Data Mining of Project Management Data: An Analysis of Applied Research Studies
GĂźrdal Ertek
Allan N. Zhang
Sobhan Asian
Murat M Tunc
Omer Tanrikulu
1
Outline • Project Management • Data Mining • Research Gap Topic Methods • Applied Framework • Results • Conclusions • Acknowledgement
Data Mining
Project Management
2
Project • “A temporary endeavor • (with a definite beginning and definite end), • with progressive elaboration (developing in steps, continuing in increments), • undertaken to create a unique • product, • service, or • result”.
3
Project Management (PM) • “Application of • • • •
knowledge, skills, tools, and techniques
• to project activities • to meet project requirements” [1]. • Very important, because projects are • undertaken at all levels of the organization, • in almost any industry, and • can have long-term effects. 4
Project Management (PM) • Vast literature on project management • Specialized academic journals, • Professional institutions, • Project Management Institute (PMI),
• dedicated to project management field.
5
Project Management (PM) • However, real world projects usually • fall behind the performance goals or • FAIL frequently.
Source: http://calleam.com/WTPF/
6
Project Management (PM) • McKinsey & Company study of 5,400 large scale information technology (IT) projects: • Large IT projects run • 45% over budget and • 7% over time, while • delivering 56% less value than predicted.
• Even more critically, • 17% of large IT projects FAIL so big to threaten the existence of the company.
7
Data in Projects (1 of 2) • Project managers and planners make use of data, while new data is generated as a project progresses. • Real world projects are becoming increasingly complex, and are involving larger amount of data. • Big strategic projects, such as the production of a satellite, already generate amounts of data that can be classified as big data.
8
Data in Projects (2 of 2) • Data • in databases • post-project reviews
• can be • major source of actionable insights and competitive advantage.
• Multitude of studies • data mining (DM) techniques for • analyzing data coming from project management (PM).
• "DM for PM"
9
Data Mining • Data mining (DM) • growing field of computer science • discovery of actionable insights from –typically large and complex- data • tap into the information hidden in databases and unstructured documents • use data for advantage.
10
Research Gap | Topic • No research on the survey of literature on • data mining applications for project management.
• While use of data mining in manufacturing was • surveyed in [9].
• Our Research: • • • •
investigation of the literature on "DM for PM", results and analysis. gaps in the current literature opportunities for future research.
• Goal: • understanding of data mining (DM) for project management (PM) researchers and project managers, • as the world is moving towards the new age of big data.
11
http://www.un.org/sustainabledevelopment/sustainable-development-goals/ http://bit.ly/1Kjkn0B
12
Data (shared at ErtekProjects.com)
searched
downloaded & skimmed
read in detail
included in analysis
3,000+ papers
1,500+ papers
250+ papers
116 papers
Source data: http://ertekprojects.com/ftp/supp/14.xlsx
13
Data (shared at ErtekProjects.com)
Source data: http://ertekprojects.com/ftp/supp/14.xlsx
14
Data
Source data: http://ertekprojects.com/ftp/supp/14.xlsx
15
Data
Source data: http://ertekprojects.com/ftp/supp/14.xlsx
16
Research Methods (1 of 2) • Data Mining • growing field of computer science and informatics • aims at discovering new and useful information and knowledge from data. • multitude of analytical methods (and algorithms) • each method or combination of methods are most suitable for a given data with unique characteristics.
17
Research Methods (2 of 2) • Association Mining • data mining method for • identifying associations between • elements (items) of a set (set of items),
• based on how these elements appear in • multiple subsets (transactions) of the set.
• gives as output • list of itemsets appearing together frequently in transactions (frequent itemsets), and • rules that describe how these associations affect each other (association rules).
• An association rule is a rule in the form “IF [Antecedent A] THEN [Consequent B]” (or simply as “A⇒B”).
18
Applied Framework
19
Results
20
Results
21
Results
22
Results
23
Results
24
Conclusions (1 of 15) • 41 of the 116 reviewed papers are coming from the construction industry, • showing the significance of construction industry from • not only from a project management (PM) perspective, • but also from data mining (DM) and information technology (IT) perspectives.
25
Conclusions (2 of 15) • Other frequently encountered industries in the papers are • information and communication • manufacturing.
26
Conclusions (3 of 15) • Papers using data from • United States (19 papers) are most frequent, followed by those that use data from • Taiwan (14 papers) and • China (5 papers).
27
Conclusions (4 of 15) • Most frequent objectives are • cost minimization, • cost estimation, • makespan and • time minimization.
28
Conclusions (5 of 15) • Visualization is the most popular data mining method, and is • followed by statistical analysis. • The application of association rule mining and text mining seems least popular, • illustrating the opportunity to conduct research that uses these methods and/or develops new algorithms within these methods, especially for manufacturing. 29
Conclusions (6 of 15) • The most popular software tool is the • SPSS statistics/data mining software, followed by • MATLAB and • WEKA.
30
Conclusions (7 of 15) • An overwhelming percentage (88.8%) of the papers used data from the real world, which is very favorable.
31
Conclusions (8 of 15) • 85.3% of the papers used only existing methods, • rather than developing new data mining methods for the project management domain, • or being applied in the project management domain.
• This shows an important opportunity for future research for • developing new data mining methods • for the project management domain.
32
Conclusions (9 of 15) • 82.9% of the papers did not present the development of a decision support system (DSS), which suggests that • future research can involve development of DSS.
33
Conclusions (10 of 15) • 78.4% of the papers looked into single project data, • showing a gap, as well as opportunity to • conduct research on multi-project management.
34
Conclusions (11 of 15) • The data type in the papers was mainly (78%) single project data, suggesting gap and opportunity to conduct • more research with multiple-project data.
35
Conclusions (12 of 15) • Research on multi-project data where projects share resources is very scarce (9%), suggesting that • research on multi-project data can especially focus on the case where resources are shared.
Money
Manpower
Equipment
Facilities
Materials
Information/technology
36
Conclusions (13 of 15) • More research can be done for • operational-level projects and • strategic-level projects,
• due to the gap and opportunity on projects at these levels.
37
Conclusions (14 of 15) • There is opportunity to do more research that involves • manufacturing, as well as • public, • defense, and • scientific projects, and projects in • health, • insurance, and • energy industries. 38
Conclusions (15 of 15) • Papers where decision support systems (DSS) were developed are • four times more likely to also contain the • development of a new method.
• So any research where DSS or a new method is developed is more likely to contain (and expected to contain by the reviewers) the other.
39
40
41
Acknowledgement • Data Cleaning & Analysis • Şevki Murat Ayan • Onur Aksöyek • Ece Kurtaraner • Mete Sevinç • Byung-Geun Cho
• Research Grant • Abu Dhabi University 42
Thank you. Your Questions?
43