What are predictive applications, how do they work and how can companies get started using predictive applications?

2. Business Reasons for
Automated Decisions
Lars Trieloff | @trieloff

3. • They are faster
• They are cheaper
• They are better
• That’s it. Thank you for your attention.
Why Automated Decisions
make Business Sense

4. 4%Worldwide average profit
margin in retail: 4%

5. 4‰German average profit margin
in retail: 4‰

6. Your Customer gives you this

7. All you got to keep is that

8. — –Libby Rittenberg
“Economic profits in a system of
perfectly competitive markets
will, in the long run, be driven
to zero in all industries.”

9. Physiological
Safety
Love/Belonging
Esteem
Self-Actualization

10. — Abraham Maslov – probably never said this. It’s true anyway.
“Data has Human Needs, too”

11. Collection
Storage
Analysis
Prediction
Decision

12. Collection
Storage
Analysis
Prediction
Decision
Physiological
Safety
Love/Belonging
Esteem
Self-Actualization

14. — W. Edward Deming
“In God we trust, all others bring
data”

15. How Data-Driven Decisions
should work
Computer
Collects
Computer
Stores
Human
Analyzes
Human
Predicts
Human
Decides

16. — Daniel Kahneman
“Prejudice against algorithms is
magnified when the decisions
are consequential.”

17. How Data-Driven Decisions
REALLY work
Computer
Collects
Computer
Stores
Human
Analyzes
C O M M U N I C AT I O N
B R E A K D O W N
Human
Decides

18. — Led Zeppelin
Communication Breakdown, It's
always the same,
I'm having a nervous
breakdown, Drive me insane!

19. • Drill-down analysis … misunderstood or
distorted
• Metrics dashboards … contradictory and
confusing
• Monthly reports … ignored after two
iterations
• In-house analyst teams … overworked
and powerless
How Data-Driven Decisions
REALLY work
CO M M U N I C AT I O N
B R E A K D O W N

20. How Decisions REALLY should
work
Computer
Collects
Computer
Stores
Computer
Analyzes
Computer
Predicts
CO M P U T E R
D E C I D E S

21. — Everyone at Blue Yonder, all the time
99.9% of all business decisions
can be automated

22. How Decisions are Being Made

23. 90% No Decision is made

24. — Robin Sharma
“Making no decision is a
decision. To do nothing. And
nothing always brings you
nowhere..”

25. Business Rules for Beginners
Not doing anything is the simplest business
rule in the world – and also the most popular

26. 90% No Decision is made

27. 9% Decision Follows Rule

28. Advanced Business Rules
Computers are machines following rules. This
means business rules are programs.

29. • Business rules are like programs – written by
non-programmers
• Business rules can be contradictory,
incomplete, and complex beyond
comprehension
• Business rules have no built-in feedback
mechanism:“It is the rule, because it is the rule”
Business rules are Programs,
just not very good ones.

30. 1% Human Decision making

31. Human Decision Making has
two systems – and only one is
rational.

32. Not quite Almost there That’s it.

33. Quick: What do you see here?

35. — Steven Pinker, describing Moravec’s Paradox
“The hard problems are easy
and the easy problems are
hard.”

36. Quick: Add all even numbers
65 7 1 0
60 63 18 80
547039100

37. 94 39 37 31
92 70 100 67
4956080
69 20 26 73

38. 51 60 23 22
5 48 43 14
9525669
23 67 1 43

39. Correct Result:

40. Correct Result: 1.024

42. — Daniel Kahneman
“All of us would be better
investors if we just made fewer
decisions.”

44. How we are making decisions
(Like the big apes we are)
Anchoring effect
IKEA effect
Confirmation bias
Bandwagon effect
Substitution
Availability heuristic
Texas Sharpshooter Fallacy
Rhyme as reason effect
Over-justification effect
Zero-risk bias
Framing effect
Illusory correlation
Sunk cost fallacy
Overconfidence
Outcome bias
Inattentional Blindness
Benjamin Franklin effect
Hindsight bias
Gambler’s fallacy
Anecdotal evidence
Negativity bias
Loss aversion
Backfire effect

46. K-Means Clustering
Naive Bayes
Support Vector Machines
Affinity Propagation
Least Angle Regression
Nearest Neighbors
Decision Trees
Markov Chain Monte Carlo
Spectral clustering
Restricted Bolzmann Machines
Logistic Regression
Computers making decisions
(cold, fast, cheap, rational)

47. • A machine learning algorithm is a system that
derives a set of rules based on a set of data
• It is based on systematic observation, double-
checking and cross-validation
• There is no magic, just data – and without data
there is no magic either
Machine Learning means
Programs that write Programs

48. Better Decisions through
Predictive Applications

49. How Predictive Applications
Work
Collect & Store
Analyze
Correlations
Build Decision
Model
Decide &
Test
Optimize

50. — Warren Buffett
“I checked the actuarial tables,
and the lowest death rate is
among six-year-olds, so I
decided to eat like a six-year-
old.”

51. More than half of the apps on
a typical iPhone home screen
are predictive applications.

52. Building Predictive Applications
Machine Learning ModelPredictive Application
Enterprise Integration

53. Story Time
(Not safe for vegetarians)

54. The Ground Beef Dilemma

55. How much ground
beef are we going to
sell on Friday?

56. How much ground beef are
we going to sell on Friday?
And how much on
Saturday?

57. Challenge #1 Accurately predict
demand

58. Great. But how much
do we need to order
each day?

59. Great. But how much
do we need to order
each day?
Let’s reduce the risk
of running out of
stock to 20%

60. Sales Forecasts for Friday
SalesProbability
0
0,01
0,02
0,03
0,04
0 4 8 12 16
Friday Sales Amount

61. Sales Forecasts for Saturday
SalesProbability
0
0,01
0,02
0,03
0,04
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Saturday Sales Amount

62. Great. But how much
do we need to order
each day?
Let’s reduce the
risk of running out
of stock to 20%
So it’s 3 on Friday and
5,5 on Saturday.

63. Sales Forecasts for Both Days
SalesProbability
0
0,01
0,02
0,03
0,04
0 4 8 12 16
Friday Sales Amount Saturday Sales Amount

64. Bad news…

65. Bad news…
We need to skip the
Saturday delivery.

66. Bad news…
We need to skip the
Saturday delivery.
How big should we
make the Friday delivery
instead?

67. If you need 3 on Friday and 5,5
on Saturday to fulfill 80% of
the demand, how much do you
need to fulfill 80% of the
combined demand?

68. 3 + 5,5 = 8,5
Common Sense isn’t it?

69. — Albert Einstein
Common sense is what tells us
the world is flat.

70. Combined Sales Forecasts
SalesProbability
0
0,01
0,02
0,03
0,04
0 4 8 12 16
Combined Sales Amount

71. If you ordered 8,5 cases, you
would waste a lot of meat,
the ideal order amount is 8
cases.

72. Predictive Apps in a Nutshell
Batch and streaming data ingestion, batch
and streaming delivery (with real-time option)
Reduce risk and cost » increase revenue and profit
Trend Estimation Classification Event Prediction
Optimize Returns
Collect Data Predict Results Drive Decisions

73. — John Maynard Keynes
“When my information
changes, I alter my conclusions.
What do you do, sir?”

74. One Common Platform for
Predictive Applications
Multi-Tenant Runtime Environment
Link Store Build Run View
Link your own and
third-party data, easily
integrated via API
Store your data in
high-performance
database as a service
Build machine
learning and
application code
Automatically run
and scale ML models
and applications
Monitor and inspect
resource usage and
model quality
Secure Micro Cloud Infrastructure
Domain Model Predictive Model Application Code

75. — Kevin Kelly
“The business plans of the next
10,000 startups are easy to
forecast: Take X and add AI”

76. How Enterprises adopt
Predictive Applications
Learn about
ADDD
Define Target
Process
Build
Predictive App
Go Live
Make Lots of
Money

77. — Daniel Kahneman
“Prejudice against algorithms is
magnified when the decisions
are consequential.”

78. How Enterprises REALLY adopt
Predictive Applications
Learn about
ADDD
Define Target
Process
Build
Predictive App
Make Lots of
Money
D O U BT S
CO N C E R N S
O B J E C T I O N S

79. Decision Quality
Status Quo Predictive Prescriptive Automation

80. — The Economist, May 2015
“The best chess players in the
world are‘centaurs’,
amalgamated teams of humans
and algorithms.”

81. Decision Quality
Status Quo Predictive Prescriptive Automation 'Centaurs'

82. Ready, set, go for ADDD?

83. Not so fast

84. Data Availability
Predictability
Understandability
Executability

85. Financial Impact of Predictive
Apps
-120
-90
-60
-30
0
30
60
90
March April May June July August September
Break-Even
after 3 months

86. • They are faster
• They are cheaper
• They are better
• That’s it. Thank you for your attention.
Why Automated Decisions
make Business Sense

87. Lars Trieloff
@trieloff