Data Quality Scorecard
Score data quality based on completeness, accuracy, and consistency rules. Enter values for instant results with step-by-step formulas.
Worked Examples
Example 1: Startup Pipeline
Problem:10 Tables, 2 Covered (20%), 0 Failures
Solution:Coverage 20%, Reliability 100%. DQI = (20*0.4) + (100*0.6) = 68 (Fair).
Result:68/100 (Blind Spots)
Example 2: Mature Enterprise
Problem:100 Tables, 90 Covered (90%), 10 Critical Failures / 500 Rules
Solution:Coverage 90%. Failures impact ~6%. Reliability ~94%. DQI = (90*0.4) + (94*0.6) = 92.4.
Result:92.4/100 (Excellent)
Frequently Asked Questions
What is Data Quality Coverage?
The percentage of your data assets (tables, streams, dashboards) that have at least one automated quality check (e.g., null check, uniqueness, freshness).
What is Data Observability?
The ability to understand the health of your data system by monitoring metrics like Freshness, Distribution, Volume, Schema, and Lineage. It's 'APM for Data'.
What is 'Reliability Engineering' for Data?
Applying DevOps principles to data. Setting SLIs (Service Level Indicators) and SLOs (Objectives) for data quality and pipeline uptime.
Is my data stored or sent to a server?
No. All calculations run entirely in your browser using JavaScript. No data you enter is ever transmitted to any server or stored anywhere. Your inputs remain completely private.