The Role and Importance of Big Data Analytics in Modern Business

This report delves into the critical importance of big data analytics in the business world. It begins with an abstract summarizing the role of big data in extracting valuable information from large datasets, highlighting the challenges businesses face in this field. The report covers the concept of big data, its attributes (volume, velocity, variety, veracity, and value), and the concept of business intelligence. It explores the challenges of big data analytics, such as lack of skilled workers and data privacy concerns. The methodology includes primary data collection through interviews and surveys. The research aims to understand the role of data analytics and big data analytics in the business world and to suggest ways to acquire skilled analysts. The study outlines a timeline, expected outcomes, and references, providing a comprehensive overview of big data analytics and its impact on business decision-making and competitive intelligence.

Running head: IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Name of the Student
Name of the University
Author Note

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Abstract
In this era where information is all around, enormous quantity of data are accessible on the
hand of decision makers. “Big data” denotes to datasets which are huge and also great in
velocity and variety that marks them problematic to nurture using traditional techniques.
Since such data have been grown rapidly, solution is needed to be studied and provided to
handle and extract valuable information from these datasets. Such value can be given using
“big data analytics”. There are certain issues to analyze big data on business. This paper aims
to find out how well can this field get the skilled analysts in the business world.

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Table of Contents
Introduction................................................................................................................................3
Background of the study............................................................................................................3
Problem statement......................................................................................................................3
Rationale....................................................................................................................................4
The aim and objectives...............................................................................................................4
Research Questions:...................................................................................................................4
Literature review........................................................................................................................4
Concept of Big Data...............................................................................................................5
Concept of Business...............................................................................................................5
Challenges of big data............................................................................................................6
Methodology..............................................................................................................................6
Data collection.......................................................................................................................6
Sampling method...................................................................................................................6
Data Analysis.........................................................................................................................7
Research ethics.......................................................................................................................7
Limitation of the study...........................................................................................................7
Proposed timeline of the study...............................................................................................7
Expected Outcome.....................................................................................................................9
References................................................................................................................................10

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Introduction
Data analytics involves usually two techniques-quantitative and qualitative to improve
business profts and productivity. There are many data analytics tools which analysts,
engineers and researchers use for different business organization. The data is unstructured
and there are tools which helps to analyze those data. These tools are mainly of two types-
analysis and storage Big Data Analytics tools. The example of those tools are Apache
Hadoop, Hive, Storm, Mongo DB and many more(Tsai et al., 2015).
Background of the study
Today noone can deny the importance of data in business world. The concept of “big
data analytics” developed at the starting of twenty first century. Now every technology giant
is building use of big data technologies(Elgendy & Elragal, 2014). “Big data” is referred to
huge data set that are of unstructured or structured data. Every business and users produce
this massive amount of data. “Big data analytics” is the analyzing process of the huge amount
of data sets to get insights and patterns. It is certainly a revolution information technology
field.
Problem statement
For many organization, the main problem is to figure out how to get value from the
data. In some cases, the large gap between the theoretical knowledge of big data and actually
placing that theory into practice exist. The another major problem faced by the people in this
field is inaccurate data. A report from Experian data quality shows seventy five per cent of
businesses be certain of their customer contact information is incorrect. According to

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Capgemini’s report, thirty seven per cent of companies are in distress in finding skilled data
analysts.
Rationale
The “big data analytics” helps firms to tackle their data and employ them to find out
new chances., IIA Director of Research Tom Davenport in his report ‘Big Data in Big
companies’ found that company got values in several ways which are reduction of cost, faster
and better making of decesion, new services and products.
The aim and objectives
Aim of the current research is to understand the importance of “big data analytics” in
business.
The research objectives are:
i) To comprehend the part of data analytics in business world
ii) To comprehend the role of “big data analytics” in business world
iii) To suggest how well can this field get the skilled analysts in the business world
Research Questions:
RQ1. What is the part of ‘data analytics” in business world?
RQ2. What is the role of “big data analytics”?
RQ3. What are the probable ways to get skilled analysts in the business world?
Literature review
According to LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N.
(2011), in every industry, it has been wondered by several leaders whether they are receiving

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full value from the vast information they already have within their organization. New
technology are gathering many data and many organization are still in search of better
techniques to acquire value from the data(Hu et al.,2014). Despite popular opinion, getting
the data right is not a big challenge. Only about 1 out of 5 respondents quoted concern with
the quality of data. Executives wishes better techniques to communicate insights which are
complex,so that they can captivate the meaning fast and take action.
Concept of Big Data
“Big Data” is mainly categorized by five vital attributes volume, velocity, variety,
veracity and value. These Vs indicates vast data type diversity, data volume and varied data
generation velocity. An example can be taken, around three lacs rows of real time data per
second can be produced by Nielsen from observing live and produce extra than one billion
records per month to do “big data analysis.”. This is of data volume. In terms of data variety ,
there are both structured and unstructured that data. For data velocity, real time access can be
enabled by “big data analytics” and information sharing can help firms to produce insights
from customer transactions, inventory monitoring, advertisement and consumer preferences,
store based video, sales management and financial over state to nationwide governments for
efficient capabilities of “decision making”. Big data analytics can be a assistance of firms so
that they can exploit better big data to improve satisfaction of customer that generates
competitive intelligence and handling supply chain risk to make important business
decisions(Sun et al., 2016).
Concept of Business
“Business intelligence” is the capability of a firm to make expressive use of
availability of data. It characterized as structure which collect, change and present structured

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data from different sources that require time to obtain important business data and allow their
efficacy use in organization decision making procedure(Kwon, Lee & Shin, 2014). At the
start, software business involved with “business intelligence”, BI used to be understood as
private insight and not open knowledge. BI comprises a wide range of areas for example,
intelligence of strategy, competitor intelligence, intelligence of market, business counter
intelligence and intelligence of product.
Challenges of big data
Though “big data” can assistance to improve making important business decisions,
The encounter of “big data analytics” are lack of intellectual “big data” foundations , the
adequate network resources available for executing application,the concern about data
privacy, the problem with data integration. Also cosltly software are being used to analyze. It
causes problem in the application of “big data analytics” for Business Intelligence(BI) (Choi,
Chan & Yue, 2016). Again there are lack of skilled workers in the field of “big data
analytics”. This research will be done to reduce these problem.
Methodology
Data collection
Current study will rely on the gathering of “primary data”. “Primary data’(Wamba et
al.,2017) will be gathered by various sources like interview and survey. Here, interview will
be conducted with the managers of three different Australian Companies. Here the sample
size will be three.

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Sampling method
The techniques of sampling that will be applied for this research includes non
probabilistic sampling techniques. Also “simple random sampling” will be applied for this
research to understand the research deeply. It is probable that around three manager of the
three different companies will be asked to take part in the interview. To conduct this research,
they will invite them to present their view point on current study.
Data Analysis
To assess the “qualitative data”, the “Thematic approach” will be commenced. From
the interview the information that has been conducted will be noted down keeping in mind
several points which has been directly associated to the research subject.
Research ethics
After conducting the complete research, severe moral guideline will be monitored. To
collect the “primary data”, none of the members will be enforced to take part in the interview.
Each and every participants will sign a agreement to make assured that they took part of the
interview willingly. There should not be any symbol or logo of the business in the survey
paper.
Limitation of the study
According to Simonsohn et al. (2017), there should be some limits of a research
analysis,irrespective of the intentions. Time frame is always a curb for every research.
Another limitation is availability of resources. Again, there are some problems related to gain
permission from the managers for the interview session. Financial barriers might be a
limitation for conducting interview session.

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Proposed timeline of the study
Activities 1st
to 3rd
Week
4th
to 10th
week
11th
to 13th
Week
14th
to 17th
Week
18th
to 21st
Week
22nd
to 23rd
Week
24th
Week
Selection of the
topic

Data collection
from the interview
 
Creating layout 
Literature
review
  
Analysis and
interpretation of
collected data
  
Findings of the
data
 
Conclusion of
the study

Formation of
draft
 
Submission of
final work

Figure 1: Gantt Chart
Source: (Created by author)

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
Expected Outcome
It is expected that this paper will show the part of “big data analytics” in world of
commercial, and will suggest the probable ways to get the skilled analysts in the business
world.

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IMPORTANCE OF BIG DATA ANALYTICS IN BUSINESS
References
Choi, T. M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.
Elgendy, N., & Elragal, A. (2014, July). Big data analytics: a literature review paper.
In Industrial Conference on Data Mining (pp. 214-227). Springer, Cham.
Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data
analytics: A technology tutorial. IEEE access, 2, 652-687.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and
acquisition intention of big data analytics. International journal of information
management, 34(3), 387-394.
Simonsohn, U., Nelson, L. and Simmons, J., 2017. Research Methodology, Design, and
Analysis. Annual Review of Psychology, 69(1).
Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and big data analytics for
smart and connected communities. IEEE access, 4, 766-773.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a
survey. Journal of Big data, 2(1), 21.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017).
Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business
Research, 70, 356-365.