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Taking portfolio benefits management to the next level with modern analytics webinar, 13 June 2018

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Taking portfolio benefits management to the next level with modern analytics webinar
Wednesday 13 June 2018

presented by Ian Stuart, Altis Consulting, Principal
hosted by Merv Wyeth, Benefits Management SIG Secretary

The link to the write up page and resources of this webinar:
https://www.apm.org.uk/news/taking-portfolio-benefits-management-to-the-next-level-with-modern-analytics-webinar/

Published in: Education
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Taking portfolio benefits management to the next level with modern analytics webinar, 13 June 2018

  1. 1. Ian Stuart Altis Consulting Taking Portfolio Benefits Management to the next level with Modern Analytics
  2. 2. Welcome
  3. 3. Goal Allow you to Develop awareness and knowledge of data analytics and data presentation Understand their use in the context of Portfolio & Programme Management How they can address real-life situations Gain insight into industry Best Practices Boost value for PM practitioners and their organisations
  4. 4. Contents • Intro • Typical Project Management Challenges • Data Visualisation in Project, Programme & Portflio Management • Real life examples • Tools • Demos • Best practices for Data visualisation • How to get Started • Questions & Summary
  5. 5. Partnerships with thought leaders The Kimball Group Widely recognised as the inventors of many of the data warehousing concepts we take for granted today. William McKnight Strategist, Lead Enterprise Information Architect, and Program Manager for sites worldwide utilising the discipline of Master Data Management, Data Quality and Data Governance. Stephen Few A leading expert, educator and author in data visualisation techniques. He is the author of four best selling books on the topic with a fifth due out soon.
  6. 6. Project Management Challenges
  7. 7. Project Management Lots of knowledge areas and responsibilities Procurement Schedule Management Costs Resources Integration Management Scope Management Issues Risks
  8. 8. Project Management Many responsibilities often means many systems To name a few: • Project Management Systems • Schedules, Budgets in Excel • Customer Data (e.g. CRM) • Resource planning solutions • Libraries, repositories (e.g. SharePoint) • Finance & accountancy systems
  9. 9. Project Management Typical Programme Reporting Issues • Silos of information • Poor data quality • Sporadic data availability • Manual processing • Complexity • Little time available and difficult reports to compile
  10. 10. Data Visualisation In Project Management
  11. 11. Data Visualisation in Project Management • Analytics techniques and tools enable • Collecting data from multiple systems • Combining and storing in a common view • Data Visualisation allows • The data to be explored • Ability to find patterns and trends • Clear way to communicate message
  12. 12. Portfolio Management Analytics - Construction Project Management Centre of Excellence • Very large complex data set available • Team used to one report per project – and many sources of data • Allowed commercial analyst to find outliers and patterns that were hidden in the data • Existing traffic lighting of project was not sufficient
  13. 13. Portfolio Management Analytics - Construction Project Management Centre of Excellence
  14. 14. Portfolio Management Analytics - Construction Project Management Centre of Excellence
  15. 15. Portfolio Management Analytics - Construction Margin Analysis of projects • Small Construction company • Analysed which projects were more successful than others
  16. 16. Data Visualisation Tools
  17. 17. Gartner BI and Analytics Magic Quadrant Microsoft (Power BI) & Tableau are clear leaders
  18. 18. Gartner BI and Analytics Magic Quadrant Microsoft (Power BI) & Tableau are clear leaders
  19. 19. Data Visualisation Demos
  20. 20. The importance of using good data visualisation principles
  21. 21. Data Visualisation The WAY we present data has a HUGE impact on • If it is understood • How it is interpreted • How much it is used “The goal is to transform data into information, and information into insight.” Carla Fiorina, Executive and president of Hewlett- Packard Co in 1999. Chairwoman in 2000.
  22. 22. We are company G. Which contributes more to market share; Company A or our company? In order, which are our major competitors? Is that easier? Example 1 – 3D Pie Charts
  23. 23. Example 2 - Area Poll: How much bigger is the right hand circle compared to the left hand? r=1 cm, 𝐴 = 𝜋𝑟2 Area = 3.14 cm 16 times bigger r=4 cm, 𝐴 = 𝜋𝑟2 Area = 50.26 cm
  24. 24. Are you sure it’s this one? Example 3 – Colour Which is the darkest of the 4 smaller rectangles?
  25. 25. Observe the apparent colour change as the shape moves across the screen Example 3 – Colour
  26. 26. Visual perception “We don’t see images with our eyes; we see them with our brains” Stephen Few
  27. 27. Data Visualisation Best Practice
  28. 28. Principles 1. Display neither more nor less than is relevant to your message
  29. 29. Principles 2. Do not include visual differences in a graph that do not correspond to actual differences in the data.
  30. 30. Principles 3. Differences in the visual properties that represent values should accurately correspond to the actual differences in the values they represent.
  31. 31. Principles 4. Use the lengths or 2-D locations of objects to encode quantitative values in graphs
  32. 32. Principles 5. Do not visually connect values that are discrete, thereby suggesting a relationship that does not exist in the data.
  33. 33. Do’s and Don’ts Do’s • Consider and plan what it is you are trying to present through your table or graph • Use bar graphs • Use 2D graphs • Keep tables clean and simple. • Use colour in meaningful ways that helps to represent the data effectively • Align labels left to right (not vertical) • Consider visual perception and how your data visualisation can be misinterpreted
  34. 34. Do’s and Don’ts Don’ts • Don’t use pie charts • Don’t use 3D graphs • Don’t use background colour fill • Don’t align labels vertically • Don’t misrepresent anything that you don’t want to by disregarding visual perception
  35. 35. How to Get Started
  36. 36. High level steps • Get a tool • The top three players (and others) all have a free version • More than adequate for single developer • Get some data • Reliable, repeatable, relevant • Create some visualisations • Follow a process • Get some help • Training, Coaching, Outsourcing
  37. 37. Data Visualisation Design Process
  38. 38. Agile Framework Typical Agile approach Project Kick off and Planning Development Demo 1 Mon 7th August Mon 14th August Thu 17th August Development Thu 24th August Demo 2 Tue 22nd August Testing Finish Development Demo 3 Tue 29th August Confirm Requirements
  39. 39. Getting help • Training • Data Visualisation Principles • Public and On Demand • Specific Tool training • Free resources available online • Pre-packaged courses • Bespoke training • Coaching • On the job training • Outsourcing • Value for money • Or a combination
  40. 40. Summary We have shown you how data can be exploited and best presented in order to address real-life situations in the context of Portfolio Management We briefly discussed how to get started with data visualisation and analytics We trust you have seen how implementing good data visualisation in your organisation could save time and money whilst boosting value
  41. 41. Tel +44 208 133 5095 or +44 7946 535 454 ians@altisglobal.co.uk www.altisglobal.co.uk Connecting with courage, heart and insight 8 consecutive years since 2010
  42. 42. This presentation was delivered at an APM webinar To find out more about upcoming webinars please visit our website www.apm.org.uk/events

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