Big Data Analysis: Characteristics, Challenges, Techniques and Business Support

This article discusses the characteristics of big data, including volume, velocity, and variety, and the challenges of big data analytics. It also covers the techniques currently available to analyze big data, such as data mining, machine learning, A/B testing, and statistics. Additionally, the article explains how big data technology could support businesses, with examples of how it has been used to boost customer retention and acquisition. The article is relevant to students studying information systems and big data analysis.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
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Table of Contents
Introduction..................................................................................................................................................................................................4
What big data is and the characteristics of big data.....................................................................................................................................4
Characteristics of Big data...........................................................................................................................................................................5
The challenges of big data analytics............................................................................................................................................................6
The techniques that are currently available to analyse big data.................................................................................................................7
How Big Data technology could support business, an explanation with examples....................................................................................7
Poster...........................................................................................................................................................................................................9
References..................................................................................................................................................................................................10

Introduction
First trace of the big data has been seen back in year 1663 when John Graunt was dealing with an overwhelming information. He also
studied bubonic plague, European was haunted that time. John Graunt was known as the first ever person who had used statistical data
analysis. Later in year 1800 this field of statistics has expanded into analyzing and collecting data. Overwhelming data was considered
as a problem in 1880 (Chang, 2018). In that span of time US census bureau has announced that it estimates Ly it would be taking eight
years in processing and handling the data which was collected in census program. In 1881 Hollerith tabulating machine has been
invented by Herman Hollerith for reducing the work, he was one of the men in bureau. Then later in 20th century data had evolved at
unbeliever speed and then big data become core of evolution. At that time computers, machines for managing and storing information,
message scanner has also been invented. In 1965 first data center was built by the US government with the capacity of storing the
millions of tax returns and fingerprint sets. In current years magnetic storage and cloud data storage has become popular. Magnetic
storage is known as the least expensive method for managing and storing the data, it was invented in 1927 and adapted various of
variety in formats, hard disk drives, magnetic drums, magnetic tape and floppies. Cloud data storage is one of the popular and firstly
appeared in year 1983, technical improvements has been combines with data storage cost. Cloud data storage saves the organization
from buying and maintaining their computer system.
What big data is and the characteristics of big data
Big data is known as diverse and large information sets that grows at exponential data. It is known as largest storing data and not
competitive traditional data management tools can drive it efficiently. Volume of data do matters in the organization but the way
organization utilizes that data affects in organizational growth (Pan, 2019). Big data can be used in analyzing for insights which leads
to better strategic moves of business and for better decision. Social media platforms are also considered as the big contributors for
surmounting the data. There are three main types of big data which is structured data in which data in processed, accessed and stored
in form of fixed format. In this data comes in similar format by which business gets maximum by performing analysis. In structed data

various advanced technologies had been invented for extracting the data-based decisions from it. Unstructured data is also one of the
types of big data in which data comes unstructured or unknown form. Processing data from unstructured data sometimes becomes
challenging for business in analyzing and somehow it makes things worse ( Jin, Xing, and Wang, 2020). For example, any
heterogeneous data that contains an combination of videos, images and text files is known as unstructured data. It simply took more
times to access and process as compared to structured data. Semi structured data is also known as the type of big data which contains
both unstructured and structured data present in it. It is found in the table format which is in the form of relational DBMS. Web
application is one of the examples of semi structured data. It contains data which is unstructured in the form of transaction history files
and log files. These are the three types of big data and is been used in businesses as per their organizational strategies for proper
analyzation and management of data. Big data and its types is a set of proper information in proper place for better strategic planning
and decision making.
Characteristics of Big data
Industry analyst in the market has derived three v’s which indicated the characteristics of big data. The characteristic of big data is
described below.
Volume: Volume which is known as the data flow and it is exponentially high in organization. Data contributes from various sources
which are videos, social medias, lot devices, industrial equipment and business transactions (Zhao, and Guo, 2018). These data can
not be stored in any physical space, in early times storage was the major issues in the organization. New technologies have eased the
burden of the organization which is Hadoop and data lakes.
Velocity: Velocity is another characteristic of big data which can be defines as with the flow of exponential data speed matters
equally. The various data sets have been in an tough spot for handling the data in timely manner and do not wait for doing it after the
data volume ups as it become difficult after that in processing, accessing and managing the data. There are some of the torrents in
which data have to deal in real time that are smart meters, sensors and RFID tags.

Variety: Variety is also considered as the characteristics of big data which means that there is no assurance of the data is collected
will fall under same category (Sivaparthipan, Karthikeyan, and Karthik, 2020). In this characteristic the data in the format of images,
videos, audios, financial transaction, emails, numeric data and text documents. These characteristics are important to consider for
better assessment of big data and for analyzing their facts.
The challenges of big data analytics
There are various challenges that business face in big data analytics as in organization they have huge volume of data and of each
segment which sometimes become challenging for the organization to manage it in an organized and systematic way, it is important in
an organization for better managing, utilizing, storing and analyzing the data (Lv, and Singh, 2021). There are various data analytical
tools which helps in handling and analyzing the data. There are various challenges that a company faces while handling the data is
described below.
Shortage of professionals: In the market there is acute shortage of data professional who has the good understanding of big data
analysis. The analyzation of data is important which is produced in each minute and similarly it is useful. With the rise in exponential
data in the market the demand of data analyst and scientist has been increased in the market for the better assessment of data.
Data quality and storage: In today’s world business organization has expanding rapidly and with the high expansion of business and
organization in the market huge data has been produced from that and requires proper handling and storage for the same. The storage
is becoming challenging in today’s market, popular known data storages like warehouses and data lakes are used for storing and
gathering the data of unstructured and structured data due to large data it encounters errors and duplication data which also challenges
the quality of data.

The techniques that are currently available to analyse big data
Data mining: This is the foremost common and effective technique used in data analyzation as it extracts the patterns for huge data
set by combining it from machine learning and statistics with database management.
Machine learning: In the field of the artificial intelligence, this machine learning technique is used for analyzing the data (Liang, and
Liu, 2018). From emerging it from computer science this technique works with computer algorithms to asses and produce a valid
assumption based on the huge data analyzation. Machine learning provide prediction to the organization that is not possible for the
human analysist to predict and analysis in this form.
A/B testing: It is another technique which is used by the organization to collect and manage the data it usually involves the comparing
of control group with the specific variety of test groups. This technique describes that what change or treatment is required to improve
an given objective variable. It improves the coercion rates on commerce sites in form of analyzing layout, text and image presented in
the data collected (Lin and Yang, 2019). These techniques can only be used when the data group is of big size and has a meaningful
difference.
Statistics: This technique works in collecting, organizing and for interpretating the data with experiments and surveys. This technique
helps in providing the data in manageable form and for deducting the errors and place more clarity in the data which is not been mixed
or mismatched. This is used to manage the data effectively, efficiently and accurately for the better analyzation and decision making.
How Big Data technology could support business, an explanation with examples
Big data technology can be considered as a big advantage in supporting the businesses in the market. As every organization produces
information and needs a proper handling and assessing of that data which helps in easy decision making and for better strategic
planning according to the output of the data analyzation (Khalid, and Zebaree, 2021). Majorly big data technology can be support
business in the form of boosting customer retention and acquisition which is expressed as customer is considered as an asset for an
organization, no business can succeed without and solid customer base. The used of big data technology provides the organization to

observe variety of customer trends and patterns. The more data will be collected more trends can be identified. With this business has
the capability of deriving critical customer behavior insights and by which organization van response quickly.
For example: A company which uses big data technology for customer retention and acquisition is Coca-Cola as they managed to
build its data strategy by deriving digital led program of loyalty. They have assessed that big data technology is the biggest reason
behind the customer base and their retention (Tao, Yang, and Feng, 2020). It not only increased the audience of Coca-Cola but also
expanded their customer base which leads in expanding market base and productivity of the organization and now it achieving high
profit and growth all over the world. Big data technology for this company is considered as the strategy.

Poster

References
Jin, L., Xing, M. and Wang, R., 2020, April. Operation Framework of the Command Information System Based on Big Data Analysis.
In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 459-462). IEEE.
Lin, H.Y. and Yang, S.Y., 2019. A cloud-based energy data mining information agent system based on big data analysis
technology. Microelectronics Reliability, 97, pp.66-78.
Tao, D., Yang, P. and Feng, H., 2020. Utilization of text mining as a big data analysis tool for food science and
nutrition. Comprehensive reviews in food science and food safety, 19(2), pp.875-894.
Khalid, Z.M. and Zebaree, S.R., 2021. Big data analysis for data visualization: A review. International Journal of Science and
Business, 5(2), pp.64-75.
Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert
Systems with Applications, 111, pp.2-10.
Lv, Z. and Singh, A.K., 2021. Big data analysis of Internet of Things system. ACM Transactions on Internet Technology, 21(2), pp.1-
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Zhao, J.C. and Guo, J.X., 2018, April. Big data analysis technology application in agricultural intelligence decision system. In 2018
IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 209-212). IEEE.
Pan, L., 2019. A Big Data-Based Data Mining Tool for Physical Education and Technical and Tactical Analysis. International Journal
of Emerging Technologies in Learning, 14(22).
Chang, C.C., 2018. Hakka genealogical migration analysis enhancement using big data on library services. Library Hi Tech.