When Creating Visualizations, Question Everything

See on Scoop.itData Nerd’s Corner

Sure, it can messy. But this Open Web workflow will test your assumptions and give you better results.

Carla Gentry CSPO‘s insight:

After, data discovery focuses on understanding the parameters of the data, because the data alone can’t offer information on the underlying relationships. What outliers exist in the data? Are they meaningful or simply adding noise? For example, when exploring sub-prime lending, economists from Stanford uncovered what they thought was an error in their data: a large, unaccountable surge in loans in the early months of the year. Upon further investigation, they discovered the anomalous rise was due to the effect of the earned income tax credit, dramatically changing the direction of their research.

See on blogs.hbr.org

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