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

This article provides an overview of Big Data, including its history, characteristics, challenges, techniques and business support. It explains the five characteristics of Big Data: value, volume, velocity, variety and veracity. It also discusses the challenges of Big Data analytics, such as data management, synchronization of data sources, lack of understanding and collection of meaningful data. Finally, it explores regression analysis, factor analysis and time series analysis as techniques for analysing Big Data.

Big Data

Contents
Contents...........................................................................................................................................2
INTRODUCTION...........................................................................................................................1
MAIN BODY..................................................................................................................................1
History of big data.......................................................................................................................1
What is big data...........................................................................................................................1
Characteristics of big data...........................................................................................................2
The challenges of big data analytics:...........................................................................................2
Techniques available currently to analysis big data....................................................................3
Support of big data technology to business.................................................................................4
CONCLUSION................................................................................................................................4
REFERENCES................................................................................................................................6

INTRODUCTION
Big Data helps to ease complex and large set of information and provides safe ways to store
them. For organisation, it provides ways to utilize the information in their working to plan better
decisions and strategic business activities. The rising in Internet of Things has resulted in
increased real time action which leads to generate more information per second.
The paper summarises research finding about history of big data, characteristics of big
data, techniques which are existing available for analysing big data and ways in which it supports
businesses.
MAIN BODY
History of big data
The formulation of Big Data starts so many years before. The first time Big Data is seen
is in 1663 by John Graunt who was dealing with wide magnitude of information when he was
studying bubonic plague which was a major issue in Europe that time. The statistical data study
is used by him firstly and in early 1800 the statistics field enlarges into gathering and analysing
data. It became difficult for US to process and restore the information collected during the census
1880. The estimated amount of period to manage the data procured was almost eight years. The
urgent need to handle the data arises at that time and the start of data processing begins when
Harman Hollerith invented Hollerith Tabulating Machine in 1884. Later in 1928, Fritz Pfleumer
explored a way where a tape stored data (Choi and Lambert, 2017). This leads to creating
magnetic tape which laid basis for movie reels, visual cassetthes etc. In 1965, U.S builds data
center building which was first of its kind to store tax returns and biometrics identifications.
What is big data
A wide, overwhelming and an unmanageable magnitude of information is known as Big
Data. Big Data highlights the huge and different sets of data that rises at rapid rate. The stock
exchange markets, social media, jet engines, etc are some examples which generates Big Data on
large scale daily. The amount of information is only useful if it is use properly. The evaluation of
huge information gives advantage to comprehend patterns in the information that helps firms to
make better decisions for business. Big Data helps in minimising the time, costs and efforts a
business put in (Hawkins and Silva, 2018). It provides new ways to manage the tremendous and
complex amount of data that none of the conventional methods could store.
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Characteristics of big data
Companies that adopts Big Data approaches have competitive benefit over others since
they plan quick and enhanced business decisions. Big Data characteristics is a set of factors that
highlights the various big data examine approach. It consists of five Vs which are described
below:
Value- This is the essential characteristic of Big Data from business point of view. The
fast pace and ample amount of data is useless if it is not reliable and helpful. The value of Big
Data can be evaluate by exploring the insights and patterns that results in improved operations,
better customer interactions and other quantitative business advantages.
Volume- The quantity of data is very important as big data includes videos, audio,
images, the clicks on webpages, etc which leads to an uncontrollable amount of information.
Volume of Big Data is the large collection of information producing every second in big
companies. Big Data helps in processing all the high volume of unorganized data (Leung, 2019).
The volume of data is essential to understand its worth for the company.
Velocity- Velocity refers to the accelerated pace at which information is received and
managed and acted upon. The speed of information is also connected to how quick the data will
be processed. The smart gadgets which are internet enabled acts on real time and provides real
time action. The examining and processing of data is important to meet the requests of users.
Variety- The different kind of data available is known as variety. The range varies from
structured to numeric data in form of text documents, visuals, image, sounds, etc. Variety
includes the different sources from the data is collected and its nature. The variety of Big Data is
the scopes and diversity of information such as structured data, unstructured data and raw data.
The information about variety is important for its evaluation and storage.
Veracity- The standards and accuracy of information are referring as veracity. The
collected data sometimes is not proper and can have missing or inaccurate information which
may not be able to provide actual insights. Thus veracity is known to enhance the truthfulness of
data collected which increases business executives’ confidence.
The challenges of big data analytics
With all the benefits, Big Data comes up with some drawbacks too. Big Data provides
better potential and great opportunities to big companies, however it also creates some
challenges and obstacles for them due to the enormous size of information. The challenges faced
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by organisations consists of quality of data, its storage, absence of data professionals, invalid
data and the assembled information from diverse sources (Pauleen and Wang, 2017). The major
challenges faced by organisation are discussed below:
Data management- The data on various sources is rising rapidly in diverse formats. For
that reasons new firms and technologies are created every day to store data appropriately. A big
problem for large entities is to find which technique is best for collecting, processing and storing
data with minimum risks and dangers.
Synchronization of Data Sources- The sources of data is very diverse with very different
nature. The need to unify all the sources into an analytical platform is very important. If this need
is not addressed it can create lags or gaps in the information procured and can lead to false
insights and decisions.
Lack of understanding- Large companies may fail even when they are able to collect
enormous amount of information if they do not have professional’s analysts who have proper
understanding of how to manage and monitor data. Without appropriate guidance a company
may face the challenges of poor quality data, insufficient storage, the backup of important data,
etc.
Collection of meaningful data- With the various sources and abundant amount of data
available, it sometimes get difficult for big companies to sort which information is useful and
which is not. The collection of data which does not add to value of business results in wastage of
the firm's cost, efforts and time.
Techniques available currently to analysis big data
The data source range is very diverse which needs various kinds of techniques for
evaluation. The current techniques of Big Data used currently are explained below:
Regression Analysis- It is a kind of statistical analysis process which highlights the
interactions of independent and dependent elements. Regression refers as the method that assists
finance or investment managers to measure the value of assets and evaluates variable
relationship in products rates and stocks (Wang and Hajli, 2017). The motive of this analysis is
to calculate the impact of one variable on other dependent variable.
Factor Analysis- This technique is use to minimize the ample amount of variables to a
smaller number of elements. It operates according to various individual, observable variables
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which connect with each other as they are all correlated with some construct. It is beneficial in
compressing huge databases into smaller units and guides to discover hidden variables.
Time Series Analysis- This technique is used to find out the different trends as well as
cycles which takes place with time. This is a statistical technique which provides a series of data
points that estimates the value of similar variable at various point of time. Time series analysis is
useful to identify time affiliated trends so that the professional analysts will be able to predict
how a variable will react in case of fluctuations (Zhu, 2019).
Support of big data technology to business
Every business needs to collect and process data for its successful operations. The
adoption of Big Data has proved to be very crucial for leading businesses to reduce the risk of
competition. Many big firms like Tesco, Unilever, Bentley, etc use Big Data techniques to gain,
compete and procure market innovatively. Even the firms operating on small level but have
online presence, needs to maintain their data in modern times. Big Data helps companies to
understand the dynamics of market and rational thoughts of customers. Each business houses
from health store to car manufactures gets enhanced insights of customers’ demands and the
channels that are widely used by them (Zomaya and Sakr, eds., 2017). Big Data amends the
internal effectiveness of organisation and improves the efficiency of operations. The techniques
of Big Data even enable the tracking of real time action that takes places and changes every
second. It has the potential to increase the quality of operations by using sensors to trail
performances of machines and by optimising routes of delivery. The another support of Big Data
to companies is that it allows to increase customer' experience and satisfaction by evaluating the
data collected. Companies operating in food and fashion industry not only use Big Data to
benefit its customers but also helps to offer new products according to their tastes and
preferences. With better business decisions, Big Data also provides various revenue sources
which boosts profitability of organisation as they give insights of trends analysis which helps
companies to use latest trends of market.
CONCLUSION
From the research finding, it has been concluded that businesses major aim to analyse the
large amount of information is to achieve insights. Characteristics of big data are value, volume,
velocity, variety and veracity. Challenges of big data analytics are data management,
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synchronization of data sources, lack of understanding and collection of meaningful data.
Techniques available currently to analysis big data comprises of regression analysis, factor
analysis and time series analysis.
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REFERENCES
Books and Journals:
Choi, T. M. and Lambert, J. H., 2017. Advances in risk analysis with big data. Risk Analysis.
37(8). pp.1435-1442.
Hawkins, K.A. and Silva, B. C., 2018. Textual analysis: big data approaches. In The ideational
approach to populism (pp. 27-48). Routledge.
Leung, C. K. S., 2019. Big data analysis and mining. In Advanced methodologies and
technologies in network architecture, mobile computing, and data analytics (pp. 15-27).
IGI Global.
Pauleen, D. J. and Wang, W. Y., 2017. Does big data mean big knowledge? KM perspectives on
big data and analytics. Journal of Knowledge Management.
Wang, Y. and Hajli, N., 2017. Exploring the path to big data analytics success in
healthcare. Journal of Business Research. 70. pp.287-299.
Zhu, C., 2019. Big data as a governance mechanism. The Review of Financial Studies. 32(5).
pp.2021-2061.
Zomaya, A. Y. and Sakr, S. eds., 2017. Handbook of big data technologies.
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