Usability Benchmarking of Data Analytic Tools with Market

Research for Decision-Making

Preethi Bitra, G K Mohan Devarakonda, Poodi Venkata Vijaya Durga, Jistnasai Upendra Kolapalli

and Ponnuru L N Prakash Kumar

Vishnu Institute of Technology, Bhimavaram, India

Keywords: Data Analysis, Business Intelligence, Data Visualization, Data Storytelling, Data-Driven Decision-Making,

Ease of Use, Governance, Security, Flexibility, Scalability.

Abstract: Data is the key for any Data Analytics application. To be a master in various fields like Data Science, Machine

Learning, Deep Learning, Computer Vision, Natural Language Processing, and Predictive analysis,

everything depends on perfect data maintenance. Also, the overfitting and underfitting analyses of various

popular models depend on the data that has been provided to them. To meet the requirements, several tools

emerged in the form of software, web versions, and command-line applications to make it easier to absorb

and analyze the data. Some of the latest data analysis tools include Tableau, Power BI, Alteryx, etc. Choosing

the right tool to perform the data analysis will give you a bit more success with the result. This paper attempts

to define the functionalities, advantages, and disadvantages of various data analysis tools on the market and

also attempts to produce a choice chart to help you select the right tool for your data.

1 INTRODUCTION

A collection of facts, statistics, measurements,

observations, or any other information recorded in an

organized or unorganized manner is referred to as

data (Sridevi, Bindu, et al. , 2017). It is the raw

material or input utilized to develop significant

insights, information, or actions through analysis,

interpretation, or processing. Text, numbers, photos,

music, video, and other forms can all exist as data.

Sensors, Polls, tests, Transactions, social media, and

other digital interactions can contribute. Databases,

Spreadsheets, files, and other data storage methods

are routinely used to store and organize data. Data is

the cornerstone for many applications in computer

science and information technology, including data

analysis, machine learning, artificial intelligence,

business intelligence (Sridevi, Bindu, et al. , 2017). In

reality, for instance, we can see the day-to-day

activities of a single person in their everyday actions

(Panesar, 2019). When a person desires to purchase a

good through an online retailer or a store, he or she

looks at the product price, quality, and many other

metrics depending on their needs. The study

conducted here can be considered a Data study in and

of itself. This little example demonstrates the

importance of data analysis in our daily lives. When

this idea is utilized with vast collections of data, we

need to be more cautious as we make decisions based

on the analysis that we have completed, which is

known as data-driven decision-making (Mario,

2017), (Xuan-Linh and Rajkumar, 2008). This has an

impact on business choices. However, due to the lack

of knowledge on tool selection, the outcome of the

work may vary in time and effectiveness (Dubey,

Patel, et al. , 2018).

2 THE DATA ANALYSIS TOOLS

2.1 Spreadsheet-based Analysis

Spreadsheet-based analysis is We did market research

on the technologies available to make it easier to

analyze the huge data sets created by large activities.

Tool selection is critical in producing effective

findings, depending on the sort of analysis being

conducted, data as behavior, and needs such as

platform as service, software as service, cloud-based

tool, non-coding, coding, cost, and so on.

600

Bitra, P., Devarakonda, G. K. M., Vijaya Durga, P. V., Kolapalli, J. U. and L N Prakash Kumar, P.

Usability Benchmarking of Data Analytic Tools with Market Research for Decision-Making.

DOI: 10.5220/0013582400004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 600-607

ISBN: 978-989-758-763-4

Once you have considered these factors, you can

start to narrow down your choices. Here are a few

popular data analysis tools to consider based on the

variant:

The process of organizing, manipulating,

analyzing, and visualizing data using spreadsheet

applications such as Microsoft Excel or Google

Sheets. Spreadsheets are frequently used for a variety

of analyses, such as financial modeling, data analysis,

project management, and others (Gunnlaugsson,

2016). Data Entry, Formatting, Cleaning, Formulas

and Functions, What-If Analysis, Macros and

Automation, Reporting and visualization, Iteration

and Validation, and Collaboration and Sharing are

some of the services available. Spreadsheet-based

analysis provides a versatile and strong set of tools for

organizing and analyzing data (Şeref, Ahuja, et al. ,

2008), making it popular across sectors and

disciplines. The main drawback of this is that you

have to save the work continuously and keep backups

in order to avoid data loss.

2.2 Data Analysis Using Programming

Languages

Data Analysis using programming languages entails

using a programming language's ability to process,

alter, and analyze data. One of the techniques used in

this kind of analysis is Data exploration, where we

load and inspect the structure, format, and behavior

of the data, which can be read from databases, files,

APIs, or other sources using appropriate Libraries.

Data Cleaning and Preprocessing, Transforming

according to the criteria Visual representation to gain

insights, Statistical, time series, and text analysis

Machine learning, Optimization, simulation of Big

data, and Interactive analysis One must be a gem in

programming to conquer the analysis using this kind

of technique.

Platforms for Programming Languages:

RStudio, Jupyter Notebook, TensorFlow, etc.

2.2.1 SQL

SQL (Structured Query Language) is a Scalable and

powerful data analysis language, particularly when

working with structured data stored in relational

databases. Here are some of the benefits of using SQL

for data analysis:

Simple Data Retrieval, Data Aggregation, and

Summarization Joining various tables; filtering and

Sorting; Sub queries; and Derived Tables, SQL

allows for sub queries and data transformation

2.2.2 Data Integrity and Security

SQL databases impose data integrity restrictions to

ensure data correctness and consistency, as well as

security measures like user authentication and

permission. The SQL is Limited to Structured Data,

Procedural Logic, Statistical Analysis, and

Performance Considerations.

2.3 Python, R, Julia, and MATLAB

Python, R, Julia, and MATLAB are examples of

programming languages that include substantial

libraries, tools, and frameworks to aid with data

analysis tasks. They provide flexibility, scalability,

and the capacity to tailor your analysis to your

individual requirements (Ross and Gentleman, 1996),

(Coleman, Maliar, et al. , 2021).

2.4 Java for Big Data

Java is a popular programming language that may be

used for large-scale data processing and analysis.

While Python and R are both well-known data

analytics technologies (Coleman, Maliar, et al. ,

2021), when it comes to big data, Java reigns

supreme. Many of the technologies needed to handle

and analyze huge datasets, such as Spark, Hadoop,

Cassandra, Knime, Storm, Talend, and Elasticsearch,

are developed in Java. Java also has solutions for

interacting with cloud-based big data systems such as

Amazon Web Services (AWS) or Google Cloud

Platform (GCP) (Saxena, Kaushik, et al. , 2016).

2.5 Pros and Cons of Programming

Languages

2.5.1 Pros

Programming languages are extremely flexible,

allowing you to customize and alter your analytic

techniques to meet your demands.

1 Extensive Libraries and Tools: Many

programming languages have robust ecosystems

that include libraries and tools for data analysis.

These libraries include pre-built functions,

methods, and data structures that can help you

save time when doing analysis tasks.

2 Performance: Depending on the programming

language and optimization techniques used,

high-performance data analysis is possible,

particularly for computationally heavy jobs.

Programming languages are readily integrated

with other tools, technologies, and databases,

Usability Benchmarking of Data Analytic Tools with Market Research for Decision-Making

601

allowing for smooth data integration, transformation,

and interoperability.

2.5.2 Cons

1. Learning Curve: Learning curves are

common in languages used in programming,

and obtaining the requisite skills for data

analysis can take time and effort.

2. Development Time: Developing code for

data analysis activities might be more time-

consuming than utilizing a GUI-based

software tool.

3. Performance Limitations: Some

programming languages may not provide

ideal performance for particular sorts of data

analysis jobs, depending on the language

and individual use case.

4. Maintenance and debugging: Code-based

data analysis necessitates constant

maintenance and debugging since errors

might arise due to manual coding or updates

in the data set.

2.6 Web-Based Applications

Web-based applications are applications or apps that

run with the help of web servers as hosts and can be

accessed with the help of a Web browser. Some of

them are Google Analytics, Tableau, IBM Watson

Analytics, QlikView, and Microsoft Clarity. If you

are willing to perform the data analysis without

installing any of the GUI tools, then you can use

them. Some of the web applications are readily

available as downloadable software, where

installation can be done and used without hesitating

to be offline. Secure multi-party computing (MPC) is

a potential cryptographic technique for enabling

sensitive data analysis while maintaining anonymity,

which is the primary purpose of web-based

applications. (Lapets, Jansen, 2018).

2.7 Open Source

2.7.1 Tableau

Tableau is one of the most famous data visualization

tools. It is a data discovery and data exploration

application that allows you to give responses very

quickly, within seconds. It's one type of drag-and-

drop tool to visualize any type of data. One can get

real-time data by connecting to a database or API for

real-time visualization. This drag-and-drop analytic

tool is completely free for students and can be

downloaded from

“https://www.tableau.com/academic/students”. The

tableau tool works as shown in Figure 1. Tableau is

an open-source tool, so everyone easily visualizes

different types of charts, maps, and graphs. Tableau

helps you build dashboards that deliver actionable

information and help your business grow. Tableau

can extract data from virtually any data store,

including Excel, PDF, Oracle, and Amazon Web

Services. Tableau technologies enable data scientists

to visualize an infinite number of analytics.

Figure 1: Tableau working environment

Tableau is used in many industries and businesses

to generate reports and worksheets very quickly.

Different organizations, like Amazon, Wal-Mart, and

Accenture, widely use Tableau. Tableau limitations

are unclean data, a lack of data modeling, and a lack

of version control while creating a data dashboard.

There are two ways to get data by using the Tableau

tool:

• Developer Tools: In development, these tools

are used to generate charts, maps, dashboards,

reports, and visualizations. Tableau Public and

Tableau Desktop are the two most important

products in this category.

• Sharing Tools: These tools are used to share

various sorts of reports, visualizations, and

dashboards that have been developed using the

developer tools. Tableau Online, Tableau

Reader, and Tableau Server are the most

important items in this area.

2.7.2 Power BI

It is a Microsoft Business Intelligence and Data

Visualization application that is used to transform

data from numerous data sources, such as interactive

dashboards and analytical reports. Power BI also

provides cloud-based services for interactive data

visualization. Power BI Desktop for Microsoft

Windows 10 is available for download, as are native

mobile apps for Windows, Android, and iOS devices.

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Microsoft Power BI is used to discover trends in a

company's data.

Figure 2: Power BI working environment

Microsoft Power BI is used by professionals and

management to develop reports and predictions to

assist sales and marketing agents, as well as data for

management on how the team or individual workers

perform towards their goals.

Users may select between three versions of Power

BI: Power BI Desktop, Power BI Pro, and Power BI

Premium. Power BI is a data visualization application

with a simple drag-and-drop interface. The BI

Desktop edition is free, and the ones that follow are

modestly priced. The three versions of Power BI are

a user-friendly, drag-and-drop data visualization tool.

The BI Desktop edition is free of charge, while the

following versions are reasonably priced.

The advantages of Power BI are that data

visualization is very inexpensive, it releases rigorous

updates every month, and it has its own gateways to

authorize unwanted traffic. The Limitations are

Power BI design involved complexity issues, no data

cleansing solution, and being unable to handle a large

amount of data.

2.7.3 Apache Spark

Apache Spark is a super-fast cluster computing

solution developed for high-performance

computation. It is based on Hadoop MapReduce and

extends the MapReduce architecture to allow for

more efficient use of it for different sorts of

calculations, such as interactive queries and stream

processing. It is available as an open-source tool

Download Apache Spark by accessing the Spark

Download page and selecting the link from

“Download Spark". Some versions are offered at

affordable prices.

Spark is utilized in the healthcare industry

because it provides a full analysis of patient

information as well as past medical data, finance to

make informed decisions, credit risk assessment, and

targeted advertising. It produces high- quality

algorithms quickly and makes machine learning

simple to use and scale. Apache Spark features are

implemented on top of the Spark core. It offers a wide

range of APIs as well as apps for programming

languages such as Scala, Java, and Python to help

with development.

Figure 3: Apache Spark working environment

2.7.4 Qlik Sense

Qlik Sense is a QlikTech application that focuses on

data visualization and analysis. It helps with the

production of interactive dashboards and reports, as

well as data extraction from multiple data sources.

Qlik Sense is a QlikTech application that focuses on

data visualization and analysis. It helps with the

production of interactive dashboards and reports, as

well as data extraction from multiple data sources.

QlikSense editions are classified into three types:

Qlik Sense Desktop is a free and open-source

application. With the Qlik Download page using the

Qlik ID as a login can download the Qlik Sense

Desktop.

Figure 4.: Qlik Sense working environment

2.7.5 Sisense

Sisense's intuitive dashboard exploration and drag-

and-drop user interface make it simple for anybody to

create, investigate, and share insights. You can

quickly visualize data in a way that is best suited for

your research with the help of Sisense, which offers

intelligent computation and charting options based on

your unique data set. Dashboards can also be altered

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to have the style and feel that you choose.

Visualization may be enhanced with images, text,

videos, and links to turn insights into clickable

actions. Numerous benefits come with using the

Sisense Cloud, such as scalability and agility, a secure

environment, proactive support, hassle-free

maintenance, etc (Lousa, Pedrosa et al. 2019). It

offers both the ability for skilled coders to design

complicated reporting apps and for non-programmers

to quickly build unique reports using straightforward

drag-and-drop features. Sisense's data visualization

features make it simple to analyze reports and show

progress to clients, shareholders, and other parties.

Figure 5: Sisense airline performance dashboard screenshot

2.8 Cloud-Based Data Analytics

Cloud-based applications are the ones where the work

will be performed with the help of remote servers, and

the actual application will be run without installing

any software (Khan, Shakil et al. 2018). All we need

to get started are the login credentials. A cloud

platform offers a particular trial period for the user.

Once the trial is completed, one has to pay according

to usage and needs, similar to some open-source and

web applications. Here are some of the applications

in the cloud-based category:

2.8.1 AWS

Amazon Web Services is one of a kind and provides

a suite of cloud computing services like computing,

storage, databases, app development and deployment,

analytics, and management services. It offers a

variety of pricing options and plans to save you

money, including reserved instances and spot

instances. The main advantage of this is that it has

data centers all over the world and can deploy its

services nearly anywhere.

AWS Automated Data Analysis:

Automated analysis is the precise process of doing

analysis with a simple click that derives meaningful

insight from a specified range of data. With the help

of a deployed automatic process, it allows the users to

use this service with a user interface that abstracts

away the underlying AWS services.

Figure 6: Automated Data Analysis on AWS

2.8.2 Vanus

Vanus is a versatile cloud platform that can be used

without any prior knowledge of coding. It does all the

data preparation and transformation according to the

requirements. It does the work of Data

transformation, quality assurance, and visualization,

which includes charts, graphs, and map displays. In

addition to these works, it has an extension to

Machine Learning where it includes tools for

Classification, Regression, and clustering.

2.8.3 AppOptics Custom Metrics and

Analytics

A data collection service that is included in a bundle

that includes infrastructure and application

monitoring. This is a cloud-based solution that can

collect statistics for analysis from various cloud

platforms as well as your on-site resources.

2.8.4 IBM Cognos

Analytics combines AI approaches with stunning

visualizations to explore and detect trends. Have

plans for companies of all sizes (Magoma, Tshepo, et

al. 2021).

2.8.5 Microsoft PowerBI

Microsoft PowerBI has excellent visualization,

dashboard creation, and easy sharing and

collaboration with others. Machine learning is

included (Lousa, Pedrosa et al. 2019 ).

2.8.6 Zoho Analytics

Available on-premises and in the cloud, with drag-

and-drop dashboard customization.

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2.8.7 TIBCO Spotfire

TIBCO Spotfire is an AI-powered, sophisticated

analytics product with robust search capabilities for

corporate users.

2.8.9 Domo

Domo collects data from third-party sources such as

Excel from Microsoft, Xero, Facebook,

Salesforce, AWS, MySQL, and others.

2.8.10 QlikSense

Qlik Sense Cloud costs $15 per month. The

Enterprise Edition of Qlik Sense is available in token

form, with each token worth $1,500. Qlik enables

users to ask any inquiry in natural language and

receive robust responses. Furthermore, as you study

your data, Qlik will automatically offer insights and

new connections to investigate.

3 DATA–DRIVEN

DECISION-MAKING PROCESS

Whatever tool you select for your analysis, the

process that we follow for analytics is the same. Once

you have decided to go for analytics for your business

or some other purpose, there are some steps you can

take to get the right insights in a short period. These

are as follows:

Figure 7: Data Analysis Process Diagram

4 TAXONOMY OF THE TOOLS

AVA I L A B L E

The Analytics tools provided the basic usage

differentiation of different tools according to the

user's requirements. In this section, we would like to

give an outline of them, depending on the Budget,

memory space availability, working feasibility, and

limitations. The following tables give you an outline

of the different data analysis tools that are making a

blare in the market.

Table 1: Various Data Analysis Tools with Paid and Free Availability

Data Analysis

Metho

Spreadsheet Programming Web-based Open source Cloud-based

Paid Microsoft Excel,

Google Sheets

Python,

MATLAB, SAS

Tableau, Power

BI, QlikView

RStudio, Anaconda

Navigator, Qlick

Sense

Amazon Web Services (AWS),

Microsoft Azure, Google

Cloud Platform (GCP)

Free LibreOffice Calc,

Apache

OpenOffice Calc

Python, R,

Octave

Google Data

Studio, Microsoft

Power BI

Desktop, Looke

R, Python, Octave AWS Free Tier, Azure Free

Account, GCP Free Tier

Table 2: Pro’s and Con’s of Individual Data Analysis Tool Styles

Data Analysis Tool Advantages Disadvantages

Spreadsheets Easy to use, versatile, and affordable Can be limited for large and complex datasets, prone to

errors

Programming Most flexible and powerful, can be used

for com

lex data anal

sis tasks

It can be difficult to learn and use and requires

knowled

e of

rammin

Web-based applications Easy to use, accessible from anywhere

with an internet connection, and scalable

May not be as powerful as programming or

spreadsheets for complex data analysis tasks

Open source software Affordable, customizable, large

community of users

Can be difficult to find support for and may not be as

well-

olished as commercial software

Cloud computing Scalable, affordable, and easy to use Can be expensive for large datasets; security concerns

Usability Benchmarking of Data Analytic Tools with Market Research for Decision-Making

605

5 REPORT ON TOOL ADOPTION

TRENDS OVER THE PAST 5

YEARS

Figure 8: Data Analysis Usage Evolution for the past 5

years

The graph shows the usage of tools over the past

5 years. The Y-axis represents the number of users

using the tools, and the X-axis represents the year.

The graph shows that the usage of all tools has

increased over the past 5 years, but some tools have

grown more than others. Cloud- based computing has

grown the most, followed by open- source software,

web-based applications, programming, and

spreadsheets. This trend is likely due to the many

advantages of cloud-based computing and other

modern data analysis tools, such as scalability,

Affordability and ease of use. Additionally, the

increasing demand for data analysis skills and the

Growing availability of data have also contributed to

the growth of these tools.

Here is a more extensive breakdown of each tool's

growth:

Cloud computing has risen at the fastest rate in the

last five years, with a 250% increase. This is most

likely owing to cloud computing numerous benefits,

such as scalability, affordability, and ease of use.

Cloud computing systems can manage massive

datasets and are frequently less expensive than on-

premises data centers. Furthermore, cloud computing

systems are simple to use and may be accessible from

any region with an internet connection.

6 CONCLUSIONS

Cloud-based computing is the fastest-growing data

analysis tool. This is likely due to its scalability,

affordability, and ease of use. Programming is the

most flexible and robust data analysis tool. However,

it is also the most difficult to learn and use. Web-

based applications are a good option for users who

need a data analysis tool that is easy to use and

accessible from anywhere with an internet

connection. However, they may not be as powerful as

programming or spreadsheets for complex data

analysis tasks. Open-source Software is a good option

for users who need a data analysis tool that is

affordable and customizable. However, finding

support for open-source software can be difficult, and

it may not be as well-polished as commercial

software. Spreadsheets are a good option for users

who need a data analysis tool that is easy to use and

versatile. However, they can be limited to large and

complex datasets, and they may be prone to errors.

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