Big Data: Characteristics, Challenges, Techniques and Business Support

This report defines big data and its nature, discusses the challenges and techniques of big data analysis, and explains how big data technology supports businesses. It also covers the characteristics of big data, including volume, velocity, variety, and veracity.

Big Data

Table of Contents
INTRODUCTION ..........................................................................................................................3
TASK...............................................................................................................................................3
Define big data and its nature......................................................................................................3
Challenges and techniques of big data analysis which are currently available ..........................4
How big data technology supports business...............................................................................6
CONCLUSION ...............................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Data is the content which is collected by the companies and are transmitted to the
computer in a large amount using electrical signals and software. Companies use big data to
improve the business activity and to give better services to its customers. It helps in increasing
the overall profit and revenue of the firm. Big data analytics is a field where the information is
being extracted and the conclusions on that content in given which helps in judgement the
substance. It helps in forecasting the future trends with the help of the skilled professionals
knowledge (Ahmed, M., Choudhury, S. and Al-Turjman, F., 2019). In this report, the use of big
data and the characteristics which are useful for the business purposes is described. It define that
how difficult it was to analyse the form of data with the tradition data managements tools. So,
the modern applications were developed to prove the inefficiency of the data software and now
how useful it has become for businesses to used the big data sets and those tools to analyse the
substances. The challenges that are faced by the business is explained and how these
technologies supports the business and helps in decision – making.
TASK
Define big data and its nature.
It is a collection of data in a large volume which is hard to manage. It can be both in a
organised and unorganized that fill up the requirements of business on a day – to – day basis.
This data is very huge that it is not possible neither for human nor traditional management
systems to interpret it. To properly analyse the big data , modern tools and software should be
used to makes the correct decisions (Desai, J.N., Pandian, S. and Vij, R.K., 2021). New software
developments have been made to tract the big data sets. It is meaningless for the humans because
it is impossible and unconnected to analyse the data.
In this type of data, the capabilities are required to augment the data. It provides a right and
better line of sight to use the existing knowledge to control and have the ability to analyse the
information. It is usually used in the to extract and interpret the exact information which can be
useful for the organisation. It requires the individual hardware programs to upgrade the data.
This process makes very easy to maintain such an immense data and it is fault tolerant.

Characteristics of Big Data
1. Volume: If refers to such a large amount of data which cannot be imagined. It is
generated from different sources such as social media, surveys, questionnaires, videos
etc.. These unstructured details collected from different different locations are stored and
use computing software to analyse it.
2. Velocity: It refers to how fast the application of big data generate the data points. It is
received, analysed and interpreted quickly to get the up – to – date finding of the data. It
will be helpful for the companies to take decisions faster (Dharayani, R. and et. al.,
2019).
3. Variety: It contains different types of data from the same unorganized database.
Traditional tools of data management used the organised information which contain only
specific type of data. But now, new applications have been developed and ambiguous
data can also help find all the correlations from the content.
4. Veracity: It is the accuracy in database and how much the companies can rely on that
data. Because the raw data is collected from various sources which is the most important
part of data quality. The data collected should be sorted first and evacuate the data errors
and then the analysis should be done.
Challenges and techniques of big data analysis which are currently available .
It includes the best way to handle the numerous amount of data ad involves the process of
storing and analysing the information of the data stores.
Challenges:
1. Lack of professional knowledge: To run the modern applications and tools for analysing
the large data, a skill for that particular software is required. So the professionals need to
be skilled for running this programs, these includes data analysts, data engineers or
scientists to work with the tools and techniques which could be proved useful for the
company (Forgó, N., Hänold, S. and Schütze, B., 2017). It is a challenge that there is lack
of people having this type of knowledge who the fulfil the requirements for the big data.
2. Lack of proper understanding the Big Data: Once the person understands the
software, but it is very difficult to understand the data. Every time the data could be
different and on the basis of the type of information analysis is being done. It happens

because of insufficient understanding and companies fail to do it. Employees might not
know what data is, its storage and importance, sources etc.
3. Data Growth Issues: It is the foremost problem of storing such a big data and using of
the knowledge properly. The quantity of data which is being stored by the companies is
increasing rapidly which is creating a challenge in storing of that information at the right
place (Joshi, P. and et. al., 2018). This created the data to be stored in an unstructured
way which result in misinterpreting of the data.
4. Securing Data: It is a massive challenge for the organisation to store the data with its full
security. Often the businesses are busy in understanding and storing the data, and not
focus on the security of those information which are very crucial for any company. This
often lead to the breaching of data.
5. Confusion of Data tool selection: In today's time, many data software has come, so it
has become difficult for the companies to select the simplest toll for the giant data
analysis, interpretation and storage.
Techniques of Big Data Analysis:
1. Data integration: It allows to handle such a big data that is process in terabytes in a way
that it can be useful for the companies as well as the customers. The data which is
collected is incorporated with the existing information of the company using the
applications such as Tableau, Python, Teradata etc.
2. Data Mining: It helps in exploring and analysing the huge amount of data to find a sort
of pattern. It is used within the business analytics and with the help of the business
intelligence (Liu, H. and et. al., 2017). It aims to deliver the relevant information by
showing the relationship between the data sets, system and processes.
3. A/b testing: It is a technology used to control the data and compare the two variables in
the variety of the test groups which helps to perform better in a control environment. It
collects the data to analyse to perform better results by making an hypothesis and create
an control and variants of that data.

How big data technology supports business
Big data technology is one the great resources for the changes in the data industry and the
business (Wang, K. and et. al., 2017). It gave a new perspective to look at the informations the
organisations have and to utilise it in a right way.
1. Better customer insight: It helps in knowing better about the changes in the
prospectives of the clients and what they expect from the companies to show the
innovations in the market. It helps is discovering the invisible possibilities which can
increase the power to view the ample sets of the information. Even the complex database
can also be helpful in developing the new products and give a competitive advantage to
the firm.
2. Faster decision making: With the growth of the technology in analytics, it has
developed the ability to analyse the data faster and accurately, which assists the
businesses in taking decisions more smartly and faster.
3. Automation: It helps in improves the efficiency of working on the data-sets internally
with the help of the robotic operations (Schaeffer, C. and et. al., 2017). It can analyse the
real – time data in no time and helps in the automated decision – making.
CONCLUSION
It can be concluded from the report that the big data is an crucial part in developing the
businesses and helps in validating the informations. It also helps in the giving a deep insight to
he data sets through which the companies evaluate the accuracy and validity of the data. It gives
the decisions in no time by analysing the information with the help of the various technological
software. It can be a game – changes for many organisations as it gives the drive to do the
strategic decision – making and offer a better customer experience. It provides a more secure
transitions for the businesses and helps in identifying the fraudulent data which can be harmful
for the organisations. It sorts the information on the basis of the preferences of the firm. It also
helps in monitoring the real – time market activity and helps in fraud detection.

REFERENCES
Books and Journals
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Dharayani, R. and et. al., 2019, October. Genomic anomaly searching with blast algorithm using
mapreduce framework in big data platform. In 2019 international workshop on big data
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Forgó, N., Hänold, S. and Schütze, B., 2017. The principle of purpose limitation and big data.
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Joshi, P. and et. al., 2018, March. Big data analytics for micro-seismic monitoring. In Offshore
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Liu, H. and et. al., 2017, November. A big data framework for electric power data quality
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Schaeffer, C. and et. al., 2017. Big data management in US hospitals: benefits and barriers. The
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Wang, K. and et. al., 2017. Wireless big data computing in smart grid. IEEE Wireless
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