overcoming top 5 misconceptions predictive analytics
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www.globalbigdataconference.comTwitter : @bigdataconf
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Overcoming the Top 5 Misconceptions aboutPredictive Analytics
Sai DevulapalliHead of Data Analytics PracticeEmerging Technology Division
@sdevulapsaidevulapalli
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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
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2. We are predicting outcomes reliably, so we are done
• Analysis is easy, action is hard• Start small and iterate quickly
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3. We are successfully taking actions on our predictions, so we are done
• Assumptions are key• Reality changes• Models need TLC
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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
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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 !
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Overcoming the Top 5 Misconceptions about
Predictive Analytics