Big Data Analysis: Techniques, Challenges, and Business Applications

This article provides an overview of big data analysis, including its history, characteristics, and challenges. It discusses the techniques currently available for analyzing big data, such as data fusion and integration, machine learning, and data mining. The article also explores how businesses can use big data technology to gain a competitive advantage, with examples from companies like Apple and Aldi.

Information Systems
and Big Data Analysis

Table of Contents
HISTORY OF BIG DATA..........................................................................................................3
WHAT IS BIG DATA.................................................................................................................3
CHARACTERISTICS OF BIG DATA.......................................................................................3
THE CHALLENGES OF BIG DATA ANALYTICS; AND THE TECHNIQUES THAT ARE
CURRENTLY AVAILABLE TO ANALYSIS BIG DATA.......................................................4
TECHNIQUES THAT ARE CURRENTLY AVAILABLE TO ANALYSIS............................5
HOW BIG DATA TECHNOLOGY COULD SUPPORT BUSINESS, PLEASE USE
EXAMPLES WHEREVER NECESSARY.................................................................................6
REFERENCES.............................................................................................................................7

HISTORY OF BIG DATA
Big data was firstly Track down in the year of 1663 when John Graunt faced the large
amount of information at the time of studied the bubonic plague. The mentioned individual
was the first one who used the statistical data analysis in order to analyse those data. Later on,
the issues of overwhelming data were highlighted in 1880s. In regard to this US census Bureau
published the article in which they highlighted that it will take around eight years to analyse
and handle these big amount of data. Later on in 20th century the data evolved with the rapid
speed due to evaluation of economies as well as increment in the source of data. Various
machines as well as computers were developed at that time which scanned he patterns of these
data and bifurcated them according to their segmented pattern. In the 1965, America
established the first data centre that used in storing the tax returns (Galetsi, Katsaliaki and
Kumar, 2020).
WHAT IS BIG DATA
Big data determines the large set of information’s which are in structured and
unstructured form. These data are indulged with higher velocity and arrives in the increasing
volume. Structured data an easily formatted as well as stored without any complexions
whereas unstructured data comes in more free form, hard to track and less quantifiable. These
data can be collected from number of sources such as websites, social networking sites, apps
and many more. In addition to this these high amount of data is stored in computer data base
with the use of specially designed software.
CHARACTERISTICS OF BIG DATA
Big data has the various sources through which data is collected. In addition to this it
can be described by five characteristics.
Volume- Big data are huge in size and have the vast volume due to number of sources that
generated the large volume of data. In the today's era various sources has developed due to the
evolution of digitalisation globally. Hence, the sources also increased due to such change in
the global economy. Some of the sources are machines activities, social media platforms,
business processes etc (Silva, Diyan and Han, 2019).
 Value- Value is one of the main characteristics of big data. As these data are analysed
and used in enhancing the business operations of firms. Hence, businesses store the

reliable big data according to their needs. Therefore, big data is considered as a
valuable resource.
 Variety- The big data generally deployed under three categories structured, semi
structured and unstructured. Structure data stores in a relational data base, semi-
structured are not come in defined formats. Example email, SV. In order to arrange
these data OLTP system is generated that used in stored these unstructured data more
efficiently, unstructured data are those data that comes in inappropriate structure such s
images file, log file etc.
 Velocity- Velocity developed the speed of the data in which they flaw from various
sources in real time. It helps in managing the incoming data, set data speed s ell as
manages the activity burst I order to classify all the functions in an efficient manner.
 Veracity- The respected shows that how much data are reliable and can be used for
further operations. In order to filter the large strength of data in reliable one various
software and technologies are adopted in order to enhance the data relatability.
THE CHALLENGES OF BIG DATA ANALYTICS; AND THE
TECHNIQUES THAT ARE CURRENTLY AVAILABLE TO
ANALYSIS BIG DATA
The big data already comes in the enormousness amount. Hence there are number of
challenges can be state which can initiated in regard to the storing, analysing and storing of
these huge data. In regard to this below are some challenges which raised in the usage of big
data.
 Lack of knowledge professionals- Big data are in the diverse form which cannot be
used without the use of modern technologies and tools. As in order to use these data for
the operational use companies need the professionals which highlights the data
analysts, data scientists as well as data engineering. In addition to this one of the major
challenges that faced by industries is the lack of industrial experts in the related field.
One of the major reason of such massive gap in data handler is because, data has
evolved rapidly but the skills of professionals haven't (Osman, 2019).
 Lack of proper understanding of massive data- companies are failing in their big
data initiatives. One of the main reason of their failure is due to lack of proper

understanding. Employees not have the clear idea in regard to what to store and what to
not. Hence, such situation created the issues in the operational functions. Employees
don't have the proper knowledge how to backup sensitive data.
 Data storage issues- one of the major challenge that facing in the today's era is regard
to the storage of big data. As the mentioned data is ever growing and increasing time
by time. Hence, it is considered as a one of major challenge for organisation to stores
these data. It increases the operational cost due to the high prices of cloud storage and
adaption of technologies (Watson, 2019).
 Confusion in selection of tool- As the big data changes rapidly and not comes in a
specified form. Hence, due to such scenarios companies got confused in order to select
the effective tool according to the data requirement. In the selection of these tools
companies got confused and took the inappropriate decisions which ultimately impact
the further process of analysation. Therefore, such process forms the data ineffective
and unreliable.
TECHNIQUES THAT ARE CURRENTLY AVAILABLE TO ANALYSIS
There are number of techniques available in the market which used in analysis of big
data such as A/B testing, machine learning, data mining, Data fusion and data integration,
statistics etc. hence below are the brief discussion of some of them from the mentioned list.
 Data fusion and integration- By the use of mentioned techniques integrated data can
be analysed from multiple sources. It is the combination of techniques that helps in
extracting the accurate and potential data according to the organisation demand.
 Machine learning- Machine learning is the inclusion of artificial learning which used
the set of codes and computer science in order to extract the reliable big data. The
mentioned techniques run on the computer algorithm which developed according to
the data. Hence such tool helps in order to process the large set of big data in a more
effective manner.
 Data mining- Data mining extracts the pattern from the large amount of data by the
inclusion of various machine learning and statistical tool. By the use of such data
pattern organisations extract the favourable information’s which used in enhance the
business operations.

HOW BIG DATA TECHNOLOGY COULD SUPPORT BUSINESS,
PLEASE USE EXAMPLES WHEREVER NECESSARY
Businesses are using the big data technology in their operational functions in order to
enhance their businesses as well as taking the competitive advantage by using the reliable
sources and information’s according to their requirement.
 Competitive advantages- In order to perform the effective functions in the operational
market companies can use the big data technology. Many businesses developing the
effective strategies with the use data. It assists them in order to develop the effective
product and services that can cater the market. For example- Apple uses the big data
technology in their operational functions in order to developed the more innovative
product in compare to their competitors. By the use of information’s, the respected
company get the support in order to developed the more interstice product. In regard to
this an organisation can use such adaptive technology as it assists in understand the
market trends as well as provides the sources to creates the effective business
strategies in order to get the competitive advantage (Sun and Huo, 2021).
 Understanding the customer demand- As the big data collects the information’s
from various sources such as social media platform, online sites, apps and many more.
People perform the number of functions on internet today. Before buying any product
they search for the specific product as well as their thought and behaviour on digital
platforms provides the overview of their requirements. As through analysation of
surveys and data companies can easily understand their customer need and demand
which they want. Hence, by creating the effective product according to their
requirement businesses can form the effective product through which they can
establish their strong brand image in the target market. I regard to this Aldi which is a
retail chain in UK uses the big data technologies in order to understand the purchase
history of their customers. Hence by analysing these data the mentioned company
created the effective business plan that facilitate them in order to sell more products on
their festive seasons sales. Therefore, businesses can use these mentioned technologies
in their operational functions in order to know their customer more efficiently and
deliver them exactly according to their demands (Wong, Zhou and Zhang, 2019).

REFERENCES
Books and Journals
Galetsi, P., Katsaliaki, K. and Kumar, S., 2020. Big data analytics in health sector: Theoretical
framework, techniques and prospects. International Journal of Information
Management, 50, pp.206-216.
Osman, A. M. S., 2019. A novel big data analytics framework for smart cities. Future
Generation Computer Systems, 91, pp.620-633.
Silva, B. N., Diyan, M. and Han, K., 2019. Big data analytics. In Deep Learning: Convergence
to Big Data Analytics (pp. 13-30). Springer, Singapore.
Sun, Z. and Huo, Y., 2021. The spectrum of big data analytics. Journal of Computer
Information Systems, 61(2), pp.154-162.
Watson, H. J., 2019. Update tutorial: Big Data analytics: Concepts, technology, and
applications. Communications of the Association for Information Systems, 44(1),
p.21.
Wong, Z. S., Zhou, J. and Zhang, Q., 2019. Artificial intelligence for infectious disease big
data analytics. Infection, disease & health, 24(1), pp.44-48.