This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
3 mistakes that will eventually make this board useless. 📉
I've reviewed several client dashboards over the years, and the same 3 problems show up over and over:
1️⃣ Too many charts, no clear takeaway — if I can't tell what to do after looking at it, it's decoration, not a dashboard.
2️⃣ Colors with no consistent meaning — red means "unacceptable" on one chart and "category 3" on the next. Pick a system and stick with it.
3️⃣ No comparison point — a number alone means nothing. "Revenue: $12,480" tells you nothing. "Revenue: $12,480 (+18% vs last month)" tells you everything.
Fix these three, and your data stops being numbers on a page — it starts being something people actually use to decide.
What's the most common dashboard mistake you've seen? 👇