Data Quality
Advanced infrastructure for superior data quality
Smart, effective data quality processes powered by machine learning
Raising the bar for data quality
Traditional, manual data mapping leaves the door open for large-scale human error and unreliable data quality. Intrinio combines powerful data quality infrastructure, advanced machine learning, and hyper-efficient human review to deliver financial data that you can trust.
Our holistic data quality process
Intrinio’s systematic approach to data quality relies on five main components:
Automated flagging
Our data processing engine flags high-risk data and potential discrepancies and suggests fixes.
Human review
A data expert reviews flagged data and applies fixes before our users encounter the issue.
Conservative publishing
We won’t publish data with known, unresolved errors, so you can trust the data you retrieve.
Continuous improvement
Our system gets smarter with each filing, and we adapt to changes in the way companies file.
Built-in ticketing
Users can quickly raise data quality concerns and speak directly to our US-based data experts.
Request a Consultation.
The most trusted brands are using Intrinio data
XBRL Standardizer
Our XBRL Standardizer leverages machine learning to standardize financial statements for easier consumption and comparison.
Data Infrastructure
We built complex infrastructure for financial data, so you don't have to. Get a peek at the mechanics behind our data.
