Ep 214: Exposing the myth of ‘perfect data’

Piotr Prędkiewicz, Senior Marketing Analyst at FORMEL SKIN, exposes the myth of ‘perfect data’ and charts the future of privacy-centric analytics in mobile growth. With a career spanning fintech, dating and healthtech, Piotr unpacks how to track users legally, ethically, and creatively before they even register. He also discusses why device data is always noisier than you want, and where most marketers go wrong on privacy, attribution, and fraud. The episode blends practical strategies for data-driven marketers with a clear-eyed critique of the industry’s alignment failures, fraud incentives, and the inescapable limitations of tracking in the GDPR era.

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• What is FORMEL SKIN, and how does it solve dermatology’s bottleneck in Germany?
• How did Piotr’s career in analytics develop across multiple verticals?
• Why is ‘perfect data’ a myth in mobile marketing?
• How do you responsibly track and aggregate users before registration?
• What’s the difference between front-end and back-end behavioral data?
• How do device/user mismatches and changes create analytics headaches?
• What are the new challenges and gray areas in privacy (GDPR, CCPA, device fingerprinting)?
• Where does fraud hide in aggregated data, and how do you find it?
• Why does fraud persist, and what incentives make it so durable?
• How could success in mobile marketing be measured differently to promote collaboration and integrity?

• (0:00) – Introducing FORMEL SKIN, Piotr’s role, and Germany’s digital dermatology
• (1:18) – Marketing analytics in dating, fintech, health
• (2:50) – Why ‘perfect data’ is a myth
• (5:00) – Assigning pseudo-user IDs, device-based tracking
• (6:00) – Aggregated data, ‘chasing ghosts,’ and its pitfalls
• (8:00) – Combining front-end and back-end data; challenges in stitching
• (9:36) – Device vs. user: confusion, mismatches, and noise
• (11:13) – Balancing privacy vs. marketing needs; legal and business conflicts
• (12:30) – Device fingerprinting: what’s legal, what’s risky, and why
• (14:22) – The end of one-to-one attribution; rise of aggregated, top-level analysis
• (16:05) – Marketing fraud: what’s changed, sneaky affiliate/network tricks
• (19:08) – Incentives, alignment failures, and why fraud persists
• (21:40) – Filtering fraud: long onboarding, compliance, and technical vigilance
• (23:38) – ‘Success’ in mobile marketing and why responsibility must be shared
• (32:08) – Wrap up

• (2:50) –“Don’t expect perfect data – especially in marketing where different data sources are being combined.”
• (15:26) - "We can use aggregated data for strategic decisions, like how to shift budgets from channel A to B."