A PMBoK Extension Proposal for Data Visualization

in Software Project Management

Julia Colleoni Couto

1 a

, Josiane Kroll

2 b

, Duncan Ruiz

1 c

and Rafael Prikladnicki

1 d

PUCRS University, Porto Alegre, RS, Brazil

University of Manitoba, Winnipeg, MB, Canada

Keywords:

Software Project Management, Data Visualization, PMBoK, Techniques, Tools, Knowledge Management.

Abstract:

Although the human brain stores images more easily than text, most of the tools adopted for software project

management are based on textual reports. The number of software projects that fail is huge, and the lack of

understanding of the project complexity by the stakeholders is among the reasons for project failure. Data

visualization techniques and tools can help to identify the project issues and reduce misunderstandings. In this

paper, we investigate how project management can beneﬁt from data visualization. To do so, we adopted a

hybrid research approach composed by a systematic mapping study, a survey, and three focus group sessions.

As a result, we identify a set of the 16 visualization techniques and tools that can be used to support software

project management and we propose a PMBoK extension that provides a reference for practitioners who are

planning to use data visualization to support software project management.

1 INTRODUCTION

People process information differently, and data visu-

alization (DV) is a great way to help people literally

get the big picture. According to Roam (2009), 75%

of our sensory neurons are dedicated to visual pro-

cessing. Furthermore, there are other studies that con-

ﬁrm the picture superiority effect in regard to words in

the human brain (Kirkpatrick, 1894; Stenberg, 2006).

In project management, visualization techniques

and tools allow the mapping of large amounts of data

to visual patterns that aid human information process-

ing (McDermott, 2019) and help in reducing cogni-

tive bias. The use of visualization can help support

project decisions (Geraldi and Arlt, 2015), improve

the relationship among the team members and achieve

project’s success (Lu et al., 2020).

Visualization techniques and tools can bring many

beneﬁts to software project management (SPM) such

as enabling people to view large amounts of data

quickly and efﬁciently. DV can help people obtain in-

sights from a problem, as well as discover a new point

of view on the data. Furthermore, it can help people

https://orcid.org/0000-0002-4022-0142

https://orcid.org/0000-0002-6700-3543

https://orcid.org/0000-0002-4071-3246

https://orcid.org/0000-0003-3351-4916

to have a shared vision in a particular situation, and

help to choose the actions to take (Sviokla, 2009).

In this paper, we investigate how SPM can beneﬁt

from using DV. We adopt the PMBoK Guide (Project

Management Body of Knowledge) (PMI, 2017) as our

main reference since it has been largely used in the

software industry to support project management. To

do so, we ﬁrst performed a systematic mapping study

(SMS) to identify the visualization techniques and

tools for project management. Next, we performed

a survey and three focus group sessions to collect ex-

pert opinions on the techniques and tools we found in

the literature. Then, we combined the results to pro-

pose an extension for the PMBoK guide.

Our main contribution is a set of 16 visualization

techniques and tools to support software project man-

agement, and a PMBoK extension proposal with two

new processes named 10.4 Plan Data Visualization

and 10.5 Implement Data Visualization. We follow

the PMBoK process standards to detail each process,

describe inputs, tools & techniques, and outputs.

The remainder of this paper is organized as fol-

lows: Section 2 provides the background on this study

while Section 3 discusses the related work. Section 4

details the research method, and section 5 presents the

results. Section 6 provides our proposal and section 7

discusses our results. Finally, we draw our conclu-

sions and future research directions in Section 8.

Couto, J., Kroll, J., Ruiz, D. and Prikladnicki, R.

A PMBoK Extension Proposal for Data Visualization in Software Project Management.

DOI: 10.5220/0010454600540065

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 54-65

ISBN: 978-989-758-509-8

2 BACKGROUND

2.1 Project Management

Project management is deﬁned as the application of

techniques, tools, skills, and knowledge to enable a

project to run smoothly. A project is a temporary en-

terprise to create a unique product, result or service

(PMI, 2017). In SPM, the product is the working sys-

tem that is delivered at the end of the project.

In the software industry, the PMBoK guide is a

standard in the ﬁeld of project management. Accord-

ing to Singh and Lano (2014) the PMBoK guide is

adopted in more than 75% of the projects, includ-

ing software projects. PMBoK splits the manage-

ment process into ﬁve groups: Initiating, Planning,

Executing, Monitoring and Controlling, and Clos-

ing. PMBoK also presents 10 Knowledge Areas (KA)

named Integration, Scope, Schedule, Cost, Quality,

Resources, Communications, Risk, Procurement, and

Stakeholder. In this paper, we focus on Communi-

cations KA because DV techniques and tools are in-

serted in the communication context. In addition,

PMBoK presents 49 processes, each one has inputs

(all things that are essential to the process), tools and

techniques to help in processes execution, and the re-

sulting outputs.

2.2 Data Visualization

Data visualization (DV) refers to the representation

of information and data. Visual representations arose

even before writing, when people used to draw daily

situations and objects on cave walls, aiming to trans-

fer the knowledge to future generations. Visual rep-

resentations depend on the type of data in which they

originate. To Ward et al. (2010), DV is a way of com-

municating information using graphics. Their book is

adopted as the basis for visualization classiﬁcations,

according to their type. The authors separate the types

of visualization according to the data that originate

them, as shown in Figure 1.

3 RELATED WORK

In the Visuals Matter book (Geraldi and Arlt, 2015),

the authors discuss several views for visual represen-

tations that support the decision in projects and port-

folios. They propose visualizations and recommenda-

tions for designing and using visual tools in projects

and portfolios.

Abad et al. (2016) performed a review on visual-

ization in requirements engineering (RE). Their pa-

per presents 18 usage patterns, which discuss visual-

ization techniques developed for three dimensions of

RE: activities, stakeholders and domains.

Shahin et al. (2014) investigated software archi-

tecture visualization techniques. They found tech-

niques, tools, types of visualization, activities, and

the purpose and domain of visualizations. They

also classify the visualization techniques in four

groups: graph-based, notation-based, matrix-based,

and metaphor-based. In the same direction, Grainger

et al. (2016) proposed a framework to improve visual

communication, to be used in a non-scientiﬁc context.

Lemieux and Salois (2006) and Baum et al. (2017)

investigated visualization techniques for a better un-

derstanding of the software. They show the genera-

tion of visualizations based on code lines.

There are at least three known proposed exten-

sions on the PMBoK subject. The oldest one is for

government projects (PMI, 2006), which brings good

practices for public sector projects. Another exten-

sion is related to SPM (PMI, 2013), developed with

the Institute of Electrical and Electronic Engineers,

and it uses prescriptive and adaptive approaches. The

most recent extension is for the construction sector

(PMI, 2016) and the proposal deals with speciﬁc do-

mains of projects of that nature, such as integrity,

safety, protection, environmental management, ﬁnan-

cial management, and claims.

When comparing our study with previous ones,

our study further investigates SPM and the usage of

DV in the software engineering ﬁeld. We believe

that by extending the PMBoK guide within two new

processes, as presented in this paper, we can provide

valuable contributions to organizations as well as the

academic community.

4 RESEARCH DESIGN

We conducted a hybrid research approach, starting

with an SMS to identify visualization techniques and

tools used for SPM. Then, we performed a survey

to understand how data visualization techniques and

tools are applied in the software industry. Finally, we

conducted three focus group sessions to collect expert

opinions on the techniques and tools we previously

found, and consolidate our results. In this paper, we

adopted the following IEEE deﬁnitions (of the IEEE

Computer Society, 1990) for software techniques and

tools:

• Software Techniques: procedures to help eval-

uate and improve the software development pro-

cess. For example, bars graphs and column charts

are used to visually present the data.

A PMBoK Extension Proposal for Data Visualization in Software Project Management

Figure 1: Data Classiﬁcation according to Ward et al.

(2010).

• Software Tools: systems for developing, testing,

analyzing and maintaining software products or

software documentation. Some examples of tools

are Microsoft Excel and Microsoft Project.

4.1 Systematic Mapping Study (SMS)

We followed Kitchenham (2007) guidelines to

conduct our study. We have split the SMS into three

phases: Plan, Conduct, and Document Review. At

ﬁrst, the main researcher created the protocol and

then a second researcher validated it. We deﬁned two

research questions (RQ):

RQ1: Which visual management techniques and tools

are used for project management in general?

RQ2: When are such visual management techniques

and tools applied to support project management in

the Software Engineering ﬁeld?

We applied our search string on Scopus database and

adopted by Rauch et al. (2013) to validate our search

string. Our search string includes the following terms:

project manage*, information visual*, data visual*,

visual management, visual*, tool*, technique*, prac-

tice*, method*, challenge*, approach*. (”*” is a

wildcard).

We ﬁrst applied the search string in November

2018 and then updated the results by applying the

search string again in November 2020. We did not

specify a period of time for the searching studies. Our

results show that the ﬁrst paper on techniques and

tools for project management was published in 1983

by Winsor (1983) and it is focused on the diagonal

network analysis technique.

Initially with our search string we got 2278 pa-

pers. In the selection step, by reading the title, ab-

stract and keywords, we rejected 1072 papers.

In the extraction phase we ﬁrst performed a re-

view in the remaining 1206 papers to identify if se-

lected papers were addressing our research questions.

Then, we conducted a further the analysis of the ac-

cepted ones. At the end, we selected 304 papers to

answer our research questions. The list containing the

selected papers is available in this link

. The papers

are sorted alphabetically by the lead author’s surname

and we have added the letter ”R” before the identiﬁer

so the references of the paper and the SMS are not

mixed (eg.: [R1], [R2], ..., [R282]).

The ﬁnal set of accepted papers matches the fol-

lowing inclusion criteria: 1) Focused on our research

questions; 2) Written only in English; 3) In the case of

a duplicate, the most complete version published was

selected. The rejected papers include at least one of

the following exclusion criteria: 1) Non-English pa-

pers; 2) Papers that are not directly linked with the

research questions; 3) Papers without bibliographic

information; 4) Posters, prefaces, incomplete publi-

cations, and duplicated studies.

4.2 Survey

We have conducted an online non-supervised, semi-

structured survey composed by open and close ques-

tions. The objective of the survey was to understand

how DV techniques and tools are applied in the soft-

ware industry, and the relationship between software

tools and visualization techniques. To perform this

study, we followed the steps given by Pﬂeeger and

Kitchenham (2001). We validate our survey through

face-to-face contact, content validation, and a pilot.

Our questions were focused on the visualization

techniques and tools identiﬁed in the SMS. For each

PMBoK process group (Initiation, Planning, Execu-

tion, Monitoring and Control, Closing), we selected

the most cited DV techniques and tools during qual-

itative analysis. Our target audience for the survey

was composed of IT professionals with SPM or soft-

ware development experience. The participants were

asked to select techniques or tools that they would

use for each process group. We also asked them to

provide recommendations on other DV techniques or

tools that were not mentioned in our list. We released

the survey by e-mail and social media such as Face-

book and LinkedIn.

Up to 1675 people may have had access to the sur-

vey, but only 101 members of the target audience an-

https://bit.ly/2J7BAp1

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

swered. Participants have 7.5 years of IT experience

on average, ranging from 0 to 28 years of experience

in IT projects. The average age of the participants was

36 years, and approximately 75% identiﬁed them-

selves as male. 85% of the respondents hold some

postgraduate degrees, such as MBA, Master or PhD.

Most of the participants were located in Brazil (92%),

while 5% were located in the United States. Most

participants in the survey hold positions directly re-

lated to project management such as project manager

(28%), project analyst (18%), or director (7%). The

remainder (47%) includes coordinator, consultant, en-

gineer, architect, and software developer. 89% of the

participants have already worked with software devel-

opment or deployment projects. Almost half of the

participants have worked with prescriptive and agile

methodologies, 25% only with prescriptive method-

ologies and 21% only with agile methodologies.

4.3 Focus Group

We developed the activities based on Morgan’s guide-

lines (Morgan, 1997) to conduct the focus group

study. We also adopted the four phases proposed by

Kirk et al. (1986) for qualitative research execution:

Planning, Observation, Analysis, and Report. Our tar-

get audience was software project experts and soft-

ware development process experts. We elaborated the

dynamics to follow during the sessions, and then we

conducted a pilot so we were able to simulate, check

and improve the process.

We had 13 participants in this study, divided into

three sessions (S1, S2, and S3). Hereby we refer to

the participants as P1, up to P13. The majority was

male, with 12 years of experience in PM and 36 years

old, on average. We asked participants to answer

an online survey in advance to the sessions. During

the sessions, in the main activity, we present the 19

proposed techniques to the participants, and then we

asked them about their applicability in SPM. We had

a group of research volunteers helping us to take notes

on the topics discussed during the sessions.

When the group agreed that a particular technique

is useful, they were asked to give examples of con-

texts in which it could be applied. For the techniques

the group found not be suitable for SPM, they were

asked about the reasons that drive them. At the end

of each session, participants were asked to cite other

techniques and tools that had not been mentioned dur-

ing the sessions. We compiled data from answers to

the previous survey, the annotations the assistant re-

searchers made, a compilation one of the members of

each session made, and the video and audio we cap-

tured during each session.

5 RESULTS

5.1 Systematic Mapping Study Findings

RQ1: Which visual management techniques and tools

are adopted in project management in general?

We found a set of 16 tools and techniques for

PM such as boxplot, dashboard, network diagram,

ﬁshbone diagram, scale, spectrogram, focus + con-

text, GIS (Geographic Information Systems-based vi-

sualization), pie chart, radar chart, kanban, timeline,

linkograph, augmented reality-based, virtual reality-

based, 3D, 4D, tree structures, bars or columns chart,

scatter plot, Gantt chart, line chart, graph, heatmap,

and matrix. These techniques and tools are applied

for different types of projects.

We categorized tools and techniques according to

the process group deﬁned by PMBoK. We observed

that bars and columns chart, dashboards, line chart,

and scatter plots are used in all process groups, ex-

cept the Closing process. However, boxplot, GIS,

matrix, and virtual reality-based appears just in one

phase each. Most visualizations are made for mon-

itoring and controlling projects. We also found that

Scope, Schedule, and Cost are the KAs where visu-

alization techniques and tools are used more in PM.

We did not ﬁnd studies focused on visualization for

project Closure.

Our ﬁndings show region-based techniques, 3D

models, and line-based techniques, followed by time-

oriented data to generate the most visualizations in

project management. Visualization techniques de-

veloped for engineering, architecture, and construc-

tion use up to ﬁve dimensions to show their project’s

data. For example, papers with 5 dimensions can

present 3D visualization plus time and cost [R253],

or emissions of pollutants and accidents identiﬁcation

[R258].

The majority of techniques and tools (46%)

are developed to support DV for Engineering, Ar-

chitecture or Construction projects, and Software

Engineering (SE) projects (46%). The remaining

(8%) support DV in the Education [R14], Scientiﬁc

Research [R94], NASA (National Aeronautics and

Space Administration) ([R70], [R194]) and Industry

([R219], [R249]) ﬁelds. Other report techniques

or tools are found in generic projects such as those

to generate visualizations of geospatial data. This

is a reﬂection of the predominance of construction

projects.

RQ2: When are such visual management techniques

and tools applied to support project management in

the Software Engineering ﬁeld?

A PMBoK Extension Proposal for Data Visualization in Software Project Management

We found 140 papers discussing DV for software

engineering (SE) project management. Multivariate

data (45%), region-based techniques (32%), and tree

structures, graphs, and network diagrams (31%) form

the most used data sets in Software Engineering (SE).

In these projects, data come from conﬁguration or

code repository, source code, e-mail, .xml ﬁles, Mi-

crosoft Project or Primavera P3. These results make

sense because these techniques are used to under-

stand the interaction between developers, monitor and

control project scope, and visualize changes the fre-

quency in code snippets.

Our results also show that some visualization tech-

niques and tools use lines of code [R59], [R71],

[R72], [R182], [R232]. They are based on met-

rics obtained from code repositories, e.g., commits,

amount of rows, developers information and their

contribution in a certain code. The most cited goal

for these code-based visualizations is to understand

the interaction between developers, monitor and con-

trol project scope, and ﬁgure out which code snippets

have changed frequently.

In SE projects most of the visualizations are meant

to display information about the KAs as Scope (51%),

Schedule (43%), Stakeholders (23%) and Quality

(21%) instead of Cost, most common in general

projects. In fact, software quality indicators, for in-

stance, the number of bugs, are important information

about project progress.

5.2 Survey Findings

The most commonly adopted DV tools reported are

Microsoft Project (59%), followed by Trello (31%),

and Microsoft Excel(15%). Other tools mentioned

were: Jira (12%), Ofﬁce Package (8%), WBS Chart

Pro (7%), EPM (6%), Microsoft Team Foundation

Server (4%), Slack (4%), Microsoft Sharepoint (3%),

Redmine (3%) and SAP PS (3%). Basecamp, Google

Drive, Kanbanﬂow, Kanbanize, OpenProj, Primavera

P6, and Trace GP hold 2% or less. Participants usu-

ally cited more than one tool and sometimes men-

tioned techniques not listed: pie chart, network di-

agram, ﬁshbone diagrams, scale, spectrogram, time-

line, focus + context, linkograph, and augmented

reality-based.

Some techniques were mentioned to be useful in

all process groups in projects: kanban, bar chart or

columns, line chart, and dashboard. 3D, tree struc-

tures and Gantt chart are used in all groups except the

Initiation group. Scatter plot and graphs are used at all

stages, but not at the end of projects, while heatmaps

are not used in the Planning phase. By process group,

in Initiation, the line charts, bars or columns chart,

and dashboards are used more; in Planning, Gantt

chart, tree structures, and line charts are most com-

monly used; in Execution, the Gantt chart again ap-

pears as the most used, followed by kanban and line

chart; and for the Monitoring and Control, the most

cited was the Gantt Chart, line charts, and bars or

columns chart.

For Closing, as no speciﬁc technique was identi-

ﬁed during the SMS, we asked for a textual answer.

30% of the participants reported they do not use any

visualization technique at this stage. 18% reported

using dashboards, and 18% used only textual reports.

We also asked about tools that participants would rec-

ommend to SPM. The three most cited tools were

Microsoft Project (59%), Trello (31%) and Microsoft

Excel (15%). One-third of them suggests DV tech-

niques are more useful in Monitoring and Controlling.

Finally, 19 tools and techniques compose the ﬁnal set

(see Table 1). Based on the SMS and survey results,

we classiﬁed the techniques and tools into the PM-

BoK process groups, so that it could be evaluated by

the experts.

5.3 Focus Group Findings

Figure 2 presents a heatmap, where we compiled the

focus group results, based on the information we ob-

tained from each session (S1, S2, and S3). We calcu-

lated the score as follows: 1) Each participant’s (P1,

P2, ..., P13) previous response in the online survey

was worth 1 point; 2) When a consensus is achieved

in S1 or S5 it is worth 5 points (each had 5 partici-

pants); 3) When a consensus is achieved in S2 it is

worth 3 points (each had 3 participants); 4) We sum

the score for each technique in each phase.

For this study, the maximum score that a tech-

nique could have in each question would be 26 points,

i.e. when all the participants and all sessions informed

that they would use the technique in a certain group

of processes. To divide the techniques into process

Figure 2: Heatmap representing Focus Group analysis.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

Table 1: Techniques identiﬁed by research method: Sys-

tematic Mapping Study, Survey, and Focus Group.

SMS Survey FG

3D, 4D, Boxplot X X

Bars or columns X X X

Canvas, Pie chart, Timeline X X

Dashboard X X X

Flowchart X X

Gantt chart X X X

GIS X X X

Graph X X X

Heatmap X X X

kanban X X X

Lines X X X

Matrix X X X

Radar chart X X X

Scatter plot X X X

Tree structures X X X

Virtual reality-based X

Count 16 19 16

groups, we used 10 as the cutoff criterion. Thus,

we reached a consensus in the three sessions, among

all participants, that 3D and 4D should be removed.

Additionally, two bigger sessions (S1 and S2) also

agreed the boxplot should be removed. This way, 3D,

4D, and boxplot will not be part of the ﬁnal set of

techniques. The ﬁnal version of the proposal would

consist of 16 techniques, as shown in Table 1. In it,

we can observe that some techniques were found in

more than one research method.

6 AN EXTENSION PROPOSAL

FOR THE PMBoK GUIDE

Based on our results, we built two processes within

the PMBoK Communications Management KA. To

do so, we analyzed each PMBoK process, especially

the Project Communications Management, then we

mapped its inputs, tools and techniques, and out-

puts. Next, we selected the items that would be useful

to help plan and implement DV in project manage-

ment. The PMBoK extension proposal systematize

and emphasize the use of DV in software development

projects.

We propose the following processes: 10.4 Plan

Data Visualization and 10.5 Implement Data Visual-

ization. Figure 3 shows an overview project commu-

nications management processes. The original ﬁg-

ure comes from the PMBoK guide, and we extend it

adding two new processes at the end. All the data rep-

resentation is based on the process presentation model

used in the PMBoK guide. The following sections de-

tail our proposal.

6.1 Process: 10.4 Plan Data

Visualization

The 10.4 Plan Data Visualization process deﬁnes how,

which models and which visualization techniques will

be used as a complement to the project’s communica-

tion planning, i.e. those that are suitable for display-

ing related data.

Our data ﬂow diagram (see Figure 4) shows the

relationship between the process’ inputs and outputs.

We can see that there is another process as input: the

10.1 Plan Communications Management. The pro-

cess 10.1 generates the communication management

plan, which together with all project documents, and

taking into account the characteristics of the enter-

prise/organization, they create a solid base for the

Plan Data Visualization process. As a result of this

process, the communication management plan and

project documents must be updated to include data

visualization techniques.

6.1.1 Inputs

There are four inputs that have to be considered to

deﬁne the data visualization plan:

1. Communications Management Plan: it aims to un-

derstand how, when, and by who the information will

be administered and distributed during the project.

2. Project Documents : all documents used during the

project, such as the ones listed in the PMBoK guide.

The PMBoK lists 33 documents that may be affected

by DV planning processes, e.g. risk report, activities

list, quality reports, requirements document, lessons

learned, test and evaluation documents, schedule.

3. Enterprise Environmental Factors (EEF): are

events that cannot be controlled by the project team

and they can inﬂuence the project outcomes. Such

factors can have a positive or negative impact on the

project. EEF that may inﬂuence the Plan Data Visu-

alization process include:

• Published material, including papers about appli-

cable DV techniques;

• Academic studies;

• Benchmarking results;

• Global, regional, or local trends, practices, or

habits;

• Organizational governance structure;

• Organizational, stakeholder, and client structure

and culture;

• Geographic distribution of facilities and re-

sources;

• Speciﬁc project document standards;

• Guidelines and criteria for deﬁning the set of vi-

sualization techniques and tools to be used;

A PMBoK Extension Proposal for Data Visualization in Software Project Management

Figure 3: Project Communications Management Overview - Extension proposal.

• Established channels, tools, and communication

systems;

• Project management information systems.

4. Organizational Process Assets (OPA): are com-

posed of the processes, plans, procedures, knowledge

bases, and policies from the organization where the

project is being executed. OPAs that may inﬂuence

the Plan Data Visualization process include:

• Standard organizational policies, processes, and

procedures;

• Project governance structure, portfolio, and pro-

gram (governance functions and processes to pro-

vide guidance and decision making);

• Standardized guidelines for the development, ex-

change, storage, and retrieval of information;

• Organizational communication requirements;

• Communications management plan template;

• Formal procedures for sharing knowledge and in-

formation;

• Document and forms templates;

• Conﬁguration management knowledge base con-

taining versions and baselines of all organiza-

tional standards, policies, and procedures.

6.1.2 Tools & Techniques

The tactics used to achieve the expected process out-

comes by using the inputs to achieve the outputs:

1. Expert Judgment: The opinion provided by a per-

son or a group of people who have expertise in a spe-

ciﬁc area of application related to the performed ac-

tivity. In this case, the people do not have to meet and

achieve a consensus, we can consider individual opin-

ions. In the DV planning process, the opinion of indi-

viduals with experience, empirical knowledge or spe-

cialized training is essential for the project’s success.

The following topics related to the project should be

considered to plan the project DV:

• Knowledge of SPM;

• Knowledge of tools and visualization techniques

that can be used to support the communication of

information;

• Data interpretation and contextualization;

• Organization communication technologies;

• Organization policies and procedures on legal re-

quirements for corporate communications;

• Communications with the public, community,

media, and in a global environment - between vir-

tual groups;

• Project and communications management sys-

tems.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

2. Benchmarking: for instance, a search to know the

most used best practices in project management. It is

used to identify best practices that could be adopted

by the institution. Benchmarking can be conducted

by reviewing or mapping the literature.

3. Focus Groups: it is an expert meeting, made to

share information about visualization techniques ap-

plicable to SPM. We conducted focus group sessions

as part of this study, where we present DV techniques

from a SMS and a survey, and three experts groups

evaluated it in software development projects context.

As a result, we identiﬁed the main techniques that can

be used to support SPM (see Section 5.3).

4. Questionnaires and Surveys: a questionnaire can

be just a data collection instrument, while surveys

comprehend an entire research protocol containing all

the process to generate and analyze the questionnaire

data. Surveys can also be used to know which tech-

niques and DV tools people are using to support com-

munication in their projects. We use a survey as a data

collection tool (see Section 4.2).

5. Communication Technologies: communication

technologies are those that help communication be-

tween different organization parties for displaying

project DVs. It includes e-mails, web portals, writ-

ten documents, social media, databases, and others.

6. Communication Models: representations of how

communication will happen over the project duration.

Communication models can be established through

standard documents adopted by the team. A standard

for communication should be always adopted as the

starting point to deﬁne the communication model for

a project.

7. Communication Methods: it is the systematic pro-

cess, procedure, or technique that is used to trans-

fer information between stakeholders. For instance,

it can include a process that indicates who commu-

nicates about a problem in the project schedule, who

will receive this communication, and how.

8. Data Representation: formed by graphical or tex-

tual representations, it indicates how data will be dis-

played, and are used to transmit data and information

about the project among stakeholders.

9. Meetings: project meetings to gather information

or discuss the project status. During the meetings,

team members can deﬁne data visualizations tools

that will be used to illustrate project information.

6.1.3 Outputs

The outputs from the Plan Data Visualization process

are described as follows:

1. Data Visualization Techniques: Project visual-

ization techniques are used to support SPM. Table 5

presents the set with sixteen visualization techniques

found in the SMS, the survey, and the focus groups.

Also, examples where such techniques can be applied.

2. Communications Management Plan Updates:

The communication strategies available in the project

management plan can be updated in order to include

techniques selected to show project data in docu-

ments.

3. Project Documents Update: All project documents

can have its models updated, considering best prac-

tices in employing visualization techniques, used for

graphical representation of information.

6.2 Process: 10.5 Implement Data

Visualization

The 10.5 Implement Data Visualization process en-

sures that project information will be clearly and eas-

ily visualized and understood by all stakeholders, ac-

cording to the information they need. It involves ap-

plying visualization techniques in different contexts

of software development projects. For this purpose,

we use visualization techniques such as those men-

tioned in Section 6.1.3.

We also develop a data ﬂow diagram to show the

relationship between the inputs and outputs of this

process (Figure 4). As input, we use the new process

10.4, Plan Data Visualization, all project documents

and characteristics of the enterprise/organization. Af-

ter applying the process, we expect to produce up-

dates on the communications management plan and

in other project documents, to include DV techniques.

It also can indicate updates on OPAs.

6.2.1 Inputs

Inputs identiﬁed in implementing the DV process are

described as follow.

1. Data Visualization Techniques to be Used: Visu-

alization techniques that can be used vary according

to the project, and can also be related to KAs. Tech-

niques that may be used include, but are not limited

to: canvas, dashboard, tree structures, ﬂow chart, GIS,

bars or columns chart, scatter plot, Gantt chart, line

chart, pie chart, radar chart, graph, heatmap, kanban,

matrix, and timeline.

2. Project Documents: All documents used in a

project can be considered inputs to this process.

3. Enterprise Environmental Factors: Environmental

factors that may inﬂuence the implementation of DV

include, but are not limited to:

• Established channels, tools and communication

systems;

• Project management information systems;

• Database with project information;

A PMBoK Extension Proposal for Data Visualization in Software Project Management

Figure 4: Processes 10.4 and 10.5: Data Flow Diagrams.

• Data from published estimates.

4. Organizational Process Assets (OPAs): that may

inﬂuence implementation of DV include, but are not

limited to:

• Document and forms templates;

• Conﬁguration management knowledge base, con-

taining versions and baselines of all organiza-

tional standards, policies, and procedures, and any

project documents;

• Monitoring and reporting methods;

• Repositories with historical information and

lessons learned;

• Databases for managing problems and defects,

with historical data from each problem and defect

resolution actions;

• Performance measurement database, that are used

to collect and make available processes and prod-

uct measurements;

• Financial databases;

• Source code repositories.

6.2.2 Tools & Techniques

Next, we present the tools and techniques that can be

used to implement the data visualization process:

1. Communication Technologies: Tools, systems, and

applications used in a project that serve to transfer in-

formation between stakeholders. Examples include

emails, chats, institutional systems, text messages and

online discussion groups.

2. Communication Methods: The systematic process,

procedure, or technique, used for the purpose of trans-

ferring information between stakeholders. It can be

mapped with a ﬂowchart, for example.

3. Project Management Information Systems (PMIS):

It is a system that contains tools and techniques

used to gather, integrate, and disseminate outputs for

project management processes. Examples of PMIS

are Microsoft Project, Trello, and Microsoft Excel.

4. Data Representation - Data Visualization Tech-

niques: These are graphical representations used to

disseminate project data and information. Graphical

representations that can be used include, but are not

limited to, those described in Table 1.

5. Meetings: Reunion among stakeholders, usu-

ally with speciﬁc guidelines, where DVs can be dis-

played. For example, videos and slide shows can be

used to illustrate information related to the project.

6.2.3 Outputs

The outputs expected from implementing DV pro-

cesses are:

1. Project Data Visualization: Visualization tech-

niques should take into account the type of data, the

KA, and the context in which it will be used. Table 5

presents some contexts where techniques can be used.

2. Communications Management Plan Updates: The

communication strategies in the management plan

can be updated, with the implementation of tech-

niques for visualizing data contained in the docu-

ments.

3. Project Documents Updates: All project docu-

ments can have their models updated, according to the

implementation of visualization techniques for graph-

ical representation of the information.

4. Organizational Process Assets: OPAs that can

be updated because of the implement DV process in-

clude, but are not limited to:

• Document and forms templates;

• Conﬁguration management knowledge base, con-

taining versions and baselines of all organiza-

tional standards, policies, and procedures, and any

project documents;

• Methods of monitoring and producing reports.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

Integration

Scope

Schedule

Cost

Quality

Resources

Communications

Procurement

Risk

Stakeholder

All

Bars or

columns

Planned x realized,

Features by sprint.

Evolution of the project;

Hours planned x

consumed.

Comparison of indicators;

Project cumulative cost;

Resource cost per

location;

Information about users of

the system; System

performance: number of

access, load, database.

Resource allocation

capacity; Number of

people per project time.

Project performance.

Canvas

Collaborative planning;

Planned x realized; User

stories; Product vision

summary; Project scope.

Collaborative planning;

Planned x realized.

Project kickoff meeting;

Status report.

Collaborative design of

the project;

Dashboard

Portfolio and program

information.

Planned x realized; Track

project execution; Project

progress bar; S curve; To

do list.

Control project evolution;

Hours of development;

Planned x realized; Sprint

planning; S curve.

Supplier information;

Project costs; Project

budget (pie chart).

Use as input to

retrospective meeting.

Supplier information.

SWOT (Strengths,

Weaknesses,

Opportunities, and

Threats) analysis.

Sprint progress; project's

progress;

Flowchart

Helping the understanding

of the business process to

elaborate the business

case; Design of

development processes;

Process mapping.

Project macro view; UML

activity and state diagram;

Expected scope x realized;

Plan project phases;

Workflow of what was

developed .

Plan tasks, like a Gantt

chart without temporal

information.

Meeting guide; Review

information from the

project to the operational

team.

View information about

information security.

Project summary;

Gantt

chart

Planned x realized;

Activity planning; View

scope changes.

Monitor activities; Plan

activities; View project

timeline.

GIS

Generation of ''attack

plan'' by area, on a map of

a company, in software

deployment

Support for business

analysis

Software deployment

planning and monitoring

in geographically

distributed teams; Plan the

execution of a project by

region.

Project cost per location.

Mapping open bug reports

by location

Visualizations for

distributed team

management.

Digital marketing strategy

Monitor in real time some

geographically sensitive

data.

Project information with

features in distributed

geographic locations;

Graph

PERT chart (Program

Evaluation and Review

Technique); User journey.

Network diagram

(relationship between

activities)

PERT chart (Program

Evaluation and Review

Technique).

Mapping communication

channels within a project.

Critical path of project

activities; Monte Carlo

simulation for risk

analysis.

Heatmap

Management of

information related to

projects in portfolios;

Visualization of

relationships between

project phase and task;

View requirements

criticality.

Timeline analysis

(headlights); Plan

minimum and maximum

range for a given task.

Cost analysis; Costs by

period.

View where most users

clicked on a web page, for

evaluation and page

design.

Team members

competencies analysis, for

training planning.

Follow-up of indicators on

critical technologies for

the project; Risk analysis;

View critical project data.

Kanban

Backlog; Control micro

tasks in short periods;

Collaborative planning;

Task planning; Activities

summary.

Project planning;

Backlog; Activities

summary.

Lines

Evaluate data obtained

during the project; Project

life cycle; S curve;

Planned x realized,

Burndown chart; Burnup

chart.

S curve.

Financial comparison;

Statement of income;

Added value.

Information comparison.

Matrix

SWOT analysis

(Strengths, Weaknesses,

Opportunities, and

Threats); Selection of

projects to start.

Matrix to map where to

invest.

Communication matrix.

Technology TIME

analysis (Tolerate, Invest,

Migrate or Eliminate);

Product or project

feasibility study; Mapping

of competitors; Risk

matrix.

Mapping of stakeholders -

who is in favor and who is

against; who has power

and who does not have.

Decision support;

Assessment of project

aspects.

Pie chart

Hours consumed per

feature; Estimated

proportion of time at each

stage of the project.

Hours consumed per

feature; Workload

information; Estimated

proportion of time by

project stage;

Statement of income for

the year per phase; Phase

information (cost, time,

etc.); Proportion of costs

of each project stage.

Types of bugs found.

Workload information;

Sizing the team.

Comparison of data within

a context; Phase

information (cost, time,

etc.).

Radar

chart

BSC (Balanced Score

Card); Portfolio

management, for

prioritization of projects

and programs.

Evaluation of teams at

retrospective meetings.

Benchmarking-

performance comparison

between two or more

systems; Comparative to

choose a technological

solution; Supplier

comparison.

Stakeholder competency

analysis.

Scatter

plot

Bugs per delivery.

Team capacity statistical

analysis.

Timeline

Deliverables (project

milestones); Macro

planning.

Schedule summary;

Temporary outline;

Deliverables (project

milestones); Macro

planning; Deadlines.

Status report; Use as input

to retrospective meeting.

Tree

structures

Plan software deliveries;

WBS; Project activities

and groups structuring;

Mind map; Project

checklist; Scope view;

Critical path map.

Monitoring of the

activities evolution; Plan

software deliveries.

Project Organization

Chart.

Use as input to

retrospective meeting

Risk analysis; Critical

path map.

Mapping expectations

Figure 5: Examples of contexts to use DV techniques for each KA.

A PMBoK Extension Proposal for Data Visualization in Software Project Management

7 DISCUSSION

Our paper brings advances in the theoretical ﬁeld for

software project management. As we developed a

SMS, we could identify the most cited visualization

techniques and tools in project management. The

mapping study also allowed us to widely understand

visual techniques that have been adopted by project

managers in software management projects.

Our results show that the code repository is useful

to generate many types of data visualization. It can

help visualize all the lineage of the code: who ﬁrst de-

veloped, who updated, tested, etc. It can also help to

understand the human relationships among the team,

as it can trace the lines of code to show which de-

velopers work together, displaying the afﬁnity among

stakeholders.

With the survey, we had access to the opinion of a

hundred project management experts. We have lis-

tened to the experts’ opinion about tools and tech-

niques that are useful in software project manage-

ment. We also identiﬁed techniques and tools used by

project managers to perform daily tasks and support

its activities. We found out that Microsoft Project,

Trello and Microsoft Excel are the most adopted soft-

ware tools for supporting project management. We

also found the use of kanban, bar or column chart,

line chart, and dashboard during the entire project,

since its beginning to the closing - different to what

we map during the SMS.

The focus group sessions allowed us to discuss

in depth the use of visualization in project manage-

ment. We had access to the opinion of thirteen ex-

perts, who helped us to select the techniques and tools

that would be most useful and also describe several

project management scenarios where the techniques

and tools could be applied. Thus, we could select a set

of techniques and tools to be used in software project

management.

Our hybrid research approach contributed to the

development of an extension proposal for the PM-

BoK Communications KA, including two new pro-

cesses. The proposed processes are 10.4 Plan Data

Visualization and 10.5 Implement Data Visualization.

Our proposal brings advances to both the theoretical

and practical software project management ﬁeld. For

the practical software project management, we deliver

two detailed processes to help select and implement

the most suitable visualization techniques according

to the project context. Notice that the Implement Data

Visualization process is dependent on the execution

of the Plan Data Visualization process. It means we

must plan ﬁrst, so we can successfully apply the im-

plementation of data visualization techniques in our

software project management.

We also present detailed instructions on how to

deploy our processes. Starting with the inputs, we

present and describe each factor that must be taken

into consideration to start the processes. Then, we

suggest tools and techniques that can be used to help

achieve the expected outcomes for each process. We

also present the data ﬂow diagram for both processes.

The data ﬂow helps to visualize the connections be-

tween inputs and outputs. Then, we present the pre-

dicted outputs for each process.

We emphasize that we follow the PMBoK model

to design our proposal. Thus, we create processes

based on a broadly used template, that are known,

easily understood, and broadly followed by thousands

of people all around the world. It reduces the learn-

ing curve for people that are already acquainted with

PMBoK and are interested in learning about how to

use DV techniques in their projects. Hence, it makes

easier for people to start following two new processes.

8 CONCLUSIONS AND FUTURE

WORK

As mentioned earlier, visualization techniques and

tools can beneﬁt software development project man-

agement since it helps to better understand the project

context and how the stakeholders could contribute to

project success. PMBoK is one of the most used stan-

dards when it comes to project management, but it

does not present details about how to plan and im-

plement the use of data visualization tools and tech-

niques within the projects.

In this paper, we survey and present how soft-

ware development project management makes use of

data visualization, identifying data visualization tech-

niques and tools used, or that can still be applied to

beneﬁt software project management.

Our research method comprises the adopting of

SMS, survey, and focus group. With the SMS we

identiﬁed sixteen DV techniques applicable to project

management. With the survey, we end up with nine-

teen techniques and mapped the three tools project

management specialists use most to visualize their

project’s data. Executing the focus group, we could

reﬁne the suggested techniques to sixteen, and collect

information about the context where tools and tech-

niques could be applied.

The extension we developed for the PMBoK can

be added to the Communications Management knowl-

edge area, and by following the two processes we cre-

ated, people could use our extension to help select the

best way to represent information alongside the ten

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

PMBoK knowledge areas. We also present examples

of contexts in which they can be applied.

As for directions of future research on DV in the

software engineering ﬁeld, we want to develop a data

repository to present the processes and how to imple-

ment them in the software development ﬁeld. For

example, selecting a KA and identifying a context

in which speciﬁc visualization techniques and tools

could be applied to better interpret the data. We also

plan to perform a case study to evaluate the use of DV

for agile projects.

ACKNOWLEDGEMENTS

We thank the study participants and acknowledge that

this research is sponsored by Dell Brazil using incen-

tives of the Brazilian Informatics Law (Law no 8.2.48,

year 1991).

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