Big Data Lazy Brain

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ThrivOn Analytics Presentation at TDWI Key to Success with Big Data and Lazy Brain March, 2013 Rajeev Kumar [email protected] 571-228-7037

Transcript of Big Data Lazy Brain

Page 1: Big Data Lazy Brain

ThrivOn Analytics Presentation at TDWI

Key to Success with

Big Data and Lazy Brain

March, 2013

Rajeev Kumar

[email protected]

571-228-7037

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ThrivOn Services

User engagement•User engagement

platform

– Enterprise portal

– Member/patient

portal

– SharePoint

– Collaboration

– Social Media

•Document Assembly

Analytics•Business analytics

•Data Warehousing

•Master Data

Management

•Big Data, Hadoop

•Now Data

Corporate Strategy•Growth Consulting

•M&A Assessment

•Value based Management

•New Market Assessment

and Entry Support

•Customer Strategies

ThrivOn closes the decision loop with combination of research, analytics and strategy

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Technology Partnerships

IBM SVP Partner: Information Management, Analytics and Big Data

Only VMware Vfabric certified partner in mid-atlantic region, Cloud, Platform as a service

Member of the Clouder Connetct partner program – Hadoop Big Data Platform

Hortonworks – Big Data Hadoop/Mapreduce partner

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Agenda

• Practical insights into “Decisioning Process" • How consumers make use of analytics • Framework for ensuring impact from analytics

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My effort to leverage behavioral psychology with big data analytics in practice

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Body and mind

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A bat and ball cost $1.10.The bat costs one dollar more than the ball.

How much does the ball cost?

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Bat and ball - Do not try to solve it but listen to your intuition

10 cents

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What affects our mind and hence our decisions

• Context• Halo effect• WYSIATI - Information availability bias• Priming• Framing• Anchoring• Substitution• Quest for causality• Loss aversion• Possibility and certainty effect• Behavior under desperate situations

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Context

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Coffee room experiment

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Priming: Coffee room experiment

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Information Availability

“Steve is very shy and withdrawn, invariably helpful but with little interest in people or in the world of reality. A meek

and tidy soul, he has a need for order and structure, and a passion for detail.”

Is Steve more likely to be a librarian or a farmer?

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Framing: What is more re-assuring

Hospital A Hospital B

The odds of survival one month after surgery are 90%”

The mortality within one month of surgery is 10%.”

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Allen Vs Ben

• Alanintelligent—industrious—impulsive—critical—stubborn—envious

• Ben envious—stubborn—critical—impulsive—industrious—intelligent

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Anchoring

Sales promotion for Campbell’s soup at about 10% off the regular price.

3 days a week – Limit of 12 per person. Other days – No limit per person.

Shoppers purchased an average of 7 cans when the limit was in force, twice as many as they bought when the limit was removed

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Substitution

• How happy are you these days• How many dates did you have last month

• How many dates did you have last month• How happy are you these days

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When are we more likely to gamble

• Get $900 for sure or 90% chance to get $1000

• Lose $900 for sure or 90% chance to lose $1000

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Possibility and certainty

Are each of the 5% increase in probability to win $1M valued equally?

– 0% to 5%– 5% to 10%– 60% to 65%– 95% to 100%

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Premium for certainty

At final stages of a settlement where your lawyer suggests you have 95% chance to win $1M, a settlement company approaches you with an offer of $910K

•Will you take it or leave it?

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What affects our mind and hence our decisions

• Context• Halo effect• WYSIATI - Information availability bias• Priming• Framing• Anchoring• Substitution• Quest for causality• Loss aversion• Possibility and certainty effect• Behavior under desperate situations

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Can we truly benefit from big data when we make decisions?

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What holds the key to achieving success with big data analytics?

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Wish we had a magical wand and a spell

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And the skeptics would say

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From The Checklist Manifesto

1.heart rate2.respiration 3.reflex4.muscle tone5.color

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ThrivOn data decisions grid

Frequently made decisions that have been pre-validated and

accepted to be “system 1 – proof”

Periodic decisions requiring insights from large data that have been pre-validated and

accepted to be “system 1-proof”

Decisions arrived at by skilled analysts based on

lots of data

Highly subjective decisions that are made in board

rooms with very little data

Big DataSmall Data

Lazy Brain

Fast Brain

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ThrivOn data decisions grid

Automated real time decisioning

Rule based batch decisions

Continuous experimentation

Checklist assisted decision making

Big DataSmall Data

Lazy Brain

Fast Brain

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Putting this in practice

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5 Point Action items

• Culture and awareness• Analytics Competency Center• Technology infrastructure• Analytics Playbooks• Culture of experimentation

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How can we protect us from ourselves

• Statistics rules• Algorithms are better (80% of the time)• Expert judgment still needed• What you see is NOT all there is• Find the answer to the right question• Psychology matters

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Golden rules

• Hire great analysts– Right mix of data science and business acumen

• Educate your top brass– To make analytics “mandatory” part of decision making

• Checklist manifesto – Establish comprehensive framework to ensure avoiding

common decision pitfalls

• Put all analysts in one room (or one floor)– Break organizational barriers, improve creativity

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Recap

• Human being are genetically wired to be irrational – for good reasons

• It’s no longer just about how decisions are made inside a company. Think how your customers make decisions

• Big data alone is not going to make you winner. Mesh big data with your lazy brain

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Questions?

[email protected]

571-228-7037