Quality Dashboard
Transparent, metadata-first data quality—driven by community feedback, coverage tracking, and expert validation—helps keep Dealcharts.org continuously improving, visible by design.
"Data quality" gets thrown around a lot—but too often, it's just a buzzword. At Dealcharts.org, it means something specific: a transparent, collaborative process you can see and participate in.
Below you'll find two key components of our data quality system: our community feedback queue and our coverage tracking system. Together, they help us maintain and improve our data quality through transparency and collaboration.
Recent Community Feedback
Displaying the 25 most recently reported open issues. We have closed 10 work items and received 3 pieces of feedback confirming data looks correct.
No documents displaying for a 2025 deal; at least one doc should be out by now.
Chart is not adding up to 100%. Only 1 current loan?
Reporting two Preliminary Prospectuses.
Data Coverage Gaps
Displaying the 2 oldest CMBS deals missing document coverage. Overall CMBS document coverage: 99.7% of deals (312 of 313 deals).
Displaying the 2 oldest CMBS deals missing prospectus documents. Overall CMBS prospectus coverage: 78.3% of deals (245 of 313 deals).
Our Approach to Data Quality
At Dealcharts.org, quality isn't a static badge—it's a living process, visible by design. We don't just collect structured finance data. We curate it, correct it, and continuously improve it, powered by a combination of metadata, human review, and user feedback.
Community-Driven Accuracy
Every chart, document, and connection on the site can be improved by our community. When you spot an issue or suggest an update, it flows directly into our public work queue. No black box. No hidden backlog. We believe everyone benefits when feedback is easy to give—and when progress is easy to see.
Rigorous Data Standards
We use a metadata-first methodology to structure and standardize data across deals. This includes schema-level validation, field-by-field version tracking, and checks to ensure consistency over time. We don't just patch data—we build systems to keep it coherent as it grows.
Collaborative Verification
Some issues need human eyes. We combine automated checks with expert review, especially for edge cases and new data types. Our verification workflows include multiple layers of validation, so errors don't just get fixed—they stay fixed.
Data quality isn't something we declare. It's something we demonstrate—right here, in public, with your help.
How You Can Help
You don't need to be an expert to make a difference. Just keep an eye out. If something looks off, click the "Spot an error? Help improve data accuracy" button in the top right of any section on the site. Your feedback enters the queue and helps improve the data for everyone.