A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses

International Journal of Computer Engineering in Research Trends

Multidisciplinary, Open Access, Peer-Reviewed and fully refereed

Review Paper Volume-10, Issue-1, 2023 Regular Edition E-ISSN: 2349-7084

A Comprehensive Review of Data Visualization

Tools: Features, Strengths, and Weaknesses

Addepalli Lavanya

1

, Sakinam Sindhuja

2

, Lokhande Gaurav

3

, Waqas Ali

4

1

Universidad Politécnica De Valencia, Valencia, Spain

2

Independent Researcher

3

Modern Education Society's College of Engineering, Pune

4

School of Information Engineering, Yangzhou University, Yangzhou 225009, China

Corresponding Author: Addepalli Lavanya | phani.lav@gmail.com

Available online at: http://www.ijcert.org

Received: 21/11/2022, Revised: 29/12/2022, Accepted: 28/1/2023, Published: 30/01/2023

Abstract: Data visualization tools have revolutionized processing, analysing, and communicating data. With the

increasing amount of data available, it has become increasingly important to present data in an easily understandable

and visually appealing way. As such, data visualization tools have become essential to data analysis and decision-

making processes in various fields, including business, healthcare, social sciences, and engineering. This review paper

aims to provide an overview of the various data visualization tools available and their features, strengths, and

weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process.

We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive

and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs,

scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when

and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of

popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like

Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of real-

world applications. In addition, we highlight the importance of choosing the right visualization tool based on the data

type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing

the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and

developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating

machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become

an essential part of the data analysis process. This review paper overviews the available data visualization tools and

their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools,

researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

Keywords: Data Analysis; Data Visualization; Information design; Visualization tools; Types of visualizations

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1. Introduction

Data visualization tools play a crucial role in data

analysis, as they help convert complex and vast

amounts of data into a visual format that is easily

understandable and interpretable. There are several

reasons why data visualization tools are essential for

data analysis. Firstly, they allow analysts to quickly

identify data trends and patterns that would be difficult

to recognize otherwise [1, 2] . With the help of

pictorial representations, such as charts, graphs, maps,

and dashboards, analysts can better understand the

underlying data and spot potential anomalies or

outliers. Moreover, data visualization tools can help

communicate complex information to a broader

audience, regardless of their level of expertise [3]. This

is especially crucial in business, where data-driven

decision-making is becoming increasingly important.

By using data visualization tools, businesses can easily

share insights and analysis with stakeholders, allowing

them to make informed decisions based on data rather

than intuition. Another benefit of data visualization

tools is that they can help in exploratory data analysis,

allowing analysts to quickly identify areas of interest

and dive deeper into the data [4]. This can be

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

especially helpful when working with large data sets,

where it is easy to get lost in the data without a clear

focus. Analysts can quickly identify areas of interest

and perform further analysis using data visualization

tools, leading to faster and more efficient decision-

making. Lastly, data visualization tools can also help in

unifying data from different sources and applying AI-

driven analytics to reveal relationships between

different data sets [5]. This can help businesses gain

insights that would be difficult to obtain by looking at

individual data sets separately. In summary, data

visualization tools are essential for data analysis as

they allow analysts to quickly identify data trends,

communicate insights to a broader audience, perform

exploratory data analysis, and gain new insights by

unifying data from different sources. Businesses can

use data visualization tools to make more informed

decisions based on data rather than intuition, leading to

better outcomes and increased efficiency.

The paper's structure begins with an introduction

that provides background information and context for

the research. The second section is the literature review

that surveys previous research on data visualization

and related topics. Section three discusses the review

of the tools that outline the visualization tools

technically and their applications; the summary section

presents the study findings; a discussion section

interprets the results and discusses their implications;

and a conclusion summarizes the key findings and

contributions of the research.

2. Background

Data visualization tools have come a long way

from their origins as static counts to interactive

displays composed of multiple data sources [6]. While

data visualization is a relatively new discipline, many

currently used techniques were developed during the

industrial revolution, and the field advanced

significantly by the middle of the nineteenth century

[7]. However, to truly understand the impact and

evolution of data visualization, it's important to look

further into history. Visualizations have been used by

mankind since the dawn of time to instruct, convey

meaning, and tell stories [8]. Maps, in particular, have

a long history and were used by ancient civilizations to

navigate and communicate important information.

Over time, visualizations have become increasingly

sophisticated and specialized, with cartography,

statistics, and science practitioners using them to

understand complex information better.

Businesses and people benefit greatly from data

visualization because it makes it possible to quickly

identify and comprehend important data characteristics

such as trends, outliers, and patterns [3]. A good data

visualization strikes a fine balance between aesthetics

and utility, presenting data in a way that is accessible to

a wide audience while yet drawing attention to the

most important findings [9]. Data visualization tools

enable fast decision-making and provide access to

understanding patterns and trends [10]. Many data

visualization tools are available today, each with

unique strengths and weaknesses. Some of the top

tools include Microsoft Power BI, which is best for

business intelligence [11]; Tableau, which is best for

interactive charts; and Qlik Sense, which is best for

artificial intelligence. However, it's important to

choose the right tool for your specific needs and to

keep in mind that the field of data visualization is

constantly evolving and changing. In conclusion, data

visualization tools have a long and fascinating history,

evolving from simple maps and charts to sophisticated,

interactive displays. Today, these tools are essential for

understanding complex information and making data-

driven decisions, and there are many different options

available for those looking to dive into the field of data

visualization.

3. Data Visualization Tools

3.1 Tableau

Tableau is frequently used in data analytics due to

its usefulness in visualizing data. Several academic

papers and articles have been written about the benefits

of using Tableau for data visualization. The authors of

one such study present an activity that shows students

how to use Tableau to make charts, put up a dashboard,

and describe the patterns they see in the data [12]. In

another study, the benefits of Tableau are outlined, and

it is concluded that Tableau is software that can help

users explore and understand their data by producing

interactive visuals [13]. Additionally, an article

discusses using Tableau as a data visualization tool and

its importance in learning data analytics [14]. Another

research paper discusses the importance of data

visualization in business and highlights the role of

dashboards and visualizations in decision-making. It

mentions Tableau as a tool for creating data

visualizations and dashboards [15]. Finally, an article

briefly mentions Tableau as one of the tools used for

data visualization, alongside Python libraries and other

tools [16]. In conclusion, the literature reveals that

Tableau is widely used in the data analytics industry as

a tool for data visualization. Several research papers

and articles discuss its advantages, uses, and

importance in data analytics.

Using Tableau, a popular data visualization tool

has benefits and drawbacks. Tableau's ability to

effectively manage massive datasets and generate eye-

catching representations that reveal insights that can't

be gleaned simply by gazing at a spreadsheet is a major

selling point [17]. Tableau is also highly rated for its

overall performance, making it easy to create different

types of visualizations and easily handle millions of

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

rows of data [18]. Moreover, Tableau's quick drag-and-

drop functions help users to create interactive reports

and dashboards within minutes, making it easy to

implement [19]. On the other hand, one strong

disadvantage of Tableau is its cost, which is considered

high for small to medium-sized businesses, making it

an expensive investment [20]. Tableau's license cost is

higher than other data visualization tools, such as

open-source software like R or Python, which can be

used at no cost [17].

3.2 Power BI

Power BI, a set of corporate analytics tools, makes

self-service data analysis and knowledge dissemination

possible. Tools in the suite work together to transform

disparate data sets into unified, interactive

visualizations and insights [21]. The platform is

scalable for enterprise-level use and user-friendly for

self-service BI, enabling users to connect to any data

source and seamlessly visualize it into their everyday

apps [5]. In Power BI Desktop, users can manage

relationships between data tables and create histograms

[22]. The platform also offers features for

documentation, such as documenting Power BI models

[5]. Power BI Service, also known as Power BI

Workspace, Power BI Web Portal, or Power BI Site, is

an on-cloud service with a web-based platform to share

and publish reports created on Power BI Desktop. It

allows users to collaborate with others and create

dashboards [22]. In addition to its core features, Power

BI can be customized to fit specific needs. For

example, users can build Power Apps to enable users to

enter notes for specific data points and use the Power

Apps custom visual on the Power BI report [23].

Power BI is a powerful and versatile platform for data

analytics and visualization. It offers a range of self-

service and enterprise BI features, allowing users to

connect to any data source and share insights with

others. Comprehensive guides are available for those

interested in learning more, such as an end-to-end

introduction guide on Power BI [24] and tools for

documenting Power BI files and reports [25].

Microsoft Power BI is a popular data visualization

and analytics tool that offers a wide range of features

to help users make sense of their data. Here is a

summary of what reviewers have to say about using

Power BI for data visualization:

Power BI is a robust data visualization solution

that efficiently generates business insights. Power BI

facilitates clear and simple data communication by

providing real-time data dashboards, interactive

reports, charts, and graphs [22].

To properly convey data through visualizations,

users must choose the proper tool. Power BI's tools are

just what you need to connect the dots between your

narrative and your data. Users are aided in their quests

to find useful information, unearth vital business

knowledge, and efficiently exchange and collaborate

on data [22].

Power BI combines powerful analytics with a

user-friendly interface and excellent data visualization

capabilities. Customers can use Power BI's pre-built

visuals or create their own to meet their needs [23].

Power BI offers many visuals, including stacked,

line, pie, doughnut, map, and scatter charts. Users can

easily create and manipulate data visualizations using

the user-friendly interface [26].

With Power BI, users can collaborate on

interactive dashboards and reports, visualize data, and

share their findings throughout the company, all

without leaving the programme [27].

The number of visuals in Power BI is vast, and it

has the potential to create most of the visual’s users

may need. Power BI provides an accessible fast way to

create data visualizations [5].

Power BI can process large amounts of data

efficiently, allowing users to run big data through it

seamlessly. This tool allows users to drill down into the

granular details, providing them with better insights

and strategic decision-making abilities [23].

Power BI enables businesses to reach strategic

decision-making by providing visibility on the broader

context and actual scenario. It manipulates the system's

transactional, interaction, and behavioural data and

external sources, such as API.

Overall, the consensus from the search results is

that Power BI is a powerful data visualization and

analytics tool with a user-friendly interface and vast

visualization capabilities. It allows users to process

large amounts of data efficiently and make strategic

decisions based on the insights gained from their data.

3.3 Google Data Studio

Google Data Studio (GDS) is a web-based data

visualization tool allowing users to create interactive

and visually appealing reports and dashboards easily. It

was first introduced in 2016 as a beta version and has

become a popular data analysis and visualization tool.

This literature review will discuss GDS's key features

and benefits for data visualization. One of the core

functions of GDS is the interpretation of social media

and web analytics data; however, it also supports data

sources like MySQL and spreadsheets, which suggests

it can be used for various data sources [28]. GDS offers

various visualization types, such as bar charts, line

charts, scatter plots, Google Maps visuals, and more,

allowing users to create customized reports and

dashboards according to their needs [29]. One of the

most significant advantages of GDS is its sub-second

performance, thanks to the BI Engine from the Google

Cloud BigQuery team. The BI Engine is an in-memory

data access and analysis service that integrates with in-

house BigQuery data warehouses, allowing live data

from hundreds of sources to be presented in one

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

dashboard that updates and loads instantaneously.

Another advantage of GDS is its user-friendly

interface, making it easy for beginners. GDS is also

compatible with multiple data sources such as MySQL,

Google Analytics, Google BigQuery, PostgreSQL,

YouTube, Excel files, and more, making it a versatile

tool for data analysis and visualization. Furthermore,

data is automatically updated when linked to another

platform, ensuring that reports and dashboards remain

current [30].

Finally, GDS has community visualizations that

enable users to display complex patterns easily,

funnels, and flows in reports and dashboards [28]. This

feature makes GDS an even more versatile and

customizable tool for data visualization. In conclusion,

Google Data Studio is an effective data visualization

tool since it supports several visualization kinds, works

with various data sources, runs in milliseconds, and is

easy to learn and use. Its community visualizations

feature further enhances its versatility and

customization options. These features make GDS a

popular data analysis and visualization tool, especially

for those looking for an easy-to-use and customizable

platform.

3.4 D3.js

D3.js, short for "Data-Driven Documents," is a

robust and widely used open-source JavaScript toolkit

for building browser dynamic and interactive data

visualizations. D3.js, created by Mike Bostock in 2011,

is frequently used to portray massive data sets in a

digestible and interactive manner [31]. It uses web

standards, such as HTML, SVG, and CSS, to create

visual representations of data that bring it to life [32].

D3.js provides various visualization options beyond

simple bar charts, including maps, tables, and

interactive visualizations. D3.js leverage the Power of

web technologies to create stunning, scalable,

responsive visualizations, making it a popular choice

for data visualization on the web [33].

One of the advantages of D3.js is its flexibility and

the ability to create custom visualizations tailored to

specific data sets. While working with HTML, SVG,

and CSS, D3.js enables data-driven transformations,

allowing users complete freedom over visualization

components and a data-driven perspective on DOM

modification. The library's commitment to web

standards means it can use all that current browsers

offer without forcing its users into any proprietary

ecosystem [34].

In conclusion, D3.js is a powerful and flexible

JavaScript library for creating dynamic and interactive

data visualizations on the web. Its use of web standards

and data-driven transformations allows for customized

visualizations tailored to specific data sets. Its

popularity and wide range of visualization options

make it a valuable tool for data visualization.

3.5 Matplotlib

Matplotlib is a widely used open-source data

visualization library in Python, primarily for

generating 2D plots and graphs for scientific,

engineering, and financial data. In this literature

review, we will explore the available resources on

Matplotlib, including its features, advantages,

disadvantages, and use cases. According to [35], A

literature review summarises the available research on

a certain issue that may be used to determine what

theories, methodologies, and gaps in the knowledge

base need to be filled. The steps involved in writing a

literature review include defining the research

question, searching for and selecting relevant sources,

evaluating and analyzing the sources, synthesizing the

information, and writing the review. Several sources

[36, 37, 38] discuss Matplotlib's features, including its

versatility in generating various types of plots and

graphs, its customization options for plot aesthetics,

and its compatibility with other libraries and

frameworks in Python. One of the advantages of

Matplotlib is its ability to provide interactive and

dynamic visualizations, making it suitable for

exploratory data analysis. However, its default plots

may not be aesthetically pleasing, requiring further

customization to enhance its visual appeal.

Additionally, while Matplotlib is widely used,

some sources suggest it may not be the most efficient

library for generating complex and large-scale

visualizations. Regarding use cases, Matplotlib suits

various fields, including scientific research,

engineering, finance, and social sciences. Sources [36]

provide examples of how Matplotlib has been used in

different applications, such as visualizing geographic

data, time series data, and statistical analyses. In

conclusion, Matplotlib is a powerful and versatile

library for data visualization in Python, with its

strengths lying in its flexibility and customization

options. However, its efficiency in generating complex

and large-scale visualizations may be a concern.

Matplotlib has various use cases in various fields,

making it a popular choice for researchers and

practitioners.

3.6 Seaborn

The Seaborn Python library is a data visualization

tool that creates beautiful and useful charts and graphs

with a simple-to-use interface. Seaborn relies heavily

on Matplotlib and incorporates the pandas' package for

data analysis. With its declarative, dataset-focused API,

it's a breeze to turn data inquiries into visually

informative charts. This response will provide a

comprehensive literature review of Seaborn Data

Visualization. One research paper provides a detailed

overview of Seaborn's capabilities and features. The

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

paper "Seaborn: Statistical Data Visualization" was

written by Michael Waskom and published on

ResearchGate [40]. The paper describes the library's

history, strengths and weaknesses and provides an in-

depth look at its functionality, syntax, and available

plot types. The paper also includes several figures

demonstrating the library's capabilities, including

histograms, bar plots, and heat maps. Another research

paper, "seaborn: statistical data visualization. Python,"

is published in The Journal of Open Source Software

[41]. This paper presents Seaborn as a statistical

visualization library and comprehensively overviews

its functionalities. The paper discusses the library's

architecture, the available plot types, the integration

with Pandas, and its customization options. A third

resource on Semantic Scholar also provides a detailed

overview of Seaborn [42]. The resource describes the

library's functionality, syntax, and plot types and

explains how it integrates with Pandas data structures.

The paper provides an in-depth look at Seaborn's

different plot types, including scatterplots, line plots,

and boxplots, and examples of using the library to

visualize real-world data. In addition to research

papers, several articles and tutorials are available

online that provide an introduction to Seaborn and its

capabilities. For example, one article on DataGy.io

overviews Seaborn's functionality and explains how to

use the library to visualize data [43]. The article

discusses the different plots in Seaborn and provides

examples of using the library to create complex

visualizations. Another article on

TowardsDataScience.com describes Seaborn as a

powerful data storytelling tool [44]. The article

provides a comprehensive overview of the library's

capabilities and features, including its built-in styles

and customization options. The article also includes

examples of how to use Seaborn to create beautiful and

informative visualizations. The Seaborn documentation

on the library's official website provides a detailed

overview of the library and its capabilities [37]. The

documentation includes a getting started guide,

tutorials, and API reference, providing a

comprehensive overview of the library's

functionalities, syntax, and plot types. The

documentation also includes examples of how to use

Seaborn to visualize different types of data, including

categorical data, time series data, and distribution data.

Finally, an article on TowardsDataScience.com

provides a tutorial on using Seaborn for data

visualization [44]. The tutorial provides step-by-step

instructions on creating plots using Seaborn, including

scatterplots, bar plots, and heat maps. The article also

includes an example of how to use Seaborn to visualize

a real-world dataset. To sum up, Seaborn is a robust

data visualization framework that offers a convenient

API for creating engaging and instructive statistical

visuals. The library has been extensively studied and

well-documented in research papers and online

resources. Seaborn's capabilities, syntax, and available

plot types are thoroughly described in these resources,

providing a comprehensive literature review for

Seaborn Data Visualization.

3.7 Plotly

Plotly is a popular data visualization tool

professionals use in various fields, including data

science, engineering, and the sciences. It is a cloud-

based or on-premises application that allows data

scientists to structure and display their findings without

custom programming. Using Python, R, and Julia,

Plotly's Plotly Dash seeks to put data science and

machine learning in the hands of business customers

[45]. Plotly is a free and open-source Python module

that can generate several graphs, including line plots,

scatter plots, bar graphs, histograms, area plots, and

many more. No prior knowledge of JavaScript is

necessary to utilize it alongside languages like R,

Python, Java, and others [46]. Additionally, it supports

dynamic charts and animations as a first principle,

which is a key difference between other visualization

libraries like matplotlib or seaborn [47].

Users have found Plotly to be applicable for data

visualization across various disciplines. It has been

used for data from buildings, building occupancy,

public health, and statistics. Plotly is also used in

online courses, such as Coursera's guided project on

Data Visualization using Plotly and the project-based

course on Data Visualization with Plotly Express [48].

In conclusion, Plotly is a versatile and user-friendly

data visualization tool that can be used in various

disciplines and programming languages. It offers

advanced visualization features and does not require

extensive coding knowledge, making it accessible to

users with varying levels of expertise.

3.8 QlikView

QlikView is a data analysis system developed by

Qlik that enables users of all skill levels to aggregate

data-reporting capabilities and make informed business

decisions [49]. QlikView's proprietary Associative

Engine sets it apart, allowing users to create and

deploy interactive guided analytics apps and

dashboards in record time [50]. Here are some of the

benefits and drawbacks of QlikView as reported by

real users:

Pros:

 QlikView allows users to find new relations within

the different data points, answer questions more

intuitively and contains many different

visualizations [51].

 The documentation presented by QlikView is very

clear and exact, making the implementation process

easy. Accessing the QlikView documents online is

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

easy if any questions arise while creating the

reports. QlikView has a Qlik Community with

different questions and answers [51].

 QlikView provides a regulated analytics

environment, which is useful for discovering and

acting upon critical business insights [51].

 QlikView is a cloud platform that integrates and

analyses real-time data, helping companies bridge

the gap between information and action [51].

Cons:

 The export process to PDF or PowerPoint could

work better for bigger documents [51].

 The solution is not as easy to configure and

program data import [51].

Overall, QlikView serves organizations of every size in

accounting, IT, pharmaceuticals, retail, logistics, and

insurance, enabling users to optimize data discovery

and reporting capabilities [52]. According to Gartner

Peer Insights, QlikView has received 1696 in-depth

reviews from real users, and users have praised its ease

of use and high performance [52].

3.9 SAS Visual Analytics

SAS Visual Analytics is an all-inclusive analytics

visualization tool that aids in the discovery of

previously hidden linkages and patterns in data. With

its out-of-the-box sophisticated analytics and self-

service BI and reporting features, anybody can gain

insights from data of any scale [53]. The solution

comes with a built-in processing engine similar to CAS

that allows users to load data directly into servers

without needing extra pipelines, making it an excellent

tool for those working in the insurance industry and

requiring a single-screen depiction of revenue growth

[54].

Data handling, analysis, and insight into the past

and present are all possible with the help of SAS

Visual Analytics' data preparation and quality tools

[55]. A non-technical user may design, collaborate, and

run BI and Analytics processes for interactive reporting

and experimental purposes. Users have praised the

software's ability to quickly gain insights about their

data and the ability to plug seamlessly with other SAS

products. They have also appreciated the dashboard

canvas for easy navigation and the easy-to-find

tutorials that teach advanced features [56]. Individual

end-users rated SAS Visual Analytics well in their

Gartner Peer Insights assessments. User reviews below

are individual opinions, not those of Gartner or any of

its partners. Overall, SAS Visual Analytics is a

powerful tool providing a complete visualization

platform with self-service BI, advanced analytics, and

governance features. It offers scalability and easy-to-

use analytics in a single in-memory environment,

making it a great choice for businesses of all sizes [57].

3.10 IBM Cognos Analytics

The business intelligence package IBM Cognos

Analytics is ideal for widespread implementation.

Enhanced self-service features make it easy to find

relevant information and take decisive action [58].

Includes Cognos Administration, Microsoft Office

integration, full-text search, dashboards, Query Studio,

Reports Studio, Analytic Studio, and Event Studio. The

package also features an AI Assistant that responds to

inquiries in plain language and recommends new data

visualizations and joins to help users find associations

they had not considered before. Users have reported

that IBM Cognos Analytics is a comprehensive

reporting tool that can help enterprises derive hidden

patterns of customer behaviour, market trends, and

business performance over a period, region, and

business line. Data visualization helps to understand

these insights effortlessly [59]. However, some users

have reported that the security (authorization) concept

is not detailed enough for their purpose. IBM Cognos

Analytics is a robust and scalable solution that can be

deployed locally or in the cloud, and it provides

business users with the freedom to design and modify

their dashboards and reports. Many companies prefer

this suite because it meets their needs at a reasonable

price and is well-supported by many experts [60].

Overall, IBM Cognos Analytics is a comprehensive

reporting tool that offers self-service capabilities and

an AI assistant to empower business users to make

confident decisions based on data. While some users

have reported security concerns, the suite remains a

popular choice for businesses due to its affordability

and availability in the market [61].

Forbes [62], Qlik [51], and FounderJar [63], it is

evident that Tableau, Power BI, and QlikView are

among the top data visualization tools in 2023. Tableau

offers a variety of ways to access the software,

including desktop, server, online, prep, and a free

public option. Tableau is ideal for creating maps and

public-facing visualizations, and it provides an

extensive collection of data connectors and

visualizations. According to Forbes, Tableau is the best

option for interactive charts [62]. Power BI is another

popular data visualization tool, particularly for

business intelligence. It is ideal for analyzing large

datasets and generating reports and offers real-time

data insights. Power BI provides a range of

visualization options and can be used with other

Microsoft products. Forbes names Power BI as the best

option for business intelligence [62].

QlikView, on the other hand, is ideal for artificial

intelligence and machine learning. It provides a suite

of supporting capabilities required for interactive data

visualization at scale. QlikView also offers a range of

visualization options and can handle large datasets.

According to Gartner's 2022 Magic Quadrant Analytics

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

BI report, QlikView is one of the three vendors named

as leaders in data visualization tools [51]. Other data

visualization tools mentioned in the sources include

FusionCharts, Highcharts, Datawrapper, Plotly, and

Sisense. Each tool has strengths and weaknesses,

making it important to carefully evaluate the user's

needs before choosing a tool. Summarizing that data

visualization tools such as Tableau, Power BI, and

QlikView offer various visualization options and

provide different features to suit specific needs. The

choice of tool will ultimately depend on the user's

requirements, such as the size and complexity of the

data, the level of interactivity needed, and the desired

output format. Careful evaluation and consideration of

the strengths and weaknesses of each tool will ensure

the selection of the most appropriate tool for the task at

hand.

4. Summary Metrics

Data visualization tools are becoming increasingly

important in today's data-driven world. With so many

available tools, choosing the best one for your needs

can be challenging. This comparison provides an

overview of the most popular data visualization tools,

their features, strengths, and weaknesses. By

understanding the differences between these tools,

users can decide which tool to use for their data

visualization needs.

Table 1: Summary metrics about the Advantages and Disadvantages of Data Visualization Tools

Name of

Tool

Advantage

Disadvantage

Citation

Tableau

User-friendly with non-Tech requirements

Professional version Expensive for

SMEs; Manual update of Data does

not allow auto-scheduling

[17, 14, 12]

Power BI

Affordable for a Desktop version suitable for SMEs;

Supportive for Non-Datawarehouse organizations;

Automated data refresh and report generations;

customized access keys for security

No data cleaning possible; higher

difficulty levels for learning

[26, 24]

Google

Data Studio

Easy integration with all Google Cloud Applications;

Flexible Integration; Built-in data connectors; Dynamic

Controls; real-time collaborations; Unlimited access to

widgets; Fully Open Source

Difficult learning curve; Limited with

functionalities;

Data processing limitations;

[30, 28]

D3.js

Flexible and powerful; Variety of visualization; supports

animation and data interactions; Active community

support; compatible with customized datasets; versatile

in visualization; built on JavaScript libraries

The difficult learning curve for non-

programmers; Complex set up; tough

for beginners needs detailed step-by-

step learning before full application;

uncertain performance issues;

[33, 32]

Matplotlib

Wide range of visualization packages; fully

customizable; cross-platform support; interactive

plotting; massive user community for support

Difficulty in learning curve; verbose

syntax; Limited for 3D and animation

plotting; Limited interactivity

between other visualization python

packages;

[4, 15]

Seaborn

Wide range of default multiple plotting; integrates

strongly with Pandas and NumPy; inbuild statistical

tests;

Rus into out of memory often; not

fully embedded into Python;

[37]

Plotly

Programmatically cost-effective; empowers

visualization with back-end and front-end Machine

learning support; Aesthetically pleasing;

No automation process at all; too

many separate libraries; slow in

speed and consumers high processes

speed;

[47]

QlikView

Allows new relationship identification among data

nodes; end-to-end solution;

Exporting plots gets challenging to a

power point or PDF, tough program

configurations;

[51]

SAS Visual

Analytics

Self-Service BI tool; highly interactive; easy to learn; no

need for programming skills; predictive modules;

powerful data visualization; a variety of outputs

provided for better visualization

Highly expensive; poor graphic

presentations

[55]

IBM

Cognos

Analytics

Highly secured for corporate data visualizations; AL and

ML-based data analytics modelling tools; greater

flexibility; cell-by-cell analytics; easy to learn; seamless

integrations with Cognos; easy file storages and report

generations system;

Expensive; too many modelling tools

creating confusion; missing simple

and small plotting features;

[60]

Data visualization is an essential tool for

businesses and organizations to make sense of their

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

data and make informed decisions. From the provided

search results, it appears that Tableau is a popular

choice for data visualization, with multiple sources

ranking it as the best tool for enterprise-level data

visualization [62, 64]. Tableau is known for its

flexibility, powerful features, and an enormous

collection of data connectors and visualizations [63].

Microsoft Power BI is also a highly recommended tool

for business intelligence and data visualization,

according to Forbes [11]. Qlik Sense is another top tool

for data visualization, especially for artificial

intelligence. Other tools like Infogram, Plecto, and

Whatagraph are also recommended for specific use

cases such as collaboration, sales teams, and visual

data analytics for social media, PPC, SEO, and email

campaigns, respectively [2]. Ultimately, the best data

visualization tool depends on the user's needs and

requirements.

5. Conclusion

In conclusion, this comprehensive review of data

visualization tools has demonstrated that data

visualization is essential for effective data analysis and

communication. Analysts and decision-makers can

easily understand complex data sets and identify

patterns, trends, and relationships by presenting data

visually. Throughout this review, we have provided an

overview of the various data visualization tools

available, including their features, strengths, and

weaknesses. We have explored different types of

visualizations, such as bar charts, line graphs, scatter

plots, heat maps, tree maps, and network diagrams, and

have shown how they can be effectively used in

different situations. We have also evaluated popular

data visualization tools like Tableau, Power BI, Google

Data Studio, D3.js, and Python libraries like

Matplotlib, Seaborn, and Plotly. Through our analysis,

we have highlighted the strengths and weaknesses of

each tool and provided real-world examples of how

they have been used to present and analyze data. In

addition, we have discussed best practices for creating

effective data visualizations, including choosing the

right colour scheme, designing for accessibility, and

avoiding common pitfalls. By following these best

practices, analysts and decision-makers can create

clear, compelling visualizations that communicate data

insights effectively. Finally, we have explored future

trends and developments in data visualization, such as

using augmented and virtual reality for data

visualization and incorporating machine learning and

artificial intelligence in data visualization tools. These

trends promise to enhance the capabilities of data

visualization tools further and make them even more

valuable in data analysis and decision-making. This

comprehensive review of data visualization tools has

demonstrated the importance of data visualization in

data analysis and communication and provided

valuable insights into the strengths and limitations of

different visualization tools. By understanding the

capabilities of different tools and following best

practices for creating effective visualizations, analysts

and decision-makers can effectively present and

analyze data, leading to better decision-making and

insights.

References

[1]

K. Kalluri, 2022. [Online]. Available:

https://splashbi.com/importance-purpose-

benefit-of-data-visualization-tools/. [Accessed

1 Dec 2023].

[2]

GeeksforGeeks, 2021. [Online]. Available:

https://www.geeksforgeeks.org/what-is-data-

visualization-and-why-is-it-important/.

[Accessed 15 Dec 2022].

[3]

Tableau, 2021. [Online]. Available:

https://www.tableau.com/learn/articles/data-

visualization. [Accessed 1 Dec 2022].

[4]

E. Amadebai, 2021. [Online]. Available:

https://www.analyticsfordecisions.com/data-

visualization-is-important/. [Accessed 12 Dec

2022].

[5]

Microsoft Power BI, 2021. [Online]. Available:

https://powerbi.microsoft.com/en-us/data-

visualization/. [Accessed 11 Dec 2022].

[6]

Chartio, "The evolution of data visualization,"

19 Oct 2020. [Online]. Available:

https://chartio.com/blog/the-evolution-of-data-

visualization/. [Accessed 3 Dec 2022].

[7]

R. Farnworth, "A short history of data

visualisation," Medium, 24 Aug 2020. [Online].

Available: https://towardsdatascience.com/a-

short-history-of-data-visualisation-

de2f81ed0b23. [Accessed 3 Dec 2022].

[8]

Tableau-1, "The history of data visualizations -

From cave drawings to tableau," 1 April 2020.

[Online]. Available:

https://www.tableau.com/whitepapers/designin

g-great-visualizations. [Accessed 4 Dec 2022].

[9]

Data.org, "Introduction to data visualization,"

data.org, 22 Dec 2022. [Online]. Available:

https://data.org/resources/introduction-to-data-

visualization/. [Accessed 22 Dec 2022].

[10]

M. Sharapa, "Data visualization: Principles,

tools, and useful tricks," Medium, 10 July

2020. [Online]. Available:

https://towardsdatascience.com/data-

visualization-principles-tools-and-useful-tricks-

b68d9c138a86. [Accessed 20 Dec 2022].

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

[11]

K. Haan, "The best data visualization tools of

2023," Forbes Advisor, 10 Jan 2023. [Online].

Available:

https://www.forbes.com/advisor/business/softw

are/best-data-visualization-tools/. [Accessed 20

Jan 2023].

[12]

S. Batt, O. R. Harmon and P. Tomolonis,

"Learning tableau: A data visualization tool,"

SSRN Electronic Journal, 2019.

[13]

P. M. Joshi and P. N. Mahalle, "Visualization

with tableau," Data Storytelling and

Visualization with Tableau, pp. 49-74, 2022.

[14]

S. Batt, T. Grealis, O. Harmon and P.

Tomolonis, "Learning tableau: A data

visualization tool," The Journal of Economic

Education, Vols. 51(3-4), pp. 317-328, 2020.

[15]

M. Y. Khalid, P. H. Then and V. Raman,

"Exploratory study for data visualization in

Internet of things," IEEE 42nd Annual

Computer Software and Applications

Conference (COMPSAC), 2018.

[16]

Y. Hasija and R. Chakraborty, "Python for data

visualization," Hands-On Data Science for

Biologists Using Python, pp. 91-122, 2021.

[17]

AbsentData, "Advantages and disadvantages of

tableau," AbsentData, 6 Oct 2022. [Online].

Available: https://absentdata.com/advantages-

and-disadvantages-of-tableau/. [Accessed 23

Dec 2023].

[18]

KnowledgeHut, "What are the 8 amazing

benefits of using tableau for your projects?,"

KnowledgeHut: Professional Bootcamps and

Certification Courses Provider for your Future,

1 Oct 2020. [Online]. Available:

https://www.knowledgehut.com/blog/business-

intelligence-and-visualization/tableau-

advantages. [Accessed 23 Dec 2023].

[19]

H2kinfosys Blog, "What are the advantages and

disadvantages of using tableau?," H2kinfosys

Blog, 12 April 2021. [Online]. Available:

https://www.h2kinfosys.com/blog/what-are-the-

advantages-and-disadvantages-of-using-

tableau/. [Accessed 20 Dec 2023].

[20]

SaM Solutions, "Pros and cons of tableau

software for data visualization," SaM Solutions,

16 Sept 2021. [Online]. Available:

https://www.sam-solutions.com/blog/tableau-

software-review-pros-and-cons-of-a-bi-

solution-for-data-visualization/. [Accessed 23

Dec 2022].

[21]

Mihart, "What is power BI? Microsoft Learn:

Build skills that open doors in your career,"

microsoft.com, 3 Oct 2020. [Online]. Available:

https://learn.microsoft.com/en-us/power-

bi/fundamentals/power-bi-overview. [Accessed

20 Dec 2022].

[22]

Davidiseminger, "Tips and tricks for creating

reports in power BI - Power BI," Microsoft

Learn: Build skills that open doors in your

career, 3 Sept 2020. [Online]. Available:

https://learn.microsoft.com/en-us/power-

bi/create-reports/desktop-tips-and-tricks-for-

creating-reports. [Accessed 30 Dec 2022].

[23]

Microsoft Power BI Community, "Writing

notes in power BI," Microsoft Power BI

Community, 4 Sept 2020. [Online]. Available:

https://community.powerbi.com/t5/Desktop/Wri

ting-Notes-in-Power-BI/m-p/683432.

[Accessed 22 Dec 2022].

[24]

Anushkakhatri , "An end-to-end introduction

guide on power BI," Analytics Vidhya, 4 May

2022. [Online]. Available:

https://www.analyticsvidhya.com/blog/2022/04

/an-end-to-end-introduction-guide-on-power-

bi/. [Accessed 23 Dec 2022].

[25]

R. Rad, "Document a power BI file and report

in a few clicks: All DAX code, visualization,

power query scripts," RADACAD, 30 May

2021. [Online]. Available:

https://radacad.com/document-a-power-bi-file-

and-report-in-a-few-clicks-all-dax-code-

visualization-power-query-scripts. [Accessed 3

Dec 2022].

[26]

47billion , "A step by step guide to data

visualization with power BI," 47billion.com, 23

Dec 2021. [Online]. Available:

https://47billion.com/blog/a-step-by-step-guide-

to-data-visualization-with-power-bi/. [Accessed

23 Dec 2022].

[27]

TrustRadius, "Microsoft power BI reviews &

ratings," TrustRadius, 2023. [Online].

Available:

https://www.trustradius.com/products/microsoft

-power-bi/reviews.

[28]

Google® Data Studio, "Cooking with Google

data studio," Hands On With Google® Data

Studio, pp. 15-56, 2020.

[29]

G. Kemp and G. White, "Starting your data

studio journey," Google Data Studio for

Beginners, pp. 1-13, 2020.

[30]

Google Data Studio, "Viewing local

organization data from Google my business,"

Hands On With Google® Data Studio, pp. 173-

219, 2020.

[31]

N. Q. Zhu, Data visualization with D3. js

cookbook, Packt Publishing Ltd, 2013.

[32]

E. Meeks, D3. js in Action: Data visualization

with JavaScript, Simon and Schuster, 2017.

[33]

M. Iglesias and M. Iglesias, "Introduction to

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

Data Visualizations with D3. js. Pro D3. js: Use

D3. js to Create Maintainable, Modular, and

Testable Charts," pp. 1-12, 2019.

[34]

Y. R. Zeng, Y. S. Chang and Y. H. Fang, "Data

visualization for air quality analysis on bigdata

platform," IEEE 2019 International Conference

on System Science and Engineering (ICSSE),

pp. 313-317, 2019.

[35]

E. Bisong, "Matplotlib and seaborn. Building

Machine Learning and Deep Learning Models

on Google Cloud Platform: A Comprehensive

Guide for Beginners," pp. 151-165, 2019.

[36]

A. Pajankar, "Data visualization with NumPy

and Matplotlib," Practical Python Data

Visualization: A Fast Track Approach To

Learning Data Visualization With Python, pp.

57-79, 2021.

[37]

P. Lemenkova, "Python libraries matplotlib,

seaborn and pandas for visualization geo-spatial

datasets generated by QGIS," Analele stiintifice

ale Universitatii" Alexandru Ioan Cuza" din Iasi-

seria Geografie, vol. 64, no. 1, pp. 13-32, 2020.

[38]

N. P. Rougier, "Scientific Visualization:

Python+ Matplotlib," 2021.

[39]

A. H. Sial, S. Y. Rashdi and A. H. Khan,

"Comparative analysis of data visualization

libraries Matplotlib and Seaborn in Python,"

International Journal, vol. 10, no. 1, 2021.

[40]

M. L. Waskom, "Seaborn: statistical data

visualization," Journal of Open Source

Software, vol. 6, no. 60, p. 3021, 2021.

[41]

M. Waskom, "seaborn: statistical data

visualization. Python 2.7 and 3.5," Open Source

Software, 2020.

[42]

I. Stančin and A. Jović, "An overview and

comparison of free Python libraries for data

mining and big data analysis," 2019 42nd

International convention on information and

communication technology, electronics and

microelectronics (MIPRO), 2019.

[43]

Nik , "Seaborn in Python for data

visualization," datagy, 20 Feb 2022. [Online].

Available: https://datagy.io/python-seaborn/.

[Accessed 20 Dec 2022].

[44]

M. Sharma, "Data visualization using Seaborn,"

Medium, 3 Nov 2018. [Online]. Available:

https://towardsdatascience.com/data-

visualization-using-seaborn-fc24db95a850.

[Accessed 30 Dec 2022].

[45]

C. Sievert, Interactive web-based data

visualization with R, plotly, and shiny, CRC

Press, 2020.

[46]

J. Van Der Donckt and E. Deprost, "Plotly-

resampler: Effective visual analytics for large

time series," 2022 IEEE Visualization and

Visual Analytics (VIS), pp. 21-25, 2022.

[47]

L. Afremov, Advanced Plotly, 2022.

[48]

L. Podo and P. Velardi, "Plotly. plus, an

Improved Dataset for Visualization

Recommendation," 31st ACM International

Conference on Information & Knowledge

Management, pp. 4384-4388, 2022.

[49]

O. Troyansky, T. Gibson and C. Leichtweis,

QlikView your business: an expert guide to

business discovery with QlikView and Qlik

Sense, John Wiley & Sons, 2015.

[50]

A. Shukla and S. Dhir, "Tools for data

visualization in business intelligence: case

study using the tool Qlikview," Information

Systems Design and Intelligent Applications:

Proceedings of Third International Conference

INDIA 2016, vol. 2, p. 3, 2016.

[51]

M. García and B. Harmsen, Qlikview 11 for

developers, Packt Publishing Ltd., 2012.

[52]

S. Redmond, Mastering QlikView, Packt

Publishing Ltd., 2014.

[53]

P. Chate, M. A. Karad and U. Patkar, "Big Data

Visualization: Challenges and SAS Visual

Analytics," International Journal of Computer

Science and Mobile Computing, vol. 4, no. 12,

pp. 44-48, 2015.

[54]

W. Majaliwa, "Discovering Patterns in Textual

Data Using SAS Visual Analytic," International

Journal of Information Technology and

Computer Science Applications, vol. 1, no. 1,

pp. 44-50, 2023.

[55]

M. Anandarajan, C. Hill and T. Nolan, "SAS

Visual Text Analytics.," Practical Text

Analytics: Maximizing the Value of Text Data,

pp. 263-282, 2019.

[56]

I. K. Fu, M. Chaves, A. Fagan and J. Hazen,

"Bringing Google Analytics, Facebook, and

Twitter Data to SAS® Visual Analytics,"

Proceedings of SAS Conference: SAS Global

Forum, pp. 1-12, 2016.

[57]

R. Styll, "Fast dashboards anywhere with

SAS® visual analytics," Proceedings of SAS

Global Forum, 2013.

[58]

A. S. Bluck, "IBM Cognos Analytics Custom

Development," IBM Software Systems

Integration: With IBM MQ Series for JMS, IBM

FileNet Case Manager, and IBM Business

Automation Workflow, Berkeley, CA: Apress,

pp. 903-990, 2023.

[59]

M. Babiak, Introduction to IBM Cognos

Business Intelligence (BI), 2014.

[60]

D. Adkison, IBM Cognos business intelligence,

Packt Publishing Ltd., 2013.

Addepalli Lavanya, Sakinam Sindhuja, Lokhande Gaurav, Waqas Ali (2023). A Comprehensive Review of Data

Visualization Tools: Features, Strengths, and Weaknesses. International Journal of Computer Engineering In Research

Trends, 10(1), pp. 10–20.

[61]

J. Richardson, R. Sallam, K. Schlegel and A.

Kronz, "Magic quadrant for analytics and

business intelligence platforms," Gartner ID

G00386610, 2020.

[62]

K. Haan, "The best data visualization tools of

2023," Forbes Advisor, 10 Jan 2023. [Online].

Available:

https://www.forbes.com/advisor/business/softw

are/best-data-visualization-tools/. [Accessed 20

Jan 2023].

[63]

M. Luenendonk, "23 best data visualization

tools of 2022 (with examples).," FounderJar, 3

Aug 2022. [Online]. Available:

https://www.founderjar.com/best-data-

visualization-tools/. [Accessed 3 Dec 2022].

[64]

TechRadar, "Best data visualization tools of

2023," TechRadar, 7 Dec 2022. [Online].

Available: https://www.techradar.com/best/best-

data-visualization-tools. [Accessed 20 Dec

2022].