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Can you mention a few more?

  1. Sampling or design of experiment not properly done
  2. Non robust cross-validation
  3. Poor communication of results to management or clients
  4. Poor data visualization 
  5. Does not solve our business problems
  6. Database misses important data or fields
  7. Failure to leverage external data
  8. Can't make business data silos to "talk to each other"
  9. Developpers (production people) and designers speak "different languages"

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Not getting all the relevant business constraints.  What a way to look bad and lose the trust of your audience.  "Oh, sorry, I did not realize that undermines our main client's contract and breaks the law.  My Bad". 

Not understanding the distribution of the population (i.e. assuming that the sample is from a normal population when it is not) and making inferences based on wrong assumptions.

Data computation and analysis takes a long time to react.

No. 5 applies to any project, of course, not just data analysis. While this mistake may seem like a 'no-brainer', my experience has been that it's common to finish an effort and not know if any real problem has been solved. While everyone will agree it has been, this is more a 'policy of success' than any actual measurable demonstration of it.

Client confusion as to what he/she needs out of Advanced Analytics to achieve for them.
Data quality is very important thingjn data analytics assignments. Not easily available. IT folks will kill you on given pain to them.


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