# Open Research Issues and Tools for Visualization and Big Data Analytics

Rania Mkhinini Gahar1,2 Olfa Arfaoui1,3 Minyar Sassi Hidri4

( 1National Engineering School of Tunis, University of Tunis El Manar, Tunisia
2OASIS Research Laboratory
3RISC Research Laboratory
4Computer Department, Deanship of Preparatory Year and Supporting Studies,
Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
)

###### Abstract

The new age of digital growth has marked all fields. This technological
evolution has impacted data flows which have witnessed a rapid expansion over
the last decade that makes the data traditional processing unable to catch up
with the rapid flow of massive data. In this context, the implementation of a
big data analytics system becomes crucial to make big data more relevant and
valuable. Therefore, with these new opportunities appear new issues of
processing very high data volumes requiring companies to look for big data-
specialized solutions. These solutions are based on techniques to process
these masses of information to facilitate decision-making. Among these
solutions, we find data visualization which makes big data more intelligible
allowing accurate illustrations that have become accessible to all. This paper
examines the big data visualization project based on its characteristics,
benefits, challenges and issues. The project, also, resulted in the provision
of tools surging for beginners as well as well as experienced users.

Keywords: Big Data, DataViz, Cloud, Big Data Analytics, Business Intelligence.

##  1 Introduction

Every day we generate 2.5 trillion bytes of data. In fact, 90% of the world’s
data was created in the past two recent years only [1]. The data comes from
different sources such as the sensors used to collect climate information,
social Media Posts, digital images, and online videos, online Purchase,
transnational records and GPS (Global Positioning System) signals from mobile
phones. This data, which has resulted essentially from the meeting of three
elements which are Internet, social networks, and smart devices (computers,
tablets, smartphones, connected objects…), is called big data or massive data.
It is considered very interesting according to the pertinent information that
may contain. Actually, we note that 13 of business leaders’ decisions are
based on information they don’t trust or don’t have and half of them say they
don’t have access to the information they need to do their job and 83% of CIOs
(Chief Information Officer) cite analytical business intelligence (BI) as part
of their plans to improve their competitiveness.

Moreover, 60% of CEOs (Chief Executive Officers) need to improve the capture
and understanding of information in order to make decisions more faster [2].
For example, data could be analyzed to i) detect customer feelings and
reactions or critical or life-threatening conditions in hospitals to intervene
in time; ii) predict weather patterns to plan the optimal use of wind turbines
and make decisions based on transactional data in real-time; iii) identify
criminals and threats from videos, sounds and data streams; iv) studying
student reactions during a lesson and predict which ones will succeed in the
basis of statistics and models gathered over the years (big data in the
education field).

Research on big data analytics is entering a new phase called fast data where
multiple gigabytes of data arrive in the big data systems every second [3, 4,
5, 6, 7, 8]. Modern big data systems collect inherently complex data streams
due to the 3 basic Vs which are Volume, Velocity, and Variety and to which are
added Veracity, Validity, Vulnerability, Volatility, Visualization which
consequently give rise to the 10Vs [9] of big data. The well-designed big data
systems must be able to deal with all 10Vs effectively by creating a balance
between data processing objectives and cost (i.e., computational, financial,
and programming efforts). Data collection and storage capabilities have
allowed researchers in diverse domains to collect and observe a huge amount of
data. However, large data sets present substantial challenges to existing data
analysis tools [10].

We will focus in our paper on one of the most important big data’ Vs which is
data visualization.

One of the most crucial and useful tools for comprehending corporate
information is data visualization. Because of this, a picture truly is worth a
thousand words. Data has been visually represented by humans for hundreds of
years. We’ve collected data, organized it, and presented it in maps, charts,
and graphs to tell a richer and deeper story than it may have otherwise. The
data boom coincides with the technological boom. Additionally, we have been
able to process ever-increasing volumes of data quickly thanks to the same
technology. Although they might not be immediately apparent in the first text
format, trends, patterns, and other insights are quickly identified utilizing
data visualization software.

The most effective strategy changes to visual data displays after reports and
dashboards take their place since they can fit a lot of information into a
little amount of space. Examining the extensive data sets and graphic
presentations that enable quick and accurate translation might take hours,
days, or even weeks. Thanks to advanced technology, many data visualization
tools allow for interactive functions. This flexibility provides the ability
to switch and change quickly, which helps the user to discover and learn about
alternative viewpoints. This comprehensive, interactive presentation can
rarely be achieved quickly by processing raw data without visualization
software. The information industry frequently faces the difficulty of the
quantitative component.

Knowing that decisions are made as a result of visual representations requires
a solid comprehension of data. In the absence of context, visuals are
ineffective. But there is an easy target is just to let the workers and the
tools perform their jobs. As long as you utilize the appropriate tools and the
individuals conducting the data analysis are aware of where the data
originated from, who can use it, and how it will be used. The data
visualization will next be translated, processed, and on a more clearer course
for making those important decisions. Data visualization’s significance in the
world of corporate information is being realized more and more every day. They
have the ability to not only supply useful data but also understand how to
process it, which guarantees the organization stays competitive. This is
because they are high-performance analytics tools that offer better ways to
analyze data faster than ever before.

Visualization is critical in today’s world. Big data is difficult to
visualize. Due to in-memory technology limitations and low scalability
(scaling up), functionalities and development time response, visualization
tools, and current big data are faced with technical challenges. Traditional
graphs cannot be relied upon to attempt to plot a billion data points. We,
therefore, need different ways of representing data. If we take into account
the multitude of variables resulting from the variety and speed of big data
and the complex relationships that unite, we can see that developing a
visualization significant is not so easy.

Spreadsheets and reports stuffed full of numbers and algorithms are far less
successful at communicating meaning than reports and charts that show enormous
amounts of complex data. Because of the constraints of in-memory technology
and their inadequate scalability, functionality, and response time, current
large data visualization solutions suffer technological difficulties. When
plotting a billion data points, we can’t rely on typical graphs, thus we need
alternative methods, such as data clustering or the use of treemaps,
sunbursts, parallel coordinates, circular network diagrams, or cone trees.
[11].

The rest of this paper is organized as follows: The DataViz’ presentations are
presented in section 2. Section 3 presents its benefits. Section 4 introduces
the characteristics of such DataViz project. Section 5 discusses some DataViz
tools surging for beginners as well as well as experienced users. Section 6
revealed the main data visualization challenges. The DataViz issues are
highlighting in section 7. Overall discussion with limitations are stated in
section 8.

##  2 The DataViz and you: presentations

Many data scientists define data visualization in different ways. Indeed, they
agree that it is indeed a visual form to visualize by facilitating access to
it. Another data experts’ group agrees that data visualization is meaningless
if it does not encompass understanding, exploitation and decision-making,
speed, and information sharing.

###  2.1 DataViz features

The value of data visualization lies in its ability to meet three main
imperatives namely interpretable, relevant and innovative.

  * •

Be interpretable: In a context where the volume of data is exploding with the
exponential growth in the use of the Internet and in particular Google, so-
called unstructured data experiences the same evolution. But a data
visualization starting from these data which would not be interpretable, that
is to say clear, would be useless. There must be some clarity regardless of
the volume or source of the data [12].

  * •

Be relevant: At a time when big data is a central issue for companies, several
techniques to process this mass of information in a relevant way must be put
in place.

Relevance is linked to interpretability. The data visualization must make it
possible to answer questions in a defined context and aimed at specific
objectives. Data sources must be reliable. Data integrity is the basis for
meaningful data visualization. You must ensure that your information is
correct and up to date. It is necessary to sort the data for optimal analysis
and to consider using all the data at your disposal. This allows you to cross-
reference information and thus bring out a more complete analysis to better
support your digital marketing department[13].

  * •

Be innovative: Finally, data visualization is only of interest if it brings
new information, and originality, if it gives a perspective unpublished on a
subject. It provides a different perspective, to illustrate an analysis [14].

###  2.2 Brief history of DataViz

Originally, it was a simple human limitation that spawned the Data
Visualization approach: our brain is simply unable to easily process large
volumes of raw data to extract useful information. Maybe we can do it
occasionally, but certainly not every time, let alone many times every day.

However, Data Visualization is not just about the graphical representation of
data. It is also a story that is told with the help of these representations.

The first successful expression of this approach is a flow map that tells the
story of the Russian campaign led by Napoleon. We owe it to the engineer
Charles Joseph Minard who represents, at the beginning of the 19th century,
the story of the colossal human losses of the Russian campaign during which
the Napoleonic army arrived in Moscow with less than a quarter of its starting
squad.

Shortly after, nurse Florence Nightingale had the idea of using graphic
representation to allow her reader to compare facts with complex correlations
[14].

She presented, for the attention of Queen Victoria, the main causes of death
of British soldiers engaged in the Crimean War. His graphic support, dated
1858, allowed him to eloquently highlight that epidemics were much more
devastating on the workforce than the injuries suffered in combat. Florence
Nightingale has therefore used a graphic support of data, presented in figure
1, to communicate information, of course, but also to convince, that is to
say, to orient the conclusion that one draws from it.

Figure 1: Florence Nightingale’s Diagram [15].

Until the 19th century, Data Visualization therefore evolved through isolated,
independent attempts, which explored both the possible fields of application
and the possible graphical approaches. In other words, the discipline was
forging a vocabulary but it still lacked common rules that is to say, a
grammar.

This grammar was laid down by Jacques Bertin who in 1967 developed the real
bases of graphic language.

In figure 2, Jacques Bertin defined graphic semiology, i.e. the elements that
can be modified in a Data Visualization to represent information. Identifying
and clearly defining these graphic variables (color, size, surface) was simply
the grammar that was missing from the graphic language.

Figure 2: Jacques Bertin’s graphic semiology [16].

It is by applying these new - but common - rules that we have gradually
managed to remove superfluous graphic elements and define what characterizes a
relevant data visualization.

###  2.3 The DataViz is pretty, but what is it for?

Data visualization concerns a wide range of business sectors. If we stick to
our panel, several areas are represented, namely consumer goods, business
services, industry, media, marketing and advertising, scientific research,
public service, telecom, transport, logistics, etc… All companies use data
visualization either to do certain things they were already doing better
(optimization), or to enrich their activity with value-added tasks
(innovation). In terms of optimization, DataViz allows, for example, to
Lagardere Active to accelerate the production of its reports and
STMicroelectronics to make its manufacturing process more efficient;

In the area of innovation, PagesJaunes discovers and rectifies, thanks to data
visualization, the shortcomings of its indexing, Voyages-sncf.com launches a
new innovative service (Mytripset), Alcatel Lucent imagines the mobile
applications of tomorrow, etc.

###  2.4 Dual purpose of optimization/innovation

More specifically, 5 use cases illustrating this dual purpose optimization and
innovation: the use of DataViz to:

  * •

Improve the company management: One of the first uses of data visualization is
to contribute to more effective management of activity and performance,
oriented towards action.

In our modern economy, all directions need to manage their organizations as
finely as possible by relying on numerous and rich data and by producing
relevant dashboards.

”A picture is worth a thousand words” and nowadays ”the image takes precedence
often on words” [17].

Data visualization is the art of telling figures in creative and gameful way
[18].

  * •

Improve the customer relationship: Marketing and customer relationship
management are two functions of choice for data visualization. We visualize
customer data to improve Customer relationship management (CRM) multi-channel.
In addition, DataViz is better to qualify the customer base.

  * •

Define the company’s offer: At the opposite end of the spectrum, data
visualization provides companies with tools to better define their offers.
Moreover, the exploration of collected customer data and the ability to test
different hypotheses prove particularly valuable.

  * •

Contribute directly to the company business: The DataViz serves to better
understand your competitive positioning. That’s why Data visualization could
be considered a relevant source of value and differentiation which immediately
identify customer postponements. Here, data visualization could directly
influence the business economics model [19].

  * •

Empowering citizens: Data visualization also finds its usefulness outside the
walls of the company. It can contribute to better informing the citizen and
therefore giving him the means to act.

In fact, company can allow citizens to think through Citizen DataViz projects
may take a longer turn activist [20, 21]. This is the case, for example, of
the pariteur of France Televisions.

###  2.5 DataViz and Infographics

The Dataviz takes the information a person needs and presents it in an easily
understandable way. Infographics are a mix of Dataviz, journalism, and
marketing. They use strategically chosen data visualizations and lexicons to
explain a complex story easily. The confusion in terminology is understandable
however the terms are not interchangeable. Both turn data into easy-to-
understand visualizations. These tools are extremely powerful when it comes to
explaining numbers in an educational way to people who are reluctant to
analyze data. This is their only common point. Here is a definition for both
forms of presentations. Some distinction points between these two terms can
take place in Table 1.

Table 1: Infographics versus DataViz Infographics |  DataViz
---|---
Promotes the information understanding that we already know by representing it in graphic form. |  Bring out information that was unknown by analyzing data presented in graphical form.
Modest data amount. |  Huge data volume.
Good design makes a product useful. |  Good DataViz makes information useful.
Good design is aesthetic. |  Good Data is aesthetic.
Good design makes a product understandable. |  Good DataViz makes information understandable.
Good design is honest. |  Good DataViz is honest.
A didactic approach focused on others. |  Self-knowledge tool.
Understanding support. |  Decision support.

###  2.6 Main reasons for using DataViz

Three main reasons explain the use of data visualization namely confirm or
refuse hypotheses on a market, educate and explore.

  * •

Confirm or refuse hypotheses on a market: The DataViz can then take the form
of a dashboard, making it possible to decide while having a global vision of
the studied market.

  * •

Educate: Internally, companies use DataViz for research work reporting or
brainstorming sessions. It can be a good complement to creative approaches
such as gamification.

  * •

Explore: This is the most futuristic aspect of data visualization, which
certainly will develop. Dataviz can help build predictive models. We are then
in the field of data analysis

###  2.7 DataViz: buzzword or real innovation?

The data that are thus available to professionals to guide them in their
decisions are increasingly numerous and multi-structured. But how not to be
overwhelmed and make it a real tool for reflection and decision-making? How to
obtain answers to fundamental questions whose answers are for the moment
unknown?

It is in the face of these requirements that DataViz takes on its full
meaning. The representation of data in the form of images makes it easier to
understand them. There are several definitions of DataViz in the academic and
industrial state of the art. These different definitions all converge on the
fact that DataViz is a way to give meaning to data in order to extract
information from it and therefore exploit it. Dataviz not only enables
intellectual understanding but also transforms a set of raw data into
actionable information.

In addition, it accelerates the understanding, decision, and action that we
have just mentioned. It is also a mode of communication, allowing data not to
remain confined to the world of BI or statistics but to infuse the entire
organization and become a support for decision-making and collaborative work.

DataViz has many uses and leads to a variety of benefits for the organization.
First, it contributes to more effective management of activity and
performance, oriented towards action. This improvement in management is
manifested by taking a step back in addition to other tools whose horizon is
in the shorter term. In other words, DataViz can be used as a decision-making,
and strategic tool, usable by a local manager to manage his performance.

Another important use of DataViz is the reinvention of customer service to
improve its efficiency. To improve the management and understanding of their
KPIs (Key Performance Indicators), SFR uses DataViz to identify causal
relationships in their data sources in order to find hidden patterns through
their main sales channels. A good customer relationship is based on perfect
knowledge of the customer himself. What are their characteristics and
behaviors? How to segment and classify them? The exploration capabilities in
the data enabled by data visualization find their full meaning in providing
answers to these questions.

Another key point about DataViz is its ability to foster innovation and its
potential to get the business to consider new possibilities. In particular, it
is a testing ground for new modes of interaction with users.

##  3 DataViz’s benefits

In a context of ever-increasing and often highly complex volumes of data,
DataViz has many advantages. Data visualization is far from being an accessory
intended to embellish your website or your presentations. Synthetically, we
can say that DataViz improves the data understanding, the data communication,
the decision-making, and the ability to innovate.

###  3.1 Dataviz makes it easier to understand data

With big data advent and the proliferation of data sources, companies are
increasingly using data visualization. These visual representations make it
easier to understand raw data and thus help in decision-making. Big data is
not merely more data; it is data that is so vast, so varied, and collecting so
quickly that typical procedures and methodologies, including ”normal” software
like Excel, Crystal reports, or other programs, are ineffective. DataViz makes
it possible to make the most comprehensible data important and what they mean,
regardless of the audience concerned. Its effectiveness is based on the fact
that a majority of us grasp and retain information better when it is
represented visually. The following image illustrates this fact, which was
studied by an American psychologist.

Unlike a table filled with figures, DataViz helps to highlight information
that seems complex or drowned in a large number of parameters. The following
example illustrates this fact well. For example, we want to analyze the life
expectancy by country. Table 2 presents an extract of values from the top 5
countries since the values file is very large.

Table 2: Life expectancy per country. | Country | Life expectancy | ISO-code
---|---|---|---
0 | Afghanistan | 64.5 | AFG
1 | Algeria | 76.7 | DZA
2 | Andorra | 81.8 | AND
3 | Angola | 60.8 | AGO
4 | Antigua and Barbuda | 76.9 | ATG

But is it ideal for ordering easily and between countries quickly? And explain
it in a few seconds to his audience? If we transcribe this information on a
map, with more or less bright colors depending on the strength of the index,
everything becomes clearer. We want to quickly understand which countries have
the highest rate of life expectancy.

Figure 3 allows us to assess at a glance countries with the highest rate of
life expectancy. Analyzing an Excel file is much faster by visualization,
especially if the data is large and complex.

Figure 3: Life expectation by country (Source: Kaggle / WORLD DATA by country
(2020).

Our brain needs less than 250 milliseconds to enter (1), understand (2), and
respond (3) to information under visual form. Whereas comparing raw data in
tabular form requires an effort of memory that quickly reaches its limits.
Ultimately, data visualization invites us to take up the classic distinction
between data, information, and knowledge. If the data are unitary, raw
elements, reflecting reality, the information is their coherence to give them
meaning.

###  3.2 Dataviz improves communication

Data not only reflect reality, but they also are not just a steering lever.
They are also communication tools. Unfortunately, few of us are fluent in our
language. Most of us need an interpreter to make us penetrate the intelligence
of the data.

This is where data visualization comes in. Moreover, Communication is the
major asset of the DataViz for coordinate the teams. Indeed, the infinite
volumes of data stored by companies are generally only within the reach of
data analysts and other technical profiles. But once put into images, this
data is accessible to everyone without any prior training. The support
generated by DataViz makes it possible to unify the discourse and convey
unambiguous messages. We can truly speak of democratization of access to given
[22].

###  3.3 Dataviz optimizes and accelerates decision-making

This is the logical continuation of an easier understanding of the data.
DataViz allows the development of interactive dashboards. Unlike static
charts, such as those in Excel, interactive DataViz allows the exploration of
data in depth, with less effort. In a few clicks, it is now possible to
release correlations between the operational actions put in place, the
performance, and their impact.

It is also much easier to compare its indicators to the market, and
competitors. Once the data has been clarified and better identified, decision-
makers can focus on the essentials and make choices in a way more simple.

###  3.4 Dataviz promotes innovation

Finally, we cannot close this part on the benefits of data visualization
without examining its potential in terms of innovation. Indeed, DataViz is
also a field of research that can encourage the company to consider new
possibilities. In particular, it is a testing ground for new ways of
interacting with users [23].

##  4 Characteristics of DataViz projects

The deployment of DataViz software is a crucial step that must be perfectly
orchestrated to guarantee the success of the project. This later must be both
fast and light [24].

###  4.1 DataViz project’s speed

The first characteristic of DataViz projects is that they are, all in all,
quick to conduct. Their big advantage is in particular to reduce the time
between the launch of the project and the ability to show a first operational
version [21].

There are several reasons for this speed. One of them is that DataViz projects
require hardware and software resources that interfere little with existing
architectures. They, therefore, do not require long and complex budget
validation cycles: the necessary environment can be available in a short time.
Another reason is that data visualization lends itself well to POC (Proof Of
Concept) type approaches experimentation, trial and error iteration loops.
Dataviz projects have a very empirical side, completely in phase with current
development approaches, such as agile methodology, rapid Agile development, or
Scrum.

###  4.2 DataViz project’s lightness

Lightness is involved in the technical resources required which are quite
light. Example: Recent technological developments, such as the development of
JSON-type formats (DS.JS3), put us in a direct connection with data. Thanks to
these formats, we can recover varied data from all horizons, using standard
applications. A real human-data interface is thus being established.

###  4.3 Success key factors

To succeed in a data visualization project, it is necessary to bring together
key success factors that can be classified into three categories. First of
all, there are the classic good practices of any project: ensuring preparation
and planning, choosing the right scope, implementing the appropriate
methodologies, etc. The second category is that the DataViz project concerns
data: their targeting, quality, respect for confidentiality, and access
authorizations, are therefore essential. Finally, of course, ergonomics and
graphic intelligence play a key role in the acceptance of DataViz and its
effective use (although, by the way, these aspects should be part of any IT
project, etc.).

####  4.3.1 Prepare the project well

The first secret to success is preparation. This is understood on two levels:
the content of the data visualization, on the one hand, and the project
approach, on the other hand.

DataViz is not a panacea and, to be useful, it must be well thought out. On
the methodological aspect, the preparation consists of implementing
establishes a process for collecting, analyzing, and representing data that is
viable over time, but also sufficiently flexible.

####  4.3.2 Target data and visualizations according to the profile of the
users

A second success factor is related to the nature of the data visualization,
namely a communication tool. However, for communicating effectively with
someone, it is important to meet his need, with clarity and in a form he
understands.

Three key questions to ask yourself to choose the best representation:

  * •

What question do we want to answer? All the graphs do not make it possible to
present the same analysis (distribution, evolution, decomposition…). Hence the
importance for the designer to question his intention.

  * •

Who are we talking to? Is he an expert or a layman? What should he do with the
information (e.g. retain the information for later or make an immediate
decision)?

  * •

In what context is the interlocutor? The good reception of the graphic does
not only depend on the graphic itself, but also the intellectual and visual
availability of the reader. All that the graphic designer can do is try to
anticipate this greater or lesser availability, in order to choose the most
suitable representation.

The difficulty increases when the data visualization must address different
audiences. It is then necessary to provide modes of representation adapted to
each of them. This is the case, for example, of the Belgian FPS Economy, which
communicates with both the general public and professionals.

####  4.3.3 Start on small perimeters, to learn

Another good practice is to ”get your hands dirty” on first restricted
perimeters, if possible as controlled as possible. This allows you to move
forward, without too many risks, in trial and error mode until a first
satisfactory solution is obtained.

####  4.3.4 Ensure data quality at source

In BI, you reap what you sow or, to put it more lapidary way: garbage in,
garbage out. In other words, if we want data visualization to be able to
communicate the right messages, make it possible to make informed decisions,
to explore unknown territories, there is one condition to be met above all: to
have quality data entrance.

In return, DataViz improves the quality of the data. First, because it compels
a certain discipline; then, because it also visualizes… the non-quality of the
data. Repeated outliers may appear at first glance as oddly placed dots, for
example.

####  4.3.5 Focus on cooperation between several departments

Another ingredient of success lies in the cooperation between the actors of
the project. DataViz thus contributes to breaking down the silos that may
exist in the company and contributes to greater cross-functionality.

####  4.3.6 Train the teams

Data visualization does not require side training users. On the contrary, we
can say that it is fully successful when it is immediately adopted. For this,
simplicity and intuitiveness are essential. But offering a simple rendering
can be extremely complicated. This is why the training of those who produce
the visualizations is an undeniable plus.

####  4.3.7 Using aesthetics as a lever for appropriating information

Data visualization cannot be reduced to a representation aesthetics of data.
One can make pretty representations that are perfectly useless. But that’s not
to say that aesthetics don’t play a role. Used well, it is an essential
criterion of efficiency for DataViz. In this concern to combine aesthetics and
efficiency, companies have every interest in being imaginative and going
beyond traditional Excel-type charts, if that makes sense.

###  4.4 Pitfalls to avoid

If certain good practices maximize the chances of succeeding in your data
visualization project, you can expect, as with any project, to encounter
difficulties such as:

  * •

The risk of overloading it with information.

  * •

A general management that does not necessarily perceive an interest of data
visualization immediately.

  * •

Skepticism about the performance of the tool.

  * •

A pitfall to avoid: forget your classics: We should not confuse simplicity
with simplism [25].

###  4.5 The DataViz’s impact on the relationship between IT and business
lines

Data visualization projects have the particularity, as we saw previously, of
offering great autonomy to users. users. This is why, in this area, the
relationship between IT and the Professions are set to evolve. It has happened
that friction has arisen, with the IT Department feeling deprived of some of
its prerogatives. These tensions have unfortunately been maintained by some
providers of DataViz solutions by addressing only the Business Lines without
going through the IT Department to win contracts [26].

Thus, we cannot speak of a loss of prerogatives of the DSI (Dimensional
Strategies Inc.) or the BI teams. Simply, data visualization raises new
questions about how to represent data, about the distribution of roles and
responsibilities between the business lines and the IT department, and about
how to conduct projects [27].

CIOs understand this. Even those who were initially reluctant are realizing
that data visualization is not a threat, and are softening their stance [28].
In truth, data visualization is a chance for CIOs and BI teams. On the one
hand, it will relieve them of time-consuming tasks, allowing them to focus on
their missions with higher added value. On the other hand, they even have a
unique opportunity to invent a new form of BI and relationship with business.
What we can remember is that data visualization, even if it provides great
autonomy to the business lines, is a question that should interest the IT
department and the BI teams, quite simply because it touches the data.
Autonomy of businesses is useful if it is implemented smartly and if it allows
them to obtain even more value from the CIO and the BI. Presumably, by
accustoming the Professions to speak the language of data, thanks to graphics,
the DataViz will contribute to the taking of awareness of the value of data.
It can therefore play a role unifier, at the service of business creation.

##  5 Which tools for which data visualization?

Information visualization faces increasing hurdles as the big data era
progresses. First of all, the amount of data that needs to be visualized
surpasses the size of the screen. Second, a typical computer cannot be used to
store and process the data.

A big data visualization solution needs to offer perceptual and performance
scalability to solve both of these issues.

In this section, we will focus on some data visualization tools for beginners
as well as for experienced users.

###  5.1 Tools accessible to beginners

Tools for beginners are available to allow them to create DataViz without
resorting to programming or its basis and no expertise is required.

####  5.1.1 Office software and extensions

##### Excel

Excel remains one of the basic tools for data visualization [29, 30]. The
maximum number of values in a column is about 1,999,999,997 [31].

Third parties create Excel add-ins to provide Excel users with extended
functionality and save their time and effort. Developing these add-ins
requires coding expertise in languages such as XML (eXtensible Markup
Language) and VBA (Visual Basic for Applications) and providing an easy-to-use
interface that complements Excel.

Table 3 overviews some Excel add-ins for DataViz. Table 3 overviews some Excel
add-ins for DataViz.

Table 3: Some Excel add-ins for DataViz. Add-in |  Description
---|---
Filled Map |  Used to display high-level chart data within a map.
3D-Mapping |  A three-dimensional (3D) data visualization tool called Microsoft 3D Maps for Excel enables you to see the data in novel ways.
Bing Maps |  With the Bing Maps add-in, Excel users can quickly plot locations and display their data using Bing Maps.
Radial Bar Chart |  It displays data from standard bar charts on a circular pattern. With this representation, visual analysis gains the advantages of both bar charts and circle graphs. The Radial Bar Chart has a distinctive style that makes it a very adaptable representation.
XLMiner Data Visualization App |  It instantly visualizes data in your Excel spreadsheet and lets you adjust the variables plotted on each axis, zoom in and out, add filters to highlight important data, and use textit size by and textit color by categorical variables for quick insights.

#####  LibreOffice

LibreOffice is a free and open-source office suite, derived from the
OpenOffice.org project, created and managed by The Document Foundation.
Extensions are provided to enable various activities, including visualization.
LibreOffice extensions are software add-ons that you can install in addition
to the core LibreOffice apps to extend the capability of the suite in one or
all of the programs (Writer, Calc, Impress, etc.).

Some examples of LibreOffice extensions dedicated to data visualization can be
presented in the table 4.

Table 4: LibreOffice Extensions. Extension |  Description
---|---
GeoMap |  Inserts map images directly into your document from address information.
GeOOo |  His free solution suggests using the LibreOffice tools to create thematic maps.
ClusterRows |  A Is a LibreOffice Calc extension that groups rows into clusters and colors the clusters to show them.
OOoHG Gallery |  Includes a Gallery that contains 1600 maps, diagrams, and graphs for geography and history organized into 96 categories (in bitmap and vector graphic format).
OpenStreetMap Presentation |  It uses penStreetMap Datas as background for nice presentations.

####  5.1.2 Online office suites

When choosing between data visualization tools, one option worth considering
is Google Sheets. Google’s spreadsheet application can be used to generate
charts, tables, and even maps that can be embedded in a website. They’re easy
to make and can be configured to update automatically [32].

It’s not for every visualization need. There are some tasks that need for more
intricate data visualization strategies and customisation than what Google
Sheets can offer.

Google drive extensions can also help novice users to perform data
visualizations, namely Fusion Tables [33], Slemma [34], Geckoboard [35],
VizyDrop [36], BIME Analytics [37], Cyfe [38], and Datahero [39], etc.

####  5.1.3 Office 365

Microsoft 365 is made up of the Office suite (Word, Excel, PowerPoint,
Outlook, OneNote, Publisher, and Access), as well as a set of online services
such as OneDrive, Exchange Online, SharePoint Online, Teams, and Yammer. The
Office suite allows work in offline mode like a perpetual suite, which
distinguishes it from Office Online, which is used from a Web browser. The
principle of Microsoft 365 is to be updated as new versions of Office are
released [40]. It provides also some integration apps to visualize your data
in an interpretable, innovative and relevant way. Table 5 describes someones
highlighting their main functionalities.

Table 5: Data Visualization Apps Integrated with Microsoft 365. Apps |  Description
---|---
Workday Adaptive Planning |  Is a cloud-based enterprise planning platform that provides modeling, budgeting, and forecasting capabilities to enable firms collaborate on planning. It offers capabilities like workforce modeling, balance sheets, and spending management, among others.
Coras |  It is an enterprise decision management platform suitable for IT, government, legal, and marketing teams. It can be adapted to any type of business and easily mapped to existing processes and governance.
Microsoft Power BI |  It enables the creation of personalized and interactive data visualizations with an interface simple enough for end users to create their reports and dashboards.
Cyfe |  Users can monitor and analyze data spread across all of your web services, including Google Analytics, Salesforce, Google Ads, MailChimp, Facebook, Twitter, and more, from a single spot in real-time with this all-in-one dashboard software.
DBxtra |  It is an ad-hoc reporting and business intelligence system that gives companies the tools to create and distribute unique reports on key performance indicators. It has capabilities including an Excel reporting service, a report and dashboard designer, online report distribution, and a report scheduler.
MicroStrategy Analytics |  It equips businesspeople with self-service tools to study data and share insights in minutes, enabling them to make quicker, smarter business decisions.
Kepion |  It is a Microsoft BI-powered cloud-based business planning tool. In a single, centralized platform, it combines budgeting, forecasting, BI reporting, and intuitive modeling technologies, empowering users to create and schedule applications that are tailored to the operations of their firm.

####  5.1.4 Simple online tools

##### Tableau

Tableau Public is a free platform for exploring, creating, and publicly
sharing data visualizations online [41].

#####  Canva

Unlike other charting tools, Canva is quick and easy. There’s no learning
curve - you’ll have a beautiful graph or chart in minutes, turning raw data
into something visual and easy to understand [42].

#####  Plot.ly

Plotly is an open-source visualization library for data visualization and
analysis. It provides many products including Dash, Chart Studio,, a Python
framework, R, and recently JULIA for building fast, easy and powerful
analytical applications. It gives the hand to draw several types of the graph
such as 3D graphs, histograms…, easy to use and handle, totally free, and very
interactive and flexible.

#####  PowerBI

Is a data analysis solution from Microsoft. It allows for the creation of
personalized and interactive data visualizations with a simple interface
enough for end users to create their reports and dashboards [43].

#####  Chartblocks

This is an online charting software. It helps to create basic charts quite
quickly and to import more data from different external sources [44].

#####  Periscope Data

Is an effective platform for data analysis. It may compile all of the
company’s data and produce reports. With this tool, we can quickly transform
our data into a report or graph that is simple to interpret. [45].

For data consumers who frequently need data, Periscope Data enables analysts
to transform their SQL searches into interactive dashboards, charts, and
reports. The ground-breaking data warehouse technology from Periscope Data
links to your databases in a flash to provide extraordinarily speedy, low-cost
query processing. Workflow hurdles are removed, and data literacy is promoted
throughout your organization, thanks to unlimited users and no query limits.
[46].

#####  Holistics

Is an intelligent data reporting and business intelligence platform that
enables us to resolve our data-related inquiries and problems without the need
for technical support. For business and data teams, it eliminates the
aggravation of request queues. Holistics allows business users to access their
data without writing SQL (Structured Query Language) or interfering with data
teams. Data teams can create and manage a set of business KPIs using Holistics
[47].

#####  Cluvio

It is a cloud-based analytics and BI tool that enables businesses to use a
dashboard to examine data. The solution enables query execution, results
filtering, and data visualization on charts and diagrams. An SQL editor,
adaptable R scripts, push notifications, customer dashboard sharing, and more
are some of its primary features [48].

#####  Klipfolio

is a cloud-based tool for building and sharing real-time dashboards for use on
mobile, TV, and online browsers. [49].

#####  Clicdata

ClicData is a 100% cloud platform. It offers a more modern vision of the
software by offering in particular the possibility of importing data
regardless of their format and cross-referencing information from different
tools [50].

#####  Qlik Sense

An effective visualization presents the relationships between many values and
allows you to analyze data at a glance. Qlik Sense offers a wide range of
visualizations and charts. Each chart excels at visualizing data in various
ways for different purposes. Charts should be selected based on the data you
want to see in them [51].

#####  Chartio

With the help of the cloud-based BI and analytics tool Chartio, users can
quickly evaluate data from business apps and visualize it using a variety of
customisable charts. Due to its straightforward Interactive and SQL modes,
Chartio is appropriate for both professionals and those with no prior
technical knowledge [52].

##### DataWrapper

Another excellent tool for data visualization is Datawrapper. With
DataWrapper, you can simply generate charts, tables, and maps that are
readable on any device. As a non-commercial platform, Datawrapper is best
suited for schools and small businesses that require simple data visualization
tools [53].

##### Venngage

Venngage is a web application for creating a range of data visualizations
including infographics, posters, reports, and promotions [54].

##### Piktochart

Piktochart is a web-based graphic design tool and infographic maker. We can
create bar charts, maps, line graphs, scatter plots, and more [55]. It can
also synchronize with Google Spreadsheet or SurveyMonkey to retrieve data and
thus create interactive graphs or tables.

##### Infogram

Infogram is a web-based data visualization and infographics platform. It
operates as a data visualization tool to make data easy to understand,
discover unknown facts/outliers/trends, visualize relationship patterns, and
ask better questions.

##### Raw

RAWGraphs is an open-source data visualization platform designed to make it
simple for anyone to visualize complex data. It tries to fill the gap between
vector graphics editors like Adobe Illustrator, Inkscape, and Sketch and
spreadsheet programs like Microsoft Excel, Apple Numbers, and OpenRefine [56].

##### Wordle

Wordle is a tool for altering word clouds [57] that include participation from
the Las Vegas 2009 Olympics. The fundamental advantage of Wordle is that it
enables neighborhood-preserving editing, which retains words in predictable
and nearby areas both during and after editing. An illustrative example made
with wordle for the DataViz context can be presented in figure 4.

Figure 4: DataViz with Wordle.

##### Easel.ly

People may simply visualize information on Easel.ly by quickly creating
infographics and data visualizations. No prior design experience is necessary.
A tool for designing infographics called Easelly can turn any visualized
content into any kind of information. It offers a variety of templates,
themes, and objects so users may alter specific details in their projects
[58].

###  5.2 Tools accessible to experienced users

####  5.2.1 JavaScript libraries

JavaScript libraries and frameworks facilitate the development of websites and
applications with a wide range of features and functionality - all thanks to
the dynamic, flexible and attractive characteristics of JavaScript. According
to a 2020 StackOverflow survey, JavaScript remains the most widely used
programming language (for the 8th grade), with 67.7% of respondents using it.
Table 6 including the top 15 libraries dedicated to visualization will be able
to describe them better.

Table 6: Top 15 JavaScript Visualization Libraries. JavaScript Library |  Description
---|---
D3JS |  Best for document modification driven by data.
Charts.js |  Best for plotting logarithmic, date, time, or custom scales on sparse and complex datasets.
FusionCharts |  For needs in data visualization and charting for web and enterprise applications, it works best.
Taucharts |  Ideal for groups creating intricate data displays.
Two.js |  Best for a 2-D shape rendering open-source library.
Pts.js |  Most effective for assembling items as you view them as points with a simple level of abstraction.
Raphael.js |  Best for quickly and efficiently producing intricate drawings and graphics.
Anime.js |  Best for producing potent user interface animation that is compatible with all of the main modern browsers.
ReCharts |  Best for teams wanting to build charts for web applications based on React.
TradingVue.js |  Best for creating complex charts, especially for stock and Forex trading applications on the web.
HighCharts |  Best suited for teams needing a large library of charts to serve web and mobile platforms.
ChartKick |  Best for producing simple charts using various libraries for programming languages, like Python, Ruby, JS, etc.
Pixi.js |  Best for groups searching for JavaScript libraries to produce HTML5-based digital content.
Three.js |  Best for creating 3-D visuals for applications on the web.
ZDog |  The creation and rendering of 3-D pictures for canvas and SVG are not best for open-sourced software.

####  5.2.2 Dashboard builders

##### Google Data Studio

It is an online application for data visualization that assists users in
transforming data into educative reports and engaging dashboards. By producing
engaging reports like this one, it is a powerful tool that enables marketers
and business owners to use their data efficiently [59]. In essence, Google
Data Studio is a condensed version of software for data visualization, such as
Tableau and Clickview. Data Studio is not a data source, in contrast to tools
like Google Analytics or HubSpot. It gathers data from several sources, does
analysis on it, and then enables you to produce interactive reports, charts,
and dashboards rather than collecting the data.

##### Toucan Toco

Is a cloud-based data visualization tool. Intended for non-technical business
executives, the objective of this highly configurable data visualization
solution is to provide essential information and data for decision-making
[60]. Toucan Toco also develops APIs that allow it to integrate with other IT
solutions, such as Cognos Analytics and Salesforce. To retrieve the data to be
used and then displayed, this advanced reporting tool is thus able to connect
to more than a hundred applications: Excel, Google Analytics, and Microsoft
SQL Server.

##### Data Hero

DataHero is a cloud computing BI software platform specializing in data
visualization and dashboards. It is the fastest and easiest way to get
insights from data. It offers the possibilty to create charts, reports, and
dashboards from business data [61].

##### Looker

Is a cloud platform dedicated to data visualization. As a BI platform, Looker
integrates several features, including many options dedicated to data
visualization including the use of reporting tools, the creation of
dashboards, and multi-cloud storage. Coders can use the LookML language to
program visualization parameters.

##  6 Data Visualization challenges

An interpretation is necessary before using quantitative data. Data
visualizations combine the meaning of unprocessed data into meaningful
conclusions. When designers put eye-catching visuals ahead of accuracy, the
result is misleading visualizations. In order to convey data in an ethical
manner, designers need to steer clear of common data visualization errors.

###  6.1 Algorithms and data inputs are susceptible to human error

Because human inputs are fallible, data visualization can only be as good as
the people who provide it. Professionals who are unaware of the variations in
applications may employ specific algorithms that emphasize specific
information while ignoring other information. They might use a specific
technique as a one-size-fits-all method for visualizing data, which can result
in concepts being misrepresented. Analysts must take into account what makes
each use case distinct and employ a system that can help them achieve their
particular objectives in order to minimize human error. Additionally, the use
of machine learning and artificial intelligence can aid in lowering the
requirement for human factors.

###  6.2 Data oversimplification

To make vast amounts of information easier to understand for viewers,
visualizations condense them into simple graphs, scatter plots, and other
visual aids. Because of this, some professionals have a tendency to
oversimplify. If they concentrate too much on the aesthetic appeal, they might
overlook important details. As a result, viewers may draw incorrect inferences
and conclusions from the imagery. In the end, this may result in poor
decisions, which could be detrimental to businesses. By enlisting the aid of
data analytics consulting firms, one can guarantee accurate representation of
data while lowering the possibility of oversimplification.

###  6.3 Reliance on visualization is inevitable

Customers are depending more and more on data visualizations to understand
their information. They make snap judgments based on visuals and aesthetics.
It’s a simple and efficient method of learning, and even as technology
develops quickly, it should continue to be applicable. But in order for
businesses to remain competitive, the trend of consumers depending too much on
visualization forces them to employ analytical tools.

###  6.4 Data overload

When working with vast and complicated datasets, researchers frequently face
the difficulty of data overload. This may lead to misunderstandings,
annoyance, and mistakes in your analysis. How do you make sense of your data
and prevent yourself from being overloaded with information? Here are some
pointers to assist you in controlling the amount of data you analyze. In data
visualization, information overload is a prevalent issue. When designers
incorporate an increasing number of datasets into the display, data overload
occurs. This makes the visualization difficult to understand as well as
difficult to construct [62]. Lack of attention and prioritizing is a common
cause of information overload. You should take a step back and assess each
data set if you find that you’ve fallen victim to data overload.

##  7 DataViz’ issues

A good visualization makes the data easy to understand so that viewers can
rapidly draw conclusions. The inclusion of excessive information is one of the
most frequent errors in data visualization. It is challenging for viewers to
come up with takeaways because of this. Similar to how visualizations suffer
from overuse of visual effects by designers.

  * •

Why are most visualization designs ineffective? Because they are created for
the incorrect audience, data visualizations frequently lack effectiveness.
Poor end-user communication causes dashboards to lose some of their apparent
usefulness. The audience for the dashboard must be identified before the data
visualization design process can begin.

  * •

What mistakes should be avoided in data visualization? Duplicate data, missed
data, unmarked NA values, etc. are examples of common errors. For instance,
the three pie chart sectors in this pie chart total up to 193%, which is
illogical. Your final visualizations would be useless if the data contained
such inaccuracies.

  * •

Does data visualization require coding? You may quickly construct an
interactive data visualization without writing any code. Spreadsheets and
reports with a lot of text are insufficient for effectively presenting the
data we found. This is why data visualization is necessary to show the data in
a form that makes it easier for everyone to understand complex ideas.

  * •

Why is misleading data bad? The audience could not notice the pertinent
information if there is too much material offered or if it is irrelevant. It
gets increasingly challenging to identify specific trends as there is more
data provided at once. The public is frequently misled using sparse but
pertinent material by over-informing them.

  * •

Can the data be misleading? Due to the sampling technique employed to collect
the data, the results may be deceptive. For instance, the type and size of the
sample used in any statistic has a significant bearing on the results. Since
many surveys and polls are directed at specific groups of people who give
particular answers, the sample sizes tend to be tiny and skewed.

Table 7: Some educated guesses about what the future may bring negatively. AI’s DataViz issue |  Description
---|---
Potential Job Losses |  Several artists were upset when an AI-generated image won an art competition last year.
|  With a description in natural language, DALL-E and its offspring can
produce realistic-looking artwork and images.
Resource Websites Tools Redundancy |  Perhaps as our methods of information acquisition change, AI tools will lessen the significance of webpages.
|  Perhaps articles, blog posts, brochure websites, and even Wikipedia pages
will become much less popular.
A Deluge of Mediocrity |  This isn’t unique to DataViz, but AI may have a very detrimental impact on the caliber of web content.
|  There will probably be a spike in the quantity of AI-generated content
available online as these tools enable faster and simpler
|  content production.
|  It’s possible that online content created by AI will eventually surpass
that created by humans.
Fake Data Visualisation |  We might observe AI producing a ton of charts in the field of data visualization to further a cause.
|  We learned how ChatGPT can create fake data in the previous post.
Therefore, it is not out of the question that AI could create
|  charts in large quantities using fictitious datasets. Even if a source is
included at the bottom of the chart, it’s possible that it was
|  created entirely by AI to deceive those who were unwilling to look further.
Impact on Design Decisions |  Over the last ten or so years, there has been a noticeable decline in attention spans.
|  The growing appeal of Tiktok and YouTube shorts, particularly among younger
audiences, makes this evident.
|  It is unfortunate to say that if this trend keeps up, it will definitely
affect how people look for and consume content that is data-focused.

###  7.1 How to secure data privacy when sharing visualizations?

A strong technique to convey patterns, insights, and narratives from large,
complicated data sets is through data visualization. However, how can you make
sure that unauthorized users cannot access your visualizations and see private
or sensitive data?

Individuals and organizations have the right and obligation to manage the
collection, use, and sharing of their proprietary or personal data. This is
known as data privacy. Data privacy is a business and reputational concern in
addition to a legal and ethical one. You run the risk of facing legal
repercussions, negative consumer feedback, and reputational harm if you
neglect to secure data privacy. Furthermore, you run the danger of losing the
confidence of your stakeholders, partners, and data providers, which could
have an impact on the availability and quality of your data.

Anonymizing data prior to visualizing it helps protect data privacy. In this
procedure, any identifiers such as names, addresses, phone numbers, or email
addresses-that could be used to connect data to particular individuals or
entities are either hidden or removed. A variety of techniques can be used to
anonymize data, including substituting random or pseudonymous values for
identifiers, aggregating or summarizing data at a finer level of detail,
introducing noise or distortion to value data, choosing or sampling a subset
of data, and switching or combining data values between records.

One practical method for guaranteeing data confidentiality is data encryption.
This entails converting data into an encoded format that is only readable or
accessible by individuals with the proper authorization and a decryption key
or password. Data can be encrypted using a variety of techniques, including
applying encryption algorithms or software, utilizing secure protocols or
platforms, and incorporating encryption functions or libraries into encryption
tools. visualization, as well as securing visualization outputs with a
password or access control. All of these techniques can aid in preventing
unwanted access to sensitive data.

###  7.2 AI’s impact on data visualisation Work

Thanks to technology advancements, data visualization once restricted to
simple graphs and tables now has a far more sophisticated aspect. As it
positions itself as a major catalyst, artificial intelligence reveals new
methods for accurately representing and interpreting the vast amount of
information at our disposal. The nexus between Artificial Intelligence (AI)
and visualization goes beyond a straightforward display; it is revolutionizing
our comprehension and utilization of data [62].

In terms of theory and consulting, Chat Generative Pre-trained Transformer
(ChatGPT) still needed improvement because it was still prone to errors and
lacked in-depth knowledge. In spite of this, it’s still amazing how quickly
and accurately it could respond in writing to my question. On the DataViz
coding side was where ChatGPT truly excelled. We can ask ChatGPT to generate
the code for a specific chart in multiple languages or libraries, and it will
be as simple as that. In order to alter the code’s appearance or
functionality, we can also ask it to update certain parts of it. Even broken
code can be fixed by ChatGPT, which can also tell you what went wrong [63,
64].

What effect will ChatGPT and similar AI tools have on DataViz work in the
future, though, if they keep getting better? The way we retrieve information
from the internet could already be revolutionized by the ChatGPT interface
[65, 66]. To what extent, then, would AI tools disrupt the data visualization
industry? In the following table 7, some educated guesses about what could
take place in the future because of the AI.

##  8 Conclusions and limitations

With the development of smart technologies that generate astronomical amounts
of data, data visualization becomes essential. Indeed, in order to be able to
analyze your Big Data and make the best use of it in your business strategy,
it is essential to be able to read it and list your business information in
visual dashboards.

By classifying, segmenting and scripting data visually, a business can uncover
previously inaccessible information at a glance. Data visualization therefore
allows any organization to manage its activity more efficiently by adopting a
data-driven and agile strategy. If data visualization was important a few
years ago, it is crucial today. In the era of Big Data, it makes it possible
to make sense of the billions of data that a company can collect every day and
which, before this transformation process, are presented in separate lines and
are therefore not easily exploitable. Data visualization is a very important
task nowadays for data scientists. The main reason for recourse is decision-
making. An interpretable, relevant, and innovative visualization can lead to
the right decision for a company knowing that this decision could be radical.
Conventional visualization techniques cannot handle the enormous volume,
variety, and velocity of data. To do this, several tools have emerged and are
constantly evolving.

Therefore, modeling large data is a useful topic right now, among other
things. Data modeling is actually a process that gives businesses access to a
simple graphical user interface for finding, designing, visualizing,
standardizing, and deploying high-quality data assets. Now, a sound data model
acts as a guide for creating and implementing databases that use higher-
quality data sources to enhance application development and help users make
wiser decisions [67]. So, we will be interested in big data modeling systems.

## References

  * [1] K. T. Torphy, D. L. Brandon, A. J. Daly, K. A. Frank, C. Greenhow, S. Hu, and M. Rehm, “Social media, education, and digital democratization,” _Teachers College Record_ , vol. 122, no. 6, pp. 1–7, 2020.
  * [2] M. Walter, R. Lovett, B. Maher, B. Williamson, J. Prehn, G. Bodkin-Andrews, and V. Lee, “Indigenous data sovereignty in the era of big data and open data,” _Australian Journal of Social Issues_ , vol. 56, no. 2, pp. 143–156, 2021\.
  * [3] R. Mkhinini Gahar, A. Hidri, and M. Sassi Hidri, “Let’s predict who will move to a new job,” _CoRR_ , vol. abs/2309.08333, 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2309.08333
  * [4] M. Sassi Hidri, S. A. Alsaif, and A. Hidri, “Performance analysis of machine learning algorithms on networks intrusion detection,” _Int. J. Comput. Appl. Technol._ , vol. 70, no. 3/4, pp. 285–295, 2022. [Online]. Available: https://doi.org/10.1504/IJCAT.2022.10056028
  * [5] S. A. Alsaif, A. Hidri, and M. Sassi Hidri, “Towards inferring influential facebook users,” _Comput._ , vol. 10, no. 5, p. 62, 2021. [Online]. Available: https://doi.org/10.3390/computers10050062
  * [6] ——, “Stacking-based modelling for improved over-indebtedness predictions,” _Int. J. Comput. Appl. Technol._ , vol. 69, no. 3, pp. 273–281, 2022. [Online]. Available: https://doi.org/10.1504/IJCAT.2022.10052783
  * [7] M. A. Zoghlami, M. Sassi Hidri, and R. Ben Ayed, “Sampling-based consensus fuzzy clustering on big data,” in _2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, Vancouver, BC, Canada, July 24-29, 2016_. IEEE, 2016, pp. 1501–1508.
  * [8] R. Mkhinini Gahar, O. Arfaoui, M. Sassi Hidri, and N. Ben Hadj-Alouane, “An ontology-driven mapreduce framework for association rules mining in massive data,” in _Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference KES-2018, Belgrade, Serbia, 3-5 September 2018_, ser. Procedia Computer Science, R. J. Howlett, L. C. Jain, Z. Popovic, D. B. Popovic, S. N. Vukosavic, C. Toro, and Y. Hicks, Eds., vol. 126. Elsevier, 2018, pp. 224–233.
  * [9] G. Manogaran, D. Lopez, C. Thota, K. M. Abbas, S. Pyne, and R. Sundarasekar, “Big data analytics in healthcare internet of things,” in _Innovative healthcare systems for the 21st century_. Springer, 2017, pp. 263–284.
  * [10] R. Mkhinini Gahar, O. Arfaoui, M. Sassi Hidri, and N. Ben Hadj-Alouane, “A distributed approach for high-dimensionality heterogeneous data reduction,” _IEEE Access_ , vol. 7, pp. 151 006–151 022, 2019.
  * [11] Z. M. Khalid, S. R. Zeebaree _et al._ , “Big data analysis for data visualization: A review,” _International Journal of Science and Business_ , vol. 5, no. 2, pp. 64–75, 2021.
  * [12] A. Lensen, B. Xue, and M. Zhang, “Genetic programming for evolving a front of interpretable models for data visualization,” _IEEE transactions on cybernetics_ , vol. 51, no. 11, pp. 5468–5482, 2020.
  * [13] F. Skender, V. Manevska, I. Hristoski, and N. Rendevski, “Investigation of dataviz as a big data visualization tool,” in _International Symposium on Intelligent Manufacturing and Service Systems_. Springer, 2023, pp. 469–478.
  * [14] A. M. S. R. N. Ibeh, “Dataviz design: A study of intentions,” in _46 th CONFERENCE_ , 2023, p. 392.
  * [15] M. Friendly, “The golden age of statistical graphics,” _Statistical Science_ , pp. 502–535, 2008.
  * [16] T. Morita, “Reflection on the development of the tool kits of bertin’s methods,” _Cartography and Geographic Information Science_ , vol. 46, no. 2, pp. 140–151, 2019.
  * [17] P. Organisciak, B. M. Schmidt, and J. S. Downie, “Giving shape to large digital libraries through exploratory data analysis,” _Journal of the Association for Information Science and Technology_ , vol. 73, no. 2, pp. 317–332, 2022.
  * [18] S. Jung, R. Xiao, O. Buruk, and J. Hamari, “Designing gaming wearables: From participatory design to concept creation,” in _Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction_ , 2021, pp. 1–14.
  * [19] L. Zhang, B. Vinodhini, and T. Maragatham, “Interactive iot data visualization for decision making in business intelligence,” _Arabian Journal for Science and Engineering_ , pp. 1–11, 2021.
  * [20] T. U. Nærland and M. Engebretsen, “Towards a critical understanding of data visualisation in democracy: a deliberative systems approach,” _Information, Communication & Society_, pp. 1–19, 2021.
  * [21] P. Raineri and F. Molinari, “Innovation in data visualisation for public policy making,” in _The Data Shake_. Springer, Cham, 2021, pp. 47–59.
  * [22] J. B. Holbrook, “Open science, open access, and the democratization of knowledge,” _Issues in Science and Technology_ , vol. 35, no. 3, pp. 26–28, 2019.
  * [23] S. Sarica, B. Yan, G. Bulato, P. Jaipurkar, and J. Luo, “Data-driven network visualization for innovation and competitive intelligence,” in _Proceedings of the 52nd Hawaii International Conference on System Sciences_ , 2019.
  * [24] C. Burnett, G. Merchant, and I. Guest, “Destabilising data: The use of creative data visualisation to generate professional dialogue,” _British Educational Research Journal_ , vol. 47, no. 1, pp. 105–127, 2021\.
  * [25] F. Deng, Z. Zhang, J. Zhang, and D. Zhang, “Building extraction from multiple images and lidar data,” in _Proceedings of the International conference on SAR and Multispectral Image Processing_ , vol. 6043, 2005, pp. 515–520.
  * [26] R. Grant, “Pretty persuasion: The advantages of data visualisation,” _Impact_ , vol. 2019, no. 2, pp. 19–23, 2019.
  * [27] S. Graessley, P. Suler, T. Kliestik, and E. Kicova, “Industrial big data analytics for cognitive internet of things: wireless sensor networks, smart computing algorithms, and machine learning techniques,” _Analysis and Metaphysics_ , vol. 18, pp. 23–29, 2019.
  * [28] O. Santolalla, “Dataviz,” in _Rock the Tech Stage_. Springer, 2020, pp. 33–49.
  * [29] H. Oike, Y. Ogawa, and K. Oishi, “Simple and quick visualization of periodical data using microsoft excel,” _Methods and protocols_ , vol. 2, no. 4, p. 81, 2019.
  * [30] N. Akhtar, N. Tabassum, A. Perwej, and Y. Perwej, “Data analytics and visualization using tableau utilitarian for covid-19 (coronavirus),” _Global Journal of Engineering and Technology Advances_ , vol. 3, pp. 28–50, 2020.
  * [31] S. Hiljazi and T. Curtis, “Developing an introductory class in business intelligence (bi) using ms excel powerpivot.” _Association Supporting Computer Users in Education_ , 2018.
  * [32] J. Dougherty and I. Ilyankou, _Hands-On Data Visualization_. ” O’Reilly Media, Inc.”, 2021.
  * [33] J. Doshi, A. Goradia, and D. Mistry, “A review of google data visualization tools,” _International Journal of Current Engineering and Technology_ , vol. 4, no. 5, pp. 3134–3138, 2014.
  * [34] J. A. S. Cardona and D. A. A. Garcia, “Evaluación y selección de herramientas de analítica visual para su implementación en una institución de educación superior,” _Revista IngEam_ , vol. 4, no. 1, pp. 1–20, 2017.
  * [35] D. Orlovskyi, A. Kopp, and V. Kondratiev, “Using dashboards for the business processes status analysis,” 2019.
  * [36] M. Minelli, M. Chambers, and A. Dhiraj, _Big data, big analytics: emerging business intelligence and analytic trends for today’s businesses_. John Wiley & Sons, 2013, vol. 578.
  * [37] M. H. u. Rehman, V. Chang, A. Batool, and T. Y. Wah, “Big data reduction framework for value creation in sustainable enterprises,” _Int. J. Inf. Manag._ , vol. 36, no. 6, p. 917–928, dec 2016.
  * [38] H. Olivér, “Kísérleti gyártáshoz kapcsolódó adatvizualizációs fejlesztés a purtár rendszerben,” _Multidiszciplináris Tudományok_ , vol. 10, no. 4, pp. 238–252, 2020\.
  * [39] T. P. Atwood and R. Reznik-Zellen, “Using the visualization software evaluation rubric to explore six freely available visualization applications,” _Journal of eScience Librarianship_ , vol. 7, no. 1, 2018\.
  * [40] K. Wilson, “Microsoft office 365,” in _Using office 365_. Springer, 2014, pp. 1–14.
  * [41] H. Kennedy and W. Allen, “Data visualisation as an emerging tool for online research,” _The Sage handbook of online research methods_ , pp. 307–326, 2016.
  * [42] M. Kaufmann, “Big data management canvas: A reference model for value creation from data,” _Big Data and Cognitive Computing_ , vol. 3, no. 1, 2019.
  * [43] N. C. Viorel and N. Lucia, “Analysis of information on tourism in the european union using the power bi business analysis service.” _Agricultural Management/Lucrari Stiintifice Seria I, Management Agricol_ , vol. 21, no. 1, 2019\.
  * [44] S. A. Fahad and A. E. Yahya, “Big data visualization: allotting by R and python with GUI tools,” in _Proceedings of the IEEE International Conference on Smart Computing and Electronic Enterprise_ , 2018, pp. 1–8.
  * [45] S. N. Pattanaik and R. P. Wiegand, “Data visualization,” _Handbook of Human Factors and Ergonomics_ , pp. 893–946, 2021.
  * [46] J. Richardson, R. Sallam, K. Schlegel, A. Kronz, and J. Sun, “Magic quadrant for analytics and business intelligence platforms,” _Gartner ID G00386610_ , 2020.
  * [47] S. Khan, “Data visualization to explore the countries dataset for pattern creation,” _International Journal of Online & Biomedical Engineering_, vol. 17, no. 13, 2021.
  * [48] G. Srivastava and R. Venkataraman, “A review of the state of the art in business intelligence software,” _Enterprise Information Systems_ , vol. 16, no. 1, pp. 1–28, 2022.
  * [49] A. M. Amer and M. M. El-Hadi, “Tableau big data visualization tool in the higher education institutions for sustainable development goals,” _International Journal of Computer Science and Mobile Computing_ , 2019.
  * [50] I. Viljanen, “Improving solutions for analytics services in a mid-sized insurance company,” 2020.
  * [51] V. Vashisht and P. Dharia, “Integrating chatbot application with qlik sense business intelligence (BI) tool using natural language processing (NLP),” in _Micro-Electronics and Telecommunication Engineering_. Springer, 2020, pp. 683–692.
  * [52] G. Sedrakyan, J. Malmberg, K. Verbert, S. Järvelä, and P. A. Kirschner, “Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation,” _Computers in Human Behavior_ , vol. 107, p. 105512, 2020.
  * [53] M. Islam and S. Jin, “An overview of data visualization,” in _Proceedings of the International Conference on Information Science and Communications Technologies (ICISCT)_ , 2019, pp. 1–7.
  * [54] T. Usova and R. Laws, “Teaching a one-credit course on data literacy and data visualisation.” _Journal of Information Literacy_ , vol. 15, no. 1, 2021\.
  * [55] S. K. Peddoju and H. Upadhyay, “Evaluation of iot data visualization tools and techniques,” in _Data visualization_. Springer, 2020, pp. 115–139.
  * [56] M. Mauri, T. Elli, G. Caviglia, G. Uboldi, and M. Azzi, “Rawgraphs: a visualisation platform to create open outputs,” in _Proceedings of the 12th biannual conference on Italian SIGCHI_ , 2017, pp. 1–5.
  * [57] F. B. Viegas, M. Wattenberg, and J. Feinberg, “Participatory visualization with wordle,” _IEEE transactions on visualization and computer graphics_ , vol. 15, no. 6, pp. 1137–1144, 2009.
  * [58] A. Weiner and K. Lorber, “Infographics: A methodology for student research presentations and other academic projects,” in _Proceedings of the International Conference on Society for Information Technology & Teacher Education_. Association for the Advancement of Computing in Education (AACE), 2021, pp. 649–652.
  * [59] M. Serik, G. Nurbekova, and M. Mukhambetova, “Optimal organisation of a big data training course: big data processing with bigquery and setting up a dataproc hadoop framework,” _World Trans. on Engng. and Technol. Educ_ , vol. 19, no. 4, pp. 417–422, 2021.
  * [60] B. Arruabarrena, “L’expert en dataviz, un métier en transition,” _I2D-Information, données documents_ , vol. 54, no. 3, pp. 7–8, 2017.
  * [61] A. M. Pedersen and C. Bossen, “Data work in healthcare: An ethnography of a bi unit,” in _Proceedings of the 8th International Conference on Infrastructures in Healthcare_. European Society for Socially Embedded Technologies (EUSSET), 2021.
  * [62] R. Rozić, R. Slišković, and M. Rosić, “Artificial intelligence for knowledge visualization: An overview,” in _International Conference on Digital Transformation in Education and Artificial Intelligence Application_. Springer, 2023, pp. 118–131.
  * [63] B. Kovalerchuk, K. Nazemi, R. Andonie, N. Datia, and E. Banissi, _Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery_. Springer, 2022.
  * [64] C. Conner, J. Samuel, M. Garvey, Y. Samuel, and A. Kretinin, “Conceptual frameworks for big data visualization: Discussion of models, methods, and artificial intelligence for graphical representations of data,” in _Handbook of Research for Big Data_. Apple Academic Press, 2022, pp. 197–234.
  * [65] A. Lavanya, S. Sindhuja, L. Gaurav, and W. Ali, “A comprehensive review of data visualization tools: Features, strengths, and weaknesses,” 2023.
  * [66] N. W. Kim, G. Myers, and B. Bach, “How good is chatgpt in giving advice on your visualization design?” _arXiv preprint arXiv:2310.09617_ , 2023.
  * [67] R. Mkhinini Gahar, O. Arfaoui, and M. Sassi Hidri, “Towards big data modeling and management systems: From DBMS to BDMS,” in _Proceedings of the IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)_ , 2023, pp. 1–6.