Big Data: Characteristics, Challenges, Techniques and Business Support

This report explains the characteristics of Big Data, the challenges faced in its analysis, and the techniques used for data analytics. It also discusses how Big Data technology could support businesses with examples. The report concludes by highlighting the importance of Big Data in today's era and its impact on development, growth, and customer satisfaction.

Big Data

Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
What big data is and the characteristics of big data?...................................................................3
The challenges of big data analytics; and the techniques that are currently available to analysis
big data.........................................................................................................................................4
How Big Data technology could support business, please use examples wherever necessary.. .5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7
APPENDIX......................................................................................................................................8

INTRODUCTION
Many businesses can utilize the outside intelligence while taking the decisions and make
strategies according to the dynamic businesses environment. 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 (Saggi and Jain, 2018).
MAIN BODY
What big data is and the characteristics of big data?
It is the collection of data that is huge 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 name Big data is itself related to a size which means huge or enormous.
Size of data plays a very crucial role in determining the value of data. Also, a specific
data can also be considered as a Big data or not, is totally dependent upon the volume of
the data. Therefore, Volume is that one characteristic which needs to be in mind while
dealing with big data solutions.
 Variety: Variety refers to the heterogeneous sources and the quality of data, both
structured or unstructured are included. During earlier days, databases and spreadsheets
were the only sources of data considered by most of the applications. Nowadays, photos
and videos are also considered in the analysis applications.
 Velocity: The term refers to the generation of speed of the data how fast a data can be
generated or processed to meet the demands and determines the potential of the data.
 Variability: This refers to the inconsistency which can be shown by the data in some
cases, therefore hampering the process of being able to handle and management of data
efficiently.

The challenges of big data analytics; and the techniques that are currently available to analysis
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.
How Big Data technology could support business, please use examples wherever necessary.
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 vast in size and entangled that none of the conventional data
management techniques and tools can store it or operate it with efficiency.
Processing Big 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. This means data should no longer be the sole domain or area of IT
departments and organisations analysists. 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 Big Data to deliver a better
service to its customers. The bank gathers information and knows a lot about its
customers what they like or dislike or what they prefer. RBS is beginning to support the
potential of this knowledge to improve its efficiency in order to meet its customer wants
or needs.
 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 procedure or events that could be automated.
 Generating an Income: Big Data is not just about rising procedure and conclusions, or
knowing more about its customer's data can be processes and decisions, or understanding
more about customer’s data can be monetised to encourage or create an auxiliary income
stream. American Express is generating income by the aid of Big Data technology.
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. big
data can be analysed with the help 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
Ghani, N.A and et.al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Mayer-Schönberger, V. and Ramge, T., 2018. Reinventing capitalism in the age of big data.
Hachette UK.
Li, J and et.al., 2018. Big data in tourism research: A literature review. Tourism
Management, 68, pp.301-323.
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.
Hawkins, K.A. and Silva, B.C., 2018. Textual analysis: big data approaches. In The ideational
approach to populism (pp. 27-48). Routledge.
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.
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.
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.

APPENDIX
Poster