Recruiter's Glossary

Examples: Regression Analysis • Tableau • Ad Hoc Analysis

Data Quality

Term from Data Analytics industry explained for recruiters

Data Quality refers to making sure information in business databases and systems is accurate, complete, and reliable. It's like being a detective for data - checking that numbers and information are correct, fixing errors, and setting up rules to catch mistakes before they cause problems. Companies need people who understand Data Quality because bad data can lead to poor business decisions. This role involves cleaning up messy data, creating standards for how information should be entered, and making sure different computer systems share information correctly. You might see it mentioned alongside terms like "data cleansing," "data governance," or "data integrity."

Examples in Resumes

Developed Data Quality frameworks that reduced error rates by 75% in customer databases

Led Data Quality assessment and improvement initiatives across multiple departments

Implemented automated Data Quality monitoring tools to maintain data accuracy

Created Data Quality reports and dashboards to track data integrity metrics

Typical job title: "Data Quality Analysts"

Also try searching for:

Data Quality Analyst Data Quality Engineer Data Quality Specialist Data Quality Manager Data Quality Coordinator Data Governance Analyst Data Quality Control Specialist

Where to Find Data Quality Analysts

Online Communities

Professional Networks

Learning Resources

Example Interview Questions

Senior Level Questions

Q: How would you implement a data quality framework in a large organization?

Expected Answer: Look for answers that discuss creating company-wide standards, setting up monitoring systems, training staff, and measuring success. They should mention working with different departments and getting management support.

Q: Tell me about a time you had to fix a major data quality issue.

Expected Answer: The candidate should describe identifying the problem's root cause, creating a plan to fix it, working with teams to implement solutions, and preventing similar issues in the future.

Mid Level Questions

Q: What methods do you use to identify data quality issues?

Expected Answer: They should mention checking for duplicate records, missing information, incorrect formats, and using tools to spot patterns of errors. Should also discuss regular monitoring and reporting.

Q: How do you ensure data stays accurate when combining information from different sources?

Expected Answer: Look for understanding of matching records across systems, checking for conflicts, and creating rules for which source to trust when information differs.

Junior Level Questions

Q: What are the main aspects of data quality?

Expected Answer: Should mention accuracy (correct information), completeness (no missing data), consistency (same format throughout), and timeliness (up-to-date information).

Q: How would you clean a spreadsheet with customer information?

Expected Answer: Should describe checking for duplicate entries, standardizing formats (like phone numbers or addresses), filling in missing information, and fixing obvious errors.