One Size Does Not Fit All - WordPress.com · 2015-07-31 · One Size Does Not Fit All Dr. Murray Cantor, Senior Consultant ... • Relearn the old lessons, e.g .Brooks law, Conway’s

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One Size Does Not Fit AllDr. Murray Cantor, Senior Consultant [email protected]

mailto:[email protected]://www.murraycantor.com


Things I have heard over the years “I have no idea.”

• Developers, when asked about how long will it take?

“We tried agile, but it didn't work for us.”• Development Managers

“Measures are a waste, they are costly, oppressive, and interfere with the real work.” • Some Methodologists

“Trust the (my) process. If the process is not working for you, you are doing it wrong.” • Some (of the same) Methodologists


Does one process ever fit all organizations? Over the years, we have seen

many ‘one true’ processes:• Water Fall• Boehm Spiral• Extreme Programming (XP)• Controlled Iteration, Rational Unified

Process• Software Factories• (Flavors of) Scaled Agile• DevOps


Each of these have generated lots of heated disagreements

4


The development leader’s choice

Follow ‘the one true method’• Advantage: It is prescriptive• Disadvantage: It is prescriptive in that it

may be blindly applied – there is enough variation in software development that blindly following even a sound process will often, but not always work.

Roll your own• You are likely to ask too much of the

practitioners – software developers want to develop software, not become experts in all these fields, so they can pick and apply the right principle.

• Relearn the old lessons, e.g .Brooks law, Conway’s law, iteration management, role of design, …

5

There is always a process. Is it what you intend?


So, What to Do.Start by understanding the work you do.

6


Choosing your methods needs to align

With your organization level and goals

With the mix of work you do

7


Achieving goals requires sense and respond loops

Key principles– Kelvin’s Principle: “To measure is to

know. If you can not measure it, you can not improve it”

• Measures are part of feedback loops

– The converse principle: “Don’t bother to measure what you do not intend to improve”

• Find a small set of measures, not a long laundry list

– Einstein’s Principle: “The best solution is as simple as possible, but not simpler.”

• Pick the right, not overly simple, statistic

8

(re)Set Goal

Take action

(practices)

Measure progress

(analytics)

React


Adapting your organization

9


Meeting goals requires analytics

10

Work item, artifact completionStaff member Commits to

Project, product deliveryProject manager, team lead Commits to

Efficiency, value deliverySenior manager Commits to

Profit, return on investment, mission fulfillment Line of business executive Commits to

Before


Aligning goals For each level to meet its goal, the

leader is dependent on the lower level.

So, the leader seeks commitments from that layer. Meeting those commitments becomes the goal of the next layer.

Hence, the analytics serve to integrate the organization

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Adapting to your mix

12


Kinds of Development Efforts: What is your mix?

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1. Low innovation/high certainty• Detailed understanding

of the requirements• Well understood code

2. Some innovation/some uncertainty• Architecture/Design in

place• Some discovery required

to have confidence in requirements

• Some refactoring/ evolution of design might be required

3. High innovation/Low Uncertainty

• Requirements not fully understood, some experimentation might be required

• May be alternatives in choice of technology

• No initial design/architecture


The methods landscape

14

Kanban

Lean startup: MVP

Agile, Scrum

Product Development Flow

Systems/Software Engineering

Lean Software

Podular Org.Liminal Thinking.

Technical Debt Management

Iterative learning: Updating estimates and plans in the face of evidence

DevOps/Continuous Delivery


1. Low innovation - high certainty: Statistics of

• Cycle, lead times• Backlogs size, growth• Time in process• Utilization• Non-value added

effort

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1 2 3

2. Some innovation -some uncertainty• Time, cost to delivery• Velocity • Burn down• Cumulative flow

diagrams

3. High innovation: Low certainty

• Time to pivot• Value of learning• Business canvas• Time, cost to delivery

Apply measures in accord with project characterization

Predictive/Bayesian

Descriptive


Example: Fitting analytics and practices to routine efforts For low innovation efforts (continuous delivery, not “real”

projects), pick product flow practices and analytics• Uncertainty is low: you have already carried out similar projects many

times• The only thing that matters is how quickly or efficiently you can carry

out the project

• Suitable for lean/VSM measures

• Tradeoff between speed/efficiency(utilization)

• The principles described by Don Reinertsen in his book Flowapply in this bucket

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http://www.amazon.com/Principles-Product-Development-Flow-Generation/dp/1935401009/ref=sr_1_1?ie=UTF8&qid=1429219228&sr=8-1&keywords=Flow+lean


Artifact-centricity is the appropriate process model for this (routine efforts) bucket Unlike activity-centric processes, artifact-centric processes

focus on describing how business data is changed/updated, by a particular action or task, throughout the process

Specifically, in the routine-effort bucket apply value stream models and flow measures (as described in the previous couple of slides) to state transitions of work products (artifacts)• Two state types:

– In process (undergoing state transitions)– In backlog (awaiting state transition)

If you consider this is a departure from traditional Agile methods, you are right:• One size does not fit all

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Semantics of artifact-centric value stream maps

18


Example: A Value Stream model for routine efforts

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Control challenges• Random arrival intervals• Variation of effort to address work items (unlike standardized

manufacturing)


Descriptive example: Cycle times

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These will be described in more detail in next webinar


To Visualize the data, use a histogram

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80% point is about 105 days


Insights and Actions Insights

• Both teams performing comparably: Not obvious skills issue

• Backlogs too large• The teams seem to be focusing on the

easier, not the most critical

Actions• With team investigate reason for backlog

size• Discovered the governance process

(decision to update statuses) is overly cumbersome leaving staff free to work elsewhere

• In response, the governance process was: – Streamlined (an approval eliminated)– Automated (less time spent finding e-mails)

• Work with teams to set and track cycle time 80% goal by priority

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This is what improvement looks like

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Example 2: Fitting analytics and practices to high innovation projects For high innovation projects, pick probabilistic methods and

the corresponding set of practices:• You really do not know what the solution would look like – you must

experiment in order to find it

Not knowing what the solution would look like, your intuition is a poor guide for estimating and scheduling under systemic uncertainty:• You must experiment in an affordable manner• The results of the experimentation need to be bi-directionally

propagated– Forward, and– Backward

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Estimating effort remaining

25

+ … + =

l e hNo

probability less than

No probability

greater than

Most probable

value

For remaining epics:• Estimate size

with triangular distributions

• Sum using forward propagation (aka Monte Carlo)


Bayesian Example:What improvement looks like: Estimate of weeks late

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sndes5.csv

Risk ObjectTime to Completion

Node NameWeeks late

Node IdM7

Summary Statistics

Mean20.3989008675

Median7.1244292625

Variance6965.2039154603

Standard Deviation83.4577972119

Lower Percentile [25.0]2.6256539409

Upper Percentile [75.0]14.5143459896

Lower BoundUpper BoundValue

-3.8125-3.250.0037084263

-3.25-2.68750.0102453268

-2.6875-2.1250.0151436822

-2.125-10.0381316783

-100.0411770466

010.0486009652

13.250.1287075694

3.255.50.1251577909

5.56.6250.0649490738

6.6257.750.0544636596

7.758.8750.0493736089

8.875100.0497583448

1011.406250.0467508094

11.4062512.81250.0388387086

12.812514.218750.0292977944

14.2187515.6250.0270954873

15.62517.031250.022749115

17.0312518.43750.0192273445

18.437519.843750.0162758327

19.8437521.250.0135382257

21.2524.06250.0228409677

24.062526.8750.017403402

26.87529.68750.0138046746

29.687532.50.011076843

32.538.1250.016636529

38.12543.750.011678273

43.7549.3750.0088374063

49.375550.0065512798

5566.250.0097117639

66.2577.50.0064853427

77.51000.0057030212

100156.250.0114008416

156.25212.50.002147603

212.53250.002100505

3255500.0042135709

55010000.0051805984

100021250.001036887

sndes6.csv

Risk ObjectTime to Completion

Node NameWeeks late

Node IdM7

Summary Statistics

Mean11.5377134142

Median2.0029441436

Variance3412.5199922607

Standard Deviation58.4167783454

Lower Percentile [25.0]-1.3278719339

Upper Percentile [75.0]7.3708289179

Lower BoundUpper BoundValue

-6.625-6.06250.0012497455

-6.0625-5.50.0071293323

-5.5-4.93750.0167844798

-4.9375-4.3750.0238249855

-4.375-3.81250.0287084292

-3.8125-3.250.0329409588

-3.25-2.68750.0367926779

-2.6875-2.1250.0417418401

-2.125-10.0858469267

-100.0767223383

010.0760630551

12.1250.0809812142

2.1252.68750.0389711388

2.68753.250.0362425983

3.253.81250.032143201

3.81254.3750.02710512

4.3754.93750.0237714734

4.93755.50.0236209415

5.56.6250.0386732752

6.6257.750.0312029359

7.758.8750.0250154734

8.875100.0219568368

1011.406250.0228879709

11.4062512.81250.0185132562

12.812514.218750.015226226

14.2187515.6250.0122600482

15.62518.43750.0200131436

18.437521.250.0144881571

21.2526.8750.020487321

26.87532.50.013263276

32.538.1250.009058378

38.12543.750.0065433908

43.75550.008780004

5577.50.0077425142

77.51000.005110334

100156.250.0057226791

156.25212.50.0014705816

212.53250.0027883363

3255500.0053544503

55010000.0027450945

100032505.59E-05


Parting Thoughts: Putting It All Together

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The ‘Secret Sauce’ of the Integrative Framework Break your portfolio to the three

buckets

Use the right kind of analytics for each of the three buckets:• Analytics ensure on-going alignment

between projects, programs and portfolios

• In particular, Bayesian analytics enables us to incrementally and iteratively put newly accrued data into consideration:

– In other words, Baysian methods enable iteratively quantified learning

This iteratively quantified learning ensures on-going alignment, hence empowerment

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The Virtuous Cycle of the Integrative Framework

Up-to-Date Shared Goals

Framework Based on the Three Buckets and Analytics

Initial Alignment

Empowered Pods

Learning through

Analytics

Realignment

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Some things I have learned over the years

To steal ideas from one person is plagiarism; to steal from many is research.

William Mizner

Human beings, who are almost unique in having the ability to learn from the experience of others, are also remarkable for their apparent disinclination to do so.

Douglas Adams

The beginning of wisdom is calling things by their right names.

Chinese Proverb


Murray CantorEmail: [email protected]

Contact Me

.

mailto:[email protected]


Murray Cantor Areas of research & consulting:

• Agile management• Lean software development• Development intelligence• Systems engineering• Software development analytics• Software governance• Development management due diligence

Major products delivered:• AIX 3.X Graphics subsystem

– Founding member OpenGL ARB• AIX 3.X multimedia subsystem• Top secret system for USAF Space

Command• RUPSE (Systems extension for Rational

Unified Process)

Books:• Object Oriented Project Management• Software Leadership

Sample accolades:• IBM Distinguished Engineer• IBM Plateau 4 Inventor• Software Leadership received 4.7/5

star rating on amazon.com

33

http://www.amazon.com/Object-Oriented-Project-Management-Murray-Cantor/dp/0471253030/ref=sr_1_1?s=books&ie=UTF8&qid=1429221580&sr=1-1&keywords=Object+Oriented+Murray+Cantorhttp://www.amazon.com/Software-Leadership-Guide-Successful-Development/dp/0201700441/ref=sr_1_1?s=books&ie=UTF8&qid=1429221505&sr=1-1&keywords=Software+leadership+Murray+Cantorhttp://www.amazon.com/Software-Leadership-Guide-Successful-Development/product-reviews/0201700441/ref=cm_cr_dp_qt_see_all_top?ie=UTF8&showViewpoints=1&sortBy=byRankDescending


This is joint work with Dr. Israel Gat Areas of research & consulting:

• Agile methods• Devops• Software as a Service (SaaS)• Software development analytics• Software governance• Technical debt & technical due diligence

Major products delivered:• BMC Performance Manager/PATROL• Microsoft Operations Manager• Tivoli Smart Handheld Device

Manager• EMC Cellera• Digital’s NetView• Nixdorf 8890

Books:• The Concise Executive Guide to Agile

Sample accolades:• Winner, 2006 Innovator Award

[Application Development Trends, May 2006]• “Nearly three times faster time to

market than industry average…… one quarter the expected number of defects based on team sizes and schedules.” [QSM Study, August 2007]

• “The change you brought to BMC with Agile is the single largest change to the development model that I have ever witnessed in my almost 20 years at BMC.” [Director, BMC Software]

• "When I deal with technical debt issues, I refer to Israel Gat regularly. His approach is the only one I've found that actually works…” [Director, Verisk Health]

34

http://www.servicetech.com.hk/bmc/patrol.htmlhttp://www.microsoft.com/systemcenter/operationsmanager/en/us/default.aspxhttp://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=ca&infotype=an&appname=iSource&supplier=897&letternum=ENUS200-452http://www.emc.com/microsites/unified/unifiedstorage.htm?CMP=KNC-UNIFIED_VNX-Celerra-NAM&activity_id=62225&division=corehttp://www-01.ibm.com/software/tivoli/products/netview/http://de.wikipedia.org/wiki/Nixdorf_Computerhttp://www.computer.org/portal/web/store?product_id=RN0000008&category_id=ReadyNotes


More granular project characterizations

After first level characterization by novelty, you might want to get into more granular characterization:• Size of the project• Criticality• Timeliness of the project• Quality level • Physical dispersion of the

organization• Logical dispersion of the

organization

A more granular characterization will enable you to:• Fine-tune your project portfolio to more than three buckets; and,• Better balance project needs vis-à-vis organizational needs

35

Guidance for so doing is provided in the fully-fledged version of this workshop

Source: Alistair Cockburn, Agile Software Development: The Cooperative Game

http://www.amazon.com/Agile-Software-Development-Cooperative-Game/dp/0321482751/ref=sr_1_1?s=books&ie=UTF8&qid=1429220428&sr=1-1&keywords=agile+software+development+the+cooperative+gamehttp://www.amazon.com/Agile-Software-Development-Cooperative-Game/dp/0321482751/ref=sr_1_1?s=books&ie=UTF8&qid=1429220428&sr=1-1&keywords=agile+software+development+the+cooperative+game


High level view of the framework Capture goals by organization

level based on• Stakeholder needs, business

context– E.g. Organization productivity,

reduced cycle times, more innovation…

• Alignment of levels• Mixture of efforts

At each level, specify sense and respond loops to achieve the goals• Analytics• Practices

36


Appendix: A quick note on carrying out more granular project characterizations

37


Appendix B: Auxiliary SlidesBackup slides

38


W. Edwards Demming Quotes “It is a mistake to assume that if

everybody does his job, it will be all right. The whole system may be in trouble.”

“It is management's job to know.”

“I am not reporting things about people. I am reporting things about practices.”


A Pragmatic Note No need to start by characterizing all programs/projects in

your portfolio unless you do so as an integral part of your annual (?) planning and budgeting process• Bootstrapping by starting with a few projects and increasing the

number of projects gradually is quite appropriate and effective in implementing the Integrative Framework

• At a certain point in time, you will need to reengineer both your organization and processes

– No need to rush with so doing before you have a critical mass of teams

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Conceptual map for tying it all together

41


Pert Estimation

• Pert Estimation: For set of a tasks (T1, …,Tn), say user stories, set the sizes (user stories)– (l1, …, ln) be the best cases (lower bounds)– (e1,…,en) be the expected cases– (h1,…,hn) be the worse cases (upper bounds)

• Let L = l1 + …+ ln, E = e1 + …+ en, H = h1 + …+ hn

• Then– mean of the sum of triangulars ~ (L+4E+H)/6– Standard deviation ~ ( H-L)/6

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We need to get a view of the data to understand team performance and set goals We will analyze the time (in hours) it took

to go from ‘created’ to ‘closed’.

We get a first look at the data with a scatter gram.

Each dot is a closed defect. The height is the number of hours.

Note that there are outliers with values as much as 1150 hours. This is typical of real data sets and may be caused by mistakes in the data capture or some abandoned defect.

With customer agreement we trim the data at 400 hours.

43


We need to get a view of the data to understand team performance and set goals

Since the curve is not a bell-shaped curve, we choose percentiles as a way to measure performance and set goals.

With this data set, we see that • Half of the defects were closed in 93

hours or less.• 80% of the defects were closed in

179 hours or less.• 90% of the defects were closed in

267 hours or less.

They set an initial goal of completing 80% of the defects in 100 hours or less.

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What improvement looks like

By revisiting the value stream map, the team applied flow principles to reduce the cycle time.

They get a new data set.

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Sheet1

idcreatedmodifiedseveritystatusformattedidtype

13676/16/146/23/14 14:31sev-4NewDE1000Defect

13686/14/146/14/14 5:51sev-4In ProgressDE1000Defect

13696/14/146/14/14 18:20sev-4ClosedDE1000Defect

13706/17/147/3/14 1:21sev-4NewDE1001Defect



13735/30/145/31/14 19:46sev-5NewDE1002Defect



13766/3/146/3/14 20:12sev-3NewDE1003Defect



13796/14/146/14/14 14:02sev-1NewDE1004Defect



13826/12/146/13/14 17:09sev-4NewDE1005Defect



13856/9/146/9/14 16:57sev-1NewDE1006Defect



13886/5/146/5/14 20:14sev-4NewDE1007Defect



13916/5/146/5/14 13:31sev-3NewDE1008Defect



13946/5/146/6/14 0:02sev-4NewDE1009Defect



13976/2/146/6/14 19:29sev-1NewDE1010Defect



14006/2/146/8/14 17:32sev-2NewDE1011Defect



14035/26/145/26/14 23:05sev-3NewDE1012Defect



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14126/7/146/7/14 10:33sev-4NewDE1015Defect



14156/3/146/6/14 5:12sev-3NewDE1016Defect



14186/6/146/10/14 8:17sev-4NewDE1017Defect



14216/4/146/5/14 13:13sev-2NewDE1018Defect



14246/10/146/13/14 0:08sev-3NewDE1019Defect



14276/15/146/17/14 2:51sev-4NewDE1020Defect



14306/5/146/13/14 22:30sev-3NewDE1021Defect



14336/2/146/2/14 8:33sev-2NewDE1022Defect



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14396/13/146/14/14 1:35sev-3NewDE1024Defect



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14456/13/146/16/14 13:30sev-3NewDE1026Defect



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14876/4/146/7/14 0:13sev-1NewDE1040Defect



14905/26/145/26/14 19:26sev-1NewDE1041Defect



14936/5/146/7/14 14:03sev-2NewDE1042Defect



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15056/15/146/15/14 19:37sev-4NewDE1046Defect



15086/12/146/13/14 23:21sev-4NewDE1047Defect



15116/8/146/8/14 4:35sev-2NewDE1048Defect



15146/14/146/14/14 10:07sev-5NewDE1049Defect



15175/26/145/28/14 3:19sev-3NewDE1050Defect



15206/13/146/13/14 5:37sev-1NewDE1051Defect



15236/16/146/16/14 1:43sev-3NewDE1052Defect



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15296/12/146/24/14 9:07sev-5NewDE1054Defect



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15446/4/146/5/14 2:52sev-4NewDE1059Defect



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15506/5/146/13/14 20:43sev-4NewDE1061Defect



15536/9/146/9/14 16:36sev-1NewDE1062Defect



15566/5/146/5/14 19:51sev-4NewDE1063Defect



15596/1/146/3/14 16:46sev-1NewDE1064Defect



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15806/7/146/13/14 8:39sev-3NewDE1071Defect



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15865/31/146/3/14 16:17sev-3NewDE1073Defect



15896/11/146/12/14 9:17sev-2NewDE1074Defect



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16766/14/146/17/14 1:24sev-1NewDE1103Defect



16796/8/146/8/14 5:15sev-2NewDE1104Defect



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We will describe the date as a probability distribution

• Height is the likelihood of the value• Area under curve is the probability of value falling within range• Mean is expected value• Standard deviation is measure of uncertainty


Reducing standard deviation improves probability

47

Sd = 10• Prob(10,30) =68%

Sd = 5• Prob(10,30) =95%

Sd = 1• Prob(10,30) = 100%


Planning to ship on the expected date is a 50/50 bet

48


Option: Move out the date

49


Option: Reduce content

50Mean = 9.5, standard deviation = 2


Reducing the standard deviation improves the odds

51

Mean = 11, standard deviation = 2

Mean = 11, standard deviation = 1


Steps for Managing Innovation: Identifying Risk• Do enough design to break the new deliverable into separate

smaller deliverables that can be partitioned to team or individuals:

• For each deliverable, elicit the 3 cases.– Lowest case, l, highest case, h, and expected case, e, for the time to

complete their tasks.

• When h is much larger than l, that constitutes uncertainty and risk– Ask why?– What would they need to know to reduce the uncertainty?

• User needs,• Usability• Technical performance• …

– How would they learn what they needed

• Prioritize work that helps them learn what is needed to reduce the uncertainty


Managing Uncertainty

• Use a prediction tool to roll up the 3 cases into a probability of completion– There are a set of techniques, beyond the scope of this workshop.

• Carry out the risk reduction tasks

• Update the task set with the new information gained

• Measure and track to narrowing of the shape of the distribution


Monte Carlo is used for doing arithmetic with distributions

54

l e h

No probability less than

No probability greater than

Most probable value

+ … + =


Bayes is the way for development teams and management to deal with uncertainties In types II and III development, quantities such as

time, cost to complete, and velocity are not known for certain. • There is not enough known to make exact predictions• You need to utilize the actual data you produce sprint by

sprint

Bayesian analysis is the centuries old method for rigorously dealing with with uncertain quantities.

Bayesian analytics allows everyone on the team to learn together.

55

Attributes of Bayes: Uncertain quantities are specified probabilities The probabilities capture both the best/worst estimates and the level of

uncertainty The probabilities/beliefs are updated as information, evidence comes in. The probability distributions can be “added,” “multiplied,” etc.


Now that you abolished the “One Size Fits All” myth at the individual project level… …. you need to determine how you would like your project

and program management organizations and processes to support t

Documents

One Size Does Not Fit All - WordPress.com · 2015-07-31 · One Size Does Not Fit All Dr. Murray Cantor, Senior Consultant ... • Relearn the old lessons, e.g .Brooks law, Conway’s