1. Advanced Project Data Analytics for
Improved Project Delivery
Webinar
4 July 2019
Martin Paver
CEO / Founder
www.projectingsuccess.co.uk
martinpaver@projectingsuccess.co.uk
+44 777 570 4044
SUPPORTED BY

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3. Experience
Chartered Engineer
Fellow
Chartered Project Professional
Professional Accreditation
Sectors
Project Manager $1bn
Programme Director $0.6bn
Portfolio lead $10bn
Roles
Icon credit: Icons8

4. Crossrail

5. Exhaust plume
from project
delivery
An Example: Crossrail

6. What Happens to the Data?

7. NASA Lessons Learned System
2012
• Not routinely used.
• Ill defined strategies
• Inconsistent funding
• Lack of monitoring
2001
• Limited sharing of lessons
• Dissatisfaction with processes
• Barriers
• Culture
• Lack of time

8. Existing Lessons Learned Analysis
http://www.treasury.govt.nz

9. Our Own Research: Research Paper
https://bit.ly/2T7yKnL

10. The Technology

11. Narrow (ANI) General (AGI) Super (ASI)
Performs one task Performs many tasks.
Equivalent to a human
Surpass most abilities
of a human
Chess Machines that
perform reasoning
Hal (2001)
Widely adopted Predicted 20-100 years
away
Imminently after AGI
Overview: What is AI?

12. The parent term
encompassing any technique
that allows a machine to act
like a human
AI, ML and Deep Learning
Artificial
Intelligence
(AI)
An AI technique that
focusses on learning from
experience
Machine
Learning
(ML)
A subset of ML that uses
neural networks based on
the brain
Deep
Learning

13. Why the Hype?
Data Cloud Algorithms
Icon made by Freepik from www.flaticon.com
In 2016, 90% of the world's data (that's 90% of all the data ever created)
had been created in the previous two years (IBM).

14. Algorithms
Credit: Google

15. Some Foundations: Graph Databases
Projects LessonsRisks$
Graph
Data Stored in Silos
Lesson X
Draw
down
Cost
impact
Time
impact
Mitigate
Cost
Mitigate
effective
-ness
Project 1
Project 2
Taxon-
omy
TechnicalSafetySecurity
Technical
issue
Security
Issue
Safety
Issue

16. Some Foundations: Tool/Platform/Data
Tool Driven
Implementation strategy driven by tool selection.
Primavera/ASTA, Risk Tool, BIM etc.
Considerable tool integration challenge.
Platform Driven
A platform that integrates multiple tools. A one stop
shop that integrates database and tools for a project
management or BIM centred use case. Vendor lock in.
Data Driven
Connected data is at the core of the solution.
Tools and platforms are used to capture, ingest,
process, visualise and provide insights.
Tool
Driven
Data
Driven
Platform
Driven
Plus integration with other corporate tools and data

17. Some Foundations:
Python, Flow, PowerApps and Power BI
Available as part of your current services. Leverage
your current investments.
Opportunity to tailor to your business, use cases
and integration of different systems

18. Some Foundations: Extracting Value from Data

19. Fundamentally:
• What is the predisposition of the work to variance?
• Can we predict it?
• How do we test for it?
• How do we treat it and change the future?
Evidence based, tempering against bias.
Project DNA

20. A Possible Future…

21. Tracking Contract
Deliverables
Project Administration
Tracking receipt
Compliance and quality assessment
Deliverable graphs
Briefs, Reports
and Dashboards
Meeting Admin,
Minutes, Actions
Gotomeeting – Transcript
Extract actions into Flow
Use Flow to progress actions
Resource
Utilisation
Quality Audits,
Maturity Reviews
Forecasting,
Budgeting
Improved benchmarking
Variance analysis
Early warnings
Automatic review of timesheets
Workflows chasing timesheets
KPIs on resource performance
Data quality/completeness analysis
Frequency of updates
Comparison against good practice
Auto-reporting
Auto-dashboards
Predictive analysis

22. EVM data
Resourcing
Weather
Supplier performance
Dependencies
Risks etc
Real time
update of
assigned tasks
WBS Elements
Scheduling Corpus and
Context Extract Triples
Benchmarking
Adaptive SchedulingRecommendations
Scheduling

23. A once through process
Risk lifecycle
Leveraging Risk Experience
Connected risks Risks-Issues-Lessons
Informed risk
registers
Risk
trends
Risk mitigations
Risk
budget
Systemic Risk
Risks

24. Benefits

25. Stakeholder Management
Credit: Praxis Framework
Or
Adaptive, dynamic networks, reflecting
real time feedback and historical
performance of specific groups/individuals
Credit: Neo4J
Static Analysis

26. P3M Maturity assessments
Audit based vs real time
• Process adherence
• Frequency of update
• Materiality of update
• Quality of inputs
• Correlation with level of experience
Caution: We do not want to create process monkeys
I want people who are
right most of the time
• Risk identification
• Risk to issues
• Schedule adherence
• Cost adherence
• Etc….
Forensic analysis on individual and team
performance

27. Buying and Deploying Black Box AI
• What is contained within the dataset?
• How relevant is the data?
• How is bias managed and accounted for?
• How was the AI trained?
• How is it validated?
• Governance of decision making: “Computer said no”
AI will need to guide and inform, but we need humans in
the loop.
Are these humans project controllers or data scientists?
We must become conversant with these capabilities

28. Definitions
"Project Controls are the data gathering, data management
and analytical processes used to predict, understand and
constructively influence the time and cost outcomes of a
project or programme; through the communication of
information in formats that assist effective management
and decision making.“ Project Controls Online
"Project Controls are the data gathering, data management
and analytical processes used to predict, understand and
constructively influence the time and cost outcomes of a
project or programme; through the communication of
information in formats that assist effective management
and decision making.“ Project Controls Online
Project Controls or Data Analyst/Scientist?

29. Data Trust: Definition

30. Positioning for a New Future
Overall approachData Strategy
Connected Data
Data harvesting
Insights and Lean Predictive Insights

31. How to Prepare

32. Positioning For a Data Driven Future
Reporting Dashboards Data cleansing
Data Graphs
Text analytics
Insights
Benchmarking
Predictive analytics
Machine Learning
Collate
Data
Auto-Collate
Data
Connect,
Qualify and
Integrate Data
Extract
Predictive
Insights

33. The Learning Curve…..
What are your aspirations?
Analyst
Or
‘Operative’

34. Getting Started
• Start with the use case and user story
• Incremental delivery
• Maintain velocity – don’t get bogged down with data challenges
• Build momentum
• Reskill or gain an awareness: Gas fitter
• Pair up project professionals with data professionals
• ‘Turn right’

35. Data Roles
Data
Scientist
Data
Engineer
Data
Analyst
• Familiarisation with roles
• Gain an overview of each
• Gap analysis
• What skills does your organisation have?
• What does your organisation aspire to?
• What does the roadmap look like?
• What would you like to do?
Make good use of:

36. Demonstrate a Passion
You are in a competitive environment
MOOCsStart
Communities
Competitions
Events
Code/Blog
Increasinglevelofcommitment

37. Barriers to Adoption
Its not on the corporate ‘to do’ list
• Lack of a shared vision
• Lack of evidence to support the vision
• Lack of skilled horsepower
• Lack of data
• Siloed
• Poor quality
• Understanding the investment case

38. Submit questions via your GoToWebinar control panel.
(sorry, function not available on mobile devices)

39. Contact
Please find me on Linkedin:
Martin Paver
Martin Paver
CEO / Founder
www.projectingsuccess.co.uk
martinpaver@projectingsuccess.co.uk
+44 777 570 4044
Project Data Analytics
Also follow the Project Data Analytics group

40. And a big thanks to Mark Constable at
For helping to make it happen