Why data matters webinar

WMG Capabilities:
• Over a decade of tailored PM education (PFQ,PPQ,PMQ) for companies
such as Arup, JLR and GE Aviation
• Degree Apprenticeships in Digital Technologies and Data Analytics
Personal Background:
• Founder of the Project Praxis Research Group. Major theme is project
data analytics.
• Work with a wide variety of stakeholders: infrastructure,
manufacturing MNCs, full stack developers, global consultancies, IPA,
APM, MPA
Naomi.Brookes@warwick.ac.uk
Naomi Brookes, Professor of Complex
Programme Management, WMG

• APM Data Advisory Group
• Project Data Analytics Research Network
• Project X Theme C – Data
• BAM Track Events
• Accenture ‘Data-driven Transformation of Capital Projects’ report
• APM ‘Project Data Analytics: The State of the Art and Science’ report
• Predicting projects performance in the context of estimating and
bechmarking using machine leaning
• Delphi Event on Barriers and Enablers of PDA
Ongoing Project Data Analytics Activities

Project data has always been with us ……….
But now we can do different things with it …….

Project Data Analytics:
The Current State of the Art
and Science

• Can show 13% of portfolio value in savings
• The ‘geeks meet business’ divide
• Data Maturity
• Attitudes to data
• Sharing
• GIGO
• Basic Statistical Challenges
• Reference class
• Unbalanced data
• Missing data
• What data do we actually need to make better decisions?
Predictive Project Data Analytics: its worth it … BUT
Courtesy of the Oakland Group

Predicting Time Estimating Capability
Importance Definition
0.3 The total number of projects the owning user of this project
owns
0.17 Location A
0.12 Number of tasks in a project
0.05 Business Unit B
0.04 The number of roles in a project created by a user
0.04 The number of roles in a project

Using data can help you make better delivery decisions –
But you need good quality data and the right tools to do this
Naomi.Brookes@warwick.ac.uk

Data driven risk analysis and assurance
Empowering project teams with data-driven, unbiased, and actionable
insights on project outcomes

Project programmes are incredibly
complex...
11
Programmes can have thousands of activities, making
them impossible for humans individuals and teams to
understand.
How can a planner be expected to estimate the duration
uncertainty? How do managers understand these? How
can executives make major investments using this
information?

… And learning from mistakes is extremely challenging…
Plans are built using team
& individual experience
Learning doesn’t
transfer back to the
creator
Unrealistic plans
that lead to poor
outcomes
Because of a long cycle
time from planning to
completion of a project
Which is limited
in availability,
scale and quality

So, we built artificial intelligence that learns how projects were executed
How long will the
project take?
What might go
wrong with it?
What can you do
to reduce risk?

Risk Teams now shift their focus to taking action, rather than data gathering
Speed
of
analysis
Objectivity of inputs
Hours
Weeks
Minutes
Subjective, experience based Objective, data-driven
Do nothing
14

Past data driving change across industries
1
5
https://www.bbc.co.uk/news/business-56164159 https://www.rollingstone.com/music/music-
features/nirvana-kurt-cobain-ai-song-1146444/

www.nplan.io
sebastian@nplan.io | +44 7555 966 202
Sebastian Williams

Copyright Projecting Success 2021
www.projectingsuccess.co.uk
12 April 2021
Fundamentally, we aspire to leverage the
experience from our portfolio of projects to
improve future project delivery performance.
Driving up delivery productivity and certainty.
Exhaust
plume from
project
delivery
Why Data
Matters

12 April 2021
Where most
organisations are
today
Where we
aspire to be
Driven by:
• Data
• Alignment between data
and our use cases
• Data volumes/quality
• Connections in data
• Relevance/Context
….and much more

12 April 2021
Does the same apply to projects?
People are
predisposed
to illnesses
Some driven
by DNA,
others by
lifestyle
factors.

12 April 2021
Certain projects are
more predisposed to
variance than others.
We know this at a
project level, but what
about WBS, conditions,
team?
Which factors have greatest influence on the variance?

12 April 2021
Every Project Is Different….
…..but at a functional level they
have a high degree of similarity
• Procurement
• Integration
• Design challenges
• Risks
• Delays
• Cost growth
• Compensation events
• Logistic challenges
• Testing/commissioning challenges
• Resourcing/bottlenecks
• Stakeholder management
• Benefits shortfalls
Are there patterns in this data?

12 April 2021
We move from this To this
Where would you prefer your career to be?

12 April 2021
Hack
Outputs
Predicting loss
making projects
example
“Absolutely over the moon with the results Martin. It gives us a really
good platform to build upon. I hope the challenge participants got as
much from it as I did setting the challenge and the fun I had watching
all the presentations and judging. I'm already thinking towards the
next Hack...
What Projecting Success and this community are achieving is industry
leading and the difference it is making is really starting to get noticed! I
look forward to working with you more closely in the future”.
Head of Business Intelligence & Analytics, Jacobs

12 April 2021
Project:Hack - Activity duration forecasts and confidence levels

12 April 2021
Survey on 14 October 2020: Project Data Analytics Panel Session
Why Data
Matters!

12 April 2021
Project Data Academy
Next cohort end May 2021
https://bit.ly/2ZVTwrL
Free* training to prepare yourself
for the next 10+ years
VickyE@projectingsuccess.co.uk
Project Data Analytics
Task Force
https://bit.ly/3nnpDes
Get involved and make a
difference
MARTIN PAVER
Projecting Success
martinpaver@projectingsuccess.co.uk

Why Data Matters - Panel
Naomi Brookes
University of
Warwick
Martin Paver
Projecting Success
Sebastian Williams
nPlan

Why data matters webinar

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