Jeffrey Nicholson Reveals How Tracer Transforms Raw Data into Actionable Business Intelligence

Jeffrey Nicholson, co-founder and CEO of Tracer, calls the company a collaborative analytics platform that helps enterprise clients organize diverse datasets—ID graphs, cost structures, revenue—to understand their business health. Tracer ingests raw data directly from sources like Google, Amazon, Facebook and TV.

They have three data ingestion methods: direct API connections, partnerships with API connector companies and a custom platform for non-API data so all client data can be stored, managed and understood.

Tracer wants to be an infrastructure layer so clients and their partners (consulting firms, agencies and brands) can get insights without Tracer being a recommendation engine.

Nicholson uses a cookie analogy to explain how Tracer organizes disparate “ingredients” (datasets) that are often scattered and uncontrolled within large organizations. He talks about their success with clients like Media.Monks where Tracer has automated data processing so human teams can focus on analysis not manual processing.

A big use case for Tracer is media data. Nicholson explains how they help brands reconcile spending across multiple channels (TV, social, search) by consolidating and organizing data from different platforms to see what worked and what didn’t.

He mentions the “three Ps” — people, process and product — and says changing how people interact with data is often the biggest challenge, but that’s why Tracer exists: to give businesses control and understanding of their data in an automated way. Nicholson is excited about using organized, contextualized datasets to feed AI models and thinks that will have a big impact for businesses in their data usage.