CMMI Explored - HM’s Fourteen: Essential Beliefs for Effective High Maturity Implementation

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© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License
CMMI® - Explored
HM’s Fourteen:
Essential Beliefs for Effective High
Maturity Implementation
1

2© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
®
CMMI and CMM are registered trademarks of Carnegie Mellon University
© Rajesh Naik, 2013
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This means you are free
(1) to copy, distribute, display, and perform the work,
(2) to make derivative works, and
(3) to make commercial use of the work
so long as you give proper attribution to the author and retain the license notice.
If you create derivative works using this work, they should also be made available under a similar license.
For further information go to http://creativecommons.org/licenses/by/3.0/
For uses outside the scope of the license, contact Rajesh Naik at naik.rajeshnaik@gmail.com

 This presentation covers fourteen essential beliefs
that need to be internalized to implement CMMI®
high-maturity practices effectively
Contents of this Presentation
®

 This presentation covers fourteen essential beliefs
that need to be internalized to implement CMMI®
high-maturity practices effectively
 The material in this presentation is derived from the
following documents:
– CMMI® for Services, Version 1.3 (CMU/SEI-2010-TR-034)
– CMMI ® for Development, Version 1.3 (CMU/SEI-2010-TR-033)
 The viewer should refer to the above documents, for
the definitive requirements of High Maturity in CMMI®
Contents of this Presentation
®

 The Fourteen essential beliefs are
1. Nothing is definite
2. Everything is interrelated
3. Nothing is permanent
4. Nobody likes variation
5. Statistics is non-intuitive
6. There are no bell-curves in real life
7. Outliers cannot be wished away
8. Skills impacts process performance
9. Statisticians should not be decision makers
10. Process performance cannot be decreed
11. Correlation is not the same as cause-effect
12. Process instability must be analyzed in real-time
13. Control of critical sub-processes impacts higher level performance
14. Simulation may be the only practical method to model reality today
HM’s Fourteen

 The Fourteen essential beliefs are
HM’s Fourteen
We cover each of
these beliefs in detail
now…

 We often like to think of outcomes as definite,
single numbers
21

single numbers
– But that is low maturity thinking, and
does not reflect reality
21

single numbers
 In reality, all activities have an inherent
variation and what we can predict is only a
probability
21

single numbers
probability
 In HM thinking, estimates or target dates are
values that have a certain chance
(probability) of being achieved
21
21 +/-

single numbers
probability
 In HM thinking, estimates or target dates are
values that have a certain chance
(probability) of being achieved
– Only when we attach probabilities to
targets/ goals, can we start looking at
ways to increase those probabilities
21
21 +/-

 Another simplistic assumption (low-
maturity thinking) is that each goal/
objective of a project/ service is
independent of other goals
Cost
Timeliness
Quality
Other
Goals

 This gives rise to naïve decisions - E.g.,
we think that by adding more resources
we can bring forward the deadline,
expecting the cost and quality to remain
the same
Cost
Timeliness
Quality
Other
Goals

 This gives rise to naïve decisions - E.g.,
we think that by adding more resources
we can bring forward the deadline,
expecting the cost and quality to remain
the same
 In real situations, everything is
interrelated, and HM is about
understanding these relationships and
taking more informed decisions
Cost
Timeliness
Quality
Other
Goals
Cost
Timeliness
Quality
Other
Goals

 Since change is constant, past performance
cannot be blindly used for predicting the
future

future
– Past performance can only be taken as
one of the inputs for future
performance

future
performance
 This needs to be combined with expected
change in performance due to evolving
technology, familiarity, skills, and risks

future
performance
 While forecasting the future, we also need to
predict the impacts of our potential actions, to
take informed decisions

future
performance
 While forecasting the future, we also need to
predict the impacts of our potential actions, to
take informed decisions
 (All this in a probabilistic, interrelated way )

 Human beings (and institutions run by
human beings) like consistency, and
dislike surprises/ variations

– Think of what happens when the summer is
hotter or cooler than what has been in the
past
– Why do people visit fast food chains? – not
for the great taste or nutritional value; but
just for the consistency of experience

past
 Our customers, employees, and vendors
also expect minimal variations from us

past
 Our customers, employees, and vendors
also expect minimal variations from us
 So, a key HM principle is to identify and
reduce variation, wherever it is
unacceptable

 Let us do a quick exercise

 If you throw a dice, what are the possible outcomes?
– An integer between 1 and 6 (both included), with a
probability of 1/6 for each result

 If you throw the dice twice, and add the two results, what
are the possible outcomes?
+

 Without adequate time, most people come up with the
wrong answer
 The right answer is
– An integer between 2 and 12 (both included), with
different probabilities for each result – see chart
on the left
+
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 101112

 Without adequate time, most people come up with the
wrong answer
 The right answer is
– An integer between 2 and 12 (both included), with
different probabilities for each result – see chart
on the left
 When we combine distributions, our gut-feel falters
 So, we need to start using stats to understand and predict
performance (and keep our guts aside )
+
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 101112

 We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves

 But human activity does not typically result in neat
bell curves

bell curves
 E.g., the air travel from Bangalore to Delhi takes
typically 2:40 hrs. On a real good day it can take
2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30
hrs (or till they run out of fuel!). So, there is no
symmetry around the typical time

bell curves
 In other words – “There is a limit to how well
you can do; but no limit to how badly you can
screw up ”

bell curves
 In other words – “There is a limit to how well
you can do; but no limit to how badly you can
screw up ”
 HM organizations will not blindly assume smooth,
neat distributions for their plans and estimates

 If I prepare a chart of the flight time taken
for all my trips from Bangalore to Delhi over
the past 10 years, the chart is odd;
there are times the plane diverted to Jaipur
till the fog/ dust/ smog cleared
Fog diversions

 I cannot ignore these events just because
they make it difficult to handle the math/
stats
– Unless, climate-change eliminates fog in Delhi
altogether 
Fog diversions

 I cannot ignore these events just because
they make it difficult to handle the math/
stats
– Unless, climate-change eliminates fog in Delhi
altogether 
 So, in HM thinking, outliers are a part of the
process (with low probability), unless the
root-cause is eliminated
Fog diversions

 Competence makes a significant difference to
performance (speed, quality, throughput)

 That is why managers fight to get the right
people in the their teams
– “Processes make us people independent” (who said
that?) - is misunderstood and misused

 That is why managers fight to get the right
people in the their teams
– “Processes make us people independent” (who said
that?) - is misunderstood and misused
 In HM organizations, estimates, plans, and
forecasts consider the skills of the people
doing the work
– Process performance baselines also factor the
skill levels

 Often, decision makers get over-whelmed
(and terrified) by analytics and abdicate
decision making to statisticians
9. Statisticians should not be decision
makers

 Statisticians can analyze data and prove or
disprove some hypothesis, but the decision
making still rests with the managers and
executives
makers

 Statisticians can analyze data and prove or
disprove some hypothesis, but the decision
making still rests with the managers and
executives
 In HM organizations, managers/ executives
make effort to understand enough stats/
analytics to remain on top of decision making
makers

 Processes do not perform differently just because executive
management “decrees” a certain performance

 In low maturity organizations, the distinction between
performance baseline (actual performance) and performance
target (desired performance) is not clear – and desired
performance is used for estimation and planning

 In low maturity organizations, the distinction between
performance baseline (actual performance) and performance
target (desired performance) is not clear – and desired
performance is used for estimation and planning
 In true HM organizations, baselines are not dictated by
management, but derived from past performance. And
management sets process performance targets (for driving
process changes)

11. Correlation is not the same as cause-
effect
• Here is an example:
– One may be able to find a good correlation
between the number of people carrying
umbrellas/ raincoats to work in the morning, and
whether it rained during the day
– But that does not mean that carrying umbrellas
and raincoats causes rainfall

effect
• Correlations can be established statistically,
but cause-effect is based on logical thinking
and requires domain knowledge (which is why
statisticians cannot be the decision makers )

effect
• Correlations can be established statistically,
but cause-effect is based on logical thinking
and requires domain knowledge (which is why
statisticians cannot be the decision makers )
• HM thinking requires us to separate
correlation from cause-effect

• Identifying process instability and doing root-
cause analysis must be as close to the event as
possible

possible
• If the root-cause analysis is done too far from the
event (more like a post-mortem), there is lesser
likelihood of identifying the true root cause

possible
– E.g., If you find out today that the time to
reach office (from home) was out-of-
control 6 weeks ago, you may not be able to
recall the conditions that caused this

possible
– E.g., If you find out today that the time to
reach office (from home) was out-of-
control 6 weeks ago, you may not be able to
recall the conditions that caused this
• HM organizations create the infrastructure, tools,
and processes to collect, analyze and report data
on a real-time basis to effectively debug their
process instability

13. Control of critical sub-processes impacts
higher level performance
• If you want your weight in control, measuring and
monitoring your weight frequently (and putting it on a
control chart) is not going to bring your weight under
control 

control 
• To control weight, you may need to monitor and measure:
– The amount and type of exercise that you do
– The amount and type of calories that you eat
You may even have to measure the calories consumed
and expended at various times during the day

control 
• Similarly, (in a project) measuring schedule variance
alone is not likely to bring the schedule under control

control 
• Similarly, (in a project) measuring schedule variance
alone is not likely to bring the schedule under control
• HM organizations identify and control sub-processes that
are critical to overall performance

14. Simulation may be the only practical method to model reality
today
• Given that:
– There are multiple, interrelated goals to achieve

today
• Given that:
– Inputs and processes have their own variations
(probabilities)

today
• Given that:
(probabilities)
– Most of these variations do not fit neat
symmetrical bell curves

today
• Given that:
(probabilities)
– We need to predict the potential impact of our
choices on the outcomes (multiple objectives)

today
• Given that:
(probabilities)
• Simple, deterministic mathematical equations
alone do not suffice to reflect reality

today
• Given that:
(probabilities)
• Simple, deterministic mathematical equations
alone do not suffice to reflect reality
• In HM organizations, random number based
computer simulations (e.g., Monte Carlo)
provide the best platform to model reality

(Recalling…) HM’s Fourteen

 Related Presentations
– CMMI® Explored – Concept of Maturity
– CMMI® - SVC Explored – Process Area Overview

®
© Rajesh Naik, 2013
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This means you are free
(1) to copy, distribute, display, and perform the work,
(2) to make derivative works, and
(3) to make commercial use of the work
so long as you give proper attribution to the author and retain the license notice.
If you create derivative works using this work, they should also be made available under a similar license.
For further information go to http://creativecommons.org/licenses/by/3.0/
For uses outside the scope of the license, contact Rajesh Naik at naik.rajeshnaik@gmail.com

Thank You!
Rajesh Naik
AlignMentor
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naik.rajeshnaik@gmail.com
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