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In collaboration with

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


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


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

Today’s Event

NEW

S

Project:Hack

£707 raised for

Next Event: 29-30 June. Looking for data & sponsors.

https://bit.ly/2HUbV0K

Sign up here:

Project:Hack

Microsoft Reactor1 weekend x 3 times a year14 Challenges5 masterclasses>100 peopleFree food and drink. Free evening bar£1000 of prizes

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

NEW

S

https://www.mckinsey.com/industries/oil-and-gas/our-insights/how-the-oil-and-gas-industry-can-improve-capital-project-performance

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

NEW

SAlso check out:

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:

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

NEW

S

Neo4j GraphTourLondon

Have we missed anything that you may be able to share?

YOU

RN

EWS

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/

https://projectdataanalytics.uk/

http://www.meetup.com/London-Project-Data-and-Analytics-meetup/

https://www.meetup.com/Bristol-Project-Data-Analytics-Meetup/

https://www.meetup.com/North-West-Project-Data-Analytics-Meetup/

Upcoming Events

Upcoming Events

LONDON

Upcoming Events

BRISTOL

Upcoming Events

NORTH-WEST

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.

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

HOW DATA ANALYTICS CAN TRANSFORM PROJECT

DELIVERY

75 years of winging it

Best Practice project

delivery?Project Data

My challenge!

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

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!)




My challenge!

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

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

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




My challenge!

Development of ‘best practice’

Practice

Research

Theory

Innovators

Development of ‘best practice’ - accelerated

Practice

Data

Analytics

Informed

decision-makers

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

[email protected]

+44 777 570 4044


Experience

Chartered Engineer

Fellow

Registered Project Professional

Professional Accreditation

Sectors

Project Manager $1bn

Programme Director $0.6bn

Portfolio lead $10bn

Roles

Icon credit: Icons8

http://www.projectingsuccess.co.uk/


Crossrail



Exhaust plume from project

delivery

An Example: Crossrail



What Happens to the Data?



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



Existing Lessons Learned Analysis

http://www.treasury.govt.nz



Research: Stephen Duffield



Our Own Research: Research Paper

https://bit.ly/2T7yKnL


The Technology


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?



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



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).



Algorithms

Credit: Google


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




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




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




Some Foundations: Extracting Value from Data




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



A Possible Future…



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



EVM dataResourcingWeatherSupplier performanceDependenciesRisks etc

Real time update of

assigned tasksWBS Elements

Scheduling Corpus and Context Extract Triples

Benchmarking

Adaptive SchedulingRecommendations

Scheduling



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



Benefits



Stakeholder Management

Credit: Praxis Framework

Or

Adaptive, dynamic networks, reflecting real time feedback and historical performance of specific groups/individuals

Credit: Neo4J

Static Analysis



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


https://www.turing.ac.uk/research/research-projects/mapping-genetic-traits-cardiovascular-disease

https://www.turing.ac.uk/research/research-projects/physical-activity-and-cardiovascular-health

https://www.turing.ac.uk/research/research-projects/heart-attack-risk-prediction-and-treatment-management

https://www.turing.ac.uk/research/research-projects/personalised-risk-management-cardiovascular-disease

https://www.turing.ac.uk/research/research-projects/blood-related-risk-factors-cardiovascular-disease

https://www.turing.ac.uk/research/research-projects/modelling-hearts-chemical-signals


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



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/



Positioning for a New Future

Overall approachData Strategy

Connected Data

Data harvesting

Insights and Lean Predictive Insights



An example


Icons made by Freepik from www.flaticon.com

Machine Learning: Bid Data – a worked example


How to Prepare


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



The Learning Curve…..

What are your aspirations?

Analyst

Or

‘Operative’



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:



Demonstrate a Passion

You are in a competitive environment

MOOCsStart

Communities

Competitions

Events

Code/Blog

Incr

easi

ng

leve

l of

com

mit

men

t



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



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.



Contact

Please find me on Linkedin:

Martin PaverMartin Paver

CEO / Founder


[email protected]

+44 777 570 4044

Project Data Analytics

Also follow the Project Data Analytics group


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