BMP4005: Information Systems and Big Data Analysis for Business

This report provides an overview of big data analysis within the context of information systems, covering its historical roots, defining characteristics (including the 5 V's: Volume, Value, Variety, Velocity, and Veracity, plus Variability), and the challenges associated with its implementation. It details various techniques available for analyzing big data, such as association rule learning, classification tree analysis, genetic algorithms, machine learning, regression analysis, sentiment analysis, and social network analysis. Furthermore, the report explores how big data technology supports businesses by enabling strategic decision-making, risk management, and productivity gains, citing examples like Unilever, Tesco, HSBC, and AstraZeneca. The document emphasizes the importance of managing and converting massive amounts of data into relevant information for business applications.

History on big Data
History on big data states that it was first seen in 1663 by John
Graunt. He was dealing with the large amount of information
while studying bubonic plague. He was the first person who
has used statistical data analysis. The term big data was
introduced by John Mashey in 1987 which was initially used to
quantify the huge amount of information. Big data era began in
2010 with advancement in technology and now there are
several cloud providers for example Amazon and Microsoft
along with Google who have the capability to manage the big
data with the help of big Data analytics and now it is emerging
in a very advanced manner to support the businesses and their
functioning (Shakhovska, Boyko, Zasoba and Benova, 2019).
Information Systems and Big Data Analysis
What is Big Data?
Big data is defined as the large volume of information which is used to
analyse and systematically extract the information from a large number
of data sets which are complicated in processing. Traditional data
processing software is not enough to manage and process the big data to
convert it into the information. Therefore several software is introduced
in order to manage the big data. Examples of big data are the stock
exchanges and social media sites along with the jet engines and many
more. Big data replicates every second in a big organisation as well as in
the small organisation if the user's the big data technology in their
functioning (Peters, Kliestik, Musa and Durana, 2020).
Characteristics of Big
data
There are 5 V's of big data considered as a characteristic of it.
Volume is the first v in which the size and the amount of big data is
managed and analysed. Value is the second v in which the
discovery and pattern recognition of the big data is identified.
Variety is the third v in which types of big data which includes
unstructured and raw data is recognised. Velocity is the fourth v in
which the speed in which the big data generates is calculated.
Veracity is the fifth v in which the accuracy and authentication of
data is measured. Variability is the sixth v which is the extended
form of the characteristics of big data that States the changing
nature of the information that captures and manages the
information accordingly (Kumar Sangaiah, Chaudhary, Tsai and
Mercaldo, 2020).
The challenges of big data
analytics
Challenges of Big Data analytics are such that there is a lack of
knowledge professionals and technical experience who can
guide and direct about the management of big data in the
suitable software. This is because until the perfect knowledge it
is difficult to analyse the big data. Lack of proper
understanding of measured data generates the data growth
issues and all the confusion while big data tool selection. It is
also difficult to integrate data from a spread of sources and also
the major issue is the securing of data from breaches and losses
(Kunanets, Vasiuta and Boiko, 2019).
How Big Data technology
could support business &
Examples
Big data Technology supports businesses in handling the
massive amounts of data and converting into the relevant
information for further use in order to have strategic decision
making. It helps in the risk management within the
organisation and helps the company in gaining the large
productivity with a large number of sales and revenue
generations. Some of the companies that use Big Data
analytics are Unilever and Tesco along with HSBC and
AstraZeneca and many more (Khanra, Dhir and Mäntymäki,
2020).
Techniques that are currently
available to analysis big data
There are several techniques that are available to analyse the big data.
Such as the business uses the implementation of big data in order to
create the value of the organisation. Techniques are the association rule
learning and classification tree analysis along with the genetic algorithms
and machine learning. Regression analysis and sentiment analysis along
with social Network analysis and mining are also the techniques.
Furthermore A/B testing and data fusion and data integration along with
the natural language processing and statistics also comes under the
techniques of big Data analytics (Leung, 2019).