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11 Business Pitfalls to Analytics

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1. Education 1. Education
2. Defining the business problem 2. Defining the business problem
3. Insufficient skills 3. Insufficient skills
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4. Data scientist expectations 4. Data scientist expectations
5. Validation of models 5. Validation of models
6. Using the right data 6. Using the right data
7. Lack of support 7. Lack of support
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8. Balance of responsibilities for the businessperson 8. Balance of responsibilities for the businessperson
9. Choosing the wrong algorithm for use 9. Choosing the wrong algorithm for use
10. Tackling too much 10. Tackling too much
11. Attention to data quality 11. Attention to data quality
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Special thanks to Claudia Imhoff and Harriet Fryman for the content of this slide show.

The lead session at the 2014 Pacific Northwest BI Summit focused on how to transition to an analytics-driven organization. Harriet Fryman of IBM and Claudia Imhoff of Intelligent Solutions and the Boulder BI Brain Trust led the discussion on the importance of anayltics, listing 11 business pitfalls hindering analytics success.

 

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