Ensuring Data Quality | Dataford Interview Questions

What is your approach to ensuring data quality in your analyses?

Using the supplied customers and measurements tables, audit every measurement record. Flag missing or unmatched customers, missing or future dates, null or negative metric values, missing source systems, and duplicate customer-date measurements.

Output

One row per measurement, including valid and invalid records.
Return measurement_id, customer_name, issue_count, issue_details, and quality_status.
Order by measurement_id ascending. Use valid when no issues exist and invalid otherwise.

Schema

customers

Column	Type	Description
customer_idPK	INT	Unique customer identifier
customer_name	VARCHAR(100)	Customer display name

measurements

Column	Type	Description
measurement_idPK	INT	Unique measurement identifier
customer_id	INT	Customer associated with the measurement
measured_at	DATE	Date on which the measurement was recorded
metric_value	DECIMAL(10,2)	Recorded metric value
source_system	VARCHAR(50)	System that supplied the measurement

Your question is Ensuring Data Quality. Start with the requirements and the two tables in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem