Recent Trends in Big Data Analytics and Role in Business Decision Making

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-12, Issue-4 (August 2022)
www.ijemr.net https://doi.org/10.31033/ijemr.12.4.6
32 This work is licensed under Creative Commons Attribution 4.0 International License.
Recent Trends in Big Data Analytics and Role in Business Decision
Making
Kamsala Jagannathachari Shiva Shankar1
, Aishwarya.S. N2
, Hemanth. D3
and Prof. Sunetra Chatterjee4
1
Student, Department of Master of Business Administration, IFIM College, E City, Bengaluru, INDIA
2
3
4
Assistant Professor, Department of Computer Applications, IFIM College, E City, Bengaluru, INDIA
3
Corresponding Author:
ABSTRACT
In this information overload era, we can see that a
large amount of data has been available for everyone, and
it helps business organizations to take strategic decisions.
The rapid increase of internet and digital marketing has
made increased in demand for data. And we can see that
the volume of the data is very high which can't be handled
using traditional systems. Data has become more valuable
nowadays for organizations because of the valuable insights
in the data. Present we can see that every day millions of
data was generated through our daily transactions to
customer interactions and various social media networks.
As we can that data is of various types like structured, and
unstructured data, the data need to be cleaned and it
should be converted into meaningful information for the
organizations.
In this paper, we are focusing on how big data will
show an impact on business decision-making, and how big
data will play a crucial role in the decision-making process.
Keywords-- Data Velocity, Data Volume, Inception,
Heterogeneous Data Sets, Predictive Modelling,
Competitor Intelligence, Ramifications.
I. INTRODUCTION
Data is info, sometimes within the kind of facts
or statistics that one will analyses or use for more
calculations. massive knowledge refers to knowledge
sets whose size is on the far side of the power of a
typical information software package tool to capture,
store, manage, analyses, and gift info in a very type that
is a lot of important for organizations or finish users.
massive knowledge Analytics describes the method of
uncovering trends, patterns, and correlations in a very
great amount of information to assist build data-
informed choices.
Analytics of massive knowledge may alter the
invention of the latest facts, knowledge, and strategy in
many fields like producing, business, finance, health
care, medicine, and education. Most definitions of
massive knowledge target the scale of knowledge in
storage. size matters, however, there square measure
different necessary attributes of massive knowledge,
particularly knowledge selection and knowledge speed.
The 3 Vs of massive knowledge (volume, variety, and
velocity) represent a comprehensive definition, and they
bust the parable that massive knowledge is simply
regarding knowledge volume. additionally, every of the
3 Vs has its own ramifications for analytics.
Since their beginning as systems for
documenting transactions, info systems have developed
to help several levels of business decision-making.
Enterprise Resource designing systems (ERPs), which
square measure internal knowledge sources, were the
most sources of data employed by ancient data system
business judgments. These datasets utilized computer
database administration and were structured. These were
utilized to support internal company selections like
inventory management, decision-making over
evaluation, decisive the foremost valued purchasers and
understanding of loss-making products, etc. to boot, a
knowledge and an information warehouse after we used
this data was created for giving analysis.
The knowledge was combined with information
from the other partners, together with the suppliers as
well as the users of the systems for business application
integration or EAI. EAI created sleek integration
potential between business partners of data systems. It
accelerated B2B communication. It improved
communication, magnified the speed of business-to-
business (B2B) transactions, and reduced the price of
inner company transactions. Early within Nineties, the
net arrived, streamlining the mixing of companies with
their operations partners. within the previous 10 years,
the mixture of data systems with the net, cloud
computing, and mobile.
Massive amounts of knowledge, or "broad
knowledge," are made due to gadgets and the web of
Things. time knowledge that is structured, semi-
structured, and unstructured is enclosed info, OLAP,
ETL, and a warehouse. With the event of computing,
vast the employment of applied mathematics ways on
massive, heterogeneous datasets. teachers and business
organizations have this study aim to analyse the role of
massive knowledge in higher decision-making and the
way broad knowledge could also be wont to build fast
and up on judgments for enhancing business outcomes.
Compared to previous analytics ways, the
massive knowledge revolution is less assailable.
Managers will build higher choices on a spread instead

of supported intuition. Businesses square measure
gathering a lot of info that is critical for giant knowledge
aids in manufacturing higher predictions and wiser
choices. massive knowledge is employed by executives
across all industries to boost social control procedures.
Their square measure numerous studies conducted
inbound fields, like massive knowledge within the
provide chain, social media knowledge, and transactional
knowledge devised original ways to extract a price from
vast knowledge.
However, a comprehensive analysis of the
decision maker’s access to massive knowledge is lacking
we have a tendency to investigate the perform numerous
of varied of assorted} massive knowledge in various
decision-making eventualities in response to the present
want by finishing the subsequent goals, this essay fills
within the following gaps: a) to analyse the present
literature on massive knowledge's core ideas and its
application to decision-making b) to look at the role
mistreatment massive data to tell operational, tactical,
and strategic choices. The analysis is useful in
developing necessary choices with the assistance of
massive knowledge. massive knowledge has been used
in several ways within the current era in the areas of
business and education.
Making higher predictions and choices as a
result. we tend to examine the current analysis of
massive knowledge and the way it is changing into a lot
of necessary for business and society within the next
half. Here, we have analysed a variety of massive
knowledge definitions from the vast knowledge and
analytics specialists. we tend to additionally discuss the
various classified applications of analysis. The third
section tells us about varied massive knowledge and
their benefits of applications. Here, we tend to reassess
how varied organizations, together with banks and
businesses, are ready to gather, analyse, and use vast
knowledge to boost their company success. for a few of
the highest organizations, the employment of massive
knowledge in analytics-based decision-making is
nothing new. However, there square measure still a great
deal of tiny and medium-sized businesses which will
begin mistreatment of this developing business. As of
the fourth section, we tend to propose a framework for
giant knowledge which will be employed by such
corporations. This structure may function as a place to
begin to develop the model that works best for his or her
firms. Finally, we conclude our study with our findings
and recommendations for future analysis.
II. LITERATURE REVIEW
According to Schmarzo, B. (2015), integrating
massive knowledge into business to drive competitive
advantage and property success massive knowledge
master’s in business brings insight and experience to
investing massive knowledge in business therefore you
will harness the facility of analytics and gain a genuine
business advantage. supported a sensible framework
with supporting methodology and active exercises, this
book helps determine wherever and the way massive
knowledge will assist you to remodel your business. you
will learn the way to take advantage of new sources of a
client, product, and operational knowledge, as well as
advanced analytics.
According to Davenport et, al (2013), broad
knowledge broke up the scene within the first decade of
the twenty-first century, and therefore the first
organizations to embrace it were online and start-up
corporations. Corporations like Google, eBay, Facebook,
etc. were designed a huge knowledge from the
beginning. They did not have to be compelled to
reconcile or integrate huge knowledge with more ancient
sources of information and therefore the analytics
performed upon them, because of they did not have those
ancient forms. They did not have to be compelled to
merge huge knowledge technologies with their ancient
IT infrastructures as a result those infrastructures did not
exist. huge knowledge might stand alone, huge
knowledge analytics could be the sole focus of analytics,
and massive knowledge technology architectures might
be the sole architecture. Consider, however, the position
of enormous, well-established businesses. huge
knowledge in those environments should not be separate,
however, should be integrated with everything else that
is happening within the company. Analytics on huge
knowledge must be compelled to be with analytics on
alternative kinds of knowledge. Hadoop clusters must be
compelled to do their work aboard IBM mainframes.
knowledge scientists should somehow get on and work
collectively with mere quantitative analysts. Knowing
this existence, we tend to interview twenty giant
organizations within the early months of 2013
concerning how huge knowledge slots into their overall
knowledge and analytics environments. Overall, we tend
to find the expected co-existence; in not one amongst
these giant organizations was huge knowledge being
managed separately from alternative kinds of knowledge
and analytics. the mixing was indeed resulting in a
brand-new management perspective on analytics, that we
will decide “Analytics three.0.” during this paper we will
describe the overall context for a way organizations
deem huge knowledge, the structure and skills required
for it, etc. We will conclude by describing the Analytics
three.0 era.
According to Tsai et. Al (2015), the age of
massive information is currently returning. however,
normal information analytics might not be ready to
manage such massive quantities of knowledge. The
question of thigh-performance would be the way to
develop a high-performance platform to expeditiously
associate degree analyses huge information and the way
to style an acceptable mining algorithmic rule to seek out
the helpful things from huge information. To deeply
discuss this issue, this paper begins with a brief
introduction to information analytics, followed by
discussions on massive information analytics. Some

necessary open problems and any analysis directions will
be conferred for the ensuing step of massive information
analytics.
According to Elgendy et. Al (2014), in the info
era, massive amounts of knowledge became accessible
and handy to call manufacturers. massive information
refers to datasets that are not solely massive but are
additionally high in selection and speed, which makes
them tough to manage the exploitation of ancient tools
and techniques. thanks to the zoom of such information,
solutions got to be studied and provided to manage and
extract price and data from these datasets. moreover, call
manufacturers got to be ready to gain valuable insights
from such varied and chop-chop ever-changing
information, starting from daily transactions to client
interactions and social network information. Such price
will be provided exploitation of massive information
analytics, which is that the application of advanced
analytics techniques on massive information. The
opportunities are provided by the appliance of huge
information analytics in numerous call domains.
According to Singh, D., & Reddy, C. K. (2015),
the primary purpose of this paper is to produce an
Associate in Nursing in-depth analysis of various
platforms accessible for performing arts huge
information analytics. This paper surveys completely
different hardware platforms accessible for giant
information analytics and assesses the benefits and
downsides of every one of those platforms supported by
numerous metrics like measurability, information I/O
rate, fault tolerance, data processing, and information
size supported, and unvarying task support. additionally,
to the hardware, a close description of the computer code
frameworks used inside every one of those platforms is
additionally mentioned at the side of their strengths and
downsides. a number of the crucial characteristics
represented here will doubtless aid the readers in
creating Associate in the Nursing hip to call regarding
the correct selection of platforms counting on their
machine wants. employing a star rating table, a rigorous
qualitative comparison between completely different
platforms is additionally mentioned for every one of the
six characteristics that are crucial for the algorithms of
massive information analytics. to produce a lot of
insights into the effectiveness of every of platform
within the context of massive information analytics,
specific implementation level details of the wide used k-
means cluster algorithmic rule on numerous platforms
are represented within the kind pseudocode.
III. OBJECTIVES OF BIG DATA
ANALYTICS IN DECISION MAKING
Developing & marketing new products &
services: It helps businesses about how to develop their
new product and services through various analysing
methods and can be able to help and forecast the
business positioning in the future.
Helps in analysing and reporting: With big data,
we can be able to analyse quickly, and this helps the
higher officials to take further decisions, due to quick
analysing of the problem the decisions can be taken as
quickly as possible.
Risk optimization: We are going to analyse the
future which helps the organizations. It shows what are
all the problems that organizations are going to face in
the future. Mostly in the financial field, it shows how it
shows the impact on our business and helps to avoid
dangerous situations.
Accurate Decision Making: As we have a huge
volume of data, we can see that contains more useful
information, which helps to predict accurate forecasting
results. The accuracy was more when we are going to
forecast by using big data.
Helps to solve the problem effectively: We can
see those problems like Data segregation, Data
encryption, real-time security monitoring, etc, can be
solved through big data analytics.
Helps to reduce cost: By using big data
analytics we can start to analyse the future ROI and what
investments we need to make, and which expenses the
company needs to avoid so that the expenses become
lesser.
Improving the business results: As we can see
that it provides accurate results and, it is cost-saving,
making accurate decisions quickly and providing
solutions for many problems which helps the growth of
the businesses.
IV. DISCUSSION ON ROLE OF BIG
DATA ANALYTICS IN DECISION
MAKING
Big information refers to the big quantity of
knowledge, each structured and unstructured, that a
business should influence daily. However, what matters
is not what quantity of information there's. What
businesses do with the info is what counts. victimization
huge information is examined for data which will be
wont to create smarter selections and business selections,
the thought of huge information is restricted to several
information which will be evaluated for several
individuals, through suggests that of technology. In its
truest definition, huge information refers to the
acceptable use of knowledge by technologies in any sure
facet.
Using huge information to help in decision-
making in diverse ways and tools improve decision-
making ability. firms like Amazon as well as Netflix
have used algorithms to see the association among
Customers' searching and previous purchases square
measure want to forecast the merchandise they are to
buy. supported their past purchases, customers square
measure hep concerning counseled merchandise or past
searches. This will increase the probability that
customers can purchase a number of the items that

square measure urged, thence increasing sales. the
maximum amount as a third of their recent sales come
back from this strategy, medium businesses sift through
enormous amounts of knowledge to forecast that they are
to lose shoppers. This aids in making policies for
consumer retention.
Nowadays data technology disperses quickly,
various information was inborn digital further as
interchanged on the web nowadays. per the estimation,
the latest information kept in digital media devices has
been already larger than ninety-two you tired of 2002,
we will see the dimensions of the latest information was
quite five exabytes. however, the issues of analysing the
big information were not instantly occurred however are
there for varied years because the formulation of
knowledge is typically easy than discovering helpful
things from the info. still pc systems nowadays square
measure quick than those within the Thirties, and
therefore the Brobdingnagian size of knowledge is less
complicated to analyse by the computers we've
nowadays.
In response to the issues of analysing large-
scale information, quite a few economical ways, like
sampling, information condensation, density-based
approaches, grid-based approaches, divide and conquer,
progressive learning, and distributed computing, are
bestowed. Of course, these ways square measure
perpetually want to improve the performance of the
operators of the info analytics method. The results of
those ways illustrate that with the economical ways at
hand, we tend could also be able to analyse large-scale
information in a ridiculously cheap time. The
dimensional reduction technique (e.g., principal elements
analysis; PCA may be a typical example that is aimed at
reducing the computer file volume to accelerate the
method of knowledge analytics. Another reduction
technique that reduces the info computations of
knowledge bunch is sampling, which might even be
wont to speed up the computation time of knowledge.
Five huge information V’s though the phrase
"big data" is recent, the apple of capturing and storing
huge volumes of knowledge for the later study is
centuries recent. Early within the new millennium,
analyst Doug Lucy Craft Laney bestowed the now-
accepted definition of huge information.
The Big Data Characteristics' Five V’s are
VOLUME: According to Davenport (2014), the volume
of large data has been doubling every 40 months in
terabytes or petabytes.
VELOCITY: Every organization and business are
accumulating data at an increasing rate.
VARIETY: Various data sources, including business
systems, social media, text, video, audio, and email,
RFID, websites, and other electronic gadgets.
VERACITY: The precision of the choice depends
heavily on the quality of the data.
VALUE: By extracting value from the heterogeneous
data, economic and social outcomes can be enhanced.
Different Big Data Sources
Big data comes from a variety of sources in
addition to traditional information systems, including
social networking sites, cloud applications, software,
social influencers, data warehouse appliances, weather
data, historical documents, business applications,
network technology, and sensor data The following
explains a few sources.
Transactional data, first Combining
transactional data with statistical tools such as regression
analysis and decision tree can aid in developing a model
to predict a result, such as a sales estimate or the degree
of a new product launch's success. The model can
historical data inputs and a predictor of the dependent
variable These models can simply be made using
statistical software such as SAS or SPSS. Transactions
are all historical data with independent variables, and
systems that keep track of transactions are commonly
referred to as "Transactional Processing Systems."
Transaction Processing Systems' primary objective is to
record an organization's operational decisions,
information, and updated data needed. Two are present.
The primary function of a TPS is to collect
information and update data for organizational OD.
There are two methods for processing transactions: BP,
which treats the data as one large batch and a temporal
frame, and RT Processing systems in which data are
processed instantly. the two in any organization,
methods are useful when making operational decisions.
Data from social media B. Due to social media's recent
surge in popularity, information is being gathered
anywhere it can be whereabouts in the world. As events
take place, they are reported. On Facebook, Twitter, or
WhatsApp, users are glad to share their opinions,
suggestions for products or services, and movie reviews
in a matter of minutes. Decision-makers now have a rare
chance to obtain market intelligence.
Through social media, people share information
that encourages consumers to buy things. making
selections by perusing reviews, client complaints, and
other services furnishing a product. Consumer sentiment
is also shared on social media, which assists businesses
to decide how to produce. Analysis for social media is
also used to compile competitive intelligence regarding
the company's goods and services that are provided by
rivals in any specific market sector. This encourages
fresh business concepts to enhance the business life
cycle.
As a result, social media are crucial for
strategic, operational, and tactical marketing decisions.
C. Internet Software As the internet has developed,
millions of users browsing numerous websites,
producing a lot of click streams, and conducting web
searches for either goods or services. There are many
online retail stores (like Amazon, Flipkart, Online
marketplaces or search engines Millions of consumers
log in and use the banking programs every day. When
they were During searches or transactions, several click
streams and records are produced that may be useful.

Modern Big Data Analytics Technology
Rapid information and data analysis are
required due to the rising level of corporate rivalry,
Rapid study of the data produces improved knowledge,
which improves decision-making. big data can be an
excellent tool for decision-making in the medical
industry. Electronic gadgets that monitor premature
newborns send data to the hospital. A vast amount of
data exists those humans are unable to interpret. the
function of Here, technology is evident. Structured data
searches for patterns that indicate when diseases will
start and shorten a person's stay in a hospital.
Additionally, new algorithms can link a patient's altered
behaviour to an infection.
Big Data’s Impact on Decision-Making
Business executives now face challenges from
high customer expectations, fierce competition, growing
labour and material prices, and shorter product
lifecycles. World trade is blending international borders.
Location and proximity to the market are no longer
relevant impediments to market entry. In those dynamic
environment, businesses must always look out for using
the information at hand and swiftly assess company risks
and possibilities. In this, we talk about how traditional
"small data" and "big data" are used to make business
decisions.
V. ADVANTAGES OF BIG DATA
FOR DECISION-MAKING
 The development of big data in recent years has
transformed the information needs of CEOs.
 Large datasets are available from a variety of
sources in addition to the standard datasets
mentioned above.
 When association rules are mined from business
transaction data, decision-makers can gain
important insights.
 Discussing bundled purchases or forecasting
demand for specific products.
 Acquiring knowledge of Patterns assist stores
like Wal-Mart in redesigning their product
arrangement and isle layout.
 Predictive modelling aids us in making
predictions about potential outcomes based on
certain knowledge. The competitiveness of
planning ahead has benefits for businesses.
Data patterns, correlations, and relationships are
beneficial for increasing sales performance,
locating the ideal clients for products, or
dividing markets into distinct segments.
Every field where data may be gathered can use
analytics. a type of supply chain analysis accessible in
the fields of fleet management, demand forecasting,
procurement planning, inventory optimization, and
optimization of route sizing. Information from social
media platforms such as reviews of current products,
ideas for new ones, and competitor intelligence is vital
for determining future strategy. Big data discovery can
yield surprising and extremely useful results. The
primary objective of data science is to increase
managers' capacity to make better business decisions in
2014,2013. Top businesses like Amazon, Wal-Mart,
Google, and Netflix have mastered the art of leveraging
analytics and data as a tool for simulations, predictions,
and occasionally for just learning new things. Amazon
and Wal-Mart use analytics to make decisions in every
area from creating demand to effectively managing the
supply chains, in which they are performing their
business.
VI. CONCLUSION AND DIRECTIONS
FOR FUTURE RESEARCH
Since the information revolution altered the
way corporate organizations operate, we have gone a
long way, using various analytical methodologies, big
data is assisting businesses in gaining a competitive
advantage. These using some methods, we can uncover
insights, patterns, connections, and associations that
might otherwise analysed using conventional small data.
These facilitate corporate decision-making executives
using competitive intelligence, cost and time savings,
and social media data strategy, analytics for the supply
chain, online analytics, etc. Companies that understand
the value of big data and create products based on it have
seen enormous profits in recent years. Profit from
analytics-based business decisions, many companies
apply analytics to every element of running their
operations.
Making decisions, we propose a framework for
creating analysis in this study capabilities and how this
latest information might assist small and medium-sized
businesses to compete with smaller resources Such
businesses can embrace it with modifications consistent
with their operations model and domain.
REFERENCES
[1] Schmarzo, B. (2015). Big data MBA: Driving
business strategies with data science. John Wiley &
Sons.
[2] Chen, H., Chiang, R. H., & Storey, V. C. (2012).
Business intelligence and analytics: From big data to big
impact. MIS Quarterly, 1165-1188.
[3] Davenport, T. H. & Dyché, J. (2013). Big data in big
companies. International Institute for Analytics, 3, 1-31.
[4] Tsai, C. W., Lai, C. F., Chao, H. C. & Vasilakos, A.
V. (2015). Big data analytics: A survey. Journal of Big
Data, 2(1), 1-32.
[5] Elgendy, N. & Elragal, A. (2014, July). Big data
analytics: a literature review paper. In: Industrial
Conference on Data Mining, pp. 214-227. Springer.
[6] Singh, D. & Reddy, C. K. (2015). A survey on
platforms for big data analytics. Journal of Big Data,
2(1), 1-20.

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