This document discusses how big data and data science can help businesses and healthcare. It provides examples of how analyzing large amounts of data can help optimize customer groups and actions to maximize results. Specifically, one case study shows how a bank increased new product conversions by 100x and profits by $1 billion by using big data to better target customer groups and proposals. The document also discusses how data science involves descriptive, predictive, prescriptive and proactive analytics and how global goal driven dynamic demographics for proaction optimization (D3PO) can combine these approaches.

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The Landscape – Analytics and Data Science

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When I say big data which of these describes what you feel?

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When I say big data which of these describes what you feel?

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When I say big data which of these describes what you feel?

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When I say big data which of these describes what you feel?

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When I say big data which of these describes what you feel?

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When I say big data which of these describes what you feel?

8. • Well, this talk is NOT about big data, but
what it can do for you
• On the way, you might just gain some clarity
of terms, and technologies
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Big Data

9. • The quantity of data allows the five pillars of
analytics to become empirical sciences
• If used right, business and medical goals are
substantially bettered
• It is not just about knowing more, it is about
zeroing in on the truth
• We will talk about they ways people miss the
truth, even seeming to use current best
practices
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Big Data

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12. • I like to start with the business questions,
the business and medical practice needs,
what leaders of businesses and medicine
would most like to do
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The Big Questions

13. • Who, where, what, how, and how much for
each group
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Who, where, what, how, and how much

14. • Business is about action, doing. Just do it!
But what to do, and to whom, and with what,
and what channel?
• What are the choices that maximize results
and ROI?
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Just do it, to maximize results and ROI

15. • Global Goal Driven Dynamic Demographics
for Proaction Optimization
(G2D3PO or just D3PO)
• What Group?
• What Actions?
• Global Goal: Profit, Customer Satisfaction,
Manufacturing Excellence, Reduced
Community Healthcare Costs, etc.
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Global Goal Driven

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Why these arbitrary fixed groupings?

17. • Groupings, affiliations and interests depend
on goals and context
• Arbitrary bounds to ranges are just
assumptions that can totally change how
statistics come out and our view of the world
• Bad assumptions lead to bad decisions
• Data discovered and confirmed foundations
lead to good decisions
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Global Goal Driven

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Case Study – Large Retail Bank – Customer Service Screen

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Goal: Sell Product – Top Pain Found and Defined
Optimal Grouping and Action

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Case Study – Large Retail Bank
Targeted Allegiance Maintenance

21. • Before: 0.07% conversion rate for new
product proposals – “shotgun” marketing
• After: 7%
– A 100X increase in closure rate
– $1B increase in new product the first year
– Rising even faster the second year
– “Good customer” attrition rate down 6%
• $200M annual saving from this churn mitigation
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Case Study – Large Retail Bank

22. • Almost no lift from “shotgun” marketing approach
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What is new and different? -- Old Profit Lift Curve

23. • The state of the art predictive approach: Ranking via scores
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What is new and different? – State of the Art Profit Lift Curve

24. • Global Goal Driven Dynamic Demographics for Proaction Optimization
• Spending only as much as needed to acquire
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What is new and different? -- D3PO Lift Curve

25. • Groupings are generated appropriate for the
sale of each product
• The best of multiple offer opportunities is
chosen
• The best proposal opportunities first
• Product, offering, or discount proposals based
on expected long-term value to company
• Cost of acquisition more focused
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Improvements come for three reasons:

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Case Study – Hospital /Managed Care

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Managed Care – Group Based on Goal: Increase population Health

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Hospital /Managed Care – Personalized Recommendations

29. • Static: Statistical Reports
• Interactive Descriptive: BI
• Predictive: Learning Algorithms (LA)
• Prescriptive: Decision Classes or Optimization
• Proactive: Optimizing Groups
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The Progress

30. • To the business user the technology should
just be something that happens in the
background
• At the same time, how the recommended
decisions are being made should be
transparent to the managers
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The Best Thing for Business and Medical Analytics

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This is a talk about:
• How Big Data can enable Data Science to be a
true science
• The large opportunities it can offer for generating
value for companies and healthcare
• How we only can know, see and forecast because
we have assumptions, assumptions that can be
wrong
• But what makes the empirical method work is the
process of testing and revising assumptions to
discover the real world

32. • What business and healthcare providers want
• How BI, and Advanced Analytics depends on assumptions
• How easy it is to attribute too much intelligence to artificial
intelligence
• True intelligence is bound up with the ability to recognize
and revise assumptions
• That methods of grouping are always multiple
• How the generation of action classes (or Proaction classes)
to appreciate groups of people (or resources) for a given goal
is the method to add great value to business and healthcare
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We will also recognize:

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Revising assumptions changes your world

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Different categorizations for different goals

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Different categorizations for different goals

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Arbitrary Assumptions -- Age vs. Income breaks

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Arbitrary Assumptions -- Age vs. Income breaks

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Arbitrary Assumptions -- Same People Different Understandings

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The interesting cluster cannot even be seen
by slice and dice methods

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Both the human eye and LA’s must make assumptions to see at
all assumptions that circumstances can reveal

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Both the human eye and learning algorithms
can impose readings that make no sense

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Both the human eye and learning algorithms
can miss important hidden patterns

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Real Intelligence is knowing how to collect the added data to
know what is really going on, like throwing a rock at the lava

44. • What is Data Science?
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Humans are natural pattern recognizers
We project out inner patterns and assumptions on the universe

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So do learning algorithms and many of the modeling methods

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These are random dots

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Our eye just naturally finds meaningless clusters

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Learning algorithms and modeling have built in assumptions too
Training on first
half of the flight
of a baseball
Attempting to predict 2nd half

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Built in assumptions – Flight of a Baseball – Non-representative data
Error

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Non-representative data

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Non-representative data

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Baseball fit – Error from just assumptions of mathematical form
Error

53. • Data generated by people, such as in markets
or from buying behavior, has far more noise
than a physical system such as a baseball’s
flight
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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?

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What is the pattern hidden in the noise?
It is a sine (sign)

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The Landscape – Analytics and Data Science

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Descriptive Statistics
BI, Dashboards, Scorecards, Reporting, Discovery, What-if

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Predictive Analytics – Learning Algorithms – Forecast Techniques

65. • Taking historical data samples and finding
patterns using learning algorithms to project
what will happen in the future, or to new
individuals to detect opportunities,
differences, or abnormalities
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Predictive Analytics

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Network Analytics – Social – Information

67. • Measuring and creating statistics about the
processes that connect individuals or
technology in potentially a complex web of
interactions
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Network Analysis

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Modeling – Simulation -- Mapping

69. • Developing a language, potentially
mathematical, whose characteristics and
relationships have close analogies and
structure to something in the real world.
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Modeling

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Optimization

71. • The process were the best or near best of
potentially an infinite number of options are
chosen or found relative to a goal
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Optimization

72. • Network Optimization
• Predictive Modeling
• Predictive Optimization
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Advance Analytics – Any of the above can be combined

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D3PO combines all four advanced analytic methods

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Big data allows us to more carefully follow scientific empirical
methods honed over 200 years to find the truth

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More next time about how our LA’s implicit assumptions fail us and
how Big Data can help to do it right to get valuable results