Big Data: Characteristics, Challenges, Techniques and Business Support

This report discusses the meaning and characteristics of big data, challenges faced by big data analytics, and techniques available to study big data. It also explores how big data technology can provide assistance to businesses. Examples of businesses using big data are provided throughout the report.

Information
Systems

INTRODUCTION :
An information system is a structure that collect, process and distribute information. It is
based on computers and people that processes the data. This is an information and
communication technology that the organisation uses and the way people interact with this
technology. To carry out their operations organisation rely on information system(Moreira and
et. al., 2019). This is very helpful system as it helps the businesses to interact with customers and
suppliers. This method is used to run organisational supply chains. Many companies are entirely
built on this system. In this report we have covered about the big data and features of big data,
challenges of big data analytics and the techniques to analyse big data and how big data
technology could support businesses.
TASK
What big data is and the characteristics of big data?
Meaning of big data:-
Big data contains large variety of data and complex to be dealt by traditional methods. It
is a combination of structured and unstructured data that is collected by organisations. These
massive data can be used to manage business problems that company did not able to tackle
earlier. It improves the customer service. Users are generating huge amounts of data but it is not
the humans who are doing it. Big data is also known as three Vs. The new york stock exchange
is an example of big data that generates of new trade data of one terabyte per day. While taking
decisions businesses can use the outside information (Beath and Orlikowski, 2019). There are
three forms of big data: structured, semi-structured and unstructured.
Characteristics of big data:-
1. Volume:- It refers to the size of the data. This plays an essential part in deciding the
value out of data. Particular information is considered as big data or not is dependent
upon the volume of data. Amount of information that is available to businesses is rising
day by day and the percentage of processing declines. Businesses have to use new
technology to tackle this problem. This is one of the characteristics to be considered
while dealing with big data. This data is yield from many origins such as business
processes, social media, interaction with humans, communication system etc.

2. Variety:- Big data can be structured, semi-structured and unstructured that are collected
from different sources. In earlier days data was gathered from databases and sheets but
now a days it can be collected in forms that is PDF, Emails, Photos and videos etc.
3. Velocity:- This refers to the speed of creation of data. How fast it is created and how
much time does it takes to handle it. This deals with speed at which data inflow from
sources like networks, social sites, processes etc. This flow of data is non stop and large.
4. Veracity:- It means the reliability of data. It is the process of managing the data
efficiently. As most of the information is unorganized, it is necessary to filter out the
unnecessary information and then use it for operation.
5. Value:- It is the most essential characteristics of big data. No matter how fast it is created
but the data should be dependable. The information which is good is ready for processing
and if it is not accurate then it cannot be stored. Only valuable and reliable data is
processed and stored(Xu and Duan, 2019). Scientist first convert the raw data into
information and then retrieve the most useful data. Researcher says that if the company
takes poor data it can lead to loss of 20% in revenue(Characteristics of Big Data: Types
& 5V’s, 2022).
The challenges of big data analytics; and the techniques that are currently
available to analysis big data
Challenges of big data analytics
Information is the necessity for every organisation. Without information no company
can perform its functions. It is generated from business transactions every seconds. Data should
be analysed to enhance decision making. But there are some challenges faced by companies.
These challenges are:-
1. Lack of knowledge professionals:- organisation needs highly skilled professionals to
manage modern technologies and large data tools. These includes data analytics,
engineers and scientists. These people work with tools and data sets. Company face the
challenge of lack of massive data professional. Data handling tools have evolved with
time but professionals have not developed.
2. Lack of proper understanding of massive data:- In big data analysis companies have
failed as there is lack of proper understanding. Employees does not know what the data
is, how to store it, its importance and sources(Marshall, 2018). Only data professionals

know but others don't know about it. If employees does not realise the importance of
storage they could not keep it safe. They could not use the data storage properly and
when this information is needed it can't be recover easily.
3. Data growth issues:- Storing these huge sets of information properly is also a challenge.
The quantity of information stored in data centres keeps increasing fast. As these increase
with time period it becomes difficult to handle. Most of the information comes from
documents, picture, sound, text files and other location and the information is
unstructured.
4. Confusion in big data tool determination:- While determining the tool for data analysis
companies gets confused. They make poor decisions and select improper technology.
This results in wastage of time, money and efforts. Confusion creates poor decision and
leads the whole organisation in trouble(Alharthi and et. al., 2018). Selection of tool is a
big task and if incorrect tool is selected then the entire organisation suffers.
5. Combining data from a spread of sources:- Data comes from different sources like
social media, emails, ERP application, client logs etc. Integrating all data to make reports
is a difficult task. Data integration is critical for analysis and reporting. Integration is a
difficult task as data comes from various sources and it requires different methods to
integrate data.
6. Securing data:- Secure these huge data is a challenge for organisations. Organization are
engaged in understanding, storing, and examining their information and they send the
safety of data ahead. Some big data is attractive target for hackers. Some companies
think that their present protection method is sufficient for securing information. This is
not a valid decision as insecure data can become grounds for malicious hackers.
7. Organisational resistance:- Technology is not the issue of big information but the
humans can be. In order to acquiring the possibility offered by big data they are going to
do many things differently. The change can be difficult for the organisations. Inadequate
organizational arrangement, absence of middle management acquisition and lack of
understanding are the obstacles in the organisation.
Techniques available to analysis of big data:-
1. A/B testing:- This method refers to examination the control groups with a variety of test
groups in order to recognize what attention or alteration will enhance given aim. As it

can test huge numbers big data again fits into this model. This can only be succeed if the
groups are of big size and to gain meaningful experience. This technique is useful when
user engage in online feature and gets satisfied(Kenneth and et. al., 2019). Big social
media sites like Facebook, linkdin uses this technique to make individual experience
successful.
2. Monte Carlo simulation:- This is a computerized technique and in this techniques
models are created of achievable outcomes and their probability distribution. It involves
the range of realizable outcomes and calculates how possible each particular outcome
will be accomplished. Data analysts used this method to conduct advance risk analysis
which allows them to forecast better about what happen in future and can make decisions
accordingly.
3. Machine learning:- This is also used for data analysis. This technique works with
algorithms to produce anticipation based on information. It renders predictions that is
impossible for humans to analyse it. It includes software that can learn from data. This
technique help in distinguishing between spam and non-spam email messages. It helps in
learning user preferences and make recommendations based on the information. It
determines the probability for success of case.
4. Associate rule learning:- This method refers to discovering interesting correlations
between variables in big organisations. This method is used to help in extract information
about visitors who visits websites, place products in better locality in order to increase
sales, monitor systems logs to find intruders and analyse biological data to show new
relation(Diaz Andrade and Doolin, 2019).
5. Regression analysis:- It involves influencing some independent variables to see how it
influences the dependent variable. Proportionate change in one variable leads to change
in another variable. It defines the rate of change in variables when one variable changes.
It works best with continuous data like speed, age or weight. This analysis is used to
determine how customer satisfaction affect customer loyalty etc.
6. Social network analysis:- Telecom industry used this technique and later on it was
acquired by sociologist to study social relation. It is now uses to analyse the relationships
between persons in many fields and commercial activities. There are two networks nodes

and ties. Nodes symbolise individual with a network and ties represent relationships
between individuals. This analysis understands the social need of a set of customers.
How Big Data technology could support business, please use examples wherever
necessary.
1. Making better business decisions:- The tools which are needed by businesses to
make better decisions are given by Big data. To improve the decision making
everyone in the organization should have approach to the information. Users of
business should be capable to investigate data so that they can respond their business
questions(Wasielewski and et. al., 2020). Walmart is a great example of this.
Walmart provides its approach to data in a controlled way. It has a data cafe where
teams across the business are invited to bring the business problems to the cafe
analytics expert and find the answer.
2. Understanding your customer:- The more company understand their customers the
better they serve. For example Facebook knows much about everyone and that
information was used to give relevant recommendations. Disney is engaging in big
data as it understands the behaviour of visitors at the theme park so that it can offer
more magical experience for guests. This is possible with the introduction of magic
band. The visitor band acts as ID, entry pass, room keys and payment devices. All
the visitors have to swipe the band at sensor to pay something or to enter in hotel
room.
3. Delivering smart services and products:- Company can design better
products/services to their customers only when they knows about customers. Royal
bank of Scotland using big information to provide improved services to its users.
Average bank knows about its customers like what they want to buy, where they
want to go to holiday etc. If the businesses have knowledge about their customers
then only they can provide smart products and services to its customer(Chu and et.
al., 2018).
4. Generating income:- Big data is not just understanding about its customers,
improving processes and decisions, the data is used to boost its revenue or make
extra line of revenue. For example American express handles more than 25% of
credit card transactions interacts with parties. Now American express uses that data

to bring businesses and customers together(How Does Big Data Help Companies?,
2021).
5. Improving business operations:- Big data helps the companies to improve their
business operations. Where big data is widely implemented businesses can handle
their customers properly. This also helps the businesses for effective waste
management. Waste consumes large portion of business resources. With the help of
big data businesses can improve their management processes(Miloslavskaya and
Tolstaya, 2020).
Poster
CONCLUSION
From the above it is concluded that big data is dynamic force in sectors such as
marketing, sales and research. It changes the business perspective of customer based and product
based. This system is also used by governments to provide cost effective services to the public.
Information is everywhere and to tackle that in an effective manner organisations depends on
information system. Organisation have large amount of data and information system organises
that data to help the businesses to solve their problems in an effective manner. Individual can
also rely on information system to interact with friends through social networking. It helps the
organisations to manage the data to achieve its objectives. This system includes hardware,

software, database, network and people. In this report we have seen features of big data,
challenges faced by big data analytics and the techniques available to study big information.
Moreover how big data technology can provide assistance to businesses.
REFERENCES
Books and Journals
Moreira, M.W. and et. al., 2019. A comprehensive review on smart decision support systems for
health care. IEEE Systems Journal. 13(3). pp.3536-3545.
Beath, C.M. and Orlikowski, W.J., 2019. The contradictory structure of systems development
methodologies: Deconstructing the IS-user relationship in information engineering.
In Postmodern Management Theory (pp. 377-404). Routledge.
Xu, L.D. and Duan, L., 2019. Big data for cyber physical systems in industry 4.0: a
survey. Enterprise Information Systems. 13(2). pp.148-169.
Marshall, B., 2018. Accounting information systems. Pearson Education,.
Alharthi, H. and et. al., 2018. A survey of book recommender systems. Journal of Intelligent
Information Systems. 51(1). pp.139-160.
Kenneth, C. and et. al., 2019. Management Information Systems: Managing the Digital Firm.
PEARSON.
Diaz Andrade, A. and Doolin, B., 2019. Temporal enactment of resettled refugees' ICT‐mediated
information practices. Information Systems Journal. 29(1). pp.145-174.
Chu, M. and et. al., 2018. Integrating mobile building information modelling and augmented
reality systems: an experimental study. Automation in Construction. 85. pp.305-316.
Miloslavskaya, N. and Tolstaya, S., 2020, April. On the Assessment of Compliance with the
Requirements of Regulatory Documents to Ensure Information Security. In World
Conference on Information Systems and Technologies(pp. 789-795). Springer, Cham.
Wasielewski, M.R. and et. al., 2020. Exploiting chemistry and molecular systems for quantum
information science. Nature Reviews Chemistry. 4(9). pp.490-504.
ONLINE
Characteristics of Big Data: Types & 5V’s, 2022 [online] available through
<https://www.upgrad.com/blog/characteristics-of-big-data/#3_Velocity>
How Does Big Data Help Companies?, 2021 [online] available through
<https://bernardmarr.com/how-does-big-data-help-companies/>