02 Aug IT Project Management and Risk As discussed in the readings, IT projects have notoriously high failure rates. I would like you to discuss issues of IT project risk. Why d
IT Project Management and Risk
As discussed in the readings, IT projects have notoriously high failure rates. I would like you to discuss issues of IT project risk. Why do you think IT projects are so subject to failure? Are they really riskier than other projects? What impact does project risk have on organizational IT value? What can organizations do to manage this risk?
March 2014 (13:1) | MIS Quarterly Executive 15
Many IT Projects Still Suffer From Poor Estimation1,2 Despite a great deal of attention in the trade and academic press, IT projects continue to fail
at an alarmingly high rate. One of the most-cited reasons for these failures is poor estimation practices.
“Unrealistic expectations based on inaccurate estimates are the single largest cause of [IT project] failure.”3
Estimation is defined as an informed assessment of an uncertain event. For IT project managers, accurate estimates are the foundation for effective project planning and execution and, ultimately, project success. Unfortunately, most project managers do a very poor job of estimating and, as a result, most IT projects are classified as failures—61% in the latest Standish Group report.4 The Standish Group’s data shows some improvement in the overall success rate since 2004 (which it partially attributes to the Agile development process and improved project management expertise). However, its figures show a slight increase in both time and cost overruns since 2010—signaling that there is still much room for improvement. According to Standish, 74% of challenged projects experience time overruns and 59%
1 Leslie Willcocks is the accepting senior editor for this article. 2 The authors would like to thank Steve McConnell and Arin Sime for their input throughout this research project. We would also like to acknowledge the McIntire School of Commerce for providing financial support for this research project, thank research assistant Meg Raymond and thank the Project Management Institute for posting a link to our survey. 3 Futrell, R. T., Shafer, D. F. and Shafer, L. I. Quality Software Project Management, Prentice Hall, 2002. 4 The Standish Group: Chaos Manifesto 2013, available at http://versionone.com/assets/img/files/ChaosManifesto2013.pdf.
IT Project Estimation: Contemporary Practices and Management Guidelines Many IT projects continue to suffer from poor estimation. Indeed, the accuracy of estimation has hardly changed from that reported in a seminal study carried out over 20 years ago. Based on findings from two recent survey-based studies, which replicated and then extended the original study, we provide guidelines for improving IT project estimation, taking account of the greater use today of Agile, rather than traditional Waterfall, development methods.1,2
R. Ryan Nelson University of Virginia (U. S.)
Michael G. Morris
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IT Project Estimation
experience cost overruns. Further evidence can be found in a review of 180 IT projects completed between 1999-2013,5 64% of which suffered from poor estimation.
In this article, we examine the practice of IT project estimation, report the findings from two studies and provide recommendations to help project managers improve project estimation.
“An estimate is the most optimistic prediction that has a non-zero probability of coming true … Accepting this definition leads irrevocably toward a method called what’s-the-earliest-date-by-which- you-can’t-prove-you-won’t-be-finished estimating.”6
Why Estimates Matter Accurate estimation is very important to IT
project managers for a wide variety of reasons: ● Avoiding the vicious cycle: poor estimation
results in more schedule pressure, which in turn creates more stress, producing more mistakes, and ultimately more schedule slips, creating even more schedule pressure, and so on.7
● Avoiding the ripple effect: slippage in one project’s completion date can have a ripple effect on other projects and stakeholders throughout the organization. Effective organizations require a portfolio of well- coordinated projects to succeed, and better estimation leads to better coordination with other tasks and projects throughout the portfolio.8
● Avoiding late-in-the-project discovery that the project has been underestimated,
5 See Nelson, R. R. “Project Retrospectives: Evaluating Project Success, Failure, and Everything in Between,” MIS Quarterly Executive (4:3), 2005, pp. 361-372; Nelson, R. R. “IT Project Management: Infamous Failures, Classic Mistakes, and Best Practices,” MIS Quarterly Executive (6:2), 2007, pp. 67-78; and Nelson, R. R. and Jansen, K. J. “Mapping and Managing Momentum in IT Projects,” MIS Quarterly Executive (8:3), 2009, pp. 141-148. 6 DeMarco, T. Controlling Software Projects: Management, Measurement and Estimation, Yourdon Press, 1982. 7 McConnell, S. Software Estimation: Demystifying the Black Art, Microsoft Press, 2006. 8 Boehm, B. Software Engineering Economics, Prentice-Hall, 1981. This book contains a thorough discussion of software estimation and scheduling, which is presented in terms of Boehm’s COCOMO cost- estimation model.
which is difficult, if not impossible to correct.9
● Facilitating better budgeting: accurate budgets depend on accurate estimates of size, effort and time.10
● Evaluating project personnel: compensation is often tied to project performance.
● Generating more credibility for the project team: being part of a team that brings a project in on budget and schedule can do wonders for an individual’s career, not to mention job satisfaction.11
In practice, estimation is both an art and a science. While the art of estimation is closely tied to personal experience, heuristics and common sense, the science of estimation can be based on complex statistical analysis, algorithms, historical data and computer-based tools.
To gain a better understanding of exactly how these various methods are used in practice, we conducted two research studies, focusing on contemporary estimation practices and their linkage to project success. The first study focused on an organizational-level analysis of project estimation. Results from 60 IT professionals suggested that, consistent with responses from a similar study carried out 20 years ago, there is still much room for improvement in how estimates are generated and used within organizations. Moving down a level of analysis, the second study focused on project-level data from 115 IT professionals. It also compared traditional Waterfall and Agile development methods with respect to estimation practices and project success. (The survey methodologies used for each of these studies are described in more detail in the Appendix.)
Research Study 1: Organization-Level Analysis of
Project Estimation In 1992, Lederer and Prasad published the
results of a survey of 115 IT professionals on their
9 Brooks, F. The Mythical Man-Month, Addison-Wesley, 1975. 10 Jones, C. Estimating Software Costs, McGraw-Hill, 1998. 11 DeMarco, T. and Lister, T. Peopleware: Productive Projects and Teams (3rd Edition), Addison-Wesley, 2013.
March 2014 (13:1) | MIS Quarterly Executive 17
IT Project Estimation: Contemporary Practices and Management Guidelines
organizations’ cost estimation practices,12 which we have used as a baseline for our research. That study provided an in-depth understanding of the estimation process in the early 1990s— an era of organizational computing marked by the rapid diffusion of the personal computer, the rise of network computing (before the takeoff of the Internet) and the beginning of the business process reengineering movement. Lederer and Prasad’s research exposed a stark contrast between the perceived importance of accurate project estimation and the state of practice, and they prescribed a set of management guidelines aimed at improving estimation practices.
The primary objective of our first study was to discover how project estimation perceptions and practices have changed over the more than 20 years since Lederer and Prasad’s original study. We modified the survey used by Lederer and Prasad and distributed it to a similar target population.13 The results of our study are compared and contrasted with the Lederer and Prasad findings below.
There is Still a Significant Need for Estimation Improvement
After 20 years, there is still a profound need for improvement in IT project estimation—as highlighted in Table 1. Although the vast majority of our respondents believe that estimation is important (88% described it as “very important”
12 Lederer, A. L. and Prasad, J. “Nine Management Guidelines for Better Cost Estimating,” Communications of the ACM (35:2), 1992, pp. 51-59. This was one of Lederer and Prasad’s original studies on cost estimation, with other informative articles being published over a 10-year period. 13 While the general profile of respondents and companies in our study is similar to the Lederer and Prasad sample, there was a significant difference in industries represented—with their largest percentage of respondents coming from manufacturing (32%), compared with service industries (31%) in our sample.
or “moderately important,” the highest two possible ratings on the five-point scale), the data also suggests that only 31% of projects are completed reasonably close to their estimated time or cost. The good news, however, is that our study shows that both estimation awareness and performance have improved somewhat since the early 1990s.
How Estimates Are Used As stated above, estimates can be important
for a variety of reasons. Table 2 shows that estimation seems to be used more for project planning and control (rows 1 to 5) than for evaluating estimators, developers and others involved in a project (rows 6 to 8). These findings are consistent with Lederer and Prasad’s findings, with the top five remaining the same, albeit in a different rank order.
Our respondents were then asked about their organizations’ most common estimation practices, and the top five responses are listed in Table 3. Interestingly, although 75% of projects use formal estimates, only 54% involved developers in the estimation process—suggesting that 21% of projects do not have any estimation input from the developers even though they use formal estimates. This finding is contrary to the first of Lederer and Prasad’s “Nine Management Guidelines for Better Cost Estimating:” “Assign the initial estimating task to the final developer.” A second Lederer and Prasad guideline is “Monitor the course of a project from the preparation of the estimate through project completion.” This practice was reported for 71% of projects in our study, which is virtually identical to the 70% reported by Lederer and Prasad in 1992.
Table 1: Importance and Accuracy of IT Project Estimation Lederer &
Prasad Study 1
The accurate estimation of IT projects is moderately/very important. 84% 88%
What percentage of all IT projects significantly overrun their estimates? 63% 56%
What percentage of all IT projects significantly underrun their estimates? 14% 13%
What percentage of all IT projects are completed at cost close to estimate. 23% 31%
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IT Project Estimation
The Estimation Process and Causes of Inaccuracies
Respondents were asked about the basis they used for their estimations. As seen in Table 4, they rely most heavily on comparisons to similar, past projects when arriving at their estimates— whether these comparisons are based on personal memory or on documented facts. It is interesting to note that the rank ordering of the bases by our respondents is virtually identical to what Lederer and Prasad found in their research 20 years ago (only items 4 and 5 were reversed in order).
Our survey included a question on peer or team-reviewed estimates, which was not included in the original Lederer and Prasad survey, because this practice is promoted by the use of Agile development. Our respondents reported that this practice was the third-most extensively used basis for estimation.
The final part of our study replicating the Lederer and Prasad study was to ask respondents to identify the factors most responsible for inaccurate estimates—giving them a total of 26 causes to choose from (the top five are listed in Table 5). Again, these findings are remarkably consistent with Lederer & Prasad’s (the notable exception being that “Insufficient user-analyst communication and understanding” dropped from fourth position in their study to ninth in ours).
The clear message continues to be that initial estimates will be inaccurate because of frequent changes by users, lack of users’ understanding of requirements, inadequate task/problem identification and insufficient analysis at the estimation stage. As a result, estimators are encouraged to revise estimates during the course of the project (reported by 63% of our respondent organizations) and to delay committing to a target for as long as possible.
Table 2: How Estimates are Used
Use of Estimate Importance of Use
(1 = Very unimportant; 5 = Very important)
1. To select proposed projects for implementation 4.40
2. To staff projects 4.20
3. To schedule projects 4.17
4. To quote the charges to users for projects 4.04
5. To control or monitor project implementation 3.93
6. To evaluate project estimators 3.26
7. To audit project success 3.33
8. To evaluate project developers 3.13
Table 3: Project Estimation Practices
Top 5 Responses Percentage of IT
1. Formal estimates are prepared 75%
2. Formal monitoring of project progress 71%
3. Estimate revised because requirements change 63%
4. Cost-benefit analysis used to justify project 61%
5. Developers participate in estimation 54%
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IT Project Estimation: Contemporary Practices and Management Guidelines
Research Study 2: Project-Level Analysis of Project Estimation
Study 1 enabled us to gain an overall view of organizational project estimation practices. We wanted to supplement these findings with a more detailed second study of project estimation practices that focused on project-level data. In Study 2, we therefore surveyed 115 IT professionals and asked them to respond with data about their most recently completed IT projects.
Another factor we wanted to explore in Study 2 (based on what we learned in Study 1 as well as input from two software estimation experts consulted throughout the research project) is the
changes in the IT development landscape since Lederer and Prasad’s 1992 study. Specifically, the growth of Agile development has fundamentally altered the way in which projects are conceived and managed. Collecting data at the project level allowed us to learn more about the usefulness and accuracy of project-estimation practices in the context of specific development environments (e.g., Agile vs. traditional Waterfall development), which obviously carries important implications for IT managers today.
As shown in Table 6, we found that traditional Waterfall methodologies (or variants) were still the most widely used. However, Agile methodologies made up a significant proportion
Table 4: Bases of the Estimating Process
Basis Extensiveness of Use
(1 = Not used at all; 5 = Extremely extensive)
1. Comparison to similar, past projects based on personal memory 3.48
2. Comparison to similar, past projects based on documented facts 3.45
3. Peer or team-reviewed estimates 3.08
4. A simple arithmetic formula (such as summing task durations) 2.96
5. Intuition 2.94
6. Guessing 2.76
7. Established standards (such as averages, standard deviations, etc.)
8. An estimation tool (e.g., software package) 1.63
9. A complex statistical formula (such as multiple regression, differential equations, etc.)
Table 5: Causes of Inaccurate Estimates
Causes (Top 5 Out of 26) Extent of Responsibility
(1 = Not resp. at all; 5 = Extremely responsible)
1. Frequent user requests for changes 3.70
2. Users’ lack of understanding of their own requirements 3.54
3. Overlooked tasks 3.35
4. Poor or imprecise problem definition 3.26
5. Insufficient analysis when developing estimate 3.11
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IT Project Estimation
(nearly a third) of all projects in our Study 2 sample. 14
Consistent with the findings of Study 1, the 94 projects in Study 2 for which estimated vs. actual costs were reported tended to come in over budget, although there was also a healthy number of projects on or even under budget in the sample (see Table 7).
Clearly, the 40% of projects reported as over budget indicates that estimation and/ or execution problems persist in IT projects. However, the 60% of projects that are on or under budget is a hopeful sign that the project management discipline is progressing. Moreover, Agile projects seem to be performing better than Waterfall projects in terms of cost performance against budget. Of the 38 (out of 94) projects that were over budget, the average percentage over the original estimate was nearly 73%, with a range between 1% and 900%. For the 29 projects under budget, the range was much smaller, from 2% to 93%, with an average of 17%.
14 Schwaber, K. and Beedle, M. Agile software development with Scrum, Prentice Hall, 2002.
In terms of schedule compared to the original estimate, on average, projects started 25 days late (with none starting early), and finished an average of 56 days late (with a maximum of 3.1 years). Once again, Agile projects tended to start and finish closer to their estimated times. Table 8 summarizes the results for project timelines.
In terms of functionality compared to the original estimate, regardless of start or finish time, 90% of the originally specified requirements were completed.
A Detailed Look at Project-Level Estimation Methods and Practices
Based on the responses in Study 1 and input from our two experts, we constructed a list of commonly accepted estimation best practices and asked respondents in Study 2 to say which of them were followed during their most recently completed IT projects (see Table 9). The good news is that several of these practices are used on projects in the organizations we sampled. Our survey showed that Waterfall and Agile projects employ similar estimation practices, with the exception of preparing formal estimates, which,
Table 6: Development Methodologies Methodology Count Percentage
Waterfall (or variant) 45 47.9%
Agile—Scrum14 11 11.7%
Agile—other 19 20.2%
Other (e.g., ad hoc and hybrid) 19 20.2%
Table 7: Cost Performance Estimates vs. Actuals Total Waterfall Agile
Over budget 40% 47% 33%
Under budget 31% 33% 33%
On budget 29% 20% 33%
Table 8: Schedule Performance Start Time Finish Time
Total WF Agile Total WF Agile
Late 36% 31% 30% 61% 67% 53%
Early 0% 0% 0% 17% 13% 13%
On Time 64% 69% 70% 22% 20% 33%
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IT Project Estimation: Contemporary Practices and Management Guidelines
not surprisingly, is more prevalent in Waterfall projects.
Table 10 lists the 11 most commonly used methods for estimating the costs of respondents’ most recent IT projects.
The rankings in Table 10 compare favorably with the organizational-level data from Study 1 (see Table 4). (Note, however, that due to the specific project focus of Study 2 the question was asked differently.) The top three cost-estimation methods were identical in both studies. Other common—and surprising—findings are the apparently wide use of “guessing” as a common basis of cost estimation (particularly in Agile projects) and the relatively low use of estimation tools to support the process (although the use of tools has increased slightly from the 17%
reported by Lederer and Prasad in 1992). Cost-estimation tools most commonly cited by respondents were Excel, internally developed proprietary tools and Construx Estimate (a freeware tool), although none of these was widely used.
Table 10 indicates some differences in the cost-estimation methods used for Waterfall and Agile projects. Whereas Waterfall projects tend to compare with past projects, use individually prepared and reviewed estimates, and use established organizational standards, Agile projects are more likely to rely on expert judgment, formulas and group-based estimates.
To provide additional detail about the estimation process and bases for estimation, respondents also identified the most common
Table 9: Estimation Best Practices In Use Best Practice Total WF Agile
A formal estimate was prepared 86% 93% 73%
Progress against the estimate was formally monitored throughout the project 80% 82% 80%
The same people who eventually executed the project plan (e.g., developers) also participated in the preparation of the initial estimate 67% 69% 63%
The estimate was revised to accompany changes in user requirements during the project 61% 64% 57%
Project scope was reduced to meet a target completion date 38% 38% 40%
Table 10: Method for Estimating Project Cost Most- to Least-used Method Total WF Agile
Comparison to similar, past projects based on documented facts 59% 67% 43%
Comparison to similar, past projects based on personal memory 58% 58% 57%
Estimates created by individuals and reviewed in a group setting 56% 58% 57%
Expert judgment without use of documented facts 54% 51% 67%
A simple arithmetic formula (such as summing task durations) 52% 47% 57%
Estimates created in a group setting 49% 38% 70%
Estimates created by individuals and reviewed by individuals 45% 51% 37%
Guessing 39% 31% 47%
Established organizational standards (such as averages, standard deviations, etc.) 34% 51% 20%
An estimation tool (e.g., software package) 21% 27% 13%
A complex statistical formula (such as multiple regression) 9% 4% 20%
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IT Project Estimation
methods used to estimate project size and complexity. Table 11 lists the five most common methods used across the large sample of Study 2 projects.
Work breakdown structure is by far the most commonly used method for estimating project size and complexity, which suggests a formality and structure that has developed over time in project management. However, the most surprising finding is the low level of use of lines of code for estimating size and complexity, despite prior studies and popular literature on software estimation reporting a heavy reliance on this method.15 There are two reasons why this method is now less popular than in the past. First, is the type of project typical in today’s IT environments. Increasingly, contemporary projects include a large number of integration or COTS (commercial off-the-shelf ) projects, which often make the use of lines of code challenging or impossible. The other contextual change includes the growth of Agile development methodologies, which often promote the use of relative size
15 See for example McConnell, S., op. cit., 2009 p. 198.
estimates (e.g., story points in Table 11). Our survey results suggest it may be necessary to reconsider and refine techniques promoted in the popular IT development literature concerned with the use (and even relevance of ) lines of code to estimate many of today’s IT projects.
Estimate Creation and Presentation In addition to the methods and practices used
to create IT project estimates, we also wanted to examine who was responsible for creating an estimate, when and how it was created and the form in which it was presented. Table 12 lists the person(s) most responsible for estimate creation.
These findings were as expected and similar for both Waterfall and Agile projects, with the exception that Agile projects were more likely to have the people doing the work (but not the technical lead) responsible for creating the estimates. Estimation experts recommend this as a best practice.
The only surprising result in Table 12 is the relatively large percentage of respondents reporting that someone outside of the project team (i.e., not directly involved with the
Table 11: Methods Used to Estimate Size and Complexity of IT Projects Most- to Least-used Method Total WF Agile
Work breakdown structure 71% 73% 53%
Work drivers (number of interface changes, number of new modules, etc.)
46% 58% 33%
Story points (relative estimate of the size and complexity of work in an Agile project)
32% 16% 67%
Function points (formal count of number of user inputs, outputs, inquiries, files and external interfaces based on IFPUG standards or other industry standard approach)
27% 36% 20%
Lines of code 8% 7% 7%
Table 12: Responsibility for Estimate Creation Person(s) Responsible (Multiple Responses Allowed) Total WF Agile
Technical lead 74% 73% 70%
Person(s) doing the work (not technical lead) 72% 64% 83%
Manager 53% 58% 43%
Independent estimator 14% 16% 13%
Someone else outside of the project team 20% 22% 17%
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IT Project Estimation: Contemporary Practices and Management Guidelines
project), but not an independent estimator, was responsible for creating the estimate. The open-ended comments provided with these responses suggested that people with little formal (or informal) estimation expertise, or even familiarity with the technology, are frequently driving project estimates. Amazingly, one respondent reported that the sales manager was responsible for creating the estimate for her most recent project.
To get a sense for the timing of estimate creation, we asked respondents when the project estimate was created. As expected, by far the most common response was that estimates were created before any work on the project was done (see Table 13). Note that a relatively high number of Agile projects (40%) were still creating estimates through product-concept-completion. Again, this is often viewed as a recommended practice.
Additional qualitative data gathered from respondents suggested that baseline estimates are commonly prepared at the proposal stage and updated as major milestones are achieved. In fact, one respondent indicated that estimates were refined at eight different points during his last IT project. Revisiting estimates throughout a
project is often a recommended best practice, so greater use of this practice could well improve estimation accuracy.
Finally, respondents reported a variety of techniques for presenting estimates to internal management or external clients. Rather than using a monolithic, single-point estimate, our survey indicates wide variation in how estimates are presented, including some differences between Waterfall and Agile projects (see Table 14).
It is surprising (and somewhat encouraging) that the use of a single-point estimate falls in the middle of Table 14, with other more contextually driven factors (ranges, feature lists or confidence factors), exceeding or rivaling the popularity of less flexible, single-point estimates.
To summarize, most of the 115 projects reported on in Study 2 were either late and/or over budget, with Agile projects faring somewhat better in both areas. A possible explanation for the better performance of Agile projects (in terms of estimation) is that they tend to rely more on expert judgment, formulas and group- based estimates. In addition, estimates for the Agile projects in the survey tended to be:
Table 13: Timing for Estimate Creation When Estimate is Created (Multiple Responses Allowed) Total WF Agile
Before any work on the project was done 72% 67% 73%
Requirements complete 46% 42% 30%
Through product-concept-completion 32% 29% 40%
At a defined gate or milestone in a stage-gate process 29% 33% 23%
Table 14: Estimate Presentation Most Common to Least Used (Multiple Responses Allowed) Total WF Agile
Cost or effort range 54% 56% 50%
Schedule range 49% 40% 53%
Feature list (e.g., definitely, probably or maybe) 49% 44% 60%
Single-point number 45% 47% 33%
Confidence factors (probability of delivering on-time and/or on-budget) 33% 36% 27%
Cases (e.g., best, most likely, worst) 29% 33% 17%
Plus-or-minus qualifiers (e.g., 6 months, +3 months, – 2 months) 26% 24% 23%
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IT Project Estimation
● Prepared by people who would also be doing the work
● Baselined at the beginning of the project and updated throughout the project
● Presented in (cost/effort/schedule) ranges rather than as single-point numbers.
Common Estimation Problems In light of the cost, schedule and functionality
estimation challenges reported above, we also asked respondents which estimation-related problems they encountered during their last IT projects. They cited the following (listed in descending order of influence as reported in prior studies):16
1. Insufficient analysis when developing the estimate
2. Lack of adequate methodology/guidelines for estimation
3. Lack of historical data on past estimates and actual results
4. Pressure from managers or other stakeholders to increase or reduce estimates, and over-reliance on a single person’s estimate
5. A lack of visibility or control over actual data compared to estimates.
Although less directly linked to estimation per se, other problems that are either antecedents or consequences of poor estimation practice identified by managers included:
● Frequent user requests for changes ● Users’ lack of understanding of their own
requirements ● Overlooked tasks ● Changing personnel ● Poor or imprecise problem definition. Qualitative data provided by project
managers also suggested that drastic budget cuts by government agencies was a factor in causing projects to miss the original estimates. A representative comment was “The program was tracking in accordance with the estimate
16 The ranked order of 26 proposed causes of inaccurate estimates from Study 1 and Lederer and Prasad (1992).
both in terms of time and budget. The Federal agency simply slashed the budge
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