case study

Improving Company Data Accuracy By 50% With Zippia

Learn how Javier Andrés, Head of Data Science at Zippia, identified a measurable increase in data accuracy and coverage by integrating with Diffbot's Knowledge Graph

We immediately verified a reduction in the percentage of wrong company information that we were showing to our users by at least half.

About Zippia

Currently people make career decisions with little knowledge about all their career options and longer term implications. Zippia is changing that.

Zippia’s vision is that every person should have access to all their career options and how those various paths will impact their professional lives. Zippia extracts intelligence from real world experiences to provide the best information and tools for people to achieve their career plans.

The Problem

To power an intelligent career platform, Zippia needs accurate and reliable company data. They were already obtaining data from a few different sources, but consistent data inaccuracies and coverage gaps left Javier and his team searching for alternatives.

The Solution

Diffbot’s Knowledge Graph of over 250M organizations came up in their search. The team immediately got to work running a series of coverage and accuracy tests, revealing a data accuracy gap of at least 50% with their existing data.

Possibly one of the easiest integrations we’ve done.

Data quality tests complete, Javier had one more challenge — to integrate Diffbot data into Zippia’s pipeline, they needed to perform specific custom queries that the existing API was not built to do.

With product and success teams working in lock step, Diffbot was able to extend the existing API to enable the custom queries Zippia requested, making data integration a breeze.

Takeaway

Zippia was not only able to improve in-product data accuracy by 50%, but also was able to easily integrate Diffbot’s Knowledge Graph data into their pipelines in record time.