This document discusses overcoming the top 5 misconceptions about predictive analytics: 1) Starting with a limited feature set and sample of data, 2) Thinking prediction outcomes are reliable enough, 3) Successfully taking actions means being done, 4) Minimizing assumptions and letting algorithms do the work, 5) Thinking data never lies so actions should follow what data shows. It emphasizes starting small and iterating, analysis being easier than action, assumptions being key, and data quality impacting outcomes.

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2. Overcoming the
Top 5 Misconceptions about
Predictive Analytics
Sai Devulapalli
Head of Data Analytics Practice
Emerging Technology Division
@sdevulap
saidevulapalli

3. 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
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4. 2. We are predicting outcomes reliably, so
we are done
• Analysis is easy, action is hard
• Start small and iterate quickly

5. 3. We are successfully taking actions on our
predictions, so we are done
• Assumptions are key
• Reality changes
• Models need TLC

6. 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

7. 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

8. Overcoming the
Top 5 Misconceptions about
Predictive Analytics