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Advanced Project Data Analytics for Improved Project Delivery
The webinar on advanced project data analytics, led by Martin Paver, discusses the importance of data in improving project delivery, highlighting lessons learned from previous projects like Crossrail and challenges in data management. It emphasizes the integration of various tools and platforms, the significance of predictive analytics, and the evolving role of data professionals in project controls. Key topics include the need for a data-driven strategy, the importance of connected data, and building skills within organizations to leverage data effectively.
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Advanced Project Data Analytics for Improved Project Delivery
- 1. Advanced Project DataAnalytics 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
- 2. Before we getstarted…. This session is being recorded. The recording and slides will be shared after the webinar. All participants are muted. Please type any questions into the “Questions” window. Your feedback will help us to improve future webinars. Please send any comments and suggestions to: martinpaver@projectingsuccess.co.uk
- 3. Experience Chartered Engineer Fellow Chartered ProjectProfessional Professional Accreditation Sectors Project Manager $1bn Programme Director $0.6bn Portfolio lead $10bn Roles Icon credit: Icons8
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- 5. Exhaust plume from project delivery AnExample: Crossrail
- 6. What Happens tothe Data?
- 7. NASA Lessons LearnedSystem 2012 • Not routinely used. • Ill defined strategies • Inconsistent funding • Lack of monitoring 2001 • Limited sharing of lessons • Dissatisfaction with processes • Barriers • Culture • Lack of time
- 8. Existing Lessons LearnedAnalysis http://www.treasury.govt.nz
- 9. Our Own Research:Research Paper https://bit.ly/2T7yKnL
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- 11. Narrow (ANI) General(AGI) Super (ASI) Performs one task Performs many tasks. Equivalent to a human Surpass most abilities of a human Chess Machines that perform reasoning Hal (2001) Widely adopted Predicted 20-100 years away Imminently after AGI Overview: What is AI?
- 12. The parent term encompassingany technique that allows a machine to act like a human AI, ML and Deep Learning Artificial Intelligence (AI) An AI technique that focusses on learning from experience Machine Learning (ML) A subset of ML that uses neural networks based on the brain Deep Learning
- 13. Why the Hype? DataCloud Algorithms Icon made by Freepik from www.flaticon.com In 2016, 90% of the world's data (that's 90% of all the data ever created) had been created in the previous two years (IBM).
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- 15. Some Foundations: GraphDatabases Projects LessonsRisks$ 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 ToolDriven 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: ExtractingValue from Data
- 19. Fundamentally: • What isthe 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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- 21. Tracking Contract Deliverables Project Administration Trackingreceipt 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 Risksetc Real time update of assigned tasks WBS Elements Scheduling Corpus and Context Extract Triples Benchmarking Adaptive SchedulingRecommendations Scheduling
- 23. A once throughprocess 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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- 25. Stakeholder Management Credit: PraxisFramework Or Adaptive, dynamic networks, reflecting real time feedback and historical performance of specific groups/individuals Credit: Neo4J Static Analysis
- 26. P3M Maturity assessments Auditbased 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 DeployingBlack 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 arethe 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?
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- 30. Positioning for aNew Future Overall approachData Strategy Connected Data Data harvesting Insights and Lean Predictive Insights
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- 32. Positioning For aData 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….. Whatare your aspirations? Analyst Or ‘Operative’
- 34. Getting Started • Startwith 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 • Familiarisationwith 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 Youare in a competitive environment MOOCsStart Communities Competitions Events Code/Blog Increasinglevelofcommitment
- 37. Barriers to Adoption Itsnot 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 viayour GoToWebinar control panel. (sorry, function not available on mobile devices)
- 39. Contact Please find meon 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 bigthanks to Mark Constable at For helping to make it happen