Network analysis applied to project risks identification

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This document discusses using an inductive approach and network analysis to analyze World Bank project data in order to better understand the factors that influence project success or failure. Over 75% of projects are rated as satisfactory or better but known variables only explain 12% of variations. The approach involves grouping similar projects by sector and theme and looking for patterns between successful and unsuccessful groups. The document demonstrates initial results from analyzing over 8,000 projects and 5 million connections in a network, and provides details on exploring particular projects and their neighborhoods. It concludes that expert analysis is still needed and the work paves the way for further exploratory research.

Technology

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

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.

Motivation : what accounts for
project success ?

Let's use an inductive approach to discovering the
determinants 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

Let's put our hands in the data !

Creating the network

Project 1
Project 2 Visual encoding
Common
themes Size : amount
Sectors Color : outcome rating
(can be → satisfactory : blue
contrained)
→ unsatisfactory : red

Results
Clusters of projects around themes/sectors
→ variety of outcomes

Closeup
Interest on a particular project

Understanding

What lays behind a set of similar projects ?

Exploration to find the common
factors between projects

2 networks :
Projects vs Themes and sectors

First iteration of the
back and forth process :

- need of experts for in-depth analysis
- identify subgroups of projects with similar
outcomes
- extend the set of characteristics
- refine the measures of connectedness
(people/social network,
semantic analysis of objectives)

we've 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-portfolio

People :
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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Network analysis applied to project risks identification

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. 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. Motivation : what accounts for project success ? Let's use an inductive approach to discovering the determinants 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 Let's put our hands in the data !

4. Creating the network Project 1 Project 2 Visual encoding Common themes Size : amount Sectors Color : outcome rating (can be → satisfactory : blue contrained) → unsatisfactory : red

5. Results ~8000 projects / ~5M edges

6. Results ~8000 projects / ~700k edges

7. Results Clusters of projects around themes/sectors → variety of outcomes

8. Closeup Focus on one example

9. Closeup Interest on a particular project

10. Closeup Interest on a particular project

11. Closeup Exploring its neighborhood

12. Understanding What lays behind a set of similar projects ? Exploration to find the common factors between projects 2 networks : Projects vs Themes and sectors

17. First iteration of the back and forth process : - need of experts for in-depth analysis - identify subgroups of projects with similar outcomes - extend the set of characteristics - refine the measures of connectedness (people/social network, semantic analysis of objectives)

18. we've 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-portfolio People : 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

Network analysis applied to project risks identification

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