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What risk factors can explain project             outcome ?             The data available :          WB Project DB + IEG ...
Motivation : what accounts for      project success ?About 75% to 80% of projects are rated  moderately satisfactory or be...
Motivation : what accounts for          project success ?  Lets use an inductive approach to discovering thedeterminants o...
Creating the networkProject 1                          Project 2   Visual encoding             Common              themes ...
Results~8000 projects / ~5M edges
Results~8000 projects / ~700k edges
ResultsClusters of projects around themes/sectors          → variety of outcomes
CloseupFocus on one example
CloseupInterest on a particular project
CloseupInterest on a particular project
CloseupExploring its neighborhood
UnderstandingWhat lays behind a set of similar projects ?     Exploration to find the common        factors between projec...
Understanding
Understanding
Understanding
Understanding
First iteration of the     back and forth process :- need of experts for in-depth analysis- identify subgroups of projects...
weve paved the way for exploratory             research !Tools : Tulip (c++/python) http://tulip.labri.fr TulipPosy (javas...
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Network analysis applied to project risks identification

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This quickly wraps up our results of the Datadive hold in Washington DC (15-17 March 2013)

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Network analysis applied to project risks identification

  1. 1. What risk factors can explain project outcome ? The data available : WB Project DB + IEG Ratings Kenneth M. Chomitz – WB IEG Senior Advisor kchomitz@worldbank.org Alex H. Mckenzie – WB Senior Information Officer amckenzie@worldbank.org Benjamin Renoust – University of Bordeaux LaBRI Ph.D. Candidate benjamin.renoust@labri.fr
  2. 2. Motivation : what accounts for project success ?About 75% to 80% of projects are rated moderately satisfactory or better.Known variables only explain 12% of the variation between projects.
  3. 3. Motivation : what accounts for project success ? Lets use an inductive approach to discovering thedeterminants of success: group similar projects together and look for successful vs unsuccessful groups A start : group projects by sectors and themes. Relevant data : projects / amounts / sectors / themes / outcome ratings Lets put our hands in the data !
  4. 4. Creating the networkProject 1 Project 2 Visual encoding Common themes Size : amount Sectors Color : outcome rating (can be → satisfactory : blue contrained) → unsatisfactory : red
  5. 5. Results~8000 projects / ~5M edges
  6. 6. Results~8000 projects / ~700k edges
  7. 7. ResultsClusters of projects around themes/sectors → variety of outcomes
  8. 8. CloseupFocus on one example
  9. 9. CloseupInterest on a particular project
  10. 10. CloseupInterest on a particular project
  11. 11. CloseupExploring its neighborhood
  12. 12. UnderstandingWhat lays behind a set of similar projects ? Exploration to find the common factors between projects 2 networks : Projects vs Themes and sectors
  13. 13. Understanding
  14. 14. Understanding
  15. 15. Understanding
  16. 16. Understanding
  17. 17. First iteration of the back and forth process :- need of experts for in-depth analysis- identify subgroups of projects with similaroutcomes- extend the set of characteristics- refine the measures of connectedness (people/social network, semantic analysis of objectives)
  18. 18. weve paved the way for exploratory research !Tools : Tulip (c++/python) http://tulip.labri.fr TulipPosy (javascript/python/d3.js) – https://github.com/renoust/tulipposy → contributors are welcome !Data : IEG Performance Rating : http://data.worldbank.org/data-catalog/IEG WB Project API : http://data.worldbank.org/data-catalog/projects-portfolioPeople : Kenneth M. Chomitz – WB IEG Senior Advisor – kchomitz@worldbank.org Alex H. Mckenzie – WB Senior Information Officer – amckenzie@worldbank.org Benjamin Renoust – University of Bordeaux LaBRI Ph.D. Candidate – benjamin.renoust@labri.fr

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