2. 2
2
When I say big data which of these describes what you feel?
3. 3
3
When I say big data which of these describes what you feel?
4. 4
4
When I say big data which of these describes what you feel?
5. 5
5
When I say big data which of these describes what you feel?
6. 6
6
When I say big data which of these describes what you feel?
7. 7
7
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
8
8
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
9
9
Big Data
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
12
12
The Big Questions
13. • Who, where, what, how, and how much for
each group
13
13
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?
14
14
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.
15
15
Global Goal Driven
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
17
17
Global Goal Driven
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
21
21
Case Study – Large Retail Bank
22. • Almost no lift from “shotgun” marketing approach
22
22
What is new and different? -- Old Profit Lift Curve
23. • The state of the art predictive approach: Ranking via scores
23
23
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
24
24
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
25
25
Improvements come for three reasons:
29. • Static: Statistical Reports
• Interactive Descriptive: BI
• Predictive: Learning Algorithms (LA)
• Prescriptive: Decision Classes or Optimization
• Proactive: Optimizing Groups
29
29
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
30
30
The Best Thing for Business and Medical Analytics
31. 31
31
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
32
32
We will also recognize:
48. 48
48
Learning algorithms and modeling have built in assumptions too
Training on first
half of the flight
of a baseball
Attempting to predict 2nd half
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
53
53
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
65
65
Predictive Analytics
67. • Measuring and creating statistics about the
processes that connect individuals or
technology in potentially a complex web of
interactions
67
67
Network Analysis
69. • Developing a language, potentially
mathematical, whose characteristics and
relationships have close analogies and
structure to something in the real world.
69
69
Modeling
