# Visualization - a vital decision driving tool for enterprises
††thanks:

Rajath Chikkatur Srinivasa, Supriya Arun, Lauren James, Ying Zhu Department of
Computer Science
Georgia State University
{rcs1, ljames26<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

This report documents the results found through surveys and interviews on how
visualizations help the employees in their workspace. The objectives of this
study were to get in-depth knowledge on what prepares an employee to have the
right skill set in constructing an informative visualization as well as the
tools and techniques that they use on their daily basis for analysis and
visualization purposes. Using the results gathered, we sorted the information
in many different ways for analysis and came to conclusions ranging from
corporation-based strategies to individualized employee and position
preferences.

###### Index Terms:

visualizations, employees, company, position

##  I Introduction

Since the ease of access to technology has ensured a data explosion, we can
say that the 21st century is the era of data. Due to this, industries have
started to make their decisions based on the data they receive from the
consumers. To make sense of the data that the companies receive, it is crucial
to have visualizations for straightforward interpretation since the raw format
of the data itself is not understandable for everyone. Besides, visualizations
are used for a straightforward interpretation of data and accessible
communication and/or explanation between the technical and non-technical sides
of the industry. This raised a question on which part of the industry utilizes
data visualizations more commonly than the other. Through this project, we
explore the impact of visualizations in the workplace by directly learning
from employees how they interact with visualizations in their day-to-day
tasks. Our main goal is to discover the challenges and benefits that employees
encounter while using them and whether or not they go through a training
process to understand better how to build and work with visualizations. The
method that we used to gather our data is through surveys and interviews. The
data was collected from a variety of positions to get a bigger picture of how
visualizations have an impact on the industry. Based on the results collected,
we categorize it according to the company and determine which roles have a
higher priority in undergoing a training process and which positions have more
knowledge on a variety of visualization techniques.

##  II Related Work

L.A.Shah et al [5] analyzes performance measurement system’s output by
leveraging graph visualizations. They distribute visualizations based on area
which helps in making targeted decisions and showcases the importance of
visualization to industries. Sauter et al [4] is a case study that studies the
development of visualization to represent correlated data. It aims to address
specific visualization discussions like choice of visualization technology and
visualization algorithms. This helps enterprises to narrow down decisions on
visualization. Alharthi et al [1] applied techniques to visualize Hajj season
data. This showcases that visualizations can be a driving factor for deciding
various types of events and activities. Cota et al [2] describe how using
advanced visualization tools within business intelligence software helps drive
decision makings within meetings. This work is an example for how enterprise
decision making leverages visualizations. Duru et al’s [3] work provides a
theoretical explanation on what impact software visualization tools creates in
software industry and how it’s lack of usage is derailing decision making. The
tested it with actual programmers to witness the difference between the
outcome of understanding a business use-case with and without visualization.

##  III SECTIONS OF THE SURVEY

###  III-A First part - Personal experience

For this study, we decided to interview employees from six companies: Bank of
America, State Farm, N2N Services, Verizon, IBM, and Informatics. Each company
surveyed had representatives from technical and non-technical positions. The
technical positions consisted of Software Developers, Software Engineers, Data
Analysts, Data Scientists, Business Analysts, Product Managers, and Product
Developers. Non-technical positions included Project Managers, Sales Managers,
Operations Team, CEOs, CTOs, COOs, MDs, Founders, Management Team, and
HR/Recruiters.

Figure 1: Years of experience at company

The study started by inquiring the employees on their personal experience with
making use of visualizations in their workplace. Questions such as how often
they use and how they incorporate it in their work were being asked to analyze
in which format visualizations are primarily being used. The most popular
response for the frequency of usage was “Sometimes,” with it having 61.5
percent of the votes, while the second-highest was “Every day” with 30.8
percent of the votes, and the last one was “Not at all.”

Figure 2: How often do the employees use data visualization in their work?

Just by viewing these results without any background details, it could be said
that the employees who voted for “Not at all” are from positions that do not
require any form of data to be viewed for decision making, such as recruiters
or administrators. On the other hand, employees who voted for “Sometimes” or
“Every day” can be predicted to be in positions like business/data analysis or
management. The majority of the employees mention that they use visualizations
for reviewing and drawing conclusion purposes, while others say for
maintaining timelines and explanations. Regardless of the employee’s direct
usage of visualizations for their work assignments, all employees surveyed
have been exposed to them in the workplace. Through this, we can conclude that
creating visualizations helps in various ways, and the type and
responsibilities of the data visualizations utilized in the workspace tend to
correspond with the job position. The job positions that use them for
reviewing and drawing conclusions could vary mainly by the employees from the
business department. Using it for explanations could be workers from the
technical department, while people using visualizations for maintaining
agendas could be from the executive department

###  III-B Second part - Prior knowledge and Training

After inquiring about personal experience, employees were then asked about any
knowledge they had on visualizations before coming into the workplace and the
training processes they may have participated in after joining the company.
This was fulfilled through questions such as what courses or tutorials they
used to prepare themselves before joining the company; did they take any
separate online courses to feel prepared; and lastly, asking to see if the
company trains them separately for visualizations and data analysis. From the
results gathered, 92.3 percent of the employees say that their company did not
train them in visualization and the same percentage of them also say that they
did not undergo any training from the company for data analysis.

Figure 3: Did the company train you in visualization?

Figure 4: Is there any training the company makes you undergo for data
analysis?

Based on this, we can conclude that data analysis and visualizations are
topics that the industry already expects the employee to know before joining
the company. Employees who answered that they went through a training process
said that it was through their company’s internal webinar, which had courses
that could be completed whenever free. This says that the training process is
not one-on-one or hands-on learning; instead, it is just an online course
similar to anyone learning data analysis outside the university through online
resources. When the topic of prior knowledge on visualizations was asked, 69.2
percent of them said ”yes,” whereas 30.8 percent reported ”no,” which could
mean that people who answered ”yes” already were exposed to this information
in their university courses or from their previous workplace. In contrast,
people who said ”no” would have acquired the knowledge in their current job
using visualizations.

Figure 5: Did you have prior working knowledge in visualizations?

Employees who answered that they did have prior knowledge on the concept of
visualizations also mentioned that they obtained the knowledge through courses
on Databases and Introduction to BI, and Introduction to Data Science. Some
also mention that they learned it in their job itself. The majority concluded
that their courses taken in their university were sufficient in preparing them
for the visualization knowledge they have currently. Some added that once they
got through their first presentation with some visualizations, they got
familiar with the concepts and became comfortable with them by their next
presentation. 76.9 percent said they did not use any online materials to
educate themselves on visualizations, whereas 23.1 percent said they used
online resources to succeed in that concept.

Figure 6: Did you do any online courses related to data visualization?

It can be said that people who did learn it in their job itself tend to be
from a department that does not require complex visualizations for decision
making and it also shows that building a foundation in visualization is not a
highly challenging task.

###  III-C Third part - Challenges and Complaints

As we continued with the survey questions, we asked the employees about the
challenges they face and their complaints regarding their visualizations, the
ones they come across at work, or even just in general. We then ask them how
it can be less challenging to get their perspective on how visualizations can
be improved or updated and we also ask what they think is a good
visualization. Employees mention that the topmost complaint is that the in-
built visualizations are not as flexible in terms of how it suits their data
set or how much data can be viewed at once. Some also say that the data can
get too cluttered, so it sometimes defeats its purpose. They added that the
end-users sometimes do not understand the meaning behind viewing the data in a
visualized format. Through these thoughts, it can be concluded that
visualizations that are already built-in are for depicting simple
visualizations such as bar graphs or line graphs that incorporate simple forms
of data. This is why employees might have complained about the built-in ones,
but if they had to code the visualizations, they would not have such
complaints on flexibility and the amount of data shown since they are manually
programming the visualization. For complaints on end-users not clearly
understanding the importance of data to be viewed in a visualized manner could
be from a background that has to do with quick decision making where they do
not have time or the necessity to view the collected information throughout
the years or the opinions from the customer. The importance of visualizations
vary from the time and the importance of the scenario, for example, if the
business is taking a huge step in how they function or how they would deal
with their stakeholders, then visualizations play a crucial role because it is
essential to see all the treads, patterns and details before making a
decision. However, if a decision does not require much research before making
a move, then a detailed visualization with many comparisons is unnecessary
even though it is good. Many employees say that the challenging part of
visualizations is to know which type is the suitable one for the data set and
which one conveys the right meaning from the results they have found. Some add
that explaining visualizations is hard sometimes, and it is due to a lack of
understanding from the end-users. Through this, it can be said that keeping
the visualizations as simple as possible can be the key for both cases. Simple
visualizations such as bar graphs, pie charts, etc., can be drawn from most of
the data sets and these types of depictions are less challenging for
explanation and for showcasing the intended meaning behind the visualization
made. Understanding the visualizations for the stakeholders and end-users can
indeed be challenging, but it depends from person to person because some
individuals want to talk business rather than using outside tools to serve
their point. When they were asked what could make it less challenging, a large
number said that knowing how the team understands better is the key. They also
say that familiarizing with the basics for succeeding in visualizations is
beneficial for any group of people. These are valid points because it takes
time to understand how the team depicts better understanding and it all comes
down to being a learning process. They also add that they come across
visualizations that are overly done in terms of color formatting and
incorporating a vast amount of data into one visualization. This is not so
appealing because it makes it less readable and distracts the viewers, which
makes them not pay close attention. They also add that some visualizations do
not show the entire data set that is being there, which is a problem with
accurate conclusions. This comes down to picking the correct type of
visualization for accurate display and easy understanding. This brings up what
makes a good visualization and the popular response was simplicity. This is
agreeable because the other party is not seeing how complex a visualization is
or their skills in creating one; they want to see the point of the
visualization, meaning what they learned from that. They also added that the
best way to present a visualization is to explain in a detailed manner.
Additionally, they say that keeping visualizations based on only one factor
per visualization is the most effective way to get the point across. This is
crucial because the more data is shown in a single visualization, the more
confusing it gets. Simplicity with the right kind of visualization is the key.

###  III-D Fourth part - Tools and Techniques

The employees were lastly asked what type of tools and software they are
familiar with and which ones they incorporate in their workplace. They were
asked to pick the tools and techniques from a given list: Google Data Studio,
Google Trends, Google Analytics, Google Charts, Kibana, Dash, R Studio, and
Azure Data Explorer. The most familiar tool is Google Analytics, while other
products of Google (Google Data Studio, Google Trends, and Google Charts) and
R Studio are also commonly recognized among the survey respondents. On the
contrary, the unknown tools were Dash and Kibana.

Figure 7: Which of the below tools have you used or heard before?

As for software applications, there were several selections listed that no
employee surveyed had been introduced to for visualizations, including
Alteryx, TIBCO SpotFire, ChartBlocks, Datawrapper, Orange, Looker, FineReport,
and LeafLet. However, the majority of the employees are familiar with Tableau
and Microsoft Excel. Our survey results showed that QlikView, PowerBI, SAS,
and Grafana are also tools the company employees have been exposed to. We can
conclude that companies may likely provide Microsoft licensing for their
employees, and Tableau is a popular data visualization creation tool for
people from non-technical and technical backgrounds.

Figure 8: Which of the following software have you used and are familiar with
for visualizations?

We asked a technical question about their exposure to libraries used to code
visualizations to continue our in-depth research on the individual’s
experience with data visualization tools and applications. The employees from
non-technical positions were not familiar with the libraries. Plotly was the
most familiar library amongst the technical employees, followed by ggplot,
seaborn, and matplotlib. This implies that the technical employees tend to
prefer using Python or R when coding visualizations.

Figure 9: Which of the following data visualization libraries have you
previously used or heard about?

This survey exposed that basic knowledge of data visualizations is fundamental
in any field. We asked our survey respondents about the plotting techniques
they are aware of and at least one person was familiar with each type of plot
listed. There was only one individual, a Product Manager, who reported that
they were unfamiliar with any techniques. The most recognized plot types are
scatterplots, bar plots, heatmaps, line plots, and cluster maps. This is a
direct result of the most basic visualizations commonly displayed in the
workplace, online training, and even university settings.

Figure 10: Which of the below types of plotting techniques have you heard of
before?

The most common domain that the employees have noticed in data visualizations
is Information Technology. This was an expected outcome as technical positions
tend to use data visualizations more often than non-technical job assignments.
The non-technical employees may have been exposed to visualizations when
viewing these technical presentations.

Figure 11: For which domain have you applied data visualization or observed
being applied?

We questioned the industry professionals on their confidence with specific
concepts related to visualizations. The professionals were most confident with
Exploratory Data Analysis and were significantly less confident with the
remaining topics: Time Series, World Cloud, and Kernel Density Estimator.
However, there are some outliers as the same Product Manager, unfamiliar with
the plotting techniques, was very confident with the Time Series topic. The
results made it very noticeable that non-technical industry positions were
highly unfamiliar with these concepts, with the overall most unfamiliar topic
being the Kernel Density Estimator.

Figure 12: How confident are you in below topics?

We concluded the survey asking about the impact of data visualizations. All
employees agreed that visualizations do not make matters more difficult, but
they either maintain or make tasks easier. In terms of communication,
understanding, and decision making, a large majority of the individuals
surveyed agree that data visualizations are beneficial for people.

Figure 13: According to you, what is visualization impact on the below
parameter?

It is a proven fact that visualizations communicate data better than words
alone as it makes information easier to process. They allow people to
comprehend data at faster and more effective rates. Visual representation
allows for clearly defined parameters, leaving less room for confusion and
miscommunication that leads to wrong decisions. Analyzers can gain insight
that they may not have acknowledged without the visualization that encourages
rightful decision-making. Large data sets can be easily comprehended by the
human brain when it is formatted in a clean, efficient visualization. These
visuals make it easier to detect patterns, trends, and outliers in groups of
data. Also, data visualizations can answer business questions that are not
necessarily intuitively answered. They provide context to make critical data-
driven decisions for the financial benefit of an organization. For these
reasons, we can assume that these employees value utilizing visualizations in
the workplace and would prefer continued usage of these methods.

##  IV Results

The results of this survey were not unanticipated. The most shocking factor to
the team was that there was a more extensive representation than expected of
employees (7.7 percent) who do not use visualizations in the workplace. We
assumed in the present day that every job position should be utilizing data
visualizations. We were partially correct in this assumption as each employee
revealed they had seen visualizations in work meetings and presentations, but
each employee does not need to use them for their job. The most important
takeaway from our research is the knowledge we have gained about the
employees’ perspective. We have learned what employees value when it comes to
visualization preferences and learning new information and the patterns in
technical versus non-technical positions. The results did not vary based on
the company but by position. Our results have also allowed us to generate
visualizations about the information companies prioritize exposing their
employees through training and presentations.

## Acknowledgment

Faiz Ahamed Iqbal,Sushmita Suresh,Sneha Krishnan,Vijay Prasad,Vaishnav B,Kiran
Kodithala,Bhaskar D,Nicolas E. Colbert,Rakesh Manda,Kaléa Dais,Sumanth
Katari,Rathnakara Achary,Joji Thomas,Suyash Kotia,Deepthi Saineni

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