Data Quality and Lead Flow Analysis Questions

Recognize common data quality issues: duplicate records, missing or null values, incorrect data types, inconsistent formatting, stale data. Understand how bad data affects downstream processes (e.g., bad email addresses lead to failed campaigns; duplicate leads confuse the sales team; missing data makes metrics unreliable). Practice analyzing lead flow data: How many leads came in by source? At what stage did they drop off? Which sources produce the highest quality leads? For entry-level, you should be able to write queries that identify data quality issues and articulate the business impact. You don't need to implement automated data cleansing pipelines, but you should demonstrate understanding of why data quality matters and how to investigate problems.