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MD Half‐day Tutorial 6/3/2013 8:30 AM
"Dealing with Estimation, Uncertainty, Risk, and Commitment"
Presented by:
Todd Little Landmark Graphics Corporation
Brought to you by:
340 Corporate Way, Suite 300, Orange Park, FL 32073 888‐268‐8770 ∙ 904‐278‐0524 ∙ [email protected] ∙ www.sqe.com
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Todd Little Landmark Graphics Corporation
Todd Little is a senior development manager for Landmark Graphics. Todd has been involved in most aspects of software development with a focus on commercial software applications for oil and gas exploration and production. He is a coauthor of the Declaration of Interdependence and a founding member and past president of the Agile Leadership Network. Todd has served on the board of directors of both the Agile Alliance and the Agile Leadership Network, is a coauthor of Stand Back and Deliver: Accelerating Business Agility, has written several articles for IEEE Software, and posts all his publications and presentations on his websitetoddlittleweb.com.
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Dealing with Estimation, Uncertainty,
Risk, and Commitment : An Outside-In Look at Agility and Risk Management
Todd Little
Sr. Development Manager
Landmark Software & Services
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Agenda
• Introduction
• Exercise
• Estimation and Uncertainty
• Exercise
• Break
• Risk Management, Real Options and
Commitment
• Exercise
• Summary
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Form into teams. Pick a name... Shout out the name.
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On a piece of paper, write 1. Your Name 2. Your Team Name 3. Team to complete task A
first 4. Team to complete task B
first
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task “A”...
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55/ Create a six sided random number generator.
Something like a dice like thingy.
Every member of the team needs to roll a double six.
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task “B”...
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55/ Create a four sided random number generator.
Something like a dice like thingy.
Every member of the team needs to roll a double six.
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Risk and Uncertainty
Risk Uncertainty
Frank Knight Immeasurable Quantifiable
PMI Risk can be positive or
negative
???
English A situation involving
exposure to danger
The state of being uncertain
Not known or established;
questionable
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Delivery Challenges/Failures
Challenged
46%
Failed
19%Succesful
35%
Standish Group 2006, reported by CEO Jim Johnson, CIO.com, ‘How to Spot a Failing Project’
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Managing the Coming Storm
Inside the Tornado
When will we get the requirements?
All in good time, my little pretty, all in good time
But I guess it doesn't matter anyway
Doesn't anybody believe me?
You're a very bad man!
Just give me your estimates by this afternoon
No, we need something today!
I already promised the customer it will be out in 6 months
No, we need it sooner.
Not so fast! Not so fast! ... I'll have to give the matter a little thought. Go away and come back tomorrow
Ok then, it will take 2 years.
Team Unity
Project Kickoff
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We’re not in Kansas Anymore
My! People come and go so quickly here!
I may not come out alive, but I'm goin' in there!
The Great and Powerful Oz has got matters well in hand.
"Hee hee hee ha ha! Going so soon? I wouldn't hear
of it! Why, my little party's just beginning!
Developer Hero
Reorg
Testing
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Why is Software Late?
Genuchten 1991 IEEE General
Manager
Project
Manager Item
1 10 Insufficient front end planning
2 3 Unrealistic project plan
3 8 Project scope underestimated
4 1 Customer/management changes
5 14 Insufficient contingency planning
6 13 Inability to track progress
7 5 Inability to track problems early
8 9 Insufficient Number of checkpoints
9 4 Staffing problems
10 2 Technical complexity
11 6 Priority Shifts
12 11 No commitment by personnel to plan
13 12 Uncooperative support groups
14 7 Sinking team spirit
15 15 Unqualified project personnel
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The Context of Feedback
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Why is Software Late?
Genuchten 1991 IEEE General
Manager
Project
Manager Item
4 1 Customer/management changes
10 2 Technical complexity
2 3 Unrealistic project plan
9 4 Staffing problems
7 5 Inability to track problems early
11 6 Priority Shifts
14 7 Sinking team spirit
3 8 Project scope underestimated
8 9 Insufficient Number of checkpoints
1 10 Insufficient front end planning
12 11 No commitment by personnel to plan
13 12 Uncooperative support groups
6 13 Inability to track progress
5 14 Insufficient contingency planning
15 15 Unqualified project personnel
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Why is Software Late?
Genuchten 1991 IEEE
General
Manager Project
Manager Item
H H Customer/management changes
L H Overall complexity
H H Unrealistic project plan
M H Staffing problems
H L Insufficient front end planning
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The Goal
On Time
To Spec
Within Budget
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Da Plan, Boss – Da Plan
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IEEE Software, May/June 2006
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Accuracy of Initial Estimate A
ctu
al
Initial Estimate
Initial Estimate vs. Actual Duration
Ideal
LGC Data
DeMarco
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Probability Distribution Curve
Distribution Curve of Actual/Estimated (DeMarco data vs. LGC)
(Demarco data is Effort/Effort; LGC data is Duration/Duration)
0
0.5
1
1.5
2
2.5
0 1 2 3 4 5 6 7 8
(Actual/Estimated)
Fre
qu
en
cy
DeMarco
LGC
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Log Normal Distribution
• Estimation Accuracy follows a Log Normal
distribution
0.1 1 10 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Median
Mean
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.1 1 10 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.1 1 10 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
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Uncertainty Bounds
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.1 1 10
CD
F P
rob
ab
ilit
y
Ratio of Actual/Estimate
Cumulative Distribution Function of Actual/Estimate Ratio
DeMarco Data
DeMarco Log-Normal
Landmark Data
Landmark Log-Normal
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How does Estimation Accuracy
Improve Over Time?
Feasibility Concept ofOperation
RequirementsSpec
Product DesignSpec
Detail DesignSpec
AcceptedSoftware
Rela
tive C
ost
Ran
ge
Cone of Uncertainty from Boehm
4.0
2.0
0.5
0.25
1.5
0.67
1.25
0.8 1.0
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Landmark Cone of Uncertainty
0.1
1
10
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Acu
tal
To
tal
Du
rati
on
/ E
sti
mate
d T
ota
l D
ura
tio
n
Percent of Actual Duration
Estimation Error over Time
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But is Uncertainty Really Reduced?
“Take away an ordinary person’s illusions and
you take away happiness at the same time.”
Henrik Ibsen--Villanden
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Remaining Uncertainty
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Remaining Uncertainty
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Sources of Error
• Bias
• Uncertainty Range
• Scope Creep
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Sources of Error
• Bias
Median
Mean
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Estimate
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Negotiation Bias
• "It is difficult to get a man to
understand something when his
salary depends upon his not
understanding it.“ » Upton Sinclair:
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Politics and Schedule Estimation
http://www.stevemcconnell.com/ieeesoftware/bp03.htm
• Developers tend to be temperamentally opposed
to the use of negotiating tricks. Such tricks
offend their sense of technical accuracy and fair
play. Developers don't want to offer lopsidedly
high initial estimates even when they know that
customers, marketers, or bosses will start with
lopsidedly low bargaining positions.
– Steve McConnell
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Space Shuttle Challenger
Engineers Management
Probability of loss of life 1 in 100 1 in 100,000
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34
Group Subject % Correct (target 90%)
Harvard MBAs General Trivia 40%
Chemical Co. Employees General Industry 50%
Chemical Co. Employees Company-Specific 48%
Computer Co. Managers General Business 17%
Computer Co. Managers Company-Specific 36%
AIE Seminar (before training) General Trivia & IT 35%-50%
AIE Seminar (after training) General Trivia & IT ~90%
90% Confidence Interval
Overconfidence in Ranges
• Most people are significantly overconfident about
their estimates, especially educated professionals
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Test 1 (Jørgensen IEEE
Software 2008)
Group Guidance Result
A 800
B 40
C 4
D None 160
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Test 1
Group Guidance Result
A 800 300
B 40 100
C 4 60
D None 160
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Test 2
Group Guidance Result
A Minor Extension
B New Functionality
C Extension 50
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Test 2
Group Guidance Result
A Minor Extension
40
B New Functionality
80
C Extension 50
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Test 3
Group Guidance Result
A Future work at stake, efficiency will be measured
B Control 100
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Test 3
Group Guidance Result
A Future work at stake, efficiency will be measured
40
B Control 100
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Understand Bias
• "What gets us into trouble is not what we
don't know. It's what we know for sure that
just ain't so.“ » Mark Twain
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task “A”...
htt
p:/
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co
m/p
hoto
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oza
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92425
55/ Create a six sided random number generator.
Something like a dice like thingy.
Every member of the team needs to roll a double six.
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Did you know? (Jørgensen IEEE
Software March 2013) • The US has 155 million more inhabitants
than Mexico, but Mexico has 100 million
fewer inhabitants than the US.
• Poland has 10 million more inhabitants
than Romania, but Romania has about the
same number of inhabitants as Poland.
• Austria’s population is 70% of Hungary’s,
while Hungary’s population is 80% of
Austria’s.
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Velocity Helps Remove Bias
•𝑆𝑡𝑜𝑟𝑦 𝑃𝑜𝑖𝑛𝑡𝑠
𝑆𝑡𝑜𝑟𝑦 𝑃𝑜𝑖𝑛𝑡𝑠
𝐼𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛
= 𝐼𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠
8/25/2009
10/14/2009
12/3/2009
1/22/2010
3/13/2010
5/2/2010
6/21/2010
8/10/2010
9/29/2010
11/18/2010
0 1 2 3 4 5 6 7 8
Iteration
Projected Ship Date
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But Velocity is not a Silver Bullet S
tory
Estim
ate
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Sources of Error
• Range of Uncertainty
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Estimation Errors
• Lan Cao - Estimating Agile Software
Project Effort: An Empirical Study
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Lan Cao - Estimating Agile Software Project
Effort: An Empirical Study
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Sources of Error
• Scope Creep
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Scope Creep
• Capers Jones
–2% per month
–27% per year
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Don’t know that
we don’t know
Knowable
Unknowable
Uncertainty
Know that
we know
Know that
we don’t know
Don’t know that
we know
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Don’t know that
we don’t know
Knowable
Unknowable
Uncertainty
Know that
we know
Know that
we don’t know
Don’t know that
we know
Uncertainty
Management
Wishful Thinking Discoverable Risks
p10
p50
p90
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Estimation Exercise
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War
They couldn't hit an elephant at this dist…
General John B. Sedgwick, Union Army Civil War officer's last words, uttered during the Battle of Spotsylvania, 1864
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Gordon the
Guided Missile
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Texas Hold’em:
Which is the best hole hand?
A B C
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Texas Hold’em:
Which is the best hole hand?
33.5% 29.6% 36.5%
A B C
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Poker Metric:
Percent of Hands Won
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Oil & Gas Exploration
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Oil & Gas Exploration
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Which Risks Are Important
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Real Options
• The right — but not the obligation — to
undertake certain actions prior to an expiry
date
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Real Options • The right, but not the obligation to take
some action prior to an expiry date
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Real Options
Value of Information
Value of Flexibility
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Value of Information
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Value of Information
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Va
lue
or
Co
st
EVI
ECI
Increasing Value & Cost of Info.
• EVPI – Expected Value of
Perfect Information
• ECI – Expected Cost of
Information
• EVI – Expected Value of
Information
$0
$$$
Low certainty High certainty
EVPI
Aim for this
range
76
Perfect
Information
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Value of Flexibility
• The right — but not the
obligation — to undertake
certain actions prior to an
expiry date
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Making An Agile Conference Agile
0
100
200
300
400
500
600
700
800
900
1000
0 10 20 30 40 50 60 70
Days Before Deadline
Submissions for 2011 compared to 2010
Y2010
Y2011-IT1
Y2011-IT2
5 Extra weeks to
review sessions
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0
200
400
600
800
1000
1200
1400
1600
1800
16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 FINAL
Weeks Out
Estimated Agile 2010 Attendance
Most Likely
Pessimistic
Optimistic
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Do you have a choice?
80
Option
Commitment
Decision
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Agile projects need risk management too
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Analyze risks
probability
imp
act
sh
ow
sto
pp
er
op
po
rtun
ity
improbable near certainty
Alien Invasion
We’ll rock at Scrum
Early Finish
Task Switching
Evaluation
Sickness
Scope creep
ne
glig
ible
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0
5
10
15
20
25
30
1 2 3 4 5 6 7
Ris
k
Iteration
Risk Burndown
Risk 5
Risk 4
Risk 3
Risk 2
Risk 1
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Risk
Technical Uncertainty
Manual Process
Complexity
Rules Engine
Complexity Lack of Standardization
Uncertain
Benefits
Technical Uncertainty
Manual Process
Complexity
Rules Engine
Complexity Lack of Standardization
Uncertain
Benefits
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Categories of risk
Delivery Failure
Business Case
Failure
Collateral Damage
Political Risk
Quality and Market
Acceptance
Page 89
Two Risk Perspectives
Individual
Perspective
Business
Perspective
Page 90
Collateral Damage
Page 91
Delivery Failure results in Collateral
Damage
Page 92
Titanic
• Time pressure
• Feedback blocks
• Unsinkable
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I’m beginning to
think it wasn’t such
a good idea to turn
off those unit tests
Page 94
Collateral Damage
Business
Perspective
Individual
Perspective
Could be catastrophic
Some individuals may be willing to
take on more risk than desired
Page 95
Collateral Damage Management
An effective roll-back strategy Honor feedback
Inspect Incremental Delivery
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Business Case Failure
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Business Case Failure
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Always or
Often Used:
20%
Never or
Rarely Used:
64% Sometimes
16% Rarely
19%
Never
45%
Often
13%
Always
7%
Features and Functions
Standish Group Study, reported by CEO Jim Johnson, XP2002
Page 99
Powerful Questions are: What are we building?
What Business are we in?
What Building are
we in?
Page 100
98
The Purpose Alignment Model
Page 101
99
In Practice
CAN WE CREATE A
DIFFERENTIATED PARTNERSHIP?
INNOVATE, CREATE
MINIMIZE / ELIMINATE
ACHIEVE AND MAINTAIN
PARITY, MIMIC,
SIMPLIFY
Page 102
Applicable at all Levels • Corporate
Strategy
• Product Strategy
Page 103
101
A View of Strategy - Apple
ATT
NEW PRODUCT DESIGN
USER EXPERIENCE
CONTENT DISTRIBUTION
MS OFFICE
INTEL HARDWARE
OTHER SOFTWARE
PERIPHERALS
Page 104
Business Case
Business
Perspective
Individual
Perspective
Sustainable competitive advantage
How can I sell this so that I can get
more budget?
Page 105
Delivery Failure
Late
Over Budget
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Uncertainty Bounds
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.1 1 10
CD
F P
rob
ab
ilit
y
Ratio of Actual/Estimate
Cumulative Distribution Function of Actual/Estimate Ratio
DeMarco Data
DeMarco Log-Normal
Landmark Data
Landmark Log-Normal
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The A/B/C List sets proper
expectations
A MUST be completed in order to ship the product and the schedule will be slipped if necessary to make this commitment.
B Is TARGETED to be completed in order to ship the product, but may be dropped without consequence.
C Is NOT TARGETED to be completed prior to shipping, but might make it if time allows.
Only “A” features may be committed to customers.
If more than 50% of the planned effort is allocated to “A”
items the project is at risk.
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A
0
0.2
0.4
0.6
0.8
1
1.2
Janu
ary
Febru
ary
Mar
chApr
ilM
ay
June
July
Augus
t
Septe
mbe
r
Octob
er
Nov
embe
r
Dec
embe
r
A/B/C List
50% 100%
Backlog Plan
Typical Delivery
25%
A B C
B C D
50% 25%
Target
Delivery Date
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0
0.2
0.4
0.6
0.8
1
1.2
Janu
ary
Febru
ary
Mar
chApr
ilM
ay
June
July
Augus
t
Septe
mbe
r
Octob
er
Nov
embe
r
Dec
embe
r
A/B/C List
50% 100%
Backlog Plan
Uncertainty Risk
25%
A B C
B C D
50% 25%
Target
Delivery Date
A
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Estimating Story Difficulty Estimation
XL
L
M
S
S M L XL
Cost
Valu
e
Page 111
Product Innovation Flow
Adaptive Activities
Pro
ject
Sanction
RTM
CORE Activities
Idea Filter
Hot Items
A Backlog
Burnup
Sales
Services
Customer Support
Product Backlog
A Items
Iteration Backlog
Flexible Scope Backlog
New
ly D
iscovere
d
It
em
s
Most Items for consideration in next release
B & C Release
Backlog
B/C/D
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No
Surprises!
Risk Management = Expectation Management
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Iteration Review Questions
• What promises have been made, to whom were
they made, and who made them?
• What key decisions or commitments might we
have to make within the next 2 iterations?
• Questions to ask team anonymously
– When will we be ready to ship?
– Will we be able to keep our promises?
– Is the team healthy and operating effectively?
– Is the team on the right path?
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Ask the Team
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2/6/2011 2/26/2011 3/18/2011 4/7/2011 4/27/2011 5/17/2011 6/6/2011 6/26/2011 7/16/2011
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Uncertainty
Time
Valu
e
Contract
Minimum scope for the
release at the latest date
that it can be released.
Plan
Planned scope for the
release at the optimal time
that it can be released.
Target
Best possible scenario if
everything went perfectly.
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Uncertainty
Time
Valu
e
C
B
A
Page 118
Tools for Delivery Risk
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2/6/20112/26/20113/18/20114/7/20114/27/20115/17/20116/6/20116/26/20117/16/2011
Stop Dreaming
Ask the Team
Cost of Delay
Page 119
Delivery Risk
Business
Perspective
Individual
Perspective
Cost of Delay
Business Credibility
Personal Credibility
Page 120
Market Acceptance
Features and Quality
Page 121
“I skate to
where the
puck is
going to
be, not
where it
has been.”
Page 122
As Knowledge
increases Leaders use
iterations to guide
project towards
enhanced goal
Allow Mid Course Corrections
Planned Path
Actual Path
Actual Completion
Start
Zone of success Planned
Completion
Incre
asin
g K
now
led
ge
Page 123
The Cone of Uncertainty
We expect uncertainty and manage for it through iterations,
anticipation, and adaptation.
Page 125
123
The Purpose Alignment Model
Page 126
Quality and Market Acceptance
Strategies
Seek and honor feedback
Incremental Delivery
Page 128
Wrong Priorities
Page 129
Compute This
"I recently asked a colleague [CIO] whether he
would prefer to deliver a project somewhat late
and over-budget but rich with business benefits or
one that is on-time and underbudget but of scant
value to the business. He thought it was a tough
call, and then went for the on-time scenario.
Delivering on-time and within budget is part of his
IT department's performance metrics. Chasing
after the elusive business value, over which he
thought he had little control anyway, is not."
Cutter Sr. Consultant Helen Pukszta
Page 130
Software Tornado Example
Item
-15M 0M 15M 30M 45M
Product Acceptance
Uncertainty ██ ███ ███ ███ ███ ███ ███
Schedule Uncertainty █ ███ ███ ██
General Market Uncertainty ██ ██
Cost Uncertainty ██ █
Page 131
Software Tornado Example
Item
-15M 0M 15M 30M 45M
Product Acceptance
Uncertainty ███ ███ ███ ███ ███ ███
Schedule Uncertainty ███ ███ ██
General Market Uncertainty ██ ██
Cost Uncertainty ██ █
Page 132
Software Tornado Example
Item
-15M 0M 15M 30M 45M
Product Acceptance
Uncertainty ███ ███
Schedule Uncertainty ███ ██
General Market Uncertainty ██ ██
Cost Uncertainty ██ █
Page 133
The Measurement Inversion
132
• Initial cost
• Long-term costs
• Cost saving benefit other than labor productivity
• Labor productivity
• Revenue enhancement
• Technology adoption rate
• Project completion
Lowest
Information Value
Highest
Information Value
Most Measured
Least Measured
In a business case, the economic value of measuring a variable is usually
inversely proportional to the measurement attention it typically gets.
Page 134
Market Acceptance
Business
Perspective
Individual
Perspective
Impact to revenue
Impact to credibility
Cost of rework
Priorities are often guided by other
perceived constraints
Page 136
Change
or
Maintain
Page 137
Political Challenges Boss and above Boss’s Peers Boss’s Indirect
Peers
You Peers Indirect Peers
Direct Reports Peers’ Reports Indirect
Page 138
Network
What’s in it for them for everyone to succeed?
What do they worry about losing if you are successful?
Page 139
Long Ago and Far, Far Away…
Page 141
Collaborating with Non-Collaborators
Agreement Disagree Agree
Col
labo
rati
on
Non
- C
olla
bora
tor
Col
labo
rato
r
Compliance Combative
Creative Tension
Collegial
Common Practice M
ore
usef
ul
Page 142
Agile Leadership
Page 143
Politics
Business
Perspective
Individual
Perspective
Politics do not add to business value
other than by luck
Perception trumps Reality
Politics forms Perception
Page 144
Final Summary Risk Business Perspective Individual Perspective Tools Business Case Strategic alignment with
market need
Demonstrate Confidence • Purpose Alignment
Model
Collateral
Damage
Could be catastrophic Make or Break career.
Individuals may be willing to
take more risk than the
organization
• Build quality in
• Feedback loops
Delivery of
Market/
Quality Need
Impact to revenue
Impact to credibility
Cost of rework
Depends on the individual -
Priorities are often guided
by other perceived
constraints
• Customer Feedback
Delivery on
Time or within
Budget
Cost of Delay
Reduction of ROI
Business Credibility
Personal credibility • Cost of Delay
• Tornado Charts
Politics Markets only care if politics
influence external
perception.
Perception trumps Reality
Politics forms Perception
• Networking &
Transparency
• Collaborating with
Non-Collaborators
Page 145
Simulation Exercise
ID: 1 Value: Sum of all dice
Major feature
Test: Roll 3 dice - 2 or more dice are the
same
Commit Points: 2
Page 146
Stories and Scoring
• Points (Value pts and Commit pts) are scored for accepted stories.
• Total points are Value + 10*Commit Points Made.
• Value is just an estimate…the market will decide
We will run 3 iterations,
with 10 rolls of the dice per
iteration
WIP limit of one story in
progress
Must commit to 15 pts for
the release, and 5 pt per
iteration
ID: 1 Value: Sum of all dice
Major feature
Test: Roll 3 dice - 2 or more dice are the
same
Commit Points: 2
Page 147
Special Actions
• Information
– At the cost of one roll, you may pre-roll your next turn
prior to selecting the story to work on.
• Flexibility
– At the cost of one roll, you may designate that the
story that you will be working on can be refactored at
a future date, i.e. you first accept the story, but if a
future roll gives an improved result, you may use that
roll for the new value
• It is ok abandon a story
• It is ok to redo a story
Page 148
Estimating Story Difficulty Estimation
XL
L
M
S
S M L XL
Cost
Valu
e
Page 149
The Progress Board Backlog Committed In Work
Accepted
Committed for
this release (A)
Cutline
Potential stories
Committed for
this iteration
In Development
Passes test
criteria
Page 150
Risk Management is not Risk Aversion
Page 151
Contact
Todd Little
[email protected]
www.toddlittleweb.com
www.accelinnova.com
Page 153
Why is
Software
Late?
Page 154
From the home office in Duncan, Oklahoma
Top Ten reasons why software is
late
Dubai, UAE
Page 155
10: Requirements, what Requirements?
What you want, baby I
got it
R-E-Q-U-I-R-E
Find out what it
means to me
Top Ten reasons why software
is late
Page 156
9: Dependencies on other groups that were late
Top Ten reasons why software
is late
Page 157
8: Over-optimistic Schedule Estimation
Always look on the bright side of code
. . . . . . .
Always look on the bright side of code
. . . . . . .
The code’s a piece of $#!^,
when we look at it
We can always overlook a minor kink . . . .
It probably compiles, it might even link . . .
Surely that must mean it doesn’t stink
Top Ten reasons why software
is late
Page 158
7: Those weren’t MY estimates
How low can you go!
Scheduling Ritual
Top Ten reasons why software
is late
Page 159
6: Not enough testers or documentation
resources.
Who needs them anyway? We
put those bugs--I mean features--
in there on purpose. Besides, it
was difficult to program, it should
be difficult to use.
Top Ten reasons why software
is late
Page 160
5: Offshore and Outsourcing issues
My source code lies over the ocean,
My source code lies over the sea .
My source code lies over the ocean,
Oh bring back my source code to me
. . . . .
Bring Back, Bring Back,
oh bring back my source code to me, to me
Bring Back, Bring Back,
oh bring back my source code to me
Top Ten reasons why software is
late
Page 161
4: One word, Ch-ch-ch-changes
Top Ten reasons why software is
late
Page 162
3: I can’t get no, System Admin
– I can’t get no, CM action
– ‘cause I try,
– ..and I try,
– ….and I try,
– ……and I try….
Top Ten reasons why software
is late
Page 163
2: You didn’t give me the headcount that
you promised
Top Ten reasons why software
is late
Page 164
1: Weren’t you doing the backups!?
Top Ten reasons why software
is late
Page 165
Successful Projects?
Page 166
Risk and Context
• One Size Doesn’t Fit All
Page 167
Context Leadership Model
Project Complexity
Un
ce
rta
inty
Cows
Bulls Colts
Sheep Dogs
Page 168
Context Leadership Model
Project Complexity
Un
ce
rta
inty
Simple, young projects.
Need agility
Tight Teams
Sheep Dogs Complex, mature market
Need defined interfaces
Cows
Bulls Agility to handle uncertainty Process definition to cope
with complexity
laissez faire
Colts
Low
Low
High
High
Page 169
Bull Product Release
Page 170
Reduce Uncertainty or Complexity
Uncertainty Complexity
Opportunities to Reduce Uncertainty:
• Use proven technologies
• Reduce project duration
Opportunities to Reduce Complexity:
• Collocate the team
• Break project into sub-projects
Attribute Score
Market ███
Technical ███
# Customers █████████
Duration █████████
Change ███
Attribute Score
Team Size █████████
Mission Critical █████████
Team Location █████████
Team Maturity ███
Domain Gaps ███
Dependencies █████████
Page 171
Partitioning
Dog
Project
Cow
Project
Colt
Project
Bull
Program
Remember: Loose Coupling and Strong Cohesion
Page 172
Project Leadership Guide
M
ark
et
Dif
fere
nti
ati
ng
High
Low
Mission Critical Low High
Invent
Manage Offload
Create
Change
Embrace
Change
Eliminate
Change
Control
Change
Ad Hoc Agile
Outsource Structured
Deploy
Page 173
Portfolio Management
RAPID Quadrant Assessment
0.0
2.0
4.0
6.0
8.0
10.0
12.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Project Complexity
Un
cert
ain
ty
Project Complexity
Un
ce
rta
inty
Page 174
Not all dogs are the same
Page 175
Financial Markets
A severe depression like that of 1920-21 is outside the range of probability.
Harvard Economic Society, Weekly Letter, November 16, 1929.
Page 178
New Product Development
I think there is a world market for about five computers.
Thomas J. Watson, chairman of IBM, 1943.
Page 179
Getting Better
0
1
2
3
4
5
6
7
8
9
1 2 3 4
Esti
mati
on
Qu
ali
ty F
acto
r (E
QF
)
Years
Page 180
How do “Risky Businesses” work
“It’s tough to make predictions,
especially about the future.”
Yogi Berra, Niels Bohr
Page 181
Exercise
Low Med High
Distance from Las Vegas to Houston, Texas
Height of the Empire State Building
Population of Sweden
U.S. Oil Consumption/day
Water in a 100 gallon vat filled with sand
Page 182
Exercise
Low Med High
Distance from Las Vegas to Houston, Texas
1222
Height of the Empire State Building
1453
Population of Sweden 9MM
U.S. Oil Consumption/day 20MM
Water in a 100 gallon vat filled with sand
35
Page 183
Uncertainty
Time
Valu
e
Failure
Maximal
Success
Schedule
Flexible
Scope
Flexible
Minimal
Success
Page 184
Estimation accuracy improves
(Eveleens and Verhoef)
Page 185
Estimation accuracy constant
(Eveleens and Verhoef)
Page 186
Estimation accuracy decreases
(Eveleens and Verhoef)