Common misconceptions in executing on predictive analytics projects

1. www.globalbigdataconference.com
Twitter : @bigdataconf

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