Using metrics to influence developers, executives, and stakeholders

@LMaccherone @TheAgileCraft
What?
So what?
NOW WHAT?
Using metrics to
influence developers,
executives, and
stakeholders
Topic presented by:
Larry Maccherone
@LMaccherone

What?
So what?
NOW WHAT?

What?

So what?

What?
Visualization is like photography.
Impact is a function of focus,
illumination, and perspective.
Credit: Edward Tufte

NOW WHAT?

Don’t
Launch!

Prevent your own
disastrous decisions

Larry Maccherone
@LMaccherone

Cognitive bias
works against good
decisions

We don't see things
the way they are.
We see
things the way we
are.
~The Talmud

Next slide is a movie
click to play

Denying the Evidence

5 Truths about cognitive bias
1. Very few people are immune to it.
2. We all think that we are part of that
small group.
3. You can be trained to get much, much better.
Douglass Hubbard – How to Measure Anything
4. We do a first-fit pattern match. Not a best-fit pattern
match. And we only use about 5% of the information
to do the matching.
5. We evolved to be this way (survival trait).

An example of overcoming
cognitive bias

Trained/Calibrated
Untrained/Uncalibrated
Statistical Error
“Ideal” Confidence
30%
40%
50%
60%
70%
80%
90%
100%
50% 60% 80% 90% 100%
25
75 71 65 58
21
17
68 152
65
45
21
70%
Assessed Chance Of Being Correct
PercentCorrect
99 # of Responses
We are overconfident when assessing our own uncertainty
Copyright HDR 2007
dwhubbard@hubbardresearch.com
But, training can “calibrate” people so that of all the times they
say they are X% confident, they will be right X% of the time

Equivalent Bet calibration
What year did Newton published the Universal Laws of
Gravitation?
Pick year range that you are 90% certain it would fall within.
Win $1,000:
1. It is within your range; or
2. You spin this wheel and it lands green
Adjust your range until 1 and 2 seem equal.
Even pretending to bet money works.
90%
10%

Agile Teams, Programs, and
Portfolios
benefit from similar
calibration exercises

How to avoid cognitive bias in decision making
 Don't focus on consensus.
Ritual dissent is a much more successful approach.
 “But that doesn’t explain _______”.
An FBI agent knew that some folks were being trained to fly
but not take off and land.
 Assign someone the role of devil’s advocate.
Israel’s 10th man.
In other words… Really consider the other ALTERNATIVES

Every decision is a
forecast!

Quality of decision depends upon:
1. alternatives considered, and
2. models used to forecast the
outcome of those alternatives.

Group decisions

1. Different Models
2. Different Values
3. Different Risk Tolerance
Why do people disagree?
favor different
alternatives
Fear-based decision
making

For a given alternative, let:
Pg = Probability of good thing happening
Vg = “Value” of good thing happening
Then:
Value of the alternative = Pg × Vg

An
agile product management
example

$8M
Best case
(25%)
$1M
Likely case
(50%)
$1M
Worst case
(25%)
1
$2M$2M$1M2
Which strategy is best…
…for your company?
PW × VW = .25 × -$1.00M = -$0.25M
PL × VL = .50 × $1.00M = $0.50M
PB × VB = .25 × $8.00M = $2.00M
-----------
$2.25M
…for your career?
PW × VW = .25 × $1.00M = $0.25M
PL × VL = .50 × $2.00M = $1.00M
PB × VB = .25 × $2.00M = $0.50M
-----------
$1.75M

If you get only 1 project then
strategy 2 is better
75% of the time
If you get ∞ projects then
strategy 1 is better
100% of the time
How many projects do you need for
strategy 1 to be better
more often than not?

Play with it yourself at:
http://jsfiddle.net/lmaccherone/j3wh61r7/

Did any of you get emotional
about the $1M loss?
Did any of you want to
question the $8M number?
We’ve totally…
…eliminated fear from the equation
…changed the nature of the conversation

Argument is about who is right.
Decision making is about what is right.
One more thing about
group decision making…

An
agile delivery date forecast
example

Monte Carlo Forecasting
What it looks like
Credit for graphic:
Rally Software

Seek to
change the nature of
the conversation

1. Different Models
2. Different Values
3. Different Risk Tolerance
Why do people favor
different alternatives?
✔

Models and Values
 Models calculate probability in terms of proxy variables
 Values translate those probabilities into money
The Monte Carlo forecast didn’t give
us dollars. It gave us a probability
curve for project duration. To translate
that to money, we need to know how
we “value” time-to-market, quality, etc.
Cost-of-delay analysis should work
here.

Example model and “values” system
Team performance feedback
 Duplicate great
performance
 Bend the curve early on
declining performance
 Decide what process
improvements to
implement next
Dimensions of performance
1. Productivity
2. Predictability
3. Time-to-market
4. Responsiveness
5. Quality
6. Customer satisfaction
7. Employee engagement
8. Build-the-right-thing
9. Code quality

Economic value weighting of team performance
 Proxy variables translated to something unit-less
 Weight (adding to 100%) applied to chosen dimensions
 Examples:
 Medical device manufacturer values QUALITY
 60% for Quality
 10% each for Productivity, Predictability, Time-to-market, and
Responsiveness
 Mobile game team values TIME-TO-MARKET
 40% for Time-to-market
 20% each for Productivity and Responsiveness
 10% each Quality and Predictability

Avoid milk toast values

Criteria for
great visualization
Credits:
Edward Tufte (mostly)
Stephen Few
Gestalt School of Psychology

1. Answers the question, "Compared with what?”
(So what?)
 Trends
 Benchmarks

2. Shows causality, or is at least informed by it.
The primary chart used by the
NASA scientists showed O-ring
failure indicators by launch date.
Tufte's alternative shows the same
data by the critical factor,
temperature.
The fateful shuttle launch occurred
at 31 degree. Tufte's visualization
makes it obvious that there is great
risk for any launch at temperatures
below 66 degrees.

3. Tells a story with whatever it takes.
 Still
 Moving
 Numbers
 Graphics
And …
 Maybe some fun

4. Is credible.
 Calculations explained
 Sources
 Assumptions
 Who (name drop?)
 Drill-down
 How?
 Etc.

5. Has business value.
(or value in it’s social context)
The ODIM framework
O U T C O M E
D E C I S I O N
I N S I G H T
M E A S U R E
THINK
EFFECT
like Vic Basili’s
Goal-Question-Metric (GQM)
but without
ISO/IEC 15939 baggage

6. Shows differences easily.
aka:
Save the “pie” for dessert Credit:
• Stephen Few (Perceptual Edge)
• http://www.perceptualedge.com/ar
ticles/08-21-07.pdf

6. Shows differences easily. (continued)
Can you compare the market share from one year to the next?
Quickly: Which two companies are growing share the fastest?
One pie chart is bad. Multiple pie charts are worse!!!

6. Shows comparisons easily. (continued)
How about now?
Can you compare the
market share from one
year to the next?

7. Allows you to
see the forest
AND
the trees.

8. Informs along multiple dimensions.

8. Informs along multiple dimensions. (continued)

9. Leaves in the
numbers
where
possible.

10. Leaves out glitter.
Examples of how NOT to do it.

Top 10 criteria for great visualization
1. Answers the question,
"Compared with what?”
(SO What?)
2. Shows causality, or is at least
informed by it.
(NOW WHAT?)
3. Tells a story with whatever it
takes.
4. Is credible.
5. Has business value or impact in
its social context.
6. Shows
differences
easily.
7. Allows you to see the forest
AND the trees.
8. Informs along multiple
dimensions.
9. Leaves in the numbers where
possible.
10. Leaves out glitter.
Credits:
• Edward Tufte
• Stephen Few
• Gestalt
(School of Psychology)

Influencing with data

The rider and the elephant
 Direct the rider
 Motivate the
elephant
 Shape the path
Jonathan Haidt
The Happiness Hypothesis
(also mentioned in Switch)

Seven Deadly Sins of Agile Measurement
If you do metrics wrong, you will harm your agile transformation
1. Sin: Measurement as a lever
Virtue: Measurement as
feedback
2. Sin: Unbalanced metrics
Virtue: 1 each for Do it
fast/right/on-time, and Keep
doing it
3. Sin: Metrics can replace thinking
Virtue: Blah
4. Sin: Expensive metrics
Virtue: 1st work with the data you
are already passively gathering
5. Sin: Using a convenient metric
Virtue: Outcomes 
Decisions  Insight  Metric
(ODIM)
6. Sin: Bad analysis
Virtue: Simple stats and
simulation
7. Sin: Single outcome forecasts
Virtue: Forecasts w/
probability

NOW WHAT?… Come to the AgileCraft Booth
• Questions answered
• Seven Deadly Sins of Agile Measurement
• Demo of how AgileCraft answers “NOW
WHAT?” and is the best way to scale
agile

They say…
Nobody knows what’s gonna happen
next: not on a freeway, not in an
airplane, not inside our own bodies
and certainly not on a racetrack with
40 other infantile egomaniacs.
– Days of Thunder
Trying to predict the future is like
trying to drive down a country road
at night with no lights while looking
out the back window.
– Peter Drucker
Never make predictions, especially
about the future.
– Casey Stengel

When you come to a
fork in the road…
take it!
~Yogi Berra
What?
the metrics and analysis
So what?
how it compares/trends
what it means
NOW WHAT?
every decision is a forecast

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