Data Analysis Project Failures

Mary Branscombe explains the causes of common failures in data analysis projects :

1. Assuming your data is ready to use — and all you need
2. Not exploring your data set before starting work
3. Expecting too much
4. Not using a control group to test your new data model in action
5. Starting with targets rather than hypotheses
6. Letting your data model go stale
7. Automating without monitoring the final outcome
8. Forgetting the business experts
9. Picking too complex a tool
10. Reusing implementations that don’t fit your problem
11. Misunderstanding fundamentals like causation and cross validation
12. Underestimating what users can understand

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-DD