Mastering Data Cleaning: A Core Analyst Skill
Data may be called the new oil, but in its raw form it’s messy, inconsistent, and often misleading. Before modelling, dashboards, or decisions can deliver value, analysts must transform imperfect inputs into trustworthy datasets. That work—detecting errors, resolving inconsistencies, and documenting choices—is data cleaning. It is rarely glamorous, yet it’s the bedrock of every credible analysis, and the reason seasoned analysts treat cleaning as a first‑class skill, not an afterthought. At its heart, data cleaning is the disciplined process of improving data quality so it meets the needs of a specific task. It’s broader than removing “bad rows”: it means standardising formats, reconciling categories, handling missing values appropriately, checking for duplicates, aligning time zones, and verifying that the final table truly represents the real‑world entities it claims to describe. Good cleaning is context‑aware: what’s acceptable for a quick trend check may be unacceptable for r...