Download Link: http://ertekprojects.com/ Outline • Project Management • Data Mining • Research Gap | Topic | Methods • Applied Framework • Results • Conclusions • Acknowledgement https://dl.acm.org/citation.cfm?id=3176714

1. Data Mining of Project Management Data:
An Analysis of Applied Research Studies
1
Gürdal Ertek Allan N. Zhang Sobhan Asian Murat M Tunc Omer Tanrikulu

2. Outline
•Project Management
•Data Mining
•Research Gap  Topic  Methods
•Applied Framework
•Results
•Conclusions
•Acknowledgement
2
Data Mining
Project Management

3. 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

4. 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

5. Project Management (PM)
• Vast literature on project management
• Specialized academic journals,
• Professional institutions,
• Project Management Institute (PMI),
• dedicated to project management field.
5

6. Project Management (PM)
• However, real world projects usually
• fall behind the performance goals or
• FAIL frequently.
6Source: http://calleam.com/WTPF/

7. 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

8. 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

9. 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

10. 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

11. 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

12. 12
http://www.un.org/sustainabledevelopment/sustainable-development-goals/
http://bit.ly/1Kjkn0B

13. Data (shared at ErtekProjects.com)
13
3,000+
papers
1,500+
papers
250+
papers
116
papers
searched
downloaded
& skimmed
read
in detail
included
in analysis
Source data: http://ertekprojects.com/ftp/supp/14.xlsx

14. Data (shared at ErtekProjects.com)
14Source data: http://ertekprojects.com/ftp/supp/14.xlsx

15. Data
15Source data: http://ertekprojects.com/ftp/supp/14.xlsx

16. Data
16Source data: http://ertekprojects.com/ftp/supp/14.xlsx

17. 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

18. 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

19. Applied Framework
19

20. Results
20

21. Results
21

22. Results
22

23. Results
23

24. Results
24

25. Conclusions (1 of 15)
25
• 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.

26. Conclusions (2 of 15)
26
• Other frequently encountered industries in the papers are
• information and communication
• manufacturing.

27. Conclusions (3 of 15)
27
• 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).

28. Conclusions (4 of 15)
28
• Most frequent objectives are
• cost minimization,
• cost estimation,
• makespan and
• time minimization.

29. Conclusions (5 of 15)
29
• 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.

30. Conclusions (6 of 15)
30
• The most popular software tool is the
• SPSS statistics/data mining software, followed by
• MATLAB and
• WEKA.

31. Conclusions (7 of 15)
31
• An overwhelming percentage (88.8%) of the papers used
data from the real world, which is very favorable.

32. Conclusions (8 of 15)
32
• 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.

33. Conclusions (9 of 15)
33
• 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.

34. Conclusions (10 of 15)
34
• 78.4% of the papers looked into single project data,
• showing a gap, as well as opportunity to
• conduct research on multi-project management.

35. Conclusions (11 of 15)
35
• The data type in the papers was mainly (78%) single project
data, suggesting gap and opportunity to conduct
• more research with multiple-project data.

36. Conclusions (12 of 15)
36
• 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

37. Conclusions (13 of 15)
37
• More research can be done for
• operational-level projects and
• strategic-level projects,
• due to the gap and opportunity on projects at these levels.

38. Conclusions (14 of 15)
38
• 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.

39. Conclusions (15 of 15)
39
• 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.

40. 40

41. 41

42. Acknowledgement
• Data Cleaning & Analysis
• Şevki Murat Ayan
• Onur Aksöyek
• Ece Kurtaraner
• Mete Sevinç
• Byung-Geun Cho
• Research Grant
• Abu Dhabi University
42

43. Thank you. Your Questions?
43