Storytelling through data
summary
Shows that poor scaling and excess styling make viewers answer basic questions incorrectly, citing an eye‑tracking KPMG study and a corrected example that drops the Y axis and adds labels; applies IBCS success rules to turn tables into grayscale dashboards with red/green variance coloring and blue highlights; demonstrates fixes in Excel PowerBI Python (matplotlib) and reports a concrete result: women's apparel is 4% below plan and the top 10 customers make up top 10 ≈50% of revenue, reducing eye movement and improving decision accuracy.
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chapters (15)
- 0:00Storytelling through data
- 1:28Data career skills
- 5:33Why communication matters
- 8:02Bad visualizations mislead
- 11:14Poor visualization reasons
- 14:05Tool limits in visualization
- 20:12Simplicity in data storytelling
- 22:26Raw data to compelling stories
- 30:25SUCCESS methodology
- 36:10Five rules for visualizations
- 43:05SUCCESS in Power BI
- 51:40Standardized visualizations
- 55:00History of data visualization
- 1:02:52Team visualization standards
- 1:05:21SUCCESS recap and final thoughts
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