Citation: Al-Sai, Z.A.; Husin, M.H.;
Syed-Mohamad, S.M.; Abdin, R.M.S.;
Damer, N.; Abualigah, L.; Gandomi,
A.H. Explore Big Data Analytics
Applications and Opportunities: A
Review. Big Data Cogn. Comput. 2022,
6, 157. https://doi.org/10.3390/
bdcc6040157
Academic Editors: Domenico Talia
and Fabrizio Marozzo
Received: 11 November 2022
Accepted: 12 December 2022
Published: 14 December 2022
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4.0/).
big data and
cognitive computing
Review
Explore Big Data Analytics Applications and Opportunities:
A Review
Zaher Ali Al-Sai
1,2,
* , Mohd Heikal Husin
2
, Sharifah Mashita Syed-Mohamad
2
, Rasha Moh’d Sadeq Abdin
2
,
Nour Damer
3
, Laith Abualigah
2,4,5,6,7
and Amir H. Gandomi
8,9,
*
1
Department of Management Information Systems, Faculty of Business, Al-Zaytoonah University of Jordan,
Amman 11733, Jordan
2
School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
3
King Talal School of Business Technology, Princess Sumaya University for Technology, Amman 11941, Jordan
4
Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan
5
Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan
6
Faculty of Information Technology, Middle East University, Amman 11831, Jordan
7
Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
8
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
9
University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
* Correspondence: [email protected] (Z.A.A.-S.); [email protected] (A.H.G.)
Abstract:
Big data applications and analytics are vital in proposing ultimate strategic decisions.
The existing literature emphasizes that big data applications and analytics can empower those who
apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature
specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri
of the pandemic for using Big Data applications is presented. The comparison is expanded to four
highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion
on the effectiveness of the four major types of data analytics across the mentioned industries is
highlighted. Hence, this paper provides an illustrative description of the importance of big data
applications in the era of COVID-19, as well as aligning the applications to their relevant big data
analytics models. This review paper concludes that applying the ultimate big data applications and
their associated data analytics models can harness the significant limitations faced by organizations
during one of the most fateful pandemics worldwide. Future work will conduct a systematic
literature review and a comparative analysis of the existing Big Data Systems and models. Moreover,
future work will investigate the critical challenges of Big Data Analytics and applications during the
COVID-19 pandemic.
Keywords:
big data; big data analytics; big data applications; big data opportunities; COVID-19
pandemic; medical applications; healthcare; education
1. Introduction
The COVID-19 pandemic has drastically changed nation’s worldwide routine life
and operations. People have been forced to study and work from home, commuting and
traveling to local and overseas destinations have become impossible, and governments
have been forced to close cities and countries’ borders [1,2].
There is undoubtedly a need for the transfer/exchange the big data systems since
decision-makers must be able to react swiftly to changes or trends in markets, investments,
interest rates, and other crucial happenings [
3
]. Decision-makers should be thoroughly
aware of the type of inputs they have and the best structure for exchange or analysis if they
are thinking about making significant investments in KM systems or big data/business
analytics systems [3].
The COVID-19 pandemic paralyzed most vital industries globally. Recently, this is
the presented fact; however, a real opportunity is hidden in the new oil in the digital
Big Data Cogn. Comput. 2022, 6, 157. https://doi.org/10.3390/bdcc6040157 https://www.mdpi.com/journal/bdcc
Big Data Cogn. Comput. 2022, 6, 157 2 of 23
economy, which is Big Data. From a theoretical point of view, researchers have identified
different BD-related capabilities and resources as a solid and potential foundation to en-
hance organizational performance. Most current works in the BD Analytics (BDA) domain
cover the technology dimensions, talent, and management that can impact organizational
performance [
4
]. The organization’s ability to benefit from different forms of massive data
is highly required, and the willingness to invest in BD is now at the center of interest [
5
,
6
].
Recently, it has been normal for organizations to be under pressure to remain in their
positions in fiercely competitive markets and identify strategies of expenditure reduction,
quality enhancement, and reduced time to market [
7
]. The new era of BD transformation
needs next-generation technologies to attain success [8–13].
Organizations will be required to manage it appropriately for competitive advantage
and durability in the modern digital market [
14
]. Organizations should be capable of
identifying vital data resources, structure, needed skills, and architecture. Moreover,
organizations must define and describe the underlying infrastructure of the process that
supports BD analysis, formulate and applicable BD strategy, and measure applications and
technologies that support the organization’s requirements regarding their BD investments.
Particularly, organizations should migrate their data collection and analysis from just being
product or service orientated to a future-oriented platform [
14
]. To grow the adoption rate,
ensure the successful implementation, and minimize the risk after implementation, it is
crucial to assess and measure BD readiness and maturity level using a maturity assessment
model and tool [15,16].
For instance, in the education sector, big data analytics played a key role in overcoming
the negative consequences of the pandemic on the educational sector. It supported tutors
and instructors to personalize the remote learning experience for educators. Additionally,
it helped bridge the unemployment gap that resulted from COVID-19 major economic
losses globally. The importance of big data applications and analytics in the transportation
field of COVID-19 has been explicitly shown to decision-makers. For instance, regulators
supported their decisions and judgments based on the data captured and analyzed via
AI techniques and predictive models. Based on the results, precautionary measures were
clearly defined, and any violations were easily detected. Furthermore, predictive models
guided decision-makers on citizens’ movement within and among cities and metropolitans;
consequently, they were able to detect and predict future endemic areas.
Existing literature review papers have covered the topic of BD applications during
COVID-19. However, this review paper presents new insights into how big data analytics
is integrated into the picture in four critical industries. More specifically, this paper will
present how big data applications and their aligned data analytics can pave the road for
industries to survive an uncontrolled and unpredictable situation. This paper explains these
applications in detail. A systematic comparison between the use of BD applications before
and after COVID-19 is presented. The paper focuses on four highly impacted industries:
Healthcare, Education, Transportation, and Banking. Additionally, this study analyzes
the alignment of big data applications with their relevant data analytics models in the era
of COVID-19.
The structure of this review paper will be presented as follows: The introduction
Section 1, then the literature review Section 2, which highlights the definition and character-
istics of Big Data. Additionally, it highlights the Big Data Analytics and types of analytics.
Then big data applications and opportunities are presented in Section 3. Sections 4 and 5
review the Big Data applications before and during COVID-19, specifically. Finally, some
future work suggestions will be presented in the conclusion and future work in Section 6.
2. Literature Review
Big Data is a critical asset in the competitive market of the digital economy. The
benefits of Big Data allow organizations to achieve various objectives under the umbrella
of Big Data insights [
17
]. The following sub-sections present the overall review of Big Data
and its applications.
Big Data Cogn. Comput. 2022, 6, 157 3 of 23
2.1. Big Data and Analytics
There has not been a standardized definition for BD among industry, business, media,
academia, and various stakeholders. Absence of a systematic definition for BD concept
leads to a sort of confusion [
12
,
18
,
19
]. BD is usually defined by individuals. It is different
from one industry to another, and according to the types of available sizes of datasets and
the software tools are common in a particular industry [8,20–22].
There have been remarkable thoughts from both industry and academia on BD defi-
nition [
23
]. By coupling the concept of BD with current grounded academic research, the
BD concept can be more understandable. A clear view of BD concept will enhance the
awareness about BD phenomenon for both practitioners and academics, resulting in faster
growth and more efficient value obtained from BD [
24
]. In spite of the fact that there is no
identified definition for BD, from a technical and business point of view, BD is identified as
the increasing flow of various types of data from different resources [25].
The first BD definition was written by scientists from NASA. The paper published
in 1997, by NASA referred to the data volume as an exciting challenge for computer
systems to increase the demand for the big volume of main memory, local disk, and in
addition to a remote disk. It was identified by NASA as the problem of BD that required to
obtain more resources [
8
–
13
,
26
]. The META Group analyst Dough Laney (now Gartner)
has defined data growth challenges and opportunities in to three-dimensional (velocity,
volume, variety) [18,27].
The researchers have defined BD concepts from different point of views (BD charac-
teristics, technology, business, Innovation, etc.). One of the definitions had been updated
by Gartner in 2013, who defined BD concept as “high-volume, high velocity and/or high
variety information assets that demand cost-effective innovative forms of information
processing for enhanced insight, decision making, and process optimization” [
26
,
28
,
29
].
The Statistical Analysis System Institute (SAS) defined BD as “Popular term used to de-
scribe the exponential growth, availability, and use of information, both structured and
unstructured” [
30
]. IBM also added a definition for BD, “Data is coming from everywhere;
sensors that gather climate information, social media posts, digital videos and pictures,
purchase transaction record, and GPS signal of mobile phone to name a few”, “BD can be
defined as large set of very unstructured and disorganized data”, “BD is a form of data that
oversteps the processing power of traditional database infrastructures or engines” [
30
–
32
].
BD was referred from more than one perspective (BD as technology, entity, and pro-
cess) [
33
]. The definition of BD analytics consists of the technologies (database and data
mining tools) and techniques (analytical methods and techniques) that organizations can
utilize to analyze vast amount and complex data for a variety of applications prepared to
increase the performance of organizations in many perspectives. BD can be considered
as both entity and process. BD as an entity includes a volume of data captured from a
variety of resources (internal and external) and consists of structured, semi-structured, and
unstructured data that cannot be processed using traditional databases and software tech-
niques. BD as a process refers to both the organizations’ infrastructure and the technologies
used to capture, store and analyze numerous types of data [10–13,33].
New insights are provided by BD to discover new values, supporting organizations to
get the benefit of a deep understanding of the hidden values [
23
]. BD is pointed out as a
technology that enables the processing of unstructured data; and BD technologies are the
systems and tools used to process BD such as NoSQL databases, the Hadoop Distributed
File System, and MapReduce [34,35].
According to [
14
], different theories and definitions on what shape BD exist in are
provided. The most often referred definition is BD oversteps the capabilities of popularly
and currently used software tools and hardware platforms to capture, manage, and process
it within an acceptable and bounded time. The concept of BD has been promoted to define
the novel and powerful computational technologies that have been provided to process an
enormous volume of data. BD has been described in various ways, however, fundamentally
is a modern technology that is primarily characterized and derived from Business Analytics
Big Data Cogn. Comput. 2022, 6, 157 4 of 23
(BA) and Business Intelligence (BI). It is capable of creating business values via its predictive
analytics, and decision support abilities, which results in the potency to deal with data that
traditional techniques cannot process [25,34].
According to the studies by [
13
,
36
], BD is defined as “a term that describes large
volumes of high velocity, complex and variable data that requires advanced techniques
and technologies to enable the capture, storage, distribution, management, and analysis of
the information”.
2.2. Characteristics of Big Data
Existing work characterized BD as novel technologies and architectures which are
designed for extracting value from enormous volumes of a wide range of data, by empow-
ering high-velocity capture, discovery, and analysis in a cost-effective way [
28
]. Since BD is
relatively new, it is significant for organizations to know what makes this trend valuable
and they should identify the “Vs” that describe the key characteristics of BD [
37
]. Still, a lot
of confusion and obscurity among the Vs of BD exists. Some pioneering studies pointed
out that there are three, four, five, and sometimes even seven characteristics of BD [38].
The large-scale feature of BD is reflected in three different characteristics of volume, va-
riety, and velocity. Traditional technologies do not have the ability to successfully deal with
the enormous data volume, which is generated at a growing velocity, via online streaming
and a variety of other different resources such as transactional systems, sensors, social
media, product/service instrumentation, and web platforms [
38
]. META Group analyst
Doug Laney (now Gartner) presented 3Vs of BD to characterize the data management in
3 dimensions represented by three main Vs of Volume, Velocity, and Variety [
39
]. Volume
represents the amount of data. Velocity represents the speed of data generation and process.
Variety refers to the diversity of resources and data types. Variety refers to the diversity of
resources and data types [
40
–
42
]. The three Vs have been mentioned by NIST and Gartner
in 2012 and extended by IBM to involve the 4th V representing “Veracity “. Contrarily, Ora-
cle avoided using the paradigm of “Vs” in its BD definition. Instead, it is highly believed
that BD is the derivation of values from traditional relational database-driven business
decision making, grown with new resources of unstructured data [18].
The 4Vs (volume, variety, velocity and value) model was presented by [
14
,
33
,
41
,
43
,
44
].
Excluding the 4Vs mentioned, another V which is veracity is identified to represent the
uncertainties of BD and data analysis outcome. Another research conducted by [
41
,
42
],
pointed out the four major Vs of BD namely volume, velocity, variety, and value that
pertains to the insight obtained by organizations from BD which not only require scalability,
but also for preferable operational procedures and strategies [
41
,
44
–
46
] pointed out five key
characteristics of BD as 5Vs (Volume, Velocity, Variety, Veracity, and Value). “Complexity”
is a “C” feature added to the 4-Vs (Volume, Variety, Velocity, Value) of BD by [
47
–
49
]
to formulate another 5 characteristics of BD. Security and management are additional
characteristics to the 3Vs (Volume, Variety, and Value) [
48
]. A study by [
48
] also presented
a critical problem of technical research that requires more investigation by scholars.
Recently, other Vs which are (Visualization/Visibility, Variability/Volatility, Validity,
Virtual, and Complexity) are added to BD characteristics by [
26
]. Another work done by [
50
],
defined the 7 Vs of BD namely Volume, Velocity, Variety, Value, Veracity, Variability which
implies inconstancy and heterogeneity; and visualization which implies the illustrative
character of data. Volume, Velocity, Variety, Veracity, and Value are the widely accepted and
common Vs by stakeholders. However, the other Vs are important for BD paradigm too.
By comparing existing definitions of BD and its related aspects, the 5Vs (volume, velocity,
variety, veracity, and value) characteristics are extracted and formulated to point out how
different traditional data and BD are [24,51,52] as illustrated in Figure 1.
Big Data Cogn. Comput. 2022, 6, 157 5 of 23
Big Data Cogn. Comput. 2022, 6, x FOR PEER REVIEW 5 of 29
pointed out five key characteristics of BD as 5Vs (Volume, Velocity, Variety, Veracity, and
Value). “Complexity” is a “C” feature added to the 4-Vs (Volume, Variety, Velocity,
Value) of BD by [47–49] to formulate another 5 characteristics of BD. Security and man-
agement are additional characteristics to the 3Vs (Volume, Variety, and Value) [48]. A
study by [48] also presented a critical problem of technical research that requires more
investigation by scholars.
Recently, other Vs which are (Visualization/Visibility, Variability/Volatility, Validity,
Virtual, and Complexity) are added to BD characteristics by [26]. Another work done by
[50], defined the 7 Vs of BD namely Volume, Velocity, Variety, Value, Veracity, Variability
which implies inconstancy and heterogeneity; and visualization which implies the illus-
trative character of data. Volume, Velocity, Variety, Veracity, and Value are the widely
accepted and common Vs by stakeholders. However, the other Vs are important for BD
paradigm too. By comparing existing definitions of BD and its related aspects, the 5Vs
(volume, velocity, variety, veracity, and value) characteristics are extracted and formu-
lated to point out how different traditional data and BD are [24,51,52] as illustrated in
Figure 1.
Figure 1. The Five Features of Big Dat.
Big data is more of a concept than an exact term. Some classify big data as a volume
problem only for petabyte-scale (>1 million GB) data collection. Some people associate big
data with different data types, even if the volume is measured in terabytes. These inter-
pretations made the big data problem situational [51].
2.3. The Types of Data Analytics
Big Data Analytics refers to the process of collecting, organizing, and analyzing high
volume, velocity, variety of data to discover the valued patterns that could use for making
decisions. Analyzing the big data need new tools, methods, and technologies such as data
mining, predictive analytics, and perspective analytics [52].
Most of existing literature identified the use of big data applications defined in the
presence of the four types of data analytics. The four types of big data analytics that can
be implemented in governments are: (i) Descriptive, (ii) Diagnostic, (iii) Predictive, (iv)
Prescriptive [53].
The following section will describe each type with their related examples in govern-
ments and more specifically during COVID-19:
• Descriptive
Figure 1. The Five Features of Big Dat.
Big data is more of a concept than an exact term. Some classify big data as a volume
problem only for petabyte-scale (>1 million GB) data collection. Some people associate
big data with different data types, even if the volume is measured in terabytes. These
interpretations made the big data problem situational [51].
2.3. The Types of Data Analytics
Big Data Analytics refers to the process of collecting, organizing, and analyzing high
volume, velocity, variety of data to discover the valued patterns that could use for making
decisions. Analyzing the big data need new tools, methods, and technologies such as data
mining, predictive analytics, and perspective analytics [52].
Most of existing literature identified the use of big data applications defined in the
presence of the four types of data analytics. The four types of big data analytics that
can be implemented in governments are: (i) Descriptive, (ii) Diagnostic, (iii) Predictive,
(iv) Prescriptive [53].
The following section will describe each type with their related examples in govern-
ments and more specifically during COVID-19:
• Descriptive
Descriptive analytics is the preliminary stage in the analytics categorization. Descrip-
tive analytics is known as business reporting, as such stage emphasize in creating summary
reports to highlight business activities, and to illustrate the answers of questions of “what
is happening or happened?” [54–56].
This type of data analysis depends on analyzing past data, visualize and understand
historical trends. An example of BDA during COVID-19 are Dashboards used in the health
care sector to monitor live data about the spread of COVID-19 in a particular area. Such
dashboards track, illustrate and statistically explain the historical records captured about
COVID-19 cases in a specific area, city or country [29].
• Diagnostic
The second data analytics type is Diagnostic data analysis. This type focuses on
illustrating the correlation, hidden patters, cause-effect relation and interrelationships
between different variables. An example would be the data captured from job portals.
Such data is used to analyze and visualize potential market sectors and match it with the
relevant workforce in the country [54].
Diagnostic analytics figure out answers to questions of “why did it happen?’. The
main goal in Diagnostic Analytics is to highlight the root causes of a challenge or problem.
Big Data Cogn. Comput. 2022, 6, 157 6 of 23
Such root causes identification depends on specialized techniques such as visualization,
drill-down, data discovery, and data mining [54–56].
• Predictive
Predictive analytics is categorized in the third level on the data analytics hierarchy.
More specifically it is the stage residing after the descriptive analytics. Based on the Data
Analytics maturity model, organizations that have matured in descriptive analytics can
move forward to the next stage to answer “What will happen?” [55,56].
The third data analysis focuses on patterns from past existing data and predict what
will happen when changes occur in such set of data. The example here is the vaccine
distribution prioritization mechanism. Data analysts predict through machine learning
model who is next in need to the vaccine and prepare patient priority list accordingly [
57
].
• Prescriptive
The last data analysis type Prescriptive analytics. It is where the best alternative
among many–that are usually created/identified by predictive and/or descriptive analytics–
courses of action is determined using sophisticated mathematical models. Therefore, in
a sense, this type of analytics tries to answer the question of “What should I do?”. Pre-
scriptive analytics uses optimization, simulation, and heuristics-based decision modelling
techniques [58].
Perspective analytics is ranked as the highest level in data analytics maturity model,
it is also viewed as the most sophisticated and complex data analysis type. Both AI and
big data analysis techniques are used in Prescriptive analytics. Utilizing such techniques
facilitate decision makers to frame the optimal strategic decision. Decision makers will
reach to these decisions via selected optimization models. For example, Prescriptive
analytics were used during COVID-19 pandemic to understand citizens’ reactions towards
the vaccine, and support decision makers to structure the optimal strategic decisions to
control citizens’ hesitant towards the vaccine [55,56].
To illustrate how the four data analytics types are classified based on the level of so-
phistication and data complexity, researchers have introduced the following two categorize
as shown in Figure 2:
•
Business Intelligent, which consist of both Descriptive analytics and Diagnostic analytics
•
Advanced Analytics, which consider the higher data analytics types in maturity level,
namely predictive and prescriptive analytics [55,56,59].
Big Data Cogn. Comput. 2022, 6, x FOR PEER REVIEW 7 of 29
Figure 2. A simple taxonomy for analytics.
3. Big Data Analytics Opportunities and Applications
Big data analytics can be described as the use of mathematical and statistical tech-
niques, to find the hidden patterns and variances in large amount of data from multiple
sources, and from different type of data (structure, semi-structured, unstructured) to gain
future insight and faster decision making [60]. Such findings will be the base for organi-
zations to provide them with valuable knowledge and support them in their strategic de-
cisions [61]. The utilization of big data analytics has shown an added value to govern-
ments and firms during COVID-19. Consequently, those who have implemented big data
analytics, outperform others. For instance, they were able to map their current status and
structure better strategic decisions [55].
In a Mckinsey’s report it was highlighted that big data analytics empowered those
who applied it, by incrementing their annual economic value between $9.5 trillion and
$15.4 trillion [62]. Furthermore, as the COVID-19 outbreak, big data analytics has empha-
sized its effectiveness in detecting the spread of COVID-19, and supported governments
to reach optimal decisions against it [63].
The main goal for organizations is the bottom line represented in their profits, market
share and customer loyalty and satisfaction levels [64]. This fact is applied for both busi-
ness firms and governmental entities. With the exponential increase in the volume of data,
the speed in which it is generated, the variety of sources generating it, and the importance
of its quality and relevance. The vital role of big data applications in various business
sectors and governmental entities have been a necessity for their success [65]. The imple-
mentation of big data applications has supported organizations to enhance their custom-
ers experience, improve cost savings, and facilitate strategic decision making [66]. Conse-
quently, organizations’ processes and operations become achieve a higher level of effec-
tiveness and efficiency [67].
New, advanced and tactical digital technologies were considered recently as a re-
sponse to the COVID-19 pandemic, such as big data applications. Countries such as Tai-
wan, South Korea, Hong Kong, and Singapore have demonstrated the significant positive
impact from adopting such applications. Those countries proved the seamless of control-
ling the pandemic expected risks effectively [61].
Big Data Applications can derive insights from various data sources to provide ideal
solutions for several sectors [52]. Organizations from a variety of industries have started
using MapReduce-based solutions for processing enormous amounts of data [68]. To meet
their needs for handling large-scale data processing, many businesses rely on MapReduce.
Figure 2. A simple taxonomy for analytics.
Big Data Cogn. Comput. 2022, 6, 157 7 of 23
3. Big Data Analytics Opportunities and Applications
Big data analytics can be described as the use of mathematical and statistical tech-
niques, to find the hidden patterns and variances in large amount of data from multiple
sources, and from different type of data (structure, semi-structured, unstructured) to gain
future insight and faster decision making [
60
]. Such findings will be the base for orga-
nizations to provide them with valuable knowledge and support them in their strategic
decisions [
61
]. The utilization of big data analytics has shown an added value to govern-
ments and firms during COVID-19. Consequently, those who have implemented big data
analytics, outperform others. For instance, they were able to map their current status and
structure better strategic decisions [55].
In a Mckinsey’s report it was highlighted that big data analytics empowered those
who applied it, by incrementing their annual economic value between $9.5 trillion and
$15.4 trillion [
62
]. Furthermore, as the COVID-19 outbreak, big data analytics has empha-
sized its effectiveness in detecting the spread of COVID-19, and supported governments to
reach optimal decisions against it [63].
The main goal for organizations is the bottom line represented in their profits, market
share and customer loyalty and satisfaction levels [
64
]. This fact is applied for both
business firms and governmental entities. With the exponential increase in the volume
of data, the speed in which it is generated, the variety of sources generating it, and the
importance of its quality and relevance. The vital role of big data applications in various
business sectors and governmental entities have been a necessity for their success [
65
].
The implementation of big data applications has supported organizations to enhance their
customers experience, improve cost savings, and facilitate strategic decision making [
66
].
Consequently, organizations’ processes and operations become achieve a higher level of
effectiveness and efficiency [67].
New, advanced and tactical digital technologies were considered recently as a response
to the COVID-19 pandemic, such as big data applications. Countries such as Taiwan, South
Korea, Hong Kong, and Singapore have demonstrated the significant positive impact
from adopting such applications. Those countries proved the seamless of controlling the
pandemic expected risks effectively [61].
Big Data Applications can derive insights from various data sources to provide ideal
solutions for several sectors [
52
]. Organizations from a variety of industries have started
using MapReduce-based solutions for processing enormous amounts of data [
68
]. To meet
their needs for handling large-scale data processing, many businesses rely on MapRe-
duce. As businesses from a variety of sectors embrace MapReduce together with parallel
databases. new MapReduce workloads have appeared that contain a large number of brief
interactive tasks [68].
Table 1 highlights the alignment of each big data application to its respective big data
analytics model. It provides an explanation on how such an implementation has supported
organizations and governments to cope with COVID-19 pitfalls. Furthermore, such an
implementation provided an optimal solution to harness its operations and decision-
making process. This categorization has been developed based on the description of each
application in their relevant fields, and on the definition highlighted in the section on the
four big data analytics types.
Big Data Cogn. Comput. 2022, 6, 157 8 of 23
Table 1. How to utilize Big Data Analytics in Healthcare, Education, Transportation, and Banking.
Field Data Analytics Type How BDA Has Been Utilized Data Processing Models Used to Analyse Big Data Reference
Healthcare
Descriptive and Predictive Data
Analytics
Proactive actions and interventions
based on predictive models to
trigger any noncommunicable
diseases.
Predictive models based on search engines and social
media data.
Smart phone applications tracking system to identify
infection hot spots
[69,70]
Perspective Data Analytics Vaccine distribution
Sentiment analysis to reduce community resistance
towards the vaccine.
[69–71]
Diagnostic and Predictive Data
Analytics
Vaccine distribution
Machine learning models to prioritize the citizens’ need
and urgency to the vaccine
Diagnostic and Prescriptive Data
Analytics
Monitoring live and frequent data
on the spread of the disease
Provide more personalized
consultations by “virtual doctors”
Dashboards
AI Chabot
[72,73]
Education
Descriptive Data Analytics
Enhance online educational
platform experience
Analyzing data captured from online educational
platforms can ease educators remote leaning experience
[69,74]
Diagnostic Data Analytics Bridge the gap of unemployment Analysis of data captured from job portals [69,75]
Transportation
Descriptive and Prescriptive Data
Analytics
implementation of precautionary
measures-Ensure social distancing
in public transportation
Capturing relevant data and use machine learning
techniques to detect incompliance actions
[76]
Detect citizens’ commute route to
store their travel history.
Use both AI and Big data applications to capture, track
and predict valuable insights about citizens movement
within and across cities and countries
[77]
Banking
Fraud Detection
Use AI and ML techniques to describe and detect
real-time abnormal activities and online transaction, and
build ML models based on classification algorithims to
predict any suspecious case.
[78]
Descriptive and Predictive Data
Analytics
Risk Assessment
Use both diagnositic and prescriptuve data analytics
models to analyze real-time data and asses the
creditworthiness to customers. Consequenlty
developing the appropriate cutomer portfolio and tailor
clients needs to their services. Cossequently boosting
customers’ satisfaction, loayality and enhance banks
botom line records.
[78]
Big Data Cogn. Comput. 2022, 6, 157 9 of 23
4. Big Data Applications Pre the COVID-19 Pandemic
In the following section, a demonstration of how big data applications have been
applied, and the opportunities captured from it will be illustrated. The section focuses on
certain fields before COVID-19, such as healthcare, education, transportation, and banks.
4.1. Big Data in Healthcare
The secret behind utilizing Big Data in the healthcare segment is its powerful ability
to highlight the correlation and patterns between different variables rather than finding the
casual inference between them. Hence, its capability to predict for the future, and therefore
facilitating the e government health sector in its decision-making process [
79
]. For example,
it can support building predictive models for risk and resource use, study the behavioral
patterns for patients, analyze the population health, facilitate diagnostic and treatment
decisions, use medical images as an input to the clinical decision support system [
80
].
Assure the safety of the drug and medical devices use on patients and serve individuals
health better through analyzing, predicting and monitoring the disease patterns [81].
The sources of data in the healthcare field have elevated exponentially, ranging from
the records captured from public hospitals, drug research studies, pharmacists, patholo-
gists, medical laboratories and radiologist. Furthermore, recently other indirect sources
are considered such as vital sources such as medical newsletters, websites, social media
platforms, health reports, and discussion forums. Additionally, mobile phone applications
such as medical smart watches can be considered in the big data process in the health-
care segment [
80
]. All of these sources of data are the fuel used to enhance performance
in health institutes such as in vaccinations, cure of diseases, insurance procedures, and
hospital management operations [82].
Big data utilization in healthcare is focused on delivering superior value to individual
patients rather than on delivering analysis on general disease cases and volumes of data [
29
,
80
]. Hence, the main goal of big data applications in the health care industry is to serve
efficiently considering both value and costs to individual cases [83].
As explained in this section, the use of BDA has been emphasized throughout the med-
ical procedure cycle, affecting the various stakeholders. From patients, medical providers,
medical insurance entities, and medical researchers [29].
4.2. Big Data in Education
The educational system is one of the main civilization pillars. Its development can
characterize the advancement level of any society. Big data has played a vital role in
restructuring the educational system. It enabled educational institutes and professionals
to personalize the educational experience for students [
84
]. The main goal for educators
and trainers is to provide a high-quality educational scheme and teaching system. This can
only happen by understanding that each student has different way of learning, level of
competence, readiness to learn, and interests [
85
]. How this process can be personalized?
Big data is the answer. Big data can analyze, find correlation between the data, highlight
patterns, provide insights and predict for the ultimate teaching-learning process. Hence,
educators and professionals in the educational field, will provide intelligent decisions to
enhance the educational regime [86].
Sources of data in the educational field can be categorized into three main levels
Micro-level data, Meso-level data, and Macrolevel data [
87
]. Micro-level data or what is
known as clickstream data, consists of the interactions between millions of learners and
their learning environment [
88
]. This includes the learners’ interaction with the virtual
gamification, simulations, online platforms, and intelligent tutoring systems. Such actions
can predict students’ interests [
87
]. Meso-level data (text data) predict the cognitive ability
of students through analyzing the computerized text-oriented writing activities [
87
]. Such
data will be analyzed through NLP techniques [89].
Big Data Cogn. Comput. 2022, 6, 157 10 of 23
Macrolevel data (institutional data) are sets of data that are captured once a year and
represents students’ demographics, all educational institutes relevant data (admission data,
courses enrolled in, major prerequisites) [90]. Done
When considering big data applications in the educational field, all the above cat-
egorization of data sources will overlap. For instance, students’ interactions through
social media represents both microlevel (duration spent, location of the student) and their
meso-level (written posts and text-oriented interaction) [
87
]. Another example is specific
simulation games offered by the educational institute, were the three levels of data will be
represented miso/micro/and macro [91].
Applying big data techniques have unleashed several opportunities. For example, big
data can improve the learning process, by optimizing the selection, of the prior teaching
techniques and newly proposed ones to meet the student actual needs and interests [
92
].
In addition, big data can facilitate choosing the best bundle of resources, tools, and skills
that of higher priority to each teaching-learning case, away from human subjectivity [
93
].
Moreover, big data can support educators in providing real time feedback and construct
development plans based on the student interaction within the virtual learning environ-
ment [
87
]. Furthermore, big data can facilitate constructing a more personalized learning
environment. It can track, analyze and predict every action and interaction taken by the stu-
dents in their virtual environment. By collecting data on students’ preferences, performance
and results, a more comprehensive picture of the student will be developed. For instance,
every click in the virtual learning environment can ease the prediction process [
94
]. This can
be tracked from their interests, their doubts, the time spent in each program, their grades,
and their preferred learning style [
87
]. Thus, big data will result in a more satisfactory
learning environment for the students [95].
Enriching the learning environment through understanding the student actual per-
formance level, areas of improvement and difficulties. For instance, big data can detect
the questions which the student may fail in or struggle on solving it. Hence, big data can
generate progress metrics to provide in depth analysis of the student performance [
87
].
Furthermore, big data provide a great tool to predict the students who may pass success-
fully or fail. Another crucial opportunity of applying big data in the educational field is
to utilize it in the marketing research, were educational institutes can attract outstanding
students [86].
4.3. Big Data in Transportation
The phenomena of big data application have raised significantly in the transportation
field, as a result of the endless flow of both mobility and city data that resides in digital
repositories, remote and in situ sensors and mobile phones and captured accordingly in
vast volumes and velocities [
96
]. These data are the base for researchers, economists and
regulators to analyze traffic flow, congestion and their social, economic and environmental
impacts [
97
]. Moreover, applying a combination of new methods of analysis such as
artificial intelligent approaches, paves the way for predicting and providing innovative
solutions for the future. Hence, creating a new revolution in big data in the transportation
field. [30].
For example, big data plays a vital role in predicting the cause effect relation between
the driving restriction policies and traffic congestion [
98
]. Big data through its predictive
capabilities and the incorporation of economic insights can exceed the ability to understand
and analyze the past and real time data, to predict the optimal legislations for traffic
congestion issues in smart cities [99].
To understand how big data applications used in transportation, we will illustrate the
categorization of various sources of data:
1.
First source of data which is the primary source is the direct physical sensing. Repre-
sented, in road-side static sensors such as LiDAR, microwave Radars, and sensors that
measure speed, noise, and traffic flow known as acoustic sensors [
100
]. Other exam-
ples are the use of mobile phone technologies such as GPS, GSM, and Bluetooth [97].
Big Data Cogn. Comput. 2022, 6, 157 11 of 23
2.
The second source of data is the social media sources “human & social Sensing”
highlighted in the use of motorists to the smartphone-compatible platforms [
101
]. For
instance Instagram, twitter and others [97].
3.
The third category of data source is urban sensing which is generated by transportation
operators. In this category data captured can analyze urban mobility in terms of
congestion and traffic flows [
102
]. This can be performed via credit cards and smart
cards scanned through urban sensors from public transit, retail scanners and digital
toll systems [97].
4.4. Big Data in Banking
In recent years, the massive use of information technology and more specifically,
big data, has reshaped the banking sector intensely. This has been remarked by the
introduction of digital banking operations and virtual banking systems [
103
]. The banking
sector is a highly competitive environment. To survive in such a competitive environment a
proactive strategy and better strategic decisions must be adapted by management. Big data
applications are utilized in the banking sector and supported by data mining techniques
to transfer customer semi-structured and un-structured data into meaningful insights
and derive the ultimate strategic decisions. Such decisions can support banks to increase
customer satisfaction, detect fraud cases, ease the merge and acquisition operations [
95
],
optimize banking supply chain performance [
104
], outperform annual profits and expand
market share. An example of how big data applications harness strategic decisions and
meet strategic goals is through applying sophisticated algorithms to categorize clients and
group them into clusters based on the analysis and interpretation of clients’ behaviors. Such
technique can facilitate banks to provide valued and satisfactory services to different clients’
categories. Moreover, the ability of big data applications to integrate internal and external
sources play a vital role in detecting fraud activities [
105
]. Furthermore, the capability of
big data to analyze, predict, and visualize both external market conditions and internal
clients’ trends and preferences can empower management in considering the ultimate
decision to invest in new markets, hence increasing their market share and enhancing
profitability [106].
Table 2 depicts a summary of the Big Data opportunities. Moreover, Table 3 provides
examples of Big Data applications in certain fields before COVID-19.
Big Data Cogn. Comput. 2022, 6, 157 12 of 23
Table 2. The Big Data Opportunities before COVID-19 Pandemic.
Field Opportunities Description Reference
Healthcare Serve efficiently considering both value and costs to individual cases
BDA have powerful ability to highlight the correlation and
patterns between different variables rather than finding the
casual inference between them and serve individual
patients’ cases.
[79–81,83,107]
Education
Improve the learning process
Provide real time feedback and construct development plans
Construct a more personalized learning environment
Enrich the learning environment
Utilize BDA in marketing research purposes for institutions
BDA enables educational institutes and professionals to
personalize the educational experience for students
[84,86,87,92–95,108]
Transportation
The base for researchers, economists and regulators to analyze traffic flow,
congestion and their social, economic and environmental impacts.
Apply a combination of new methods of analysis such as AI approaches, to
pave the way for predicting and providing innovative solutions for the future
in the field of transportation.
BDA predictive capabilities and the incorporation of
economic insights can exceed the ability to understand and
analyze the past and real time data, to predict the optimal
legislations for traffic congestion issues in smart cities.
[30,96–99,109]
Banks
Detect fraud cases
Ease the merge and acquisition operations Optimize banking supply chain
performance
Interpret clients’ behaviors.
Provide valued and satisfactory services to clients.
Analyze, predict, and visualize both external market conditions and internal
clients’ trends and preferences
Increase market share and enhance profitability.
BDA supported the introduction of digital banking
operations and virtual banking systems
[103,105,106]
Big Data Cogn. Comput. 2022, 6, 157 13 of 23
Table 3. Examples of Big Data applications by field before COVID-19.
Field in Charge Application Name Description Reference
Health
Ebola Open Data Initiative
West Africa-data has been utilized to develop an open-source global model for tracking
the cases of Ebola cases in in 2014
[29,110,111]
HealthMap
a platform used to visualize diseases trends and provides an early trigger on the proper
response
[110,112]
Proactive listening, mobile phone-based
system
Brazil-to govern the issue of bribes in the health services, and handle any related issues
and take an immediate and effective action against corruption.
[110]
Education
ENOVA
Mexico, through the utilization of data and data analytics can analyze and predict
students’ interactions. Consequently, boosts the educational strategies and enhances the
used tools and techniques in the teaching-learning process.
[113,114]
(PASS) Personalized Adaptive Study Success
The Open University Australia-Predicts course material, beside a more personalized
studying environment. The predictive data analytics model is based on analyzing
students, individual characteristics, beside other student related data captured from
other systems.
The main goal of the application is to develop a more customized environment that
ensures students involvement, engagement, and retention in an e-learning environment.
[115]
Transportation
OpenTraffic platform
An application to support in urban infrastructure decisions, based on data captured from
both vehicles and smartphones, to analyze it and visualize it into both historic and
real-time traffic situations.
[110,116]
Seoul, South Africa-the application is used to support night bus drivers to ease their
journey from origin to destination. This will occur through capturing data from
tremendous number of calls and text data points, as well as private and corporate taxi
data sources.
[110]
Banks Avaloq, Finnova, SAP, Sungard and Temenos
OCBC is the largest bank in terms of market capitalization in Singapore. It operates in
more than 15 countries globally. It is a success example of the utilization of BDA. For
instance, the bank responded to customer actions, customers’ personalized events and
their demographic profiles. Hence, OCBC Bank succeed in achieving higher customer
engagement and increasing the level of customer satisfaction by 20% in comparison to a
control group.
These core banking applications, such as Avaloq, Finnova, SAP, Sungard or Temenos for
example, were designed to handle large amounts of transactions in back-office processes
for basic financial products and services, such as bank accounts, deposits, etc.
[104,117]
Big Data Cogn. Comput. 2022, 6, 157 14 of 23
5. Big Data Applications Peri the COVID-19 Pandemic
In 2020, the world has been experiencing a critical pandemic, COVID-19. Most govern-
ments were not expecting such a drastic change in their citizens daily routine and life. From
cities in lock down, individuals’ quarantine, people working from home to the emphasize
on online services [
97
,
118
]. Moreover, many of the government’s portals, e services and
applications were not up to the required standard to fight against such a disaster. That
triggered the importance of data analytics and the utilization of Big Data Applications.
Many governments were forced to react in a short period of time [
97
,
119
]. A variety of
applications have been introduced in different fields, to ease individuals’ lives, support
governments decisions and control the pandemic effect globally [
120
]. Furthermore, big
data analytics, have facilitated governments to embrace remarkable strategic decisions
efficiently and effectively. Moreover, data analytics proved its importance in predicting
and managing risks associated to supply chain safety, and to external economic, social and
legal risks [55,118].
According to a study by EY in 2021, governments are reconsidering the importance
of big data to overcome the pitfalls of the pandemic and to recover from it. For instance,
nations all around the globe have invested in a visionary action towards utilizing BDA.
Example of countries such as, Hong Kong, US, Switzerland, and India [121].
In 2020, a study by United Nations illustrated some of the most important applications
used worldwide during COVID-19. The main goal for governments was to share reliable
and transparent information about COVID-19, to enhance citizens’ awareness about the
situation and allow policy makers to plan appropriate actions accordingly. This has been
empowered by dashboards, such as in Vancouver and Australia, to track the number of
cases and allocate the required community resources accordingly [
122
]. Furthermore, to
ensure social distancing, governments in India and New York has urged their citizen to rely
more on online services such as online parking payment in New York City and e- Doctor
tele-video consultation to prevent crowds in hospitals in India. Also, China monitored its
citizens commuting to work, grocery stores, and shopping malls via the QR health code.
Many other BDA were offered by governments such as platforms for e leaning wither for
schools or universities. Not to mention the countless services offered for entertainment
online for citizens in quarantine periods [72].
The following sections describe the utilization of big data applications during
COVID-19 pandemic in four vital sectors: Healthcare, Education, Transportation and
Banking. It highlights the significant results approached in each sector and its role during
COVID-19 pandemic.
5.1. Big Data in Healthcare
The first application of BDA in health care sector post COVID-19, is to support govern-
ment agencies to detect a specific disease in particular area, monitor the health condition of
the citizens and provide a preventive action accordingly. All of such actions will be based
on predictive models which will be supported by input from smart phone connected to
thermometers and tracking systems, search engines and social media data [118].
Another use of BDA post COVID-19 is the management and control of vaccine distri-
bution. For example, governments will easily understand the community reaction towards
the vaccine [71]. This will be figured through applying sentiment analysis to the data cap-
tured from social media platforms, and develop strategies that will control the community
resistance towards the vaccine [
69
]. Furthermore, applying machine learning techniques
will anticipate and prioritize who will be more vulnerable to the disease, and who should
be provided with the vaccine first. Also, BDA guarantee a better technique to store the
vaccine. This will be through monitoring the optimal temperature level [72].
Finally, big data solutions helped governmental entities in their vaccine distribution
efforts. For instance, big data facilitated in the storage mechanism of the vaccine, since
they were kept and stored within precise temperature range. Hence, ensuring the quality
level of the vaccine won’t be affected by any environmental circumstances through the
Big Data Cogn. Comput. 2022, 6, 157 15 of 23
distribution chain. Furthermore, machine learning was applied to highlight analyses of the
populations. For example, a categorization of the population with health vulnerabilities
were easily distinguished. Consequently, a prioritization mechanism and plan for vaccine
delivery was prepared [
69
]. Also, sentiment analysis on citizens’ casual conversation on
social media were performed by governmental entities. Such large text-data, helped in
understanding the public view on immunization. As a result, governments were able to
develop the proper communication strategies, to persuade citizens about vaccinations and
overcome any hesitancy from it [123].
5.2. Big Data in Education
COVID-19 has forced many schools, and educational institutes to shift their physical
presence to online educational platforms [
74
]. BDA supported educators via analyzing data
captured from such platforms, to analyze and predict students current and future learning
abilities and develop the educators’ teaching styles accordingly [
69
]. Furthermore, an
instant need to bridge the gap of unemployment required the implementation of BDA. This
has been evident through analyzing the data captured from job portals, communicating
the job market needs to educators and thus develop the appropriate curriculum, and
communicating it to the targeted segments [75].
5.3. Big Data in Transportation
COVID-19 has stimulated governments to reconsider its transportation decision man-
agement systems and empower it with big data applications [
77
]. For instance, Dubai is
a leading example in the use of big data applications to ensure social distancing between
bus passengers and detect any incompliance. It invested both in using AI and big data
applications to capture relevant data such as, data and time of the trip, driver details, the
frequency of the vehicle incompliance, and the route number. Hence facilitate applying
disciplinary actions accordingly [76].
Another example is to detect citizens commute route in order to store their travel
history. This will ease the regulators to detect whom infected patients with COVID-19
virus have contacted. Hence, government regulators can predict which areas might be
potentially more affected than others. Therefore, preventive actions will be taken on a more
methodical base. Therefore, in a broader context, countries can predict the flow of infected
citizens between cities and countries and consequently declare travelling constraints and
guidance [78].
5.4. Big Data in Banking
The incident of the global COVID-19 pandemic has exponential increased banks’
clients use for online transactions using big data applications [
124
]. A study prepared by
the world bank on 29 June 2022, depicted global financial figures as follow: around 76%
of adults created personalized accounts wither with financial institutes or mobile money
providers compared to 51% in 2011. Also, the increase has been applied to the use of
digital payments. For instance, since the hit of the pandemic more than 80 million adults
conducted their first digital purchase and payment in India, and more than 100 million
adults in China [
125
]. Additionally, Big data applications and Analytics crucial role was
evident to bankers in their strategic and daily operations during COVID-19. Examples in
banking are the use of descriptive and predictive data analytics models in Fraud Detection,
and the diagnostic and prescriptive data analysis models such as in Risk Assessment [
78
].
Moreover, financial intermediaries use AI-based systems for fraud detection and analyze
the degree of interconnectedness between borrowers, which in turn allows them to better
manage their lending portfolio [
126
]. Banks are increasingly using big data and analytics to
assess the creditworthiness of prospective borrowers and make underwriting decisions,
where both functions at the core of finance [126].
Big Data Cogn. Comput. 2022, 6, 157 16 of 23
5.5. Big Data Analytics across Industry
Big Data Applications can derive insights from various data sources to provide ideal
solutions for several sectors [
52
]. Table 4 highlights the alignment of each big data applica-
tion to its respective big data analytics model. It provides an explanation on how such an
implementation has supported organizations and governments to cope with COVID-19 pit-
falls. Furthermore, such an implementation provided an optimal AI solution to harness its
operations and decision-making process. This categorization has been developed based on
the description of each application in their relevant fields, and on the definition highlighted
in the section on the four big data analytics types.
Big Data Cogn. Comput. 2022, 6, 157 17 of 23
Table 4. How to utilize Big Data Analytics in Healthcare, Education, Transportation, and Banking.
Field Data Analytics Type How BDA Has Been Utilized Method/Model Reference
Healthcare
Descriptive and Predictive Data
Analytics Models
Proactive actions and interventions
based on predictive models to
trigger any noncommunicable
diseases.
Predictive models based on search engines and social media data.
Smart phone applications tracking system to identify infection hot spots
[69,70]
Perspective Data Analytics Vaccine distribution Sentiment analysis to reduce community resistance towards the vaccine. [69,71]
Diagnostic and Predictive Data
Analytics Models
Vaccine distribution
Machine learning models to prioritize the citizens’ need and urgency to
the vaccine
Diagnostic and Prescriptive Data
Analytics Models
Monitoring live and frequent data
on the spread of the disease
Provide more personalized
consultations by “virtual doctors”
Dashboards
AI Chabot
[72,73]
Education
Descriptive Data Analytics Model
Enhance online educational
platform experience
Analyzing data captured from online educational platforms can ease
educators remote leaning experience
[69,74]
Diagnostic Data Analytics Model Bridge the gap of unemployment Analysis of data captured from job portals
[69,75]
Transportation
Descriptive and Prescriptive Data
Analytics Models
implementation of precautionary
measures-Ensure social distancing
in public transportation
Capturing relevant data and use machine learning techniques to detect
incompliance actions
[76]
Descriptive and Predictive Data
Analytics Models
Detect citizens’ commute route to
store their travel history.
Use both AI and Big data applications to capture, track and predict
valuable insights about citizens movement within and across cities and
countries
[77]
Banking
Fraud Detection
Use AI and ML techniques to describe and detect real-time abnormal
activities and online transaction, and build ML models based on
classification algorithims to predict any suspecious case.
[78]
Risk Assessment
Use both diagnositic and prescriptuve data analytics models to analyze
real-time data and asses the creditworthiness to customers. Consequenlty
developing the appropriate cutomer portfolio and tailor clients needs to
their services. Cossequently boosting customers’ satisfaction, loayality
and enhance banks botom line records.
[78]
Big Data Cogn. Comput. 2022, 6, 157 18 of 23
6. Conclusions
The unstable status resulting from COVID-19 forced organizations to realize the
real importance of big data applications. It has been evident during pandemics that
Big Data adoption enables decision-makers to make smarter decisions in real time. The
technologies behind Big Data support organizations to gain valuable insights from their
data. Big Data facilitates transforming organizations’ practices to a new generation of
digital services ensuring that added value for customers will be achieved. Organizations
utilize Big Data to detect and analyze the trends and patterns of people’s behavior on
social networking. Hence, an organization’s decision-makers can provide optimal decisions
and better, effective, and efficient services and products for the public. This review paper
investigated the existing literature to define Big Data, and the types of Analytics, and
compared the Big Data applications before and after COVID-19. The comparison was
supported by examples from four vital sectors in the industry of Healthcare, Education,
Transportation, and Banking as examples of sectors affected by COVID-19. The paper
presented a detailed description of the role of data analytics and its alignment with specific
big data applications in those fields. Such applications supported organizations and
nations to navigate through the COVID-19 pandemic confidently. Hence, they could not
only overcome challenges but also unleash opportunities and create value. The limitation
of this paper is related to the limited previous studies that investigated the applications
and opportunities of big data during the COVID-19 Pandemic. The future work will
start by investigating the challenges faced by organizations on different levels, it will also
investigate the critical success factors of Big Data and their categories toward developing a
conceptual model for Big Data implementation.
Author Contributions:
Conceptualization, Z.A.A.-S., M.H.H., S.M.S.-M., R.M.S.A., N.D., L.A. and
A.H.G.; methodology, Z.A.A.-S.; formal analysis, Z.A.A.-S.; writing—original draft preparation,
Z.A.A.-S., M.H.H., S.M.S.-M., R.M.S.A., N.D., L.A. and A.H.G.; writing—review and editing, Z.A.A.-
S., M.H.H., S.M.S.-M., R.M.S.A., N.D., L.A. and A.H.G.; visualization, L.A.; supervision, L.A. and
A.H.G.; project administration, Z.A.A.-S., M.H.H., S.M.S.-M., R.M.S.A., N.D., L.A. and A.H.G. All
authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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