Big Data Analytics: Techniques, Challenges and Business Support

This report discusses the concept and characteristics of big data, challenges faced in big data analytics, techniques available to analyze big data, and how big data technology could support businesses. The report also includes a poster and summary paper on the topic. Course code and college/university not mentioned.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
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Contents
Contents
Introduction...............................................................................................................................2
What big data is and the characteristics of big data..................................................................2
The challenges of big data analytics..........................................................................................3
The techniques that are currently available to analyse big data...............................................3
How Big Data technology could support business, an explanation with examples..................4
Conclusion..................................................................................................................................5
References..................................................................................................................................6
Appendix 1: Poster.....................................................................................................................7
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Introduction
Big data describes as the data which is in large quantity as well as it becomes
more difficult in order to process through traditional approach (Galetsi, Katsaliaki and
Kumar, 2019). It incorporates store and access of relevant data in respect of information
for obtaining analytics. In past times, there was complexity of big data process without
the use of technology. The following report describes about the big data concept and its
characteristics. It further describes challenges of big data analytics along with the
techniques that are presently available for analysing big data. It also describes how big
data technology could support business along with an explanation and examples.
What big data is and the characteristics of big
data
The big data describes as the indicators which help in indicating the wide amount
of data which has been used by the data analyst for reaching towards the conclusion and
solution. It is also refers to as complex process as larger data like interpretation and
processing for obtaining data proper analytics. Many kinds of sources are available
which helps in collecting data which includes feedback forms, surveys along with the
other digital platforms (Papadopoulos and Balta, 2022). This will used by the companies
in order to collect data that help in understanding about the customer preferences and
their buying behaviour to make their product and service effective for fulfilling the their
demand as well as make them satisfy. The characteristics of big data analysis are
described below:
Volume- In big data involves large quantity of data. This makes essential to
manage all data in effective as well as arrange in structured way in order to make
process quite easier. The procedure of data must be organised in order to make process
of data easier.
Variety- There are many kinds of data like structured or semi-structured. The
structured data is placed in organised and structured manner like graphical or tabular
form which makes easier for the user to understand the data in proper manner. Whereas
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unstructured data is not arranged in structured or in prescribed format. The data can be
of heterogeneous or homogenous type.
Velocity- It described the speed of collecting data form any kind of sources. The
speed of data collection process is known as velocity. It helps company in order to fulfil
the needs and wants of customers in effective manner (Queiroz and Telles, 2018).
Veracity- It described as truthfulness of data as well as shows the validity or
accuracy of data which has been collected. The unstructured data makes difficulties for
the user in order to sort data in proper manner. The validity of data shows its accuracy.
Value- It refers to as one of the major factor of big data which can be gained from
efficient and effective operations as well as building strong relationship with their
customers.
The challenges of big data analytics
There are many challenges of big data analytics which is faced by the user while
using it. Some of the challenges are described below:
Integrating data from different sources- Big data involves combination from
different data which has been taken from different sources as well as becomes more
difficult in order to integrate big data. There are many sources which help in collecting
data which includes email, social media handles as well as reports and presentations. It
is important for the user to analyse the data and then interpret.
Lack of skilled professional- For big data analytics, a skilled professional is
required which helps in understanding the trends as well as process the data in better
manner for obtaining the accurate results from it. It is essential to provide proper training
sessions to the new professionals which help in understanding the data in well manner.
With the help of proper training session, ensure company to reduce the its hiring cost for
new individuals.
Growth issues- This makes difficult to process big data because it was growing in
continuous manner. This makes important for the user to have latest technological
software for storing and processing the big data in every size,
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The techniques that are currently available to
analyse big data
There are several techniques available which helps in analysing the data in better
manner. Some of these are described below:
A/B testing- This techniques can be described as bucket testing. The main
purpose of using this technique is to analyse the effectiveness of presented options. With
the help of this technique, user can effectively choose the best one from two different
options. Various steps is incorporated in this technique like gathering the data, choosing
the best alternatives that are available (Galetsi and Katsaliaki, 2020).
Language processing- This kinds of technique mainly used in artificial
intelligence as well as computer science. This technique used different algorithm for
solving particular problem. It also used flow chart for representing algorithm in graphical
manner. It render the data which can be easily be understand by the persons.
Statistics- This technique incorporates collection of data, organised in structured
way as well as analyse it in order to provide accurate and effective analysis. As per the
primary and secondary data, the decision is made by the user. Primary data is refers to
as first hand data which has been collected by the person itself whereas secondary data
is refers to as existing data which has been taken from other sources such as internet,
brochures as well as other magazines.
Data fusion and integration- Data fusion refers to a process which helps in
breaking down the data in various parts. On the other hand, data integration refers to as
process which helps in organising the data in a single database.
Data mining- It refers to a process which helps in finding the correlations as well
as patterns of large quantity of data. Data mining basically refers to process of analysing
and extracting data in well manner. Predictive as well as descriptive are refers to as
certain kind of data mining.
How Big Data technology could support business,
an explanation with examples
The main aim of big data analyse is to help company in order to understand the
different trends of business as well as buying behaviour of the customers towards the
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product of the company (Sahoo, 2021). The ways which makes big data analytics in order
to support business are described below:
Helps in taking decisions- it helps managers in order to analyse the data in
proper manner in order to make effective decision which helps in achieving the
organisational goals and objective. This creates difficulty for the manager in order to
make decision as per the big data. Effective decision is important for every organisation
as it helps in achieving the objectives in time.
Improving existing products- Big data helps company in taking feedbacks from
their potential customers. As the feedback was taken from customers should be negative
or positive which allows company to take proper measure for improving their product and
service through innovation. The company need to ensure that its new offerings have the
potential in order to fulfil the needs and wants of customer in better manner.
Safety of data- The software which has been used in for analysing the data should
be safe. It helps in keeping the data of company safe as well as secure in order to retrieve
the needs within time (Zhao and Hu, 2019).
Align the customer- This makes essential for every organisation to focus on
wants and needs of customer in order to work according to it. The company should be
customer centric which means the every activity of the company should be align with the
preferences of customers. Big data refers to as tools that allow company in order to align
their task in better manner.
Conclusion
From the above mentioned report, it has been concluded that big data is crucial
for defining company’s success in better manner. With the help of this manager can take
better decision by understanding, collecting and analysing the data in order to obtain
accurate data from it. Structured and unstructured are two kinds of data which helps in
making business strategies in effective manner in order to helps in achieving the
organisational objectives. Several software has been developed which provide proper
protection from any kind of cyber-attacks. The professional is required to use of big data
analysis which makes company in order to proper training to them in order used it in
effective manner.
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References
Books and Journals:
Galetsi, P., Katsaliaki, K. and Kumar, S., 2019. Values, challenges and future directions
of big data analytics in healthcare: A systematic review. Social science &
medicine, 241, p.112533.
Papadopoulos, T. and Balta, M.E., 2022. Climate Change and big data analytics:
Challenges and opportunities. International Journal of Information
Management, 63, p.102448.
Queiroz, M.M. and Telles, R., 2018. Big data analytics in supply chain and logistics: an
empirical approach. The International Journal of Logistics Management.
Galetsi, P. and Katsaliaki, K., 2020. A review of the literature on big data analytics in
healthcare. Journal of the Operational Research Society, 71(10), pp.1511-1529.
Zhao, P. and Hu, H., 2019. Geographical patterns of traffic congestion in growing
megacities: Big data analytics from Beijing. Cities, 92, pp.164-174.
Sahoo, S., 2021. Big data analytics in manufacturing: a bibliometric analysis of research
in the field of business management. International Journal of Production
Research, pp.1-29.
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Appendix 1: Poster
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