Data Cleaning Best Practices for Teams That Have to Explain the Numbers
Seven rules that hold up in a real team, where the person who cleaned the file is not the person defending the number three weeks later.
Read the postPractical posts about the files people actually get sent: what the spreadsheet method does, exactly where it stops working, and what to do about it instead.
Seven rules that hold up in a real team, where the person who cleaned the file is not the person defending the number three weeks later.
Read the postEight techniques, in the order they should run. Getting the order right matters more than any single technique, because normalization changes what matching can see.
Read the postThree ways to combine workbooks into one sheet, and the part nobody warns you about: the merged file is where all the formatting differences finally collide.
Read the postHighlighting duplicates is safer than removing them, because you get to look first. Three methods, from one click to a normalized helper key that catches near matches.
Read the postThe built-in tool takes four clicks and matches exactly. Here is how to run it, and the three situations where it will leave your duplicates in place or delete something you needed.
Read the postA definition you can use in a meeting, the six problems that make up almost every cleaning job, and the difference between cleaning, transforming and validating.
Read the postLoad one of the messy samples and read the diff. It runs in your browser and no card is required.
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