Big Data Analytics: Features, Challenges, and Business Benefits

This report provides a comprehensive overview of big data, defining its core features such as volume, variety, velocity, and variability. It delves into the challenges organizations face when implementing big data analytics, including data complexity, integration issues, security concerns, and inter...Read More

Big Data

Contents
Contents...........................................................................................................................................2
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Define big data is and the features of big data?...........................................................................3
The issue faced by the organisation in context with the big data analytics; and the methods that
are presently accessible to study big data....................................................................................3
Mention how big data technology is useful for business. Examples to support the answer........5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7
APPENDIX......................................................................................................................................8

INTRODUCTION
Situations in today's world are dynamic any organisation working in this environment needs
data analysis so that ever changing environment does not harm the stability of the entity.
Therefore, there is need of data analytics. It helps in improving their customer service. In the
accompanying report, the big data is explained with the various characteristics that will be
helpful for the business organisation. Further, the techniques and the issues that could be faced
by the business will be discussed in the report, Moreover, the technologies that is used by the
business organisations for supporting business.
MAIN BODY
Define big data is and the features of big data?
It is the accumulation of data that is large in volume, still has a potential or power to grow
rapidly with time. The capacity of data is so huge and large in size that it is very difficult to store
the process efficiently or by any traditional tool data management.
 Volume: The big data as the name suggest indicates that it is large in size. Accumulating
from enormous source it is available in wide variety range. Data's size is judged
according to the situation. If the market is such which requires lot of information data
volume need to be huge and vice versa as the case may be.
 Variety: data is available in different forms and is gettable at number of source. It is
upon the organisation what kind of resources it need and which is more reliable. The data
addressable after filtration and analysis.
 Velocity: A concern needs data to flow regularly and smoothly so that it is available, at
the right place at the right time so that decision making process can be quick and
effective.
 Variability: collected from different sources, data is available in wide range and variety.
It may differ in structure, values and size which is because it is complex. Data is useful
according to the organisation suitability. It can be moulded referring to the needs.

The issue faced by the organisation in context with the big data analytics; and the methods that
are presently accessible to study big data.
In the today's era where everything is digitalized from shopping to schooling from education to
work, post pandemic everything is highly digitalized. Big data analytics is the process of using
this data available in different forms structured, unstructured various sizes in order to analyse
and apply in the organisational uses. Big data have following characteristics:
 High volume
 High velocity
 Artificial intelligence
 Mobile
 Social
 Internet of things
These features make the data big and complicated. Though the big data helps researchers, analyst
and decision maker to make a right decision at the right time, there are various challenges are
faced in its application at strategic level (Bansal, Chana and Clarke, 2020).
1. Data Complexity: Lack of useful data, complex data handling, traditional approach to a
modern data makes data difficult to read and analyse. The raw data flows from
salesperson to operation and which gets complex at every level and make decisions
difficult.
2. Data Integration: Unreliable source for collecting data, system error in managing data,
this mainly happens when the requirement like updating of the system is neglected and
testing is not done.
3. Data Security: Just collection, sorting, and using isn't enough in today's world of data
theft it is really important to keep data safe and restricts unauthorized entries. A small
mistake can lead to huge loss.
4. Data Collection: In today's era of IOT, information is available more than required, which
is really confusing to choose between option and their application.
5. Data Interpretation: When such wide variety of information is available analysis takes
time and using the right technique for interpretation is a hard task.
6. Difficulty in getting timely insights: When the data is complex it takes to time in
interpretation which delay the process and unable to deliver solution at the correct time.

Techniques used for data analytics:
 A/B Testing – This technique helps in comparing the control group and the test group. In
order to determine what changes brings improvement. Bid data fits in the model and
helps to run test in big data.
 Data Mining – It is most common tool to resource only the useful data from the huge data
and study the data which is required, saves time and resources and provides results fast.
 Machine Learning – It belongs to the field of artificial intelligence; it works on computer
algorithm to produce solutions to the problem based on the data available.
 Statistics – The technique used to collect, organize, interpreted and experiment.
Mention how big data technology is useful for business. Examples to support the answer.
Big data is an assemblage of data that is enormous in quantity, still has a potential or power
to grow rapidly with time. It is so immense in size and entangled that none of the traditional
approach to data management can store it or operate it with efficiency.
Processing data carries multiple advantages with it such as:
 Clear and improved consumer or customer service
 Improved and healthier functional efficiency
 Primal determination of risk to the product or service
 Organisations can utilize external intelligence service while taking decision.
With Big Data, organisations can utilize analytics, and figure or build out the most valued or
precious customers. It can also aid industries in creating new experiences or content, services and
products. Big data technology can assist businesses in five ways which are discussed below:
 Making improves business decisions: Big data helps the businesses in making smarter
decisions that are based on data and not on assumptions. Everyone in the organisation
must have a right to approach to the data they need to amend or improve decision making
of the organisation. Data uses is not restricted to only the IT department or the particular
department. Users of the company across the world are capable in investigating and
questioning data so that they can response their most urgent business questions. This
organisation's broad access to data is referred to as data democratisation. Walmart is an
outstanding example of this data democratisation. Importantly, Walmart supplies its

people to approach to data in a disciplined way ensuring that people who are not aware of
the technology do not get engulf by data and can easily discover the solution they want.
 Delivering Smarter services or products: When an organisation come to know about its
customers, it starts delivery smarter or suitable products or services for its customers
which fulfil their needs completely and satisfies them to the fullest. Royal Bank of
Scotland (RBS) is a great example of organisation using it for better customer experience.
The bank gathers information about the customer's preference, particularly about their
wants and needs. RBS is starting to make best out of the available data so that they can
attain the highest level of customer satisfaction.
 Improving business operations: The outgrowth in automation is supported by Big Data.
Robotics and high technology may be outdated in production industry lines. But,
progressively, a number of business sectors and operations are becoming more efficient,
effective and automated. PeopleDoc, a HR software company, that has launched a
Robotic Process Automation platform, that operates besides present system of the
company and perceive for process or outcome that could be automatize.
 Income maximisation: This is not just limited to knowing customer's preference,
processes and outcomes. It more linked to how the customer data can be useful in
generating additional income. American express with the help of big data is creating huge
income.
CONCLUSION
Big data analysis is method of interpretation of huge and complex data available in
structured & unstructured form. From above discussion it can be concluded that there are
challenges in order to use this data due to its complexity, unavailability and reliability issues. It
can be analysed by the aid of proven techniques and methods. These data are really important in
today's era to run a successful organisation; it can be really helpful for a business entity in
various ways. This helps in development, growth, betterment of the company and also facilitates
customer satisfaction.

REFERENCES
Books and Journals
Bansal, M., Chana, I. and Clarke, S., 2020. A survey on iot big data: current status, 13 v’s
challenges, and future directions. ACM Computing Surveys (CSUR), 53(6), pp.1-59.
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations
management. Production and Operations Management, 27(10), pp.1868-1883.
Ghani, N.A and et.al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Hawkins, K.A. and Silva, B.C., 2018. Textual analysis: big data approaches. In The ideational
approach to populism (pp. 27-48). Routledge.
Jiang, D and et.al., 2019. Big data analysis based network behavior insight of cellular networks
for industry 4.0 applications. IEEE Transactions on Industrial Informatics, 16(2),
pp.1310-1320.
Li, J and et.al., 2018. Big data in tourism research: A literature review. Tourism
Management, 68, pp.301-323.
Mayer-Schönberger, V. and Ramge, T., 2018. Reinventing capitalism in the age of big data.
Hachette UK.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big
insights for value-creation. Information Processing & Management, 54(5), pp.758-790.

APPENDIX
Poster