Uncovering $250M in campaign spending patterns for the first time - how CrossroadsCX used Zingg on Snowflake to resolve 500,000 political organizations from 50 million transactions.
"Zingg helps identify if records are related to each other, like if names are spelled differently or if there are duplicated records. We can see these are clearly the same entity, but it’s not machine-readable. That’s where Zingg comes in."
Jimmy Steinmetz, Co-founder
CrossroadsCX
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The North Carolina Free Enterprise Foundation and the NC State Board of Elections (NCSBE) wanted to improve transparency around campaign finance data. While the NCSBE already stored campaign finance data, they lacked the capability to provide comprehensive historical patterns and insights.
Understanding who was spending money on campaigns and how that influenced political decisions within North Carolina is critical for public transparency.
Political funding data in North Carolina presented two major challenges: some records were available in digital format, while others were scanned PDFs that required manual transcription.
Moreover, the data was fraught with inconsistencies and inaccuracies due to manual entry errors. The same organization appearing in up to 22 different ways, different name spellings, and incomplete addresses. Previous entity resolution attempts had failed entirely. With data spanning digital files and transcribed PDFs, there was no reliable way to understand who was spending money on North Carolina politics.
These issues made it nearly impossible to establish funding patterns or consolidate insights.
CrossroadsCX built a cloud-based data pipeline on Google Cloud and Snowflake, using Zingg to resolve entities across name, address, city, and other metadata — not just name alone. This multi-field approach was essential given the inconsistency of manually entered campaign data.
Jimmy Steinmetz, Co-founder
CrossroadsCX
See how Zingg resolves entities across name, address, and multi-field variations at scale.
See how it works →
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