected speculatively. Techniques like aggregation, anonymization, and synthetic data can often substitute for raw individual-level PII in training and analytics use cases.
Run a focused audit against the framework above, flag fields with no clear active purpose, and set a deletion or de-identification plan for them — treating it the same as any other discovered technical debt, prioritized by risk and effort rather than attempted all at once.
It’s an explicit principle under most modern privacy regulations, including DPDPA and GDPR, not merely a best practice — though the specific enforcement emphasis varies, and it’s worth confirming current requirements with counsel for your specific regulatory exposure.