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In collaboration with
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PROJECT DATA ANALYTICS COMMUNITY
Project Management
ProgrammeManagement
Portfolio Management
DataAnalysis
DataScience
& AI
Founded by
• Exploit the rich seam of project data• Demonstrate what can be possible• A community - A force for change• Develop a new cadre of skilled
professionals• Forge a collegiate network, helping
each other• Help companies to form and grow
Spread the wordVolunteer some of your time
In collaboration with
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PROJECT DATA ANALYTICS COMMUNITY
LONDON BRISTOL NORTH-WEST
The UK’s biggest Project Data Analytics Community
REGIONAL EVENTS
NATIONAL EVENTS
1st meetup Dec 20172392 members
ConferencesProject:Hack Product Demos
1st meetup 2nd April 2019123 members
Huge thanks to
our sponsors
Masterclasses
1st meetup 30 April 201975 members
In collaboration with
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Leveraging the Experience of the Past to Transform the Future
Consultancy
Capability
Providing consultancy to ignite the professional imagination and revolutionalise business practices
Delivering expert training and masterclasses, as well as creating a pipeline of expertise
Project Data Analytics, Data Trusts, Data Quality &
Completeness, Predictive Bid Insights, Data Strategy,
Visualisations, P3M Delivery
C-Suite Training, Masterclasses, Foundation Courses, Apprenticeships,
STEM Conversion,Conferences
Community
Building a Project Data Analytics Community to spark innovation and impact future approaches to leveraging data
Project:HACK, Conferences,
Product Demos, Meetups
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Today’s Event
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NEW
S
Project:Hack
£707 raised for
Next Event: 29-30 June. Looking for data & sponsors.
https://bit.ly/2HUbV0K
Sign up here:
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Project:Hack
Microsoft Reactor1 weekend x 3 times a year14 Challenges5 masterclasses>100 peopleFree food and drink. Free evening bar£1000 of prizes
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NEW
S
https://www.gartner.com/en/newsroom/press-releases/2019-03-20-gartner-says-80-percent-of-today-s-project-management
https://gtnr.it/2Thqg8Q
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NEW
S
https://www.mckinsey.com/industries/oil-and-gas/our-insights/how-the-oil-and-gas-industry-can-improve-capital-project-performance
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NEW
S
https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven
Companies Are Failing in Their Efforts to Become Data-Driven
• Despite increasing investment in big data and AI initiatives:• 72% have yet to forge a data culture• 69% have not created a data-driven organization• 53% are not yet treating data as a business asset• 52% are not competing on data and analytics
93% of respondents
identify people and
process issues as the obstacle.
https://bit.ly/2Gc5sgY
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NEW
SAlso check out:
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NEW
S
https://projectdataanalytics.uk/vlog
Key Influencers Visual:• First AI visualisation• Uses Machine Learning to automatically analyse your data• Find insights in a very natural way
Power BI Desktop February 2019 Update – Highlights:
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NEW
S
https://www.microsoft.com/en-us/microsoft-365/blog/2018/09/24/bringing-ai-to-excel-4-new-features-announced-today-at-ignite/
Bringing AI to Excel
• Take a picture of a data table
• Excel automatically converts it to a table
• Uses image recognition
• Eliminates the need to manually enter data
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NEW
S
Neo4j GraphTourLondon
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NEW
S
Neo4j GraphTourLondon
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NEW
S
Neo4j GraphTourLondon
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Have we missed anything that you may be able to share?
YOU
RN
EWS
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https://ProjectDataAnalytics.uk
www.facebook.com@ProjectDataAnalytics
www.linkedin.com/company/@ProjectDataAnalytics
www.twitter.com/ProjectDataAna
www.meetup.com/London-Project-Data-and-Analytics-meetup/
Community Communications
www.meetup.com/Bristol-Project-Data-Analytics-Meetup/www.meetup.com/North-West-Project-Data-Analytics-Meetup/
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Upcoming Events
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Upcoming Events
LONDON
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Upcoming Events
BRISTOL
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Upcoming Events
NORTH-WEST
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Raffle
We are on a quest to provide the community with access to many of the major data analytics events.
1 free ticket to AI & BigData Expo
The winner is…..
RYANMcCALL
Congratulations!
We’ll endeavour to provide at least 1 raffle a month, so stay plugged in to gain access to the latest.
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Many Thanks To Our Sponsors…
Please spread the word and help the community to grow
The PDU Code for tonight’s event is:
See Martin for details
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HOW DATA ANALYTICS CAN TRANSFORM PROJECT
DELIVERY
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75 years of winging it
Best Practice project
delivery?Project Data
My challenge!
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75 years of winging it
Best Practice project
delivery?Project Data
My challenge!
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What is ‘Best Practice’ project delivery?
Wikipedia definition
a technique, method, process, activity, incentive or reward that is believed to be
more effective at delivering a particular outcome than any other technique, method,
process, etc.
Best practices can also be defined as the most efficient (least amount of effort) and
effective (best results) way of accomplishing a task, based on repeatable
procedures that have proven themselves over time for large numbers of people
My definition
A way of doing something that has been demonstrated in practice as being better
than other ways for the given context – and that it can be described and then
repeated
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What is ‘Best Practice’ project delivery?
Wikipedia definition
a technique, method, process, activity, incentive or reward that is believed to be
more effective at delivering a particular outcome than any other technique, method,
process, etc.
Best practices can also be defined as the most efficient (least amount of effort) and
effective (best results) way of accomplishing a task, based on repeatable
procedures that have proven themselves over time for large numbers of people
My definition
A way of doing something that has been demonstrated in practice as being better
than other ways for the given context – and that it can be described and then
repeated
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Expectations from ‘best practice’ – why?
Best Practice “Winging It”
Provides an optimal process
(not too much, not too little)
No process
Adds some management costs (cost of quality) Adds “fire fighting” costs (cost of non-conformance)
Improves predictability Requires a leap of faith – trust me
Repeatable People dependent
Comparable projects/programmes enable informed
decision making
Pet projects/programmes prevail
Ensures strategic alignment Silo decisions
Dependencies clear Subject to surprise
Change is controlled (not prevented) Change is opaque (what baseline?)
Success can be measured Success defined retrospectively
(I said you could trust me!)
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75 years of winging it
Best Practice project
delivery?Project Data
My challenge!
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How many data points do we use?
Time (SPI)
Cost (CPI)
Quality (v-model)
Others:
• Risks & Issues
• Changes
• Outcomes / benefits
• Cost-benefit ratio / ROI
• Complexity
• Safety
• Stakeholder engagement / satisfaction
• Team engagement / satisfaction
• Team experience / qualifications
• Supply-chain engagement / satisfaction
• Recruitment/retention
• Productivity
• Procurement model
• Delivery model (thin/thick client)
• Delivery approach (waterfall, agile)
• Delivery method (e.g. PRINCE2)
• etc
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Candidate data points - causes of failure
OGC Common Causes of Failure, 2006
Poorly defined or poorly
communicated vision
Insufficient board level support
Leadership is weak
Unrealistic expectations of the
organisationalcapacity and
capability
Insufficient focus on benefits
Organisation fails to change its culture
Insufficient engagement of stakeholders
No real picture (blueprint) of the future capability
An incorrect toolset is used
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Candidate data points – causes of confidence
Strong Leadership
Clear Scope, Aims, Benefits
Strategic Alignment
Skills & Expertise
Living the Values
Time Cost Quality
Assurance
Stakeholder Commitment
Robust Governance &
Controls
Clear Roles & Responsibilities
OGC’s causes of confidence, 2010
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75 years of winging it
Best Practice project
delivery?Project Data
My challenge!
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Development of ‘best practice’
Practice
Research
Theory
Innovators
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Development of ‘best practice’ - accelerated
Practice
Data
Analytics
Informed
decision-makers
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How Project Data Analytics Can Transform How We Deliver Projects
Bristol Project Data Analytics Meetup2 April 2019
Martin Paver
CEO / Founder
www.projectingsuccess.co.uk
+44 777 570 4044
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www.projectingsuccess.co.uk
Experience
Chartered Engineer
Fellow
Registered Project Professional
Professional Accreditation
Sectors
Project Manager $1bn
Programme Director $0.6bn
Portfolio lead $10bn
Roles
Icon credit: Icons8
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www.projectingsuccess.co.uk
Exhaust plume from project
delivery
An Example: Crossrail
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www.projectingsuccess.co.uk
NASA Lessons Learned System
2012• Not routinely used.
• Ill defined strategies
• Inconsistent funding
• Lack of monitoring
2001• Limited sharing of lessons
• Dissatisfaction with processes
• Barriers
• Culture
• Lack of time
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www.projectingsuccess.co.uk
Existing Lessons Learned Analysis
http://www.treasury.govt.nz
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www.projectingsuccess.co.uk
Our Own Research: Research Paper
https://bit.ly/2T7yKnL
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The Technology
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www.projectingsuccess.co.uk
Narrow (ANI) General (AGI) Super (ASI)
Performs one task Performs many tasks. Equivalent to a human
Surpass most abilities of a human
Chess Machines that perform reasoning
Hal (2001)
Widely adopted Predicted 20-100 years away
Imminently after AGI
Overview: What is AI?
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www.projectingsuccess.co.uk
The parent term encompassing any technique that allows a machine to act like a human
AI, ML and Deep Learning
Artificial
Intelligence
(AI)
An AI technique that focusses on learning from experience
Machine
Learning
(ML)
A subset of ML that uses neural networks based on the brain
Deep
Learning
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www.projectingsuccess.co.uk
Why the Hype?
Data Cloud Algorithms
Icon made by Freepik from www.flaticon.com
In 2016, 90% of the world's data (that's 90% of all the data ever created) had been created in the previous two years (IBM).
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www.projectingsuccess.co.ukwww.projectingsuccess.co.uk
Some Foundations: Graph Databases
Projects
Lessons
Risks$
Graph
Data Stored in Silos
Lesson XDrawdown
Cost impact
Time impact
Mitigate Cost
Mitigate effective
-ness
Project 1
Project 2
Taxon-omy
Technical
SafetySecurity
Technical
issue
Security Issue
Safety Issue
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www.projectingsuccess.co.ukwww.projectingsuccess.co.uk
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
DrivenPlatform
Driven
Plus integration with other corporate tools and data
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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
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Some Foundations: Extracting Value from Data
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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
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Tracking Contract Deliverables
Project Administration
Tracking receiptCompliance and quality assessment
Deliverable graphs
Briefs, Reports and Dashboards
Meeting Admin, Minutes, Actions
Gotomeeting – TranscriptExtract actions into Flow
Use Flow to progress actions
Resource Utilisation
Quality Audits, Maturity Reviews
Forecasting, Budgeting
Improved benchmarkingVariance analysis
Early warnings
Automatic review of timesheetsWorkflows chasing timesheetsKPIs on resource performance
Data quality/completeness analysisFrequency of updates
Comparison against good practice
Auto-reportingAuto-dashboardsPredictive analysis
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EVM dataResourcingWeatherSupplier performanceDependenciesRisks etc
Real time update of
assigned tasksWBS Elements
Scheduling Corpus and Context Extract Triples
Benchmarking
Adaptive SchedulingRecommendations
Scheduling
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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
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Stakeholder Management
Credit: Praxis Framework
Or
Adaptive, dynamic networks, reflecting real time feedback and historical performance of specific groups/individuals
Credit: Neo4J
Static Analysis
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Health
• Mapping genetic traits of cardiovascular disease
• Physical activity and cardiovascular health
• Heart attack risk prediction and treatment management
• Personalised risk management of cardiovascular disease
• Blood related risk factors for cardiovascular disease
• Modelling the heart's chemical signals
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Does Your Data Give You An Edge?
Protect my data Pool my data
Compete on basis of:
Data availability / QualityCompete on basis of:
Innovation and Quality of Insights
Short term tactical advantage but cannot compete long term with pooled data
Strategic advantage by leveraging the broad pool of data, including client data
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Data Trust: Definition
In a data trust, the trustors may include individuals and organisations that hold
data. The trustors grant some of the rights they have to control the data to a
set of trustees, who then make decisions about the data – such as who has
access to it and for what purposes.
The beneficiaries of the data trust include those who are provided with access
to the data (such as researchers and developers) and the people who benefit
from what they create from the data.
It is a legal structure that provides independent
stewardship of some data for the benefit of a group of organisations or people.
Not wholly applicable in our case. Stewardship will be by
data providers
Source: https://theodi.org/article/defining-a-data-trust/
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Positioning for a New Future
Overall approachData Strategy
Connected Data
Data harvesting
Insights and Lean Predictive Insights
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An example
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Machine Learning: Bid Data – a worked example
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How to Prepare
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Positioning For a Data Driven Future
Reporting Dashboards
Data cleansingData Graphs
Text analyticsInsights
BenchmarkingPredictive analyticsMachine Learning
Collate Data
Auto-Collate Data
Connect, Qualify and
Integrate Data
Extract Predictive
Insights
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The Learning Curve…..
What are your aspirations?
Analyst
Or
‘Operative’
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Data Roles
DataScientist
DataEngineer
DataAnalyst
• 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:
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Demonstrate a Passion
You are in a competitive environment
MOOCsStart
Communities
Competitions
Events
Code/Blog
Incr
easi
ng
leve
l of
com
mit
men
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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
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How Quickly Will This Happen?
It depends on….
• Corporate pressure: Transparency, delivery performance
• Demonstrating the return on investment.
• Willingness to share data.
• Leaders: Next 12-24 months
• Others: 2-5 years
Consider: Large vs small organisations.
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Contact
Please find me on Linkedin:
Martin PaverMartin Paver
CEO / Founder
www.projectingsuccess.co.uk
+44 777 570 4044
Project Data Analytics
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