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An overview on big data analytics methods and
applictions in different sectors
Togare pratik ashok 1,
bhosale john samuel 2
, jiswar vishal triloknath 3
, c.kalpana4
1information technology ,s.s.t. college of arts and commerce,
pratik.mit21011@sstcollege.edu.in
2information technology ,s.s.t. college of arts and commerce,
john.mit21012@sstcollege.edu.in
3information technology ,s.s.t. college of arts and commerce,
vishal.mit21009@sstcollege.edu.in
4 asst. Professor, 1information technology ,s.s.t. college of arts and commerce,
rkalpz@gmail.com
Abstract
Big data is a very large collection of data. And it comes from almost everything, this data is so large,
fast,or complex that it is difficult to process using traditional methods. And we are going to see how it
gets used in various sectors and the benefits of it. Data analytics helps to make better decisions in
businesses and organizations, by analyzing bigdata companies, organizations get more ideas to help
improve their profits and services or we can say they can take full advantage of their assets,to 93% of
companies bigdata is very extremely important. The big data can 2predict the future, the bigdata can
help understand their customers more, the bigdata is helpful in almost every sector such as agricultural
production, healthcare, social media, etc. To companies it reduces cost, it is much faster and better
decision making, new products, and services . This research paper is addresses how big data analysis
changes our lives and how it is useful in the future.
Keywords: big data analytics, big data analytics uses, social media, supply chain, healthcare, e-
commerce
I introduction
The digitization of a lot of fields has led to the generation of massive amounts of data from various
sources. This data will only grow exponentially in the coming future due to the advancement in cloud
computing, iot, and social networking services. The data generated through these sources are very
diverse. The existing methods to process the data which used to work well are not scalable enough to
provide the same good results in the case of big data. Due to all this, it has become an unprecedented
challenge to process this ever-increasing massive amount of data and provide meaningful insight into
the data for taking important decisions.
To analyze such data needs large amounts of computational power, complexity, and time. The data is
also not available in a standardformation and there are many diversities, inconsistencies, and anomalies
in the data which is difficult to predict due to which complex computational methods are required to
analyze this data. Also, a lot of this data may not be useful for the required use cases. Hence big data
analysis has become an important topic for research. Analysis of such data could provide an insight in
predicting the future patterns and important decisions could be taken to minimize losses, maximize
profits, mitigate risks, provide personalized experiences and improve the quality of life.

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Big data analysis has a lot of uses such as security, healthcare, transportation, commerce, education,
entertainment, manufacturing, retail, energy, government, etc. Sectors, some of which we will see in
this research.
Iii literature review
Definition of big data is “high-volume, high-velocity and/or high-variety information assets that
demand cost-effective, innovative forms of information processing that enable enhanced insight,
decision making, and process automation.” In it glossary of gartner website.
3v’s concept is also used to define big data by doug laney in 2001.
The vp of engineering at facebook in 2012 told that more than five hundred terabytes of knowledge are
being handled at facebook per day which includes 300 million pics, around 5 billion uploads of content,
and 2.6 billion likes. This massive amount of data is processedin just a couple of minutes which enables
facebook to get an insight into the reactions of users which in turn helps facebook to modify or provide
its offerings.
Big data analytics in social media allows companies and organizations to notice new opportunities
which enables them to make the right business decisions to increase their overall profit and make the
customers happy.
Iii methodology
Big data analytics examines large and different types of data to uncover hidden patterns,
correlations, and other insights.
A) Need for big data analytics
1 making smarter and more efficient organizations
2. Optimize business operations by analyzing customer behavior
3. Cost reduction
4. Next-generation products
5. Predicting future
B) Characterisyics of big data analytics

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!. Volume:- the amount of data generated every second for eg active users on facebook in 2021 is 2.80
billion ,twitter tweets per minute are 98000+,698445 per. The second search on google more than 500
hours of videos get uploaded on youtube every second.identifying a data bigdata volume is crucial
Velocity:- how fast the data is being generated and how fast the data is moving from one place to
another place for eg social media, online multiplayer games, sensor data from the iot sector, etc. When
data is moving so fast and to process this kind of data we need special tools for it to analyze in real-
time such as apache kafka – open-source stream processing platform, akka streams – open-source
stream processing solution, oracle tuxedo – middleware message platformby oracle.
5 V's of
big data
analytics
Volume
Velocity
Veariety
Veracity
Value

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Variety:- the data we get can be in various forms there are mainly three different types of data is being
generated
Structured data: it owns a dedicated data model, it also has a well-defined structure, it follows a
consistent order and it is designed in such a way that it can easily be accessed and used by a person or
a computer. Structured data is usually stored in well-defined columns and also databases. In short, any
data that can be stored accessed,processed in the form of fixed-format is called structured data, in old
days to store structured data or getting content from it was really difficult but now that technology has
evolved its much easier nowadays the sours of the big data can we a machine and human
2. Semi-structured data: it can be considered as another form of structured data. It inherits a
few properties of structured data,but the major part of this kind of data fails to have a definite structure,
and also, it does not obey the formal structure of data models. In the short semi, structured data is a
collection of data where it is a mixture of structured data and unstructured data or we can say it is a
combination of structured data and semi-structured data
Structured data
Semi-Structured
data
Unstructured data

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3. Unstructured data: this is completely a different type of which neither has a structure nor
obeys to follow the formal structural rules of data models. It does not even have a consistent format and
it is found to be varying all the time. But rarely it may have information related to data and time. Data
created from everything in it the 80% of the data is unstructured data and this type of data is really
difficult to process for eg. Audio, video, social media conversation
Veracity:- trustworthiness of data, so basically meansthe degree of reliability that the data hasto offer.
Since a major part of the data is unstructured and irrelevant, bigdata needs to find an alternate way to
filter them or to translate them out as the data is crucial in business developments
Value: it is not just the amount of data that we store or process. It is the amount of valuable, reliable,
and trustworthy data that needs to be stored, processed analyzed to find insights. Value of data is
determined by the quality of the data. If we process raw data then we can get valuable data
Types of data elements:- continuous data, categorical – nominal, ordinal , binary
C need for big data analytics
1 making smarter and more efficient organizations
2. Optimize business operations by analyzing customer behavior
3. Cost reduction
4. Next-generation products
C) Stages in big data analytics
1. Identifying problem
2. Designing data requirement
3. Preprocessing data
4. Performing analytics over data
5. Visualizing data
Making smarter and
more efficient
organisations
Optmize business
operations by
analyzing customer
behavior
Cost reduction
next generation
products

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D) Types of big data analysis
1. Descriptive analysis:
Descriptive analytics answers your question about what has happened and how does
descriptive analytics answer all these questions it uses data aggregation in data mining
techniques to provide insight into the past and then it answers what is happening now
based on incoming data. It describes or summarizes the raw data and it makes it
something understandable to us and the past context basically can be one minute ago
or even a few years back
The descriptive analysis uses a variety of statistical techniques, including the measure
of the frequency of data, central tendency, dispersion, and position. How exactly you
conduct descriptive analysis will depend on what you are looking to find out. So to do
that the steps are collecting, cleaning, and finally analyzing data.
so the best example for descriptive analytics
Is the google analytics tool so google analytics is aiding organizations or different
businesses by analyzing their results through google analytics tool so the outcomes that
help the businesses understand what has happened in the past and then they evaluate if
a promotional campaign was successful or not based on the basic parameters like
pageviews so descriptive analytics is, therefore, an important seoul should determine
what to do next
2. Predictive analytics:-
Predictive analytics uses statistical models and focus techniques to understand the
future and answer what could happen, so basically as the word suggests it predicts and
we can understand through predictive analytics what are the different future outcomes
are possible so basically predictive analytics provides the companies with actionable
insights based on the data so through sensors and other machine-generated data.
So an example of this type of analytics is the airlines
Using predictive analytics they can analyze their sensor data on the planes to identify
the potential malfunctions or safety issues so basically this allows the airline to address
the possible problems and then make repairs without interrupting the flights or putting
the passengers in danger this is a very great use of you know predictive analytics to
how basically reduce their downtime and losses and aswell asyou know preventdelays
and various other factors like accidents
Another good example of predictive analytics is marketing(amazon, flipkart)
Identifying
Problem
Designing Data
Requirement
Preprocessing
Data
Performing
Analytics Over
Data
Visulizing Data

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By analyzing customers purchase history they can give the information of the product
related to your searches
3. Prescriptive analytics:-
The application of logic and mathematics to data to specify a preferred course of action.
Prescriptive analytics prescriptive analytics uses optimization and simulation algorithms to
advise on the possible outcomes and answer the question what should we do so basically it
allows the users to prescribe a number of different possible actions and then guide them
towards a solution so in a nutshell these narratives are all about providing advice so
prescriptive analytics they use a combination of techniques and tools such asbusiness rules,
algorithms, machine learning and computational modeling procedures so then these
techniques are applied against input from many different data sets including historical and
transactional data real-time data feeds and then big data so these analytics go beyond
descriptive and predictive analytics by recommending one or more possible courses of
action and the best example for this is the google self-driving car basically google self-
driving car analyzes the environment and then decides the direction to take based on the
data so it decides whether to slow down or speed up to change the lanes or not to take a
long cut to avoid traffic or prefer short routes etc so in this way it functions just like a
human driver by using data analytics at scale. Prescriptive analytics is a little complex type
of analytics and it is not yet adopted by all companies but when implemented correctly it
can have a large impact on how businesses make their decisions
4. diagnostic analysis:-
Diagnostic analytics is used to determine why something happened in the past, so it is
characterizedby techniques like drill-down data discovery data mining and correlations
to diagnostic analytics it takes a deeper look at the data to understand the root cause of
the events it is helpful in data mining what kind of factors and events contributed to a
particular outcome so mostly it uses probabilities likelihoods and the distribution of
data for the analysis so for example in a time-series data of sales the agnostic analytics
would help you to understand why the sales of a company have decreased or increase
for a particular year and so on

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So examples for diagnostic analytics could be a social media marketing campaign so
you can use diagnostic analytics to assess the number of posts mentions followers fans
pageviews reviewspens etcetera soandthen you can analyze the failure and the success
rate of a campaign at a fundamental level so therefore they can be thousands of online
mentions that can be distilled into a single view to see what worked in your past
campaigns and what did not so
E) Tools used in big data analytics
There are severaltools used for big data analytics such as hadoop apache spark, talend, kafka,
splunk, apache hbase, hive
1. Hadoop
2. Apache spark
3. Talend
4. Kafka
5. Splunk
6. Apache hbase
7. Apache hive
1. Hadoop:
Tools
Hadoop
Apache
spark
Talend
Kafka
Splunk
Apache
Hbase
Apache
Hive

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A framework that allows you to store big data in a distributed fashion so that you can process
it separately
In diagram
a. Mapreduce: mapreduce is a programming model that simultaneously processes and
analyzes huge data sets logically into separate clusters. While map sorts the data, reduce
segregates it into logical clusters, thus removing the bad data and retaining the necessary
information
b. Jvm stands for java virtual machine
c. Nodes: a computer becomes a node/workstation as soon as it is attached to a network
d. Yarn= yet another resource negotiator ( it is a resource manager) created by separating the
processing engine and the management function of mapreduce
It monitors and manages workloads, maintains a multi-tenant environment, manages
the high availability security controls
2. Apache spark: it is an in-memory data processing engine that allows us to efficiently execute
freeman machine learning and sql workloads and it requires fast i trade of access to data sets.
It is used for real-time processing
3. Talend:it is an open-source software integration platform that helps you to analyze effortlessly
and then turn the data into business insights so it helps the company in taking real-time
decisions and become more data-driven
4. Kafka: it is a messaging system (a messaging system is something responsible for transferring
data from one application to another so the applications can focus on the data so we do not need
to worry about sharing it.)

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5. Splunk: it is a log analysis tool (what are logs so logs are generated on computing as well as
non-computing devices and they stored in particular location or directory so they contain details
about every single transaction or operation that we have made
6. Apache hbase:it is a no sequeldatabase it allows you to store semi-structured and unstructured
data with ease and provides real-time read or write access
7. Apache hive:it is a data ware-housing tool it allows us to perform big data analytics hive query
language which is similar to the sequel
And in this data contain users social data (social data is information that social media users publicly
share)
Which social media tracks analytics the answer is pretty much simple it's all of them
All the most popular social media platforms have some analytics built into them there is youtube
analytics, facebook analytics, twitter analytics, instagram analytics, linkedin analytics, and even tik to
analytics
You can manage the analytics within any of these individual social media platforms you can also third
party platforms to extract information
What type of data is available on these platforms there is a fair amount of variation from platform to
platform about what's available and naming for different analytics or metrics can be wary but there are
a few key things that seem to show up on every platform such as depending on the platform this could
be video views, link clicks, likes, etc there are some additional metrics including information like how
people found the content where they referred to it did they find it in the search was it a suggested video
on youtube these types of a matrix can be very helpful for building future strategies for how to continue
growing on a channel or platform .
IV Applications of bigdata
1.big data analytics in healthcare
Using big data for the application of predictive, prescriptive, and descriptive-analytical methods enables
us to provide opportunities to improve the different areas of healthcare. (mittal and kaur, sharma 2018).
The literature put forward various opportunities provided by big data analytics in the healthcare areas
as follows:

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A) medical diagnosis: data-driven diagnosis could help to detect a lot of diseases at the initial stage
which might help to decrease the complications that may arise while performing a treatment. (gu et al.
2017; raghupathi and raghupathi 2014).
B) preventive steps could be taken by the authorities at community healthcare to manage the risks of
chronic diseasespredicted among the people. (lin et al. 2017) and the outbreak of diseasesof contagious
nature (antoine-moussiaux et al. 2019).
C) monitoring of hospitals in real-time could help government authorities to ensure that the service
quality is well maintained. (archenaa and anita 2015)
D) big data analysis can facilitate customized care for the patient which could provide quick relief to
the patients (salomi and balamurugan 2016) and decrease the rates of patients being readmitted in
hospitals (gowsalya, krushitha, and valliyammai 2014).
Citation: sayantan khanra, amandeep dhir, a. K. M. Najmul islam & matti mäntymäki (2020) big data
analytics in healthcare: a systematic literature review, enterprise information systems, 14:7, 878-912,
doi: 10.1080/17517575.2020.1812005
Thus the inclusion of big data analytics in healthcare will have major implications in maintaining the
quality of healthcare systems, preventing or managing diseases by using data-driven predictions, and
improving the overall patient experience at healthcare facilities.
2.big data analytics in e-commerce
The bestexample where big data analytics hasimproved the business value for an online firm is amazon.
Big data analytics resulted in the generation of 30 percent sales at amazon through the use of its
recommendation engine which uses big data analytics. As reported by the economist in 2011 and kiron
et al. In 2012, match.com increased its subscriber's numbers to 1.8 million for its core services, and its
revenue wasincreasedto 50 percent in the last 2 yearsasa result of big data analytics.around 30 percent
in revenue and 7 million us dollars in profitability was increased for automercados plaza’s as found by
schroeck et alin 2012 as a result of implementing the integration of information within its organization.
In addition losses of more than 30 percent of losses were prevented by the company by scheduling the
selling of perishable goods at a reduced price on time.
Big data analytics can not only add value in terms of finance but it could also add other value in terms
of customer retention, customer satisfaction and also help in improving business processes. It is clear
from the above analysis that big data analytics is playing a vital role in increasing the business value of
e-commerce companies while increasing the customer outlook on the e-commerce companies.
3.big data analytics in supply chain

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As per an article that was published on computerworld, organizations could overcome the challenges
in the supply chain by prioritizing the development of a strategy based on big data analytics. A supply
chain should focus on aiming at predicting customer needs, overall analysis of supply chain efficiency,
time of reaction, analysing risks by using big data analytics(computerworld, 2018).
 Improvement in predicting needs of the customer: if the customer demands are not met, a
company could lose such customers. Also, the reputation of a company can be degraded if it
fails to fulfill the orders or fulfills only some part of the orders. The most important aspect for
maintaining customer retention, loyalty, and satisfaction in providing the right product to the
correct customer at a proper time. Big data analytics can help provide a better view of the
customer and their needs which can help smart organizations to understand and predict their
customer preferences,needs and provide a great customer experience thereby increasing the
value of the brand
 Improvement in supply chain efficiency: the prime business concern in supply chain
management is to get analytics for proper cost-efficiency, reduction, and expenditure with the
help of big data analytics.
 Improvement in assessing risks for supply chain: an important aspect of big data analytics is its
predictive analytics which could help to assess the probability that a certain problem will occur
and what would be its impact on the business. Analysis of historical data in huge volumes by
using big data predictive analysis and techniques for mapping risks could help to predict the
risks in supply chain. Tools and techniques could then be developed to minimize the damage
associated with risks that could happen by accurate predictions is such risks.
 Improvement in supply chain traceability: big data analytics could help in effective tracking of
goods from production till it reaches retail. This helps to improve control over the different
processes in the supply chain.
 Most companies agree that speed and agility are very important in the business world. The
second most important thing that provides a competitive edge to the businesses across the
industries is the capability to meet the customer needs rapidly and in a flexible manner. Big
data analytics can help organizations improve their reaction time to the issues of the supply
chain to about 41% which can lead to around 4.25 times enhancement in order-to-cycle times
for delivery as per accenture.
It is evident from the above that big data analytics plays a crucial role in improving the overall modern
supply chain processes.
Conclusion
There has been an explosion in the generation and collection of large amounts of data by various
machines, processes,and services and it's growing rapidly every day. This has given rise to big data
which is vast amounts of data that cannot be processed with traditional computational methods. To find
patterns in this vast amount of data and uncover valuable insights from it gave the rise to big data
analytics.

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Big data analytics involves various stagessuch asidentifying the problem, designing data requirements,
preprocessing the data, performing analytics, and visualizing the data. In big data, there are different
types of analysis some of which are descriptive, predictive, prescriptive, and diagnostic analytics.
Various tools such as hadoop apache spark, talend, kafka, splunk, apache hbase, hive are employed to
perform big data analytics.
References
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