This document discusses how teaching computers to think like decision makers can help bridge the gap between data/analytics and human decision making. It argues that while data/analytics are helpful, decision making requires modeling systems and understanding uncertainties, sensitivities, and risks. The document proposes "Decision Intelligence" software that would integrate data into computable systems models to help decision makers understand how different decisions may impact outcomes and risks. It envisions new types of visualizations that represent decision variables rather than just input data to provide insights on various decision scenarios.

1. Teaching Computers to Think Like
Decision Makers
Mark Zangari
CEO, Quantellia LLC
San Francisco University
May 23, 2014
Mark.zangari@quantellia.com
303 717 4221
Copyright © 2014 Quantellia LLC. All Rights Reserved.

2. Robert McNamara
• Secretary of Defense (1961-68)
• Ford Motor Co. (1946-61)
• USAF “Statistical Control” (1943-46)

3. Data System
Analysis
Decision

4. http://sunsite.berkeley.edu/FindingAids/dynaweb/calher/jvac/figures/j12EB-644A.jpg
http://www.whatswrongwiththeworld.net/office-interior-1940s.jpg
http://www.biega.com/bcbphotos/biega-engineer.jpg
Data Acquisition… Data Mining…
Analytics…
Data

5. http://sunsite.berkeley.edu/FindingAids/dynaweb/calher/jvac/figures/j12EB-644A.jpg
http://www.whatswrongwiththeworld.net/office-interior-1940s.jpg
http://www.biega.com/bcbphotos/biega-engineer.jpg
Data Acquisition… Data Mining…
Analytics…
Data

6. Data
Instrumented
Code / Sensors
Data
Management
Analytics
Presentation
System
Analysis
Decision
Data
Instrumented
Code / Sensors
Data
Management
Analytics
Presentation
Demarcation between
automated (computer-centric) and
manual (human-centric)
information processing.
Gap between computer and human
bridged by Data Visualization.

7. Units Cost Per Unit
1-100 $12.00
101-500 $10.00
501-1000 $9.00
1001-10000 $7.50
10001+ $6.00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
SalesVolume/MarketSize
Retail Price
Base Demand
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
$5,000
$5,500
$6,000
$6,500
$7,000
$7,500
$8,000
$8,500
$9,000
$9,500
$10,000
$10,500
$11,000
$11,500
$11,750
Pct.IncreaseinDemand
Marketing Spend
Marketing Driven Demand Uplift
Manufacturing Unit Cost by Volume
The Product Manager’s Decision:
To maximize profit…
a) How many units do I order from the
manufacturer?
b) What retail price do I charge?
c) How much of my profit do I re-invest
in marketing?
(Mkt Size = 50,000)

8. Even with all the data you need,
and clear visualizations, making good
decisions is still very hard to do.
Why?
Data
System
Analysis
Decision


9. Because:
a) Humans are not good at running
Systems in their heads.
b) Unlike Data, there is little
mainstream computerized support
for modeling and analyzing Systems.

10. Build a Computable Systems Model Visually
• Attributes
• Dependencies
The Product Manager’s Model

11. Identify Model Elements:
• Outcomes / Goals
“What are we trying to achieve?”
• Levers
“What can we control?”
• Externals
“What affects our outcomes
that we can’t control?”
Build a Computable Systems Model Visually

12. Identify Dependencies
Dependencies
“How are A, B and C related to X, Y and Z?”
Intermediates
When outcomes are not directly related to levers or externals.
Build a Computable Systems Model Visually

13. Quantify Dependencies
Dependencies
“How are A, B and C related to X, Y and Z?”
Build a Computable Systems Model Visually
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
SalesVolume/MarketSize
Retail Price
Base Demand
Expressions
External Data Sources / AnalyticsSketch Graphs

14. Quantify Dependencies
Dependencies
“How are A, B and C related to X, Y and Z?”
Build a Computable Systems Model Visually
Models also provide a systematic way to assess the impact
of uncertainty, sensitivity, precision and risk on the
decisions they support.

15. While humans are not good at processing systems models,
we are much better at analyzing and designing them. This
leads to a natural human-computer partnership.
Build a Computable Systems Model Visually

16. The Product Manager’s Decision:
a) How many units do I order from
the manufacturer?
b) What retail price to I charge?
c) How much of my profit do I
re-invest in marketing?
… to maximize profit?
But wait, there’s more.
38,000
$15
7%

17. The Product Manager’s Decision:
Most decisions are made not just
to optimize outcomes, but to manage
risk.
A bi-product of the optimization search
is data that can be used to:
• Assess sensitivity of the desired
outcome to particular levers and
externals.
• Assess downside risk associated with
each positive outcome.
Opportunity envelope
Risk envelope
Gradient shows sensitivity

18. Some Interesting Structural Characteristics of Models…
Build a Computable Systems Model Visually
Feedback Loop

19. … Lead to Important Behaviors.
Equilibrium and Transient States
• Real-life systems, even if they are stable, are
not static, but in a steady state or equilibrium.
• When such systems are perturbed, they
oscillate, or experience a transient.
• Effective decision makers need to be able to
understand the effects their decisions will
have both on the transient phase and on the
new equilibrium.
Build a Computable Systems Model Visually
Equilibrium with
price at $12
Price raised
to $15
New equilibrium
with price at $15
Transient
phase

20. Data System
Analysis
Decision
Big Data / Business Intelligence:

21. Data
System Analysis
Decision
Decision Intelligence
Analyze system
Build model
Integrate Data to
specify dependencies
Search the space of decision levers
and externals to determine
optimal outcomes and risk profiles
Gap between computer and human
bridged by Data Visualization of
Decision Variables, not the Input
Variables as before.

22. Decision Intelligence:
• Gives decision makers what they need most, and they cannot get
from Business Intelligence: help answering the question “If I make
this decision, then what will be the likely results, and what risks am
I exposed to?”
• Provides a framework for the most effective use of existing data
and analytics tools in a given problem.
• Provides visual and other artifacts that assure team alignment and
act as a form of “institutional memory”

23. New Kinds of Visualizations
• Familiar data visualizations still have their place in
Decision Intelligence, but note that the “axes” are now
more meaningful to decision makers as each represents
an “actionable” quantity.
• In addition, there is a powerful role for new dynamic
System Visualizations.

24. Call to Action:
Now that the “Big Data” problem is mostly solved,
we need invest our talents to return to the “Big
Picture”.
We must develop software tools and
methodologies that integrate data and systems to
produce the kinds of insights real users really need.

25. Download a free trial
of World Modeler
from
www.quantellia.com
Mark Zangari
CEO, Quantellia LLC
San Francisco University
May 23, 2014
Mark.zangari@quantellia.com
303 717 4221
Copyright © 2014 Quantellia LLC. All Rights Reserved.