Music clients had no unified attribution view of their data. Streaming numbers lived in Spotify for Artists, revenue data in distributor portals, audience data in social tools — none of it connected.

Without a unified attribution view, promotional budget was being allocated based on anecdote rather than platform-level contribution data. Labels needed a single source of truth to answer: which campaign drove streams, which DSP is growing the highest-value audience, and where to allocate spend next release cycle.

Prosecutors were manually labeling hundreds of exhibits per trial using handwritten tags and spreadsheets. The process was slow, inconsistent, and legally risky — any gap in data provenance can compromise an entire case.

The unit needed a system that enforced schema integrity, automated audit logging, and maintained full data lineage from retrieval through courtroom presentation — the same class of problems that make attribution infrastructure reliable in any data-critical environment.

Evidence retrieval near active crime scenes often required officers or technicians to physically enter unsafe or inaccessible areas — narrow crawl spaces, compromised structures, or active scene perimeters.

Standard equipment couldn't fit or couldn't be safely operated. The unit needed a remote-operated solution that could navigate tight spaces, capture footage, and extract device data without human exposure.

Built to demonstrate applied analytics methodology on real-world problems — multi-platform attribution, audience LTV cohort modeling, and decision-layer dashboard design — not synthetic or classroom data.

Each project demonstrates a specific measurement discipline: attribution pipeline design, LTV model deployment, audience segmentation, or warehouse architecture built for analytical querying — not just notebooks.