Data Analytics is already beginning to impact how projects are delivered. We can now automate minute taking and capturing actions, we can use Flow to progress chase, Power BI reduces the burden of reporting.
But we are just scratching the surface. It won’t be long before we can leverage the rich dataset of experience to predict what risks are likely to occur, understand which WBS elements will be susceptible to variance, deduce what the optimum resource profile looks like, define a schedule by leveraging data from those projects that have gone before.
The role of a project professional is about to change dramatically. In this webinar we will explore the challenges and opportunities, and how we should respond. It’s a call-to-action for the community to mobilise, help to reshape project delivery and understand the implications for you and your organisation.
Presenter Martin Paver is a Chartered Project Professional, APM Fellow and Chartered Engineer. In December 2017 he established the London Project Data Analytics meetup which has quickly spread across the UK and expanded to 3000+ members. Martin has major project experience including leading a $billion projects with a team of 220 and a multi-billion PMO with a team of 50. He has a detailed grasp of project management and combines this with a broad understanding of recent developments in the field of data science. He is on a mission to ensure that the project management profession readies itself for a transformed future.
Learning outcomes:
- Understand the implications of advanced data analytics on project delivery
- Understand the scope of which functions it is likely to impact
- Help you to develop a strategy for how you engage with it
- Understand how to leverage the benefits and opportunities that will emerge from it
Presenter:
Martin Paver, CEO & Founder, Projecting Success Ltd
Measures of Central Tendency: Mean, Median and Mode
Advanced Project Data Analytics for Improved Project Delivery
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
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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
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).
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
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
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?
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
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