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Overcoming the Top 5 Misconceptions aboutPredictive Analytics

Sai DevulapalliHead of Data Analytics PracticeEmerging Technology Division

@sdevulapsaidevulapalli

1. We need to start small and iterate, so we start with a limited feature set

• Limited set of most pressing business problems• Representative sample of data points• Business actions applied to a limited set of instances

B R O A DEEP

2. We are predicting outcomes reliably, so we are done

• Analysis is easy, action is hard• Start small and iterate quickly

3. We are successfully taking actions on our predictions, so we are done

• Assumptions are key• Reality changes• Models need TLC

4. Let’s minimize our assumptions and let powerful analytics algorithms do the heavy lifting

• Assumptions capture domain knowledge• Life cycle management vs. model complexity

5. Data does not lie, so we need to act on what the data tells us

• Poor quality / incomplete inputs• Off-key assumptions and models• Interpretation bias

Business Intuition has been proven !

Overcoming the Top 5 Misconceptions about

Predictive Analytics