Relating GIS&T and project management bodies of knowledge to projects perceived as successes.
The most common metric used to assess effective project management is project success or project failure. Certain measures are used for determining failure rates in the oft-cited Chaos Reports of 1995 and 2004 (The Standish Group). Using data collected from surveys, interviews, and focus groups, projects were assigned to three categories based on measures of cost and time overruns, as well as assessments of content deficiencies (The Standish Group 1995). According to the 2004 Chaos Report, 18 percent of projects failed, 53 percent were challenged, and 29 percent succeeded.
These studies have merit for providing straightforward evaluations of project success when detailed data on cost, timing, and scope--the so-called triple constraint of project management--are available. Their focus on project performance in a small number of managerial knowledge areas, however, may oversimplify planning approaches to achieve project success. For example, issues of cost may have arisen because of poor communication practices, which might be remedied in future projects at no additional cost.
When projects include requirements in specific technical areas such as geospatial technology, consideration of project success must encompass both general project management issues as well as domain specific issues. One way to conduct an analysis of successful geotechnical projects would be to consider all areas of knowledge related to geospatial technology and project management simultaneously. Such analysis is facilitated by geospatial technology and general project management both having reference frameworks, the geographic information science and technology (GIS&T) and project management (PM) bodies of knowledge (BoK), respectively.
Although these frameworks are well established as a series of knowledge areas, extensive datasets of geospatial projects are not readily available, and procedures for mapping project components to the BoKs are not well established. This study looks at 101 reports on predominantly geospatial projects written by geospatial industry professionals. Their reports discussed geospatial projects, focused on geospatial and managerial issues that arose, and included their opinions on whether the projects were successful. This study uses these reports to map geospatial components of the projects to the GIS&T BoK knowledge areas, and management issues to PM knowledge areas. This study also offers the author's perception, based on observations in the report and the author's opinion, on each project as successful or failure-prone. The procedure for mapping geospatial components to the GIS&T BoK, managerial issues to the PM BoK, and criteria for judging projects perceived as successful and failure-prone are discussed in the methodology.
Within these frameworks, the overall objective of this study is to determine how the perceived success of geospatial projects is related to both project management issues and geospatial knowledge. The specific objectives of this study are to determine:
1. How frequently projects perceived as failure-prone are associated with geotechnical issues;
2. If projects that integrate more numerous GIS&T knowledge areas are more often perceived as failure-prone;
3. If projects that experience problems in a greater number of PM knowledge areas are more often perceived as failure-prone;
4. If projects experiencing problems in any pair-wise combinations of PM functions (summary categories of PM knowledge areas) are more often perceived as failure-prone; and
5. What types of project management problems might be expected with projects utilizing various GIS&T knowledge areas, and which of these cross-discipline combinations are most often associated with projects perceived as failure-prone.
This analysis extensively utilizes the GIS&T and PM BoKs, two examples of professional fields that recognize the importance of comprehensively inventorying areas of knowledge. BoKs also have been documented in other disciplines, including civil engineering (The Body of Knowledge Committee 2008), software engineering (Abran et al. 2001), software quality measurement (Schneidewind 2002), enterprise architecture (Hagan 2004), configuration management (The Configuration Management Community 2009), and business analysis (Brennan 2000). In addition to serving as inventories of skills and knowledge, these BoKs can be used for endeavors important to the health and development of a profession or organization, including certification, accreditation, strategic planning, and curriculum assessment or development (Prager and Plewe 2009). Following is a brief overview of the GIS&T and PM BoKs.
The GIS&T BoK (DiBiase et al. 2007) is organized in a strongly hierarchical fashion. At the highest level are the ten knowledge areas listed with two-letter abbreviations that follow:
* Analytical Methods (AM)
* Conceptual Foundations (CF)
* Cartography and Visualization (CV)
* Design Aspects (DA)
* Data Modeling (DM)
* Data Manipulation (DN)
* Geocomputation (GC)
* Geospatial Data (GD)
* GIS&T and Society (GS)
* Organizational and Institutional Aspects (OI)
At the level beneath are a total of 73 units, with the number of units per knowledge area varying from three to 12. In this study, components of projects were mapped to the unit level. The level beneath units includes the most detailed topics, with the number of topics per unit varying from two to nine.
The PM BoK does not have so detailed a hierarchy, but it does organize knowledge areas at a higher level into three categories called functions (Project Management Institute 2004). These functions are core, facilitating, and integrative. The latter consists of only one knowledge area (project integration management) that integrates managerial components from all other PM knowledge areas. The functions and their underlying knowledge areas * are listed as follows:
* Core functions
* Facilitating functions
** Human resources
* Integrative functions
** Project integration
Beneath the knowledge area level, there is no more detailed structural breakdown. Instead, these knowledge areas are discussed in terms of specific tools, techniques, methodologies, and best practices that may be utilized to ensure project success (Project Management Institute 2004, Schwalbe 2009). Many project management studies focus on how to best avoid issues in one or more knowledge area, such as quality (Futrell et al. 2001, Crosby 1979) or risk (Raz and Michael 2001, Wideman 1992).
The methodology for analyzing project reports involved the following four components. First, geospatial reports were collected over a span of three years. Second, each project was categorized as either being perceived as successful or failure-prone. Third, components of each project were mapped to units of the GIS&T BoK. Finally, issues identified in each project were mapped to knowledge areas in the PM or GIS&T BoK, depending on whether the nature of the issue was managerial or geotechnical.
GEOSPATIAL PROJECT REPORTS
The data for this study are 101 project reports, varying in length from three pages to five pages. The authors of these reports generally were full-time workers in geospatial technology and part-time graduate students beginning a geospatial technology project management class in a professional Master's of GIS degree program. These reports were collected and evaluated by this author, while serving as the course instructor, over a period of nine terms during three years.
Reports were designed to allow students to reflect on their perception of a project in which they participated, before a more formal survey of the field of geospatial project management. Specific instructions for writing a portion of this report are given as follows:
Document a project, preferably a geospatial project from your organization. In documenting the project, include information that you perceive as important to understanding how the project progressed from a geotechnical and managerial perspective. You may include information on cost, timing, scope, quality, or other aspects you think were key. You also should make a determination of whether the project was a success or a failure. You should describe the project in your own words, but indicate the source of your information.
PERCEPTION OF PROJECT SUCCESS
This author/class instructor, taking the opinions and supporting evidence of the students into account, made a determination of projects he perceived as probable successes. It is important to stress that the author has no additional information other than that supplied by the students, so in nearly all cases a traditional declaration of project success was difficult or impossible to make. Instead, this author categorized all projects into two nominal classes. The first class consists of projects perceived as failure-prone, which includes those projects that students perceived as being failures, with significant pitfalls and uncertain outcomes. Projects with significant pitfalls, although sometimes deemed successful by students, generally had such severe issues that their scope or quality seemed seriously compromised. Projects of uncertain success generally were so poorly scoped that the student and/or instructor could not evaluate whether the project objectives were met.
The second class consists of projects perceived as successful. It includes all 78 projects perceived by students as successful. Twenty of these projects included metrics that, if reported properly by students, indicate success in terms of meeting project objectives on schedule and budget. The remaining 58, although lacking such evidence, did not include any elements such as cost overruns, missed deadlines, or failure to meet project objectives that would explicitly indicate failure. The two pending projects are not included in the analysis.
PROJECT COMPONENTS AND THE GIS&T BOK
This author/instructor examined geospatial project reports and identified all components of projects that corresponded to a GIS&T unit and were utilized to meet the project's geotechnical needs. Geospatial projects may use specialized knowledge from a combination of any or all knowledge areas, or might require expertise from only one specific topic of one particular unit of a single knowledge area. For example, a project to develop a "custom tool to map attributes of residential meters" involved a design aspect (DA) to design the tool, a data manipulation (DN) component to put attributes in the proper format, and a GIS&T and Society (GS) component to provide information to the customers.
In their reports, students were not required to discuss how components of their projects fit into the GIS&T BoK. Instead, this author/instructor reviewed all reports and mapped student discussion to the BoK. Students described technical components of projects in sufficient detail for the author/instructor to identify specific "units" of GIS&T knowledge areas utilized, with units often but not always occurring in different knowledge areas. Any uncertainty of mapping to specific units should be mitigated by analysis for this study being conducted at the higher knowledge area level.
RELATING ISSUES TO THE PM BOK
Most project reports included discussions of some issues or problems that arose during the projects. Some were geospatial technology issues, but the vast majority were managerial issues. Based on the report's description of the issue, this author nominally mapped each issue to one of the PM knowledge areas. In some cases, such as a project's duration taking much longer than proposed, the choice of knowledge area (Time) was straightforward, given the information provided. When possible, the author attempted to look at causality and be as consistent as possible with the information provided. For example, a team lacking some of the geotechnical skills necessary to complete a project may face issues of meeting deadlines (Time), staying on budget (Cost), or meeting requirements (Scope). The author, however, mapped this issue to the "Human Resources" PM knowledge area, as an appropriately skilled team member or technical training could eliminate this issue.
DATA COMPILATION AND DISPLAY
Data on perceived success, PM knowledge areas in which issues arose, and GIS&T knowledge areas to which project components correspond were collected in a summary table and used to create the graphs in the Results section. The summary table also allowed for creation of a display unique to this study and referred to as a knowledge matrix.
A knowledge matrix considers pair-wise combinations of knowledge areas from the two BoKs, mapping problems in PM knowledge areas to all the GIS&T knowledge areas that these projects contain. These cross-pairings do not consider whether a particular PM issue arose because of efforts in one GIS&T knowledge area or another in the project, and in most cases such causality was impossible to determine. Thus, a project utilizing three GIS&T knowledge areas and having problems arise in three PM knowledge areas would be mapped to nine separate cross-pairings represented by grid cells in the knowledge matrix.
Given ten GIS&T knowledge areas that could represent components of geospatial projects and nine PM knowledge areas were potential problems could arise, a maximum of 90 grid cells is possible between the two BoKs. In this study, projects included technical components from only eight of the GIS&T knowledge areas (none from Geocomputation (GC) or Conceptual Foundations (CF)). Additionally, some GIS&T knowledge areas were never associated with problems in particular PM knowledge areas. As a result, this study mapped 262 cross-pairings to a total of 59 grid cells in the matrix.
A table summarizing the analysis and listing report names, edited to ensure anonymity, is included in the Appendix. This table was constructed to address all the objectives outlined in the introduction of this study. Each row represents a project. The table includes three columns to account for the maximum number of managerial problems reported (PM1 to PM3), and five columns for the maximum number of GIS&T components integrated into a project (GIST1 to GIST5). This format allows projects to be categorized and evaluated on perceived success, the number or category of PM knowledge areas in which issues were discussed, and the number or category of GIS&T knowledge areas included as components in the project.
Of the 101 reports, 78 percent were perceived to be successful, 20 percent were perceived to be failure-prone, and 2 percent were pending. This degree of perceived project failure is similar to failures found in the CHAOS Report (2004), which averaged 18 percent of projects studied. Of the 20 failure-prone projects, 13 included serious issues with cost, time, or scope. Such issues often are interrelated and known to make project success unattainable. Of the remaining seven, four had critical issues with communication among partners, clients, or workers and management. The other three had issues with integration that in two cases arose from personnel turnover or reassignment.
One result of this study is that the reports are much more likely to discuss project management issues rather than geotechnical issues. While 80 percent of the reports discussed at least one management issue, only 9 percent reported technical issues worthy of discussion. Of those reporting technical issues, however, six of nine were associated with projects perceived as failure-prone. The common thread among most of these technical failures was that the project was a first or early attempt to use a particular technology within the organization.
In addition to technology failures, this study examined the degree to which technical knowledge from multiple GIS&T knowledge areas is perceived as being effectively integrated into a project. The bar graph in Figure 1 shows an average of 2.3 GIS&T knowledge areas are incorporated into the projects of this study, with a range from zero to five. Also of note is that 80 percent of geospatial projects technically integrate two or more units from different knowledge areas of the GIS&T BoK to achieve desired objectives. Three nonspatial projects were able to be classified in the GIS&T BoK because of the overlap of GIS&T with related fields such as information technology, while only two projects were not able to be assigned to at least one knowledge area.
The points displayed as diamonds in Figure 1 shows the percentage of projects perceived as failure-prone that occurred for projects containing various numbers of GIS&T knowledge areas. The trend indicates that projects that combine more numerous GIS&T knowledge areas are associated less often with failure-prone projects. There is also a minor increase in the average number (X) of GIS&T knowledge areas discussed in successful projects (X = 2.2) as compared to failure-prone projects (X = 2.0), but this difference is not significant based on the Mann-Whitney rank sum test at P = < 0.05.
The most frequently discussed GIS&T knowledge areas are likely a reflection of the interests of and type of work performed by geospatial professionals/students in this particular Master's program. The first two most frequently cited knowledge areas in this study are Geospatial Data (GD) and Design Aspects (DA), accounting for 50 percent of GIS&T units cited in the reports. Including the next three most common knowledge areas, GIS&T and Society (GS), Organizational and Institutional Aspects (OI), and Analytical Methods (AM), accounts for more than 90 percent.
This study also looks at the number of issues that arise in various PM knowledge areas. The bar graph in Figure 2 indicates that problems occurred in an average of 1.2 PM knowledge areas per project, with a range from zero (no problems discussed regarding a successful project) to three (three different issues related to three unique project management knowledge areas). The points in Figure 2 do not follow a linear trend, but show that projects reporting issues in more than one PM knowledge areas are more often perceived as failure-prone, as might be expected. This also is apparent in the large disparity between the average number (X) of knowledge areas experiencing problems discussed in reports on successful projects (X = 1.1) and reports on failure-prone projects (X = 2.0). This is a significant difference based on the Mann-Whitney rank sum test at P = < 0.001.
A problem in only one PM BoK knowledge area does not often lead to failure, but these occurrences are worth noting. Of the six failure-prone projects with one management problem, four proved to be communication issues. Specifically, failure to establish or utilize key communication channels among coworkers, managers, clients, etc., in the planning and implementation phases of the project life cycle often resulted in projects perceived as failure-prone. This can be compared with a dozen projects perceived as successful that experienced some communication problem. In all of these cases, the proper channels of communication were properly established, but unclear communication led to less severe problems.
In most cases, projects perceived as failure-prone had issues in more than one knowledge area of the PM BoK (Figure 2). To investigate, this study evaluated pair-wise combinations of PM knowledge areas when more that one problem occurred in more than one area. These are generalized to the function level of the PM BoK to provide a more general summary of results.
Figure 3 shows the project management "functions" associated with pair-wise combinations of issues in PM knowledge areas for projects perceived as successful and failure-prone. The bar graph shows the number and breakdown of projects perceived as successful and failure-prone. The points show the percentage of projects perceived as failure-prone. The detrimental nature of issues that arise in core or integrative knowledge areas is apparent. Any issue in a core or integrative knowledge area occurring in conjunction with a problem in any other knowledge area results in a perceived failure-prone rate of at least 50 percent. This contrasts sharply with projects that have problems within Facilitating-Facilitating functions. None of these projects were considered failure-prone.
Cross-Discipline Knowledge Matrices
Each problem that arises in a specific PM knowledge area can be associated with at least one specific GIS&T knowledge area for the projects in this study. By looking at these cross-discipline pair-wise relationships and mapping results to a knowledge matrix, it is possible to see the type of managerial problems most likely to arise for projects that contain geotechnical components in particular GIS&T knowledge areas. Generalizing the PM knowledge areas to the function level aggregates greater numbers of problems into fewer categories. It also indicates whether projects utilizing particular GIS&T knowledge areas are more often perceived as failure-prone if issues arise in core versus facilitating PM functions.
The top row of Figure 4 shows the type of GIS&T knowledge areas utilized in the projects that reported no managerial problems. Summing this row, there were 39 geospatial components discussed in these 20 problem-free projects.
The remainder of Figure 4 represents a knowledge matrix with problems that arose in particular PM knowledge areas mapped to all GIS&T knowledge areas utilized in projects perceived as both successful and failure-prone. Numbers indicate the total number of problems discussed for each combination. At this granular level of inquiry, where more than one-third (31 of 72) of the cells contain zero, one, or two problems, and only six cells have values of ten or more, the percentage of problems associated with projects perceived as successful versus failure-prone is not shown.
The rows are PM knowledge areas and are grouped by the core, integrative, and facilitating functions from top to bottom. The columns are the GIS&T knowledge areas and are ordered by the sum of each column in the matrix proper. Moving from left to right, these sums vary from 65 to 5. This wide disparity of problems is more associated with how many projects reported on particular GIS&T knowledge areas being utilized (and the career interests of the geospatial professionals writing the reports), and should not be taken as an indication that projects that incorporate certain knowledge areas are more or less likely to be problem-free. Nevertheless, variations in values found in columns of the matrix reveal where problems are most and least likely to arise.
Focusing on the PK knowledge areas in which problems occur most frequently, the facilitating knowledge areas of communication and human resources always rank first and second for the four most discussed GIS&T knowledge areas (the four left columns of Figure 4). Project managers utilizing these GIS&T knowledge areas should expect such problems outside of any discussion of perceived project failure and success.
Table 1 summarizes the data in Figure 4 to the function level of the PM BoK in its rows entitled Total. Because the integrative function includes one knowledge area (Integration), while core and facilitating functions each contain four, the former was omitted from Table 1. The subsequent rows report on how many problems in each grouping were associated with projects perceived as failure-prone, and the final rows report percentage of projects failure-prone.
This study is not designed to assign causes of failure to projects perceived as failure-prone. It does, however, reveal trends in the type of GIS&T project components and the problems that arise in PM knowledge areas that are most frequently associated with projects perceived as failure-prone by geospatial professionals. Project managers can use these results to help plan geospatial projects or to track important project metrics once the project is initiated.
Project managers are served well by understanding geospatial technology and its integration. Most project reports in this study (91 percent) do not include a discussion of technological problems. Also, projects that incorporate more GIS&T knowledge areas are less often considered failure-prone (Figure 1). Both of these observations reflect well on the technical maturity of the geospatial industry and its workforce and their abilities to integrate disparate GIS&T knowledge areas from a technology standpoint.
Exploring the reasons that projects that incorporate more numerous GIS&T bodies of knowledge have lower failure rates is beyond the scope of this study, but it may simply be a reflection of the necessary geotechnical complexity of achieving desirable project objectives. It also may be the result of projects with a geospatial technology focus requiring more experienced geospatial professionals. A detailed look at the dozen projects utilizing four or five GIS&T knowledge areas indicates that contributions from all GIS&T knowledge areas cited seem essential to achieving the desired objectives.
A geospatial manager should expect managerial problems to arise, as 80 percent of the projects in this study reported some such problems. If the first problem to arise is a communication problem, it is essential that the manager ensure that all proper channels of communication have been well established. For example, a "kickoff meeting" that includes all important team members and stakeholders makes these channels of communication apparent and personal, and can help to avoid this potential problem (Kerzner 2007).
If a geospatial project has more than one managerial issue, it is important for the project manager to recognize the PM functions in which these problems occur. If one of the problems is in a core function, this study would indicate that the project has only about a 50 percent chance of being perceived as successful (Figure 2). One important observation difficult to extract from this study is the level of severity of the problems, especially in the core functions. This author observed, however, that in some reports, steps were taken early to mitigate the effect of issues with scope, timing, cost, or quality, tending to make projects more often perceived as successful. Such early recognition underscores the importance of project management tracking tools such as earned value management (Fleming and Koppelman 2000, 2002) to recognize and correct such problems at an early stage in the project.
Although issues in core functions are critical in projects with more than one problem, project managers cannot afford to ignore the importance of integration management. Integration management is defined as coordinating all other PK knowledge areas throughout the project's life cycle. It includes six main processes: developing the project charter, developing the project management plan, directing and managing project execution, monitoring and controlling project work, performing integrated change control, and closing the project (PMBOK Guide 2009). For geospatial professionals taking on new project management roles, some of these processes may be less familiar or require more specialized knowledge than do the core functions of scoping a project, tracking a budget, or staying on schedule. Effective mastery of these integrative processes may require training in project management or guidance from more experienced project managers.
Although all these integrative processes are well defined from the project management perspective, what is less well defined is how a geospatial manager can bring his or her knowledge of the technology to bear on these critical processes, such as when developing the project management plan. Although a work breakdown structure (WBS) associated with these plans can be created with readily accessible software and a somewhat formulaic approach of breaking down objectives to summary tasks and then to smaller tasks (Project Management Institute 2006), it would be difficult or impossible for a geospatial project manager to create such a WBS without a working understanding of all the technical requirements of such projects.
Communication and human resources issues seem especially prevalent facilitating problems. For communication issues, problems with communication channels never being established often lead to projects perceived as failure-prone, but frequent issues associated with miscommunications are not uncommon in projects perceived as successful. In human resources management, changes in personnel often are associated with projects perceived as failure-prone, while issues associated with training, time off, or team conflicts often are more minor issues that frequently occur in projects perceived as successful.
Further trends are revealed in the cross-discipline knowledge matrix of Figure 4 and its summary in Table 1. The two most frequently cited GIS&T knowledge areas, Geospatial Data (GD) and Design Aspects (DA), are approximately twice as likely to be considered failure-prone from problems arising in the core versus facilitating knowledge areas, even though more problems are reported in the facilitating areas. Such projects reported in this study tended to be "technocentric"; they focused on the technology, were worked by a team familiar with geospatial technology, and had fairly limited requirements for interpersonal interactions outside of the team. Facilitating issues, especially in communication and human resources, will continue to arise, but seem more easily resolved by the teams in this study's projects, which often were interacting collaboratively on a daily basis.
These results can be compared to the next two most frequently cited GIS&T knowledge areas, GIS&T and Society (GS) and Organizational and Institutional Aspects (OI). Projects that include such components are just as likely to be perceived failure-prone from issues that arise in facilitating or core PM knowledge areas. These projects tended to more "people-centric," focused on the ways in which organizations, partners, or the public either accesses or interacts with geospatial technology. Some steps that could have mitigated problems in projects from this study include identifying champions or sponsors, communicating with important stakeholders, and a more thorough understanding or appreciation of the organizational culture (Keen 1981).
Fewer examples of problems were reported for other GIS&T knowledge area columns in Figure 4, making subsequent percentages in Table 1 more difficult to interpret. One number worth noting, however, was that for projects with a Data Modeling component that experienced problems in the core PM functions, 73 percent (8 of 11) were perceived as failure-prone. Such projects were generally "technocentric," and often represented a first foray of an organization into a project requiring knowledge from highly technical units of this knowledge area.
To be able to utilize the knowledge matrix presented as Figure 4 to report on projects perceived as successful or failure-prone at the knowledge area level would require GIS&T projects utilizing a greater variety of knowledge areas and a much greater number of examples. With such data, each cell could show the percentage of projects perceived as failure-prone. Although currently some cells are associated with just a few projects, they still provide some glimpse into concerns with specific combinations of GIS&T knowledge area project components and PM knowledge area issues.
For example, project managers must understand the risk associated with a project, and certain GIS&T knowledge areas may be more risk-prone. Although the sum of the risk row (16) in Figure 4 is the lowest of any, one GIS&T knowledge area stands out as being associated with failure-prone projects. Four projects using Analytical Methods reported problems with risk, and three (75 percent) were associated with projects perceived as failure-prone. Looking at the next unit level of the GIS&T Bok, these projects included elements of Analysis of Surfaces, Data Mining, and Network Analysis. In each case, the risk was recognized in the planning stage and the project represented the first attempt of an organization to take on a project requiring knowledge from these highly technical units.
Quality is another important knowledge area for project managers, but one that was not commonly discussed in the reports included in this study. This could indicate that geospatial project managers have a good handle on quality planning, assurance, and control. Alternatively, it could mean that quality management is not frequently utilized or recognized by geospatial professionals in projects and presents an opportunity for obtaining a competitive edge from a management perspective. Of the eight reports identifying issues in quality, only two indicated that quality was considered during early stages (i.e., planning) of the project life cycle. In these two cases, the authors specifically discussed their company's quality assurance/quality control plans. In another project without quality problems, the author specifically referenced quality standards, in this case the International Organization for Standardization quality management system used by his organization (ISO 9001: 2,000).
This study was designed to be different from previous studies such as the Chaos Report (2004) that measure project success and failure, and thus has serious caveats but interesting potential. While project success studies typically rely on quantitative measures of success, this study focuses more on the subjective perception of geospatial professionals. Although not so easy to quantify, such data can be as easy to collect as a manager asking the team how a project is progressing. For such a conversation to be effective, however, all parties should have some understanding of how critical work functions, both technical and managerial, are necessary for success.
These strategies are greatly facilitated by the recent efforts of the geospatial industry in association with the U.S. Department of Labor Employment and Training Association (DOLETA) in creating the Geospatial Technology Competency Model (http://www. careeronestop.org/Competency Model/pyramid.aspx?GEO=Y). A competency model is defined as "a collection of multiple competencies that together define successful performance in a defined work setting," and a competency is "the capability to apply or use a set of related knowledge, skills, and abilities required to successfully perform 'critical work functions' or tasks in a defined work setting." (Ennis 2008) Associated with "critical work functions" are "technical content areas," the background knowledge on which skills and abilities are based. The GIS&T BoK serves as the basis for these technical content areas in the industrywide and industry-sector tiers (4 and 5, respectively).
A key component left undefined by any industry until recently has been a management competency model, the uppermost tier (9) of the competency model. In 2012, the geospatial industry became the first to specify a management model, the Geospatial Management Competency Model (GMCM) that can be found at http://www.urisa.org/gmcm. DOLETA incorporated URISA's GMCM into its Competency Model Clearinghouse after a rigorous process of drafting, public review, and approval. The GMCM is designed to define this critical interface between geospatial management and technology.
Although the design of the GTCM is focused more on valued knowledge, skills, and abilities that will assist workers on a career path within the geospatial or other related industries, its use as a framework within which project success can be monitored appears promising. Competencies indicate that all critical work functions have been successfully performed. If this can be accomplished from both geotechnical and managerial perspectives, the likelihood of overall project success seems improved.
Perceived project success requires considering both geospatial knowledge and project management issues. It is helpful to think of these within some type of framework, such as those offered by the GIS&T and PM bodies of knowledge, respectively. The former identifies knowledge areas of geospatial technology, any number of which may be necessary to achieve project objectives. The latter includes knowledge areas of project management, all of which should be addressed in planning and closely monitored for issues that might lead to projects perceived as failure-prone. With technical components and managerial issues thus classified, this study was able to achieve the objectives listed in the introductory section, with key outcomes of objectives summarized in the following section.
Geotechnical problems are not the most frequent type of problems to arise in geotechnical projects. Only 9 percent of projects in this study reported geotechnical issues, and these were generally the first foray of an organization into a project involving a new or different technology. A majority of these projects were perceived as failure-prone.
Projects that integrate more numerous GIS&T knowledge areas show a trend of being less often perceived as failure-prone. This seems to reflect well on the technical maturity of the geospatial industry. Beyond the GIS&T BoK, the establishment of the Geospatial Technology Competency Model with the U.S. Department of Labor Employment and Training Association (DOLETA) (http://www.careeronestop.org/CompetencyModel/ pyramid.aspx?GEO=Y) recognizes geotechnical competencies both industrywide and for industry sectors. All this indicates a high level of coordination among geospatial professionals working throughout the industry. It also could act as a road map for managers with little or no experience in the geospatial industry to pursue cross-training within specific knowledge areas.
In a similar manner, this study shows the benefits of training geospatial professionals in project management. This training seems critical, for projects that experience problems in a greater number of PM knowledge areas are more often perceived as failure-prone. Specifically, problems that occur in core PM knowledge areas are frequent and lead to projects perceived as failure-prone.
The importance of comprehensive and integrative training of geospatial professionals in project management is apparent in the frequent occurrence of problems that arise in project integration management. With such an issue, along with another problem in the core or facilitating functions, the project was considered failure-prone more than 50 percent of the time. This indicates a geospatial project manager must move beyond geotechnical understanding and the ability to scope, schedule, and budget a quality project. He or she must additionally be adept at the following types of managerial tasks:
* Develop a project charter,
* Develop a project management plan,
* Direct and manage project execution,
* Monitor and control project work,
* Perform integrated change control, and
* Close the project.
This study also found that certain GIS&T knowledge areas experienced specific types of managerial problems more often, and that certain types of managerial problems were more often associated with projects perceived as failure-prone. Managerial problems that most commonly arise are in communications and human resources. In projects with a "data-centric" focus, those being worked by expert teams to solve a technical problem or implement a technology for themselves or their clients, projects most often perceived as failure-prone have problems arise in the core PM functions. This is contrasted with projects with a "people-centric" component, those requiring buy-in or consensus from groups outside of the technical team, such as a larger organization or the public. These projects are perceived as failure-prone equally often when problems arise in the facilitating or core PM functions.
This study sees merit in the Geospatial Management Competency Model, http://www.urisa.org/files/GMCM%20final. pdf, tier 9 of the Geospatial Technology Competency Model. As a competency is defined as "the capability to apply or use a set of related knowledge, skills, and abilities required to successfully perform 'critical work functions' or tasks" (Ennis 2008), projects completed with competence in geospatial technology and management seem apt to be successful.
With the geospatial industry recognized as a high growth sector by the U.S. Department of Labor, demand for geospatial managers is likely to increase. The GTCM and its associated GMCM can help to identify individuals with competencies in both geospatial technology and management. These workers and studies such as this could serve as a guide for helping individuals to understand how the components of geospatial technology and management are inextricably interwoven, how they can be evaluated, and how methods can be advanced to address issues most often associated with projects being perceived failure-prone.
List of geospatial projects, their perceived success, the project management body of knowledge (PM BoK) knowledge areas in which problems arose (PM1-PM3), and the geographic information science and technology body of knowledge (GIS&T BoK) knowledge areas with which the technical components of projects were associated (GIST1-GIST5).
Project Perception PM1 PM2 PM3 Countywide basemap Failure-prone Time Cost Risk creation Water utility Failure-prone Scope Comm Integ geodatabase from CAD drawings Tracking forms Failure-prone Scope Time Cost Software tool to manage Failure-prone Scope Procure Cost inventory and customer orders Inventory of properties Failure-prone Time Integ Comm Statewide broadband gap Failure-prone Risk Time project Measure spatial Failure-prone Risk Time segregation for race groups in urban areas Tesselation of lidar Failure-prone Time Scope data Road sign inventory Failure-prone Scope Qual Top ten technical system Failure-prone HR Integ issues ad-dressed Develop sanitary/storm Failure-prone Time HR sewer network for city Electronic zoning map Failure-prone Integ HR for city's Web site Migrate data library Failure-prone Comm Cost into geodatabase Development of GIS for Failure-prone Scope Comm local municipality Public health portal Failure-prone Time Digitizing paper maps Failure-prone Integ Routes and matrices from Failure-prone Comm onscreen digitization Permits at lakeside Failure-prone Comm recreation area Digitizing and analyzing Failure-prone Comm county datasets Digitizing a zone map Failure-prone Comm Bald eagle electrocution Successful Risk Cost Time risk model Developing a countywide Successful Integ HR Qual GIS Analyze and integrate Successful Cost Time Procure geospatial information Develop a GIS layer with Successful Cost HR Procure trails and invasive species Streams dataset Successful Cost HR Procure Tools for visualization, Successful Scope Procure HR analysis, and decision making Opening new drop zones Successful Scope Qual Develop a new version of Successful Cost Qual existing software Nationwide mapping Successful Scope Integ project Digitizing land parcels Successful HR Integ E-map book product Successful Comm Integ interface Comprehensive forest Successful Procure HR inventory Trail mapping study Successful Procure HR Map, track, and manage Successful Comm HR utility lines Sugarcane farming study Successful Scope Cost Creation of DFIRM maps Successful Risk Cost Electronic data Successful Procure Comm conversion Watershed-based water Successful HR Comm quality study Utilities GIS Successful Time development Workflow streamlining Successful Time for property appraisal Fleet management and Time tracking software Successful Central repository for Successful Scope spatial data Remediation of old Successful Scope military ordinance Restructuring of an Successful Scope organization, systems architecture, and data Stream monitoring for Successful Scope coal mining FEMA hazards mitigation Successful Scope planning Facility spill analysis Successful Scope program Custom tool to map Successful Risk attributes of residential meters Mapping invasive species Successful Risk Web-based app for Successful Risk search/rescue/ security Creating GIS layer of Successful Qual trail network Digitizing historic maps Successful Qual in raster/vector format Automation of existing Successful Qual hyperspectral imagery analysis algorithms Adjusting annotation of Successful Qual parcels Tracking gang activity Successful Procure geospatially Data conversion/viewer Successful Procure project Aerial photo Successful Procure interpretation and GIS Groundwater data portal Successful Procure with open source software Special needs survey Successful Procure application Spatial videorecording Successful Procure device Geospatial data for Successful Integ continentwide biodiversity study Using ArcIMS for map Successful Integ distribution and cartographic needs Creating a network of Successful Integ GPS stations for better mapping control Orthoimagery acquisition Successful Integ and coordinating needs of multiple organizations Creek restoration Successful Integ project Determining tree crown Successful HR area on forested acres Providing internet Successful HR access to GIS data Locating recruits for Successful HR biobank Determining GIS format Successful HR for weather hazards Countywide DFIRM study Successful Cost Creating a bike and Successful Cost trail guide 3-d Pipeline distance Successful Cost calculation 911 Commication Successful Comm distribution Viewers for data access Successful Comm Internet-mediated Successful Comm commity of practice tool for data access Integrating multiple Successful Comm geospatial systems Designing an enterprise Successful Comm GIS Allowing public to view Successful Comm and download hydrographic surveys Biannual election Successful Comm results collection and analysis Digitizing and analyzing Comm county datasets Successful Facility management N/A system Security for Olympics Successful N/A games Creating a base map from Successful N/A aerial imagery Standardizing data Successful N/A storage for multiple projects Personnel deployment Successful N/A Preliminary integrated Successful N/A geologic map database Converting to a Successful N/A GIS-based tropical cyclone daily warning system Finding unexploded Successful N/A ordinances Developing GIS data and Successful N/A creating print maps Acquiring true color Successful N/A orthoimagery National realignment to Successful N/A build balanced sales territories Migrating pipeline data Successful N/A into geodatabase Natural gas resource Successful N/A mapping Viewshed modeling Successful N/A Locating satellite Successful N/A offices based on workload data Metadata creation Successful N/A Deriving features with Successful N/A imagery analysis Everglades restoration Successful N/A study Efficient follow-up Successful N/A testing based on routing Creating photorealistic Pending HR Procur Cost buildings for visualization Identifying desirable Pending N/A land tracts for Project GIST1 GIST2 GIST3 GIST4 GIST5 Countywide basemap GD AM creation Water utility DN GS geodatabase from CAD drawings Tracking forms DM Software tool to manage DA DM inventory and customer orders Inventory of properties DA GS IO Statewide broadband gap AM project Measure spatial AM GS DA segregation for race groups in urban areas Tesselation of lidar DM data Road sign inventory DA GD Top ten technical system OI issues ad-dressed Develop sanitary/storm DA GD sewer network for city Electronic zoning map GS IO DA for city's Web site Migrate data library into geodatabase Development of GIS for DN GD DA GS local municipality Public health portal DM GS Digitizing paper maps GD DA Routes and matrices from AM GD onscreen digitization Permits at lakeside DA GS recreation area Digitizing and analyzing GD county datasets Digitizing a zone map GD GS Bald eagle electrocution AM risk model Developing a countywide GS OI DA GIS Analyze and integrate AM AM DA geospatial information Develop a GIS layer with GD GD GS OI trails and invasive species Streams dataset AM GD GD Tools for visualization, DA CV AM analysis, and decision making Opening new drop zones AM Develop a new version of DA DA existing software Nationwide mapping DA DM project Digitizing land parcels GD E-map book product DA OI interface Comprehensive forest GD GD DA inventory Trail mapping study GD GS Map, track, and manage GD DA OI CV DA utility lines Sugarcane farming study GS GD DA Creation of DFIRM maps GS OI GD Electronic data DA DN conversion Watershed-based water OI GS GD quality study Utilities GIS DA DA DA development Workflow streamlining DA GS for property appraisal Fleet management and DA DA tracking software Successful Central repository for OI OI DA DA spatial data Remediation of old GD GS DA military ordinance Restructuring of an IO IO organization, systems architecture, and data Stream monitoring for coal mining FEMA hazards mitigation GS AM planning Facility spill analysis AM program Custom tool to map GS DA DN attributes of residential meters Mapping invasive species DA OI Web-based app for DA search/rescue/ security Creating GIS layer of GD GD GD trail network Digitizing historic maps GD GD in raster/vector format Automation of existing OI DA hyperspectral imagery analysis algorithms Adjusting annotation of CV parcels Tracking gang activity GD DA GS IO geospatially Data conversion/viewer GD GD DN DA project Aerial photo GD OI interpretation and GIS Groundwater data portal GS OI with open source software Special needs survey DA application Spatial videorecording GD device Geospatial data for DA IO GS GD GD continentwide biodiversity study Using ArcIMS for map IO IO DA CV distribution and cartographic needs Creating a network of GD DG GS GPS stations for better mapping control Orthoimagery acquisition GD OI and coordinating needs of multiple organizations Creek restoration GD GD project Determining tree crown GD GD AM area on forested acres Providing internet DA GS access to GIS data Locating recruits for NA biobank Determining GIS format GS for weather hazards Countywide DFIRM study AM GD GD GS Creating a bike and GD GD CV trail guide 3-d Pipeline distance AM GD calculation 911 Commication GD DA OI GS distribution Viewers for data access DA AM GS Internet-mediated GD DA DA commity of practice tool for data access Integrating multiple OI OI geospatial systems Designing an enterprise GS GIS Allowing public to view GS DA and download hydrographic surveys Biannual election NA results collection and analysis Digitizing and analyzing GD county datasets Successful Facility management GD DM DA GD system Security for Olympics GD GD OI games Creating a base map from GD GD DN aerial imagery Standardizing data GD OI DA storage for multiple projects Personnel deployment DA CV Preliminary integrated GS OI geologic map database Converting to a DA GS GIS-based tropical cyclone daily warning system Finding unexploded GD GD ordinances Developing GIS data and GD GD creating print maps Acquiring true color GD GD orthoimagery National realignment to OI DA build balanced sales territories Migrating pipeline data DA DA into geodatabase Natural gas resource GS DA mapping Viewshed modeling AM AM Locating satellite OI AM offices based on workload data Metadata creation GD Deriving features with imagery analysis Everglades restoration DM study Efficient follow-up AM testing based on routing Creating photorealistic DM CV DA buildings for visualization Identifying desirable AM land tracts for
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* This study preceded "Project Stakeholder Management" being added as a tenth knowledge area in the PM BOK Guide, 5th Edition (2012).
Patrick J. Kennelly is a visiting professor in the online Master's of GIS program at Pennsylvania State, where he teaches geospatial technology project management. He was a co-organizer with David DiBiase and Greg Babinski of the panel that worked with URISA and the U.S. Department of Labor to create the Geospatial Management Competency Model. He also is a professor of geography at LIU Post, where he recently helped to initiate an online certificate program in mobile GIS app development. He serves as editor of Cartographic Perspectives, an open-access journal.
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Table 1. Comparisons of problems in core and facilitating PM functions for projects with a component from each of the eight GIS&T knowledge areas discussed in this study Geospatial Design GIS&T & O & I Analytical Data Aspects Society Aspects Methods Total Core 21 23 15 13 11 Facilitating 39 33 22 19 13 Perceived Failure-Prone Core 6 11 5 2 4 Facilitating 6 7 7 3 4 Percentage Perceived Failure-Prone Core 29% 48% 33% 15% 36% Facilitating 15% 21% 32% 16% 31% Data Data Cartog. & Modeling Manip. Vis. Total Core 11 3 3 Facilitating 4 2 1 Perceived Failure-Prone Core 8 2 0 Facilitating 1 2 0 Percentage Perceived Failure-Prone Core 73% 67% 0% Facilitating 25% 100% 0%
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|Author:||Kennelly, Patrick J.|
|Date:||Jan 1, 2013|
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