BSc BMP4005 Information Systems and Big Data Analysis Report

This report provides an overview of big data, its characteristics (volume, velocity, variety), and the challenges associated with its analysis. It discusses techniques currently available for analyzing big data, such as A/B testing, data fusion, data integration, data mining, machine learning, NLP, and statistical methods. The report also explores how big data technology can support businesses by analyzing customer behavior, understanding financial market dynamics, improving sales and supply chains, estimating selling patterns, making data receptive, and interpreting both quantitative and qualitative data. Examples of companies like Tesco, M&S, and Walmart are provided to illustrate these applications.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
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TABLE OF CONTENTS
INTRODUCTION..........................................................................................................................3
WHAT BIG DATA IS AND THE CHARACTERISTICS OF BIG DATA.................................................3
THE CHALLENGES OF BIG DATA ANALYTICS...............................................................................4
THE TECHNIQUES THAT ARE CURRENTLY AVAILABLE TO ANALYSE BIG DATA .........................5
REFERENCES ..............................................................................................................................7
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INTRODUCTION
Big data refers to numerous amount of data which is unmanageable by traditional techniques
of data managements. This poster will be discussing the big data and its characteristics and
how the modern techniques of big data analysis can leverage the data analytical performance.
It will further discuss the usefulness of such data analytical techniques with sharing example
in context to the using companies.
WHAT BIG DATA IS AND THE CHARACTERISTICS OF BIG
DATA
Big data and Its Characteristics- The term big data applies to the information which can't
be processed or analysed using traditional process or tools. Further it can also be defined as
the data that contains greater variety, arriving in increasing volumes and with more velocity.
Unlike traditional business today the overload of data is a genuine phenomenon in the
business and it surges the problem of taking out something meaningful out of that. It is a
burning challenge for the modern business management to manage such huge amount of data
and then making it useful in business decision-making (Ghani, 2019.)
Characteristics- the term big data is self-explanatory in itself which cites the sense of
intensive constellation of data which is hard nut to analyse and infer. Having these
characteristics which are also known as V3 which are as follows- Volume- refers the size of data. The amount in which it is available. There may a great
size of data which can be called row or unstructured, unprocessed. Such mammoth
size of data may consist the feedbacks of customers, reviews, or suggestion. It might
be tens of terabytes of row data. Which is not having a well-articulated form or
structure to be defined and inferred. Some entities generate terabytes of data every
hour. Velocity- cites how quickly the data is being received and being stored. Some
businesses may have such nature of their existence which can lead their data
generating sources to produce the data on rapid and regular interval. The volume
character of data depends on this character that how quickly or rapidly the data is
being retrieved. If the velocity of data is higher than it is a clear indication of being on
the way of having big data.
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 Variety- defines the types or forms of data that are available. With changing
technological dimensions the forms and types of data are also hiked. Earlier in
traditional form only written or documents based data was out there but nowadays it
may be in text form, audio, video, MP3, MP4, in picture form etc. Other varieties are
row data, semi-processed data, fully processed as well (Mehta and Pandit, 2018.)
THE CHALLENGES OF BIG DATA ANALYTICS
it has always been a tough work to analyse the big data due to vast availability of data which
makes it complex. To make it simple modern technologies introduced analytical tools, which
are containing these challenges-
 Lack of knowledge professionals- for running these technologies and tools the entities
are having strong need to deploy their professionals who can operate it and can
eradicate all potential troubles. Such giant data sets kick off problems like skipping
important elements or may be leaving analytical gaps behind.
 Lack of proper understanding of massive data- Sometimes insufficient understanding
of massive data may also lead to fuzzy conclusions. In early stage of collection where
there is a sizeable involvement of employees they might not know what data is. its
storage, processing importance, sources etc. Data professionals may know what's
happening, but others might not have proper idea of the data.
 Data growth issues- With time the data is growing rapidly. Storing and managing such
enormous amount of information is not a handy thing. The numerous growth of data
is creating obstacles in analytics of it. With this surge there is a huge hiking in
unstructured data. Which is making the analytical task more complicated.
 Tool selection problem- In the market there are ample number of tools for data
analytics. It is also raising the issue of selection of tool for data analytics. Which is
not only confusing the professionals of this field but also the derived results and
conclusions are also depicting blur picture of final outcomes.
 Integration of data from disseminated sources- As earlier discussed there are so many
sources of data are existing. Bringing those all data together is also a tough task. It
may create discrepancy in analytical processing. It may also create the perils like
presentational or reporting problem.
 Data security- Securing such numerous amount of data is one of the daunting
challenges. Generally, the data protection issues are seen in the companies.
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Unprotected or doubted data is not reliable in data analytics due to its character of
misleading conclusion (Galetsi, Katsaliaki and Kumar, 2020.)
THE TECHNIQUES THAT ARE CURRENTLY AVAILABLE
TO ANALYSE BIG DATA
Techniques to analyse big data- Over the time with growing data there is a huge emergence
in the available techniques as well. A few of the most usable techniques are as follows-
 A/B testing- this technique involves doing comparison of a control group with a
variety of test group. This technique use comparison model for analysing big data.
This technique formulate hypothesis and then create control group and test group then
conduct test and compare results then it reaches to final conclusion that the null
hypotheses should be rejected or selected.
 Data fusion and data integration- this is the process of integrating information from
multiple sources to produce specific, comprehensive, unified data about an entity.
Whereas data integration cites the process of merging data from several disparate sources. It
will be following reprocessing method that includes merging data from set of heterogeneous
data sources.
 Data mining- data mining is the process of generating new set of information by
applying technique on available data. In It we generate new data by using techniques
like- extrapolation. We derive new data with new patterns and in new from the
existing material.
 Machine learning- This tool of data analytics study the algorithms of computer that
can improve the use of data. It builds a model based on sample data which ultimately
helps in making predictions or decisions without being explicitly programmed to do
so.
 NLP- Stands for Natural Language Processing. In general human being uses
homonyms, homophones, sarcasm, idioms, metaphors, grammatical errors too. So this
is the programme which make such content meaningful with bringing proper sense to
it.
 Statistics- It consists wide range of tools and techniques to analyse the big set of data.
For instance- spatial analysis, predictive modelling, network analysis, regression,
correlations etc. It is more dynamic in nature since it provides a great cluster of
techniques as per the specification of the objective of collection of data (Fathi, 2021.)
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HOW BIG DATA TECHNOLOGY COULD SUPPORT BUSINESS,
AN EXPLANATION WITH EXAMPLES
Big data technologies can be defined as tools or software which help in analysing, processing,
and extracting data from a vast and complex set of big data. Manually doing this work is not
possible which traditionally carried out the process.
It supports modern businesses by many ways like-
 To analyse the behaviour of the customers- Where, why and when their customers are
buying or consuming their products. The technique AI assists in this regard. Currently
lots of tech giants are using this tool to analyse the personal behaviour of their
customers.
 It helps in understanding the financial dynamics of financial market- Since it is fast
changing in nature and needs strong surveillance. The Securities Exchange
Commission is using NLP to catch illegal trading activity in the financial markets.
 Improving sales and supply chain- Tesco ltd also uses the techniques like “Broccoli
Cam” to improve their supply chain management. It helps in managing sales of the
outlets and bringing more ease to the customers.
 In estimating selling it also helps- How the people are purchasing in a store in a
particular time period. It helps in providing goods timely to the stores. For instance-
Tesco ltd is using “clustering methods” to know the way the items are being bought.
 Making huge amount of data receptive- Like M&S is using “Azure Synapse
Analytics” to manage an extensive amount of data. This performs the acts integration,
data mining, data processing etc. which makes the analysis easier.
 Analysing structured, unstructured and semi-processed data- Since the volume of big
data may consist a wide range of data with different characteristics. For instance-
Walmart which is being operated in so many countries with its subsidiaries and retail
stores so having data of different nature is so common for them so for analysing such
dispersed data Walmart uses “Hadoop Cluster” which performs this task for it.
 Quantitative and qualitative data interpretation- Using the big data analytical tool like
statistics the quantitative and qualitative both types of data can be processed and
interpreted (Delanoy and Kasztelnik, 2020.)
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REFERENCES
Ghani, 2019. Social media big data analytics: A survey. Computers in Human Behavior. 101.
pp.417-428.
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A
systematic review. International journal of medical informatics. 114. pp.57-65.
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.
Fathi, M., 2021. Big data analytics in weather forecasting: A systematic review. Archives of
Computational Methods in Engineering, pp.1-29.
Delanoy, N. and Kasztelnik, K., 2020. Business open big data analytics to support innovative
leadership and management decision in Canada. Business Ethics and Leadership.
4(2). pp.56-74.
Mohamed, 2020. The state of the art and taxonomy of big data analytics: view from new big
data framework. Artificial Intelligence Review. 53(2). pp.989-1037.
Mikalef, P. and Krogstie, J., 2020. Examining the interplay between big data analytics and
contextual factors in driving process innovation capabilities. European Journal of
Information Systems. 29(3). pp.260-287.
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