A framework to manage uncertainty in early planning of projects, an ICT project

Abstract

A framework to manage uncertainty in early planning of projects, an ICT project Abstract Identifying sources of uncertainty and tailoring decision-making approaches to meet specific contexts, creates opportunities to reduce effort expended in the early planning phases of project planning. Practical application of these approaches in not yet being widely reported in research on Business Case and Decision-making Frameworks, so this paper seeks to fill the gap by describing an approach based on the Cynefin Framework (C. F. Kurtz & D. J. Snowden, 2003; Snowden, 2018) which distinguishes between complicated and complex decision contexts based on the types of operational constraints (governing and enabling) and nature of practices involved (good and emergent). Recognising the differences during project initiation, improves accessibility to streamlined decision-making, by ensuring 'fit-for-purpose' methodologies are chosen rather than relying on an undifferentiated single method. This paper describes how use of the Cynefin framework, during initial project planning, enables better alignment of plans with situational constraints, and ensures effective calibration of plans to meet required outcomes.

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A framework to manage uncertainty in early planning of projects, an ICT project
Abstract
Identifying sources of uncertainty and tailoring decision-making approaches to meet specific
contexts, creates opportunities to reduce effort expended in the early planning phases of project
planning. Practical application of these approaches in not yet being widely reported in research on
Business Case and Decision-making Frameworks, so this paper seeks to fill the gap by describing an
approach based on the Cynefin Framework (C. F. Kurtz & D. J. Snowden, 2003; Snowden, 2018)
which distinguishes between complicated and complex decision contexts based on the types of
operational constraints (governing and enabling) and nature of practices involved (good and
emergent). Recognising the differences during project initiation, improves accessibility to
streamlined decision-making, by ensuring ‘fit-for-purpose’ methodologies are chosen rather than
relying on an undifferentiated single method. This paper describes how use of the Cynefin
framework, during initial project planning, enables better alignment of plans with situational
constraints, and ensures effective calibration of plans to meet required outcomes.
Dr Saeed Shalbafan, PhD, PMP, MIEA, UTS, Faculty of Built Environment, Senior Advisor to NSW
Government and Founder of Hooshmand-Intelligence Advisory
Kim Ballestrin, Principal Consultant in Elabor8
Corresponding author. Saeed.Shalbafan@hintplex.com
Keywords. Cynefin, Complex project, ICT, uncertainty, business case, planning, decision framework
Introduction
In the initial planning phase of projects - especially those with multiple stakeholders and intricate
sets of outcomes, there are opportunities to avoid wasted effort through considered application of
the Cynefin Framework (David J. Snowden & Mary E. Boone, 2007), used as a categorisation model
to address ambiguity in goals and scope definitions through tailoring approaches for problem solving
and disagreement resolution. Planning conversations can be easily derailed when issues associated
with complex and complicated problems are mixed together such as happens in analysis-style
workshops, or reliance on experts sorting through the issues to arrive at recommended action
strategies. Derailment often occurs when workshops and meetings planned to last a couple of hours
result in ongoing conversations and disagreements lasting sometimes for weeks or months.
Sources of complexity have been identified in regard to research on projects (Remington & Pollack,
2008a), business case frameworks (van Putten, Brecht, & Günther, 2013) and linked to the degree of
uncertainty associated with interpreting real-world events via use of case studies (S. French, 1995b).
This paper reports insights emerging from use of the Cynefin framework during the early planning
phase prior to developing the business case for an ICT project. The approach allowed team members
to categorise the types of decisions required in early planning, consequently allowing tailoring of the
decision-making formats to suit differing levels of uncertainty and complexity for each item. This
resulted in a significant reduction in the effort required to make key decisions, allowing a set of
decisions that had remained unresolved for 3 months to be finalised in two days.
Relevant theoretical background —including the theory of complexity, navigation of uncertainty, the
Cynefin Framework itself and decision making methodologies—is introduced and the methodology
for implement the approach in a particular ICT project is described before the outcomes are
explained. A concluding discussion illustrates the connections between practice and theory.

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Complexity and Uncertainty
Complexity and uncertainty are acknowledged as regular disruptors of decision making, especially
when senior managers are required to make decisions without the availability of sufficient
information (Gorzen-Mitka & Okreglicka, 2014) conditions which usually happen during the early
stages of planning new projects. To better understand such complexity Remington and Pollack
(2008b) identified four types of project complexity: structural; technical; directional; and temporal.
Directional uncertainty arises from uncertainty and lack of agreement about project goals, and
stakeholder disagreements: unshared goals, unclear meanings and hidden agendas (Remington &
Pollack, 2007, p. 7). When technical and directional complexity are not well managed the result is
prolongation of early planning and emergence of wicked problems. Mis-managing technical
complexity in early planning leads to over estimation of requirements or poor understanding of
values and benefits realisations (Ward, Daniel, & Peppard, 2008). A framework has been proposed
for managing complexity of projects in the initiation phase through breaking down key decisions into
15 areas. This is relevant to creating a business case for large IT Projects and table 1 (van Putten et
al., 2013) sets out three categories for key topics relevant to the final decision. Reuse topics indicate
use of information from similar business cases; Adaptation topics refer to items which can be adapt
from previous business cases and Collaboration topics are those where project teams can
collaborate to generate specialised information for each business case.
Table 1 – Research areas within the solutions for a business case framework [from van Putten 2013]
Topic
Reuse
Adaptation
Collaboration
Reuse
of
content
X
Reuse
of
structure
X
Aggregation
X
Comparison
X
Provider
vs.
Customer
Perspective
X
Market
Potential
Estimation
X
Changing
Assumptions
X
Product
Innov
ation
Lifecycle
X
Business
Model
Adaptability
X
Clarifying
Reasoning
X
Stakeholders
‘opinions
X
Information
Sources
X
Information
Quality
X
Sharing
X
Security
X

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Whilst these topics resulted from implementation of a Business Case Framework (BCF) over a 14-
month period, the quality of information developed during creation of the business case was fine
questionable and even ambiguous. And this ambiguity, if unresolved during early planning for
adaptation and collaboration, can turn into wicked problems (Childs & McLeod, 2013) needing
further research to establish the actual requirements for particular frameworks for action.
Conversely Table 2 (adapted from S. French, 1995a) categorises the types of uncertainty identified
during analysis of a real-world example. This categorisation framework provides indicators to detect
sources of uncertainty in an inductive process of making sense from a real-world case study (Simon
French, 2017).
Table 2 – sources of uncertainty (adapted from S. French, 1995a)
Sense
-
making
-
Uncertainty
about
•
meaning
/
ambiguity
• what might happen (the science)
• Likely potential impacts (values)
• released decisions
A
nalysis
-
Uncertainty
because
of
•
physical
randomness
• lack of knowledge
Analysis
-
Uncertainty
about
the
•
evolution
of
future
beliefs
and
values
• accuracy of calculations
Induction
-
Uncertainty
about
•
depth
to
which
to
conduct
an
analysis
Table 2 lists four types of uncertainty likely to create complexity in early planning stages, however,
there is, as yet, limited application to practice of the frameworks discussed in this section.
Furthermore, while mechanisms to manage uncertainty and their application to early planning
wicked problems are important, they too are yet to be fully integrated into contemporary practice.
Navigation of Uncertainty
Uncertainties are usually events beyond the analysts’ ability to predict, and thus cannot be
measured in terms of risk (Quade, 1989). Quick-changing environments are a reality of business
environment, and the kinds of uncertainties called 'Black Swans’ (Taleb, 2007) can derail business
operations. Sudden changes influence decision makers’ perceptions about choosing actions and
identifying decision criteria. Christiansen and Varnes (2008) suggest that decision makers have to
deal with multiple criteria and sometimes conflicting interests at the same time. Thus, decision
makers often find themselves moving away from rational thinking and towards intuitive thinking
(Huang & Pearce, 2015) inevitably adapting a sub-optimal problem solving approach (Shalbafan,
Leigh, Pollack, & Sankara, 2017).
Seeking to make sense of complexity leads to more proactive identification of sources of uncertainty
and a watchfulness for early signs of failure. In this regard Kallelman, Mckeeman and Zhang (2006)
and Weick (1995) argued that people apply sense-making as a tool to overcome ambiguity and
associated interpretations of such conditions. Access to a framework for making sense of complex
situations can help planners to manage uncertainty in the early planning stages. “Cynefin provides a

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framework in which to discuss different forms of uncertainty from the deep uncertainty through the
growth of knowledge as we learn about the world” (Simon French, 2017`, p. 1636).
Cynefin framework
According to Kurt and Snowden (2003), the Cynefin framework provides ways to open up
discussions, identify barriers, stimulate attractors and encourage dissent and diversity, thus enabling
planners to better manage starting conditions, monitor for emergence of uncertainties and manage
complexity in order to find the appropriate actions to stabilise uncertain conditions.
Figure 1- Cynefin Framework (Cognitive-Edge 2019)
Figure 1 is a current depiction of the Cynefin framework including the domains of Obvious,
Complicated, Complex and Chaotic and the central condition of Disorder. Table 3 illustrates
characteristics of the Cynefin domains as they were adapted in the analysis conducted for this
research. The use of a Cynefin framework to analyse complex and complicated domains and fluidity
of decision making approaches is discussed in the context of managing multiple projects in Childs
and McLeod (2013) and Shalbafan and Leigh (2017).

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Table 3- Adapted from a Leader’s Guide (David J. Snowden & Mary E. Boone, 2007`, p. 73)

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This article aims to present how an application of familiar language representing three domains in
the table 3. This approach helped an ICT Project team to categorise critical decisions and adapt
appropriate approaches for each set of decisions by aligning as either obvious, complicated or
complex problems. This is an original approach to researching the concepts within a single project.
The story of an ICT Project
Experiment and design of methodology
The Cynefin Framework (Cynthia F. Kurtz & David J. Snowden, 2003) is a great tool for early project
planning and decision-making (Shalbafan et al., 2017). In approaching the project, it was decided
that team members could best benefit from its principles by using common (non-technical) words to
connect participants’ knowledge of factors emerging in the context to the theoretical framework.
After consultation with key stakeholders, the decision was made to use a trio of common terms
suited to the project and the team. Thus, the Obvious domain was identified as Easy, the
complicated domain was designated as requiring Analysis, and the complex domain became the Can
of Worms. Use of these terms meant the principles behind the Cynefin Framework could be applied
immediately without a detailed explanation being required (Ballestrin, 2015). If project team
members wanted to learn more about the underlying principles, the full Cynefin Framework
explanation could be explored later.
This Easy / Analysis / Can of Worms approach uses familiar terminology and definitions that are
quickly understood. The three terms were initially chosen ‘at the moment’ of time-pressure to start
a project and are now used in workshops and conference presentations as a practical explanation of
the Cynefin Framework. The term ‘Can of Worms’ has been particularly well-received. In one
instance, after the approach was outlined to a team, there were reports of it being used in the very
next meeting to challenge an item threatening to derail the conversation by identifying it as a ‘can of
worms. At first the approach was applied to lists of items of work required to deliver a project so
that workshops and other early project activities used time more effectively during business case
development. However, it has been found to apply broadly to other sets of activities with various
levels of un/certainty. Prior to the development of this approach, it was common to hold 4-5 days of
workshops with 10-15 participants in order to ‘discover’ the work needed to complete the project.
The Easy, Analysis, and Can of Worms approach means that much smaller and shorter workshop
activities can be designed and the project team members assigned to ‘Easy’ types of decisions can be
freed up to get on with other work.
Revised Project Methodology
in terms of ‘methodology' the shift to using proxy definitions for the relevant three Cynefin domains
creates enabling constraints (Juarrero, 2015) and allows project team members to more easily
categorise work items into increasing levels of uncertainty.
 Easy – is proxy for the Obvious domain where there is one best practice and we can use the
pattern Sense, Categorise, Respond. The description - as applied to project planning is ‘We
can name a person we can speak with and in a conversation of 20 min or less they are likely
to tell us that it will take X long and cost Y much’
 Analysis – is proxy for the Complicated domain where there are often several good ways to
achieve an outcome and we can use the pattern Sense, Analyse, Respond. The description as
applied to project planning is ‘We can name the experts that we could give the work to; or

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we can design a workshop of 2-3 hours and by the end of the analysis we would have a
scope with which we can determine cost and timeframe.’
 Can of Worms – is proxy for Complex where the linkages between cause and effect are not
easy to determine and we need to use the pattern Probe, Sense, Respond. The description
applied to project planning is ‘Everything that does not fit into Easy or Analysis.’
Once all the known items of work are categorised, project activities can be planned because the
people to be involved can be aligned with the work assigned to each of the 3 categories. For
example, if the same group of experts is required for all analysis items, one large workshop can be
facilitated to tackle all - and only - the relevant items.
The Results
Commencement of the ICT project, which is the basis of this paper had been was stalled because of
lack of clear and agreed decisions about new software elements to be included.
In order to keep focused on the work, proxy definitions were used for three of the Cynefin
framework domains.
The organisation had an aging technology stack that was mission-critical and required to operate 24
x 7. It required replacement because any new features and updates added to the systems increased
the risk of catastrophic failure, it was at ‘end of life’ for systems support. For 3-4 months there had
been an architectural white paper circulating and no clear decision about the new technology
choices for the replacement systems. The desired technology would make it easy to implement
continuous delivery and automated release management.
A set of 140 technology decisions were documented by a continuous delivery expert consultant.
These were determined based on several workshops and conversations to determine the nature of
the issues with the current technology stack and the desired functionality keeping the mission-
critical functions and removing the fragility associated with the decades-old legacy codebase. These
questions were then classified in collaboration with the lead enterprise architect using the proxy
definitions as described above, for the three critical domains in the Cynefin Framework.
 Easy - meant that the technology question could be answered in less than 20 minutes and
often would be related to the SOE (Standard Operating Environment). For example, ‘Do you
use Windows or Linux?’
 Analysis meant that it was agreed that a team of known experts could discuss the question
and the length of the discussion could be estimated with confidence ( maximum 1 hour)
 Can of Worms meant everything else. For example, when answers began with “I think…" or
the length of analysis conversations could not be estimated, the issue automatically became
a Can of Worms
A 2-day workshop was designed to tackle all the remaining Analysis and Can of Worms decisions
(there were only about 10 ‘Easy’ questions).
Day one was scene-setting so that the 20 or so attendees fully understood the desired outcomes for
the technology replacement project.
On day two, there were 3 teams of experts in one room answering the Analysis questions and in a
separate room, all the other attendees addressed the Can of Worms (Complex) questions.
Figure 2 shows that the group identified assumptions about the question, placing them on a grid
indicating low to high risk. If the assumption was invalid, Risk would increase on the Y-Axis and the

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Ease of testing would increase on the X-Axis. A key success factor was that the group tackling the
Complex questions did not have to be experts in software architecture. It is much easier for non-
experts to articulate assumptions because the role of an expert is to provide the answer. In the top
right corner were the assumptions that were high-risk if invalid and easy to test. The group then
called experts, searched the internet etc. to determine if the assumption was valid or invalid and this
drove enough certainty into the question for it to be delivered to the teams of experts for an
answer.
Figure 2 – Prioritisation of assumptions for each decision/question
Discussion
By the end of the 2-day workshop, all 140 questions were answered and the project to replace the
aging technology had a good enough starting point. Until the execution of the two-day workshop,
the project had stalled due to the lack of clarity about which technology could be used for the
replacement systems.
The Easy, Analysis, and Can of Worms (EAC) approach uses the Cynefin Framework as a classification
tool by imposing a definition of the Obvious, Complicated and Complex domains so that they
become enabling constraints for project planning.

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Table 4 shows key expected actions for decisions in each category.
Table 4- Actions expected in each category of EAC approach
C
ategory
Typical
Actions
Easy
(Obvious)
Identify
the
people
who
can
provide
the
information
and
create
a
schedule
for
those conversations/ tasks
Analysis
(Complicated)
Identify
the
people
and
groups
that
have
the
required
expertise
and
plan/schedule workshops or other analytical activities
Can
of
Worms
(Complex)
Keep
these
isolated
from
the
other
activities
to
avoid
the
risk
of
derailing
that
work and then design ‘probes’ or experiments with very rapid feedback cycles to
explore the items and move them across to the Complicated or Obvious domains
This allows us to blend techniques for project planning. In the case study we were able to very
quickly decide the Easy items and then ensure that we keep the Analysis and Can of Worms items
isolated from each other.
It has been observed that most workshop styles are suited to analysis (Complicated) decision-making
and that when a can of worms (Complex) topic arises, the workshop can be derailed, sometimes for
weeks. Complex topics require special design to allow for surfacing and testing of assumptions. The
workshop used in this case study, was one of successful approaches to explore complexity
effectively.
The workshop described was designed to move the Complex decisions into the Complicated domain
by surfacing and testing assumptions for validity/invalidity. This process drove sufficient certainty
into the question that the teams of experts could answer it. This is a common movement pattern on
the Cynefin framework and with the aim of ICT projects being to use technology to deliver an
outcome, it is one of the key patterns that is useful to the early planning of projects.
Table 5- EAC model impact on decision makers in early planning decisions
Stage
Sources
of
uncertain
ty
relevant
to
each
stage
Observation
in
the
ICT
Project
Sense
Making
Uncertainty
about
meaning
/
ambiguity
T
he
EAC
framework
helped
to manage ambiguity by
applying targeted
approaches to each
category of decisions
Uncertainty
about
what
might
happen
(the
science)
N/A
Uncertainty
about
how
much
impacts
matter
(values)
N/A

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Uncertainty
about
related
decisions
All
140
decisions
were
related to identification of
the new technology
required for the project.
The EAC framework
removed some of this
uncertainty by grouping
them and treating the types
of decisions differently
Analysis
Uncertainty
because
of
physical
randomness
N/A
Uncertainty
because
of
lack
of
knowledge
The
‘
can
of
worms
’
category
identified the decisions that
could not easily be
answered by experts (the
people who had the
knowledge) and the
facilitated approach to
identify and test
assumptions meant that
people without expert
knowledge could make
progress with the decision
until it had enough certainty
for the experts to answer it
Uncertainty
about
the
evolution
of
future
beliefs
and
values
N/A
Uncertainty
about
the
accuracy
of
calculations
e.g.
Not
applicable
to
this
case study
Induction
Uncertainty
about
depth
to
which
to
conduct
an
analysis
N/A
This case study demonstrates a way to effectively manage uncertainty in a complex ICT project.
Table 5 shows the main causes of uncertainty in the case study and the impact from applying the
EAC model to facilitate making complex decisions. were in the Sense-making and Analysis stages.
 There was uncertainty about related decisions – the architectural whitepaper had stalled
because it was a set of complex inter-related decisions about what types of software could
provide the required functional and non-functional needs.
 There was uncertainty about lack of knowledge – the project team had insufficient
experience and expertise with modern software to be able to determine a good enough
starting point
The Easy, Analysis, Can of Worms approach provided a clear pathway of facilitation for the 140 key
decisions to be made. Had this approach not been taken, there was a high likelihood that the project
would have been delayed further due to the mixing of complex and complicated questions. When a

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group is focused on analysis and a ‘can of worms’ comes up, it halts the progress on the analysis
component and leads to the feeling of ‘spinning wheels’ as conversations go around in circles.
Another data point was also captured as part of this case study. There had been many observations
of ‘can of worms’ topics derailing analysis work – however, at one point during the workshop, a
couple of the analysis experts walked over to the ‘can of worms’ room to see what they were doing.
They nearly disrupted the session when they started to ask the group why they had not considered
this or that about a particular question and were very quickly asked to leave. The thinking required
to surface, and test assumptions is completely different to that needed for analysis and it is not
effective nor efficient to mix the two together.
Table 6- Observed impacts of using EAC model on BCA Framework
Topic
Reuse
Adaptation
Collaboration
Reuse
of
content
N/A
Reuse
of
structure
NA
Aggregation
N/A
Comparison
N/A
Provider
vs.
Customer
Perspective
N/A
M
arket
Potential
Estimation
N/A
Changing
Assumptions
N/A
Product
Innovation
Lifecycle
N/A
Business
Model
Adaptability
N/A
Clarifying
Reasoning
O
Stakeholders
‘opinions
O
Information
Sources
O
Information
Quality
O
Sharing
O
Security
N/A
Referencing the Business Case Framework, Table 6 shows those elements which were observed with
letter O and NA for not applicable. This case study reflects elements from the collaboration area
 Stakeholders opinions - prior to the engagement, the white-paper reflected the stakeholder
opinions about the required new technology
 Clarifying reasoning - the consultation clarified the current technology landscape and
classifying the set of 140 decisions identified effective collaborative approaches to finding
the answers

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 Information sources – for the Analysis (Complicated) decisions, the experts held the
information and for the Can of Worms (Complex) decisions, many information sources were
used collaboratively to drive sufficient certainty into the decision so that it could be
determined by experts
Conclusions
Whilst Planning for projects can go beyond a complicated problem into complex or chaos domains,
the paper provides insights how implementation of the categorisation model known as Easy,
Analysis, Can of Worms (EAC) can facilitate critical decisions during early planning. EAC has provided
a collaborative approach to extend stakeholders opinions, the through consultation with diversified
expertise and increased certainty on sources of information in order to help decision makers with
approval of final business case.
The case study was used from an ICT project, and data and the model were analysed against Cynefin
Framework, business case framework and the categorised sources of uncertainty to interpret real-
world in a case study.
The paper concludes the EAC model as an effective categorisation model for complex decision
making and early planning for projects. “Fixing deep uncertainties or strong disagreements about
societal values in interesting scenarios might help us inform debate and make sense of very complex
issues” (Simon French, 2017`, p. 1643). Further assessment of the model across different industries
can result in generalisation of the EAC as a viable ad-hoc to the business case framework for
planning purposes.
Acknowledgement
We acknowledge that Elabor8 pty ltd has provided data for the case study based on services they
delivered in an ICT project. The authors would like to thank professor David Snowden for his advice
on development of the model based on Cynefin Framework, and we acknowledge support from
Cognitive-Edge Pty Ltd for graphics of Cynefin figures in this paper. Finally, we thank Dr Elyssebeth
Leigh for her kind support with final editing of the paper for the PGCS conference submission.

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