Data Quality Metrics: Completeness, Validity, Uniqueness, Timeliness & DQ Scorecards
Measure data quality like a data steward: completeness with COUNTBLANK, validity rules, duplicate keys, orphan records across tables, entry lag and freshness against SLAs, accuracy against a verified sample, a weighted DQ scorecard with icon sets and a trend dashboard with the cost of poor data: 8 graded labs on CRM and order data.
The first lesson is free — no account needed.
About this course
A data quality metrics course that measures whether the CRM and order data of one fictional wholesaler, Harbourline Office Supply, can be trusted, in a spreadsheet, for data stewards, data and BI analysts, CRM and operations staff and data governance teams. You turn blank cells into completeness per field and per record with COUNTBLANK, test emails, countries and credit limits against validity rules, flag duplicate customer IDs and emails, find orphan orders and conflicting regions across tables, measure entry lag, SLA compliance, the 90th-percentile lag and freshness, check a sample against verified values with a tolerance, combine five dimensions into a weighted DQ score with a Green/Amber/Red status and icon sets, and chart the weekly trend and the cost of poor data. Fictional company, synthetic records. The first lab is free.
Lessons
- Completeness: Blank Cells, Field Completeness & Complete RecordsFree
- Validity: Format, Allowed-Value and Range Rules Pro
- Uniqueness: Duplicate Keys, Distinct Counts & Duplicate Emails Pro
- Consistency across Tables: Orphan Keys and Conflicting Values Pro
- Timeliness: Entry Lag, SLA Compliance, p90 and Freshness Pro
- Accuracy: Checking a Sample against Verified Values Pro
- The DQ Scorecard: Weighted Scores, RAG Status & Icon Sets Pro
- DQ Trend Dashboard & the Cost of Poor Data (with a Chart) Pro