How does big data challenge traditional data protection principles?
Big data fundamentally conflicts with data protection principles like purpose limitation and data minimization, because it hoards data now for unknown future uses.
Big data technology enables massive data aggregation that goes far beyond what was previously possible. This creates four core tensions with privacy:
| Challenge | Description |
|---|---|
| Data volume | Never before have so many data been collected, stored, and analyzed. |
| Data linkage | Combining different data sources enables deep insights that individual datasets could never reveal. |
| Profiling | Non-sensitive data can be combined to derive unauthorized knowledge. Your shopping habits can predict your health conditions. |
| Loss of control | Affected individuals have almost no overview of who processes which data for what purpose. |
The fundamental tension: Traditional data protection says "collect only what you need, for a stated purpose, and delete it when done." Big data says "collect everything, store it forever, because you might find a use for it later."
These two philosophies are directly opposed. Purpose limitation requires knowing what you'll do with data before collecting it. Big data's entire value proposition is discovering unexpected patterns after the fact.
This is why new approaches like differential privacy, synthetic data, and Privacy by Design are becoming essential, because the old principles alone can't bridge this gap.
Go deeper:
Differential privacy (Wikipedia) — the PET that bridges the big-data/purpose-limitation gap.