Big Data Analytics: Techniques, Challenges, and Business Support

This report from Desklib explores the world of big data analytics, covering its characteristics, challenges, and available techniques. It also explains how big data can support businesses, with examples of how it can be used to create new revenue streams, redevelop products, and perform risk analysis. The report concludes that big data is an essential tool for any business looking to understand consumer preferences and make better decisions.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
Submitted by:
Name:
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Contents
Introduction 3
What big data is and the characteristics of big data 3-4
The challenges of big data analytics 4-5
The techniques that are currently available to analyze big data 5
How Big Data technology could support business, an explanation with
examples 6
Conclusion 7
References 8
Appendix 1: Poster 9
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Introduction
Big data is a word which describes huge volumes of data,which come in business
on daily basis. It is used for providing information to take effective decisions and plan
business strategy. It is large complex and impossible to process it using old softwares.
This project will mention about big data ,what are its features. It will also tell about the
challenges faced in big data analytics and what are the techniques used for analysis. Big
data support to business will also be explained.
What big data is and the characteristics of big data
Big data refers to the collection of the data from various sources like traditional
and digital internal and external sources of organization which represents continuous
discovery and analysis(Bag and et. al.,2021). People constrain big data by inputs like
web behavior and social network interactions. Big data consists of large volumes of
data which is growing at fast speed. There are different types of big data like
unstructured data which can not be organized or interpreted by traditional software or
data models. Twitter,Facebook are some examples of this type of big data. Multi
structured data includes various data formats which can be gathered by interactions
between people and machines such as web applications and social networks. The big
data is high volume,high velocity and high variety information which is cost
effective,innovative in processing of information which enhances insight,decision
making and automation of the process(Dey and et, al., 2019). It helps companies to put
data on work for finding new opportunities and build models of business.
Characteristics of big data areas follows-
 Volume- The huge volumes of the information helps to define systems of
big data. They can be larger than old traditional data sets ,which
demands more thinking at each step of storing and processing life cycle.
Management of clusters and algorithms are capable of breaking activities
in to small pieces becomes very important(Galetsi, Katsaliaki and Kumar,
2019).
 Velocity- It is unique as range of both sources are processed and their
quality. Data can from internal systems such application and server logs
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from feed of social media and APIs also from physical devices. Big data
process data from various sources and consolidates it in single system.
 Veracity- Various sources and the complex nature of processing which
leads to challenges for evaluation of the quality data to make it useful.
 Variability- Difference in data leads to difference in quality. Other
resources are required to identify,process and filter low quality data to
make it useful.
 Value- This is the biggest challenge of big data which is delivering value.
Systems and processes are complex and it is difficult to derive value.
The challenges of big data analytics
The challenges of big data includes processing large amount of data which
involves storage,analysis of information. These are various challenges that come across
the big data analytics-
 Lack of knowledge professionals- For running these new end
technology ,highly skilled and qualified professionals are required. They are
data scientists,data analysts and data engineers for working with tools and
derive value from data sets. As data tools are changing rapidly but not the
skills of professionals. Organizations are investing money to provide training
to professionals and hiring skilled workers(Wamba and et, al.,2020).
 Lack of proper understanding of massive data- Business fail in their
projects of big data due to lack of understanding by their employees as they
do not know what is data,storage,processing,its importance and sources.
Professional know this but other do not. If the workers do not understand the
importance of knowledge,they can not use the data bases.
 Data growth issues- Storing all these data which is very huge is the main
challenge. The quantity stored by companies is increasing day by day. It is
becoming challenge to handle these data. Data is unstructured and mainly
comes from documents ,texts and various other sources. Business choose
modern tools for this problem.
 Confusion while big data tool selection- Business gets confused while
getting tools for data analysis and storage. They ate stuck in choices and do
not find answers. So sometimes they take poor decisions and wrong strategy
which wastes time and efforts.
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 Integrating data from a spread of sources- Companies get data from
different sources like ERP applications,customer logs,financial reports,mails
etc. Compiling data is a big task for companies. It is important for
analysis,reporting and business intelligence.
 Securing of data- Data sets must be secured but companies work on storage
and analysis of data. Companies can get cost high for this.
The techniques that are currently available to analyze big
data
Data analytics refers to the process of analyzing data into insights. They are used
to take better decision. It is important for making business strategy. Here are some of
the techniques which are currently available to analyze the big data-
 Regression analysis- It is used to draw the relationship of variables set. It
sees correlation among dependent variable and independent variables. Its
aim is to measure how does one variable affect another variable and
identify patterns. It is very useful in doing predictions and for forecasting
future trends.
 Monte Carlo simulation- It is computerized method used to generate models
of various possible outcomes and their probability. It includes range of
outcomes and calculates how it is determined. Data analysts use this type
of method for advance risk analysis and forecasting and take decisions.
This model uses mathematical data such as spreadsheet.
 Factor analysis- It is the technique for reducing large numbers of variables
into small factors. It works by multiple variables relation with each other. It is
useful for converting large sets of data into small manageable samples. It
provides to study concepts which cannot be easily measured(Queiroz and
Telles, 2018).
 Cohort analysis- It is a set of behavioral analytics which takes data from data
set and looks all users as one unit. They share common features. Cohort is
a group consists of people who have common features in given time. In this
analysis consumers are divided into groups and their behavior insights is
noticed.
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How Big Data technology could support business, an
explanation with examples
Each business requires valuable data and insights. For understanding target
consumer preferences big data plays important role. It forecasts your needs. Right data
must be effectively presented and properly analyzed. It helps business organizations to
achieve their goals and objectives. These are some of the points through which it can
support business-
 Create new revenue streams- It provides insights by analyzing the markets
and consumers. The data is very valuable for business. It will play a
important role in different industries across all sectors. It can do wonders for
the company. Proper management of data will make the business more
productive and more efficient(Papadopoulos and Balta, 2022).
 Re develop products- It is the best way for collection and use
feedback,which helps in understanding the consumers behavior about
goods and services. Company can make necessary changes and re
develop the goods. Big data allows to test various designs and gather info
about lead times ,performance etc.
 Dialogue with customers- Consumers are smart and know their priorities.
Big data allows companies to profile consumers in efficient manner. This
gives business to engage with consumers in real time. It also plays in
integrating online and offline spheres. For ex- consumer in bank,they can
check their preferences and desires(Ghasemaghaei, 2020).
 Perform risk analysis- Success of business depends on many factors. There
are many factors affecting it like economic and social factors. Big data
provides predictive analytics. It scans reports and measures trends and
developments in the sector.
 Data safety- It allows you to map the landscape in the company. It analyzes
all threats internally. Companies focus on data safety due to financial
information and other practices.
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Conclusion
This report concludes that big data has changed the way of analyzing data. Many
new trends have emerged which requires infrastructure for data sets analysis as it is
cost effective and time efficient. It tells about the characteristics of data and challenges
with it. Techniques of big data analytics were also mentioned and how it can support the
growth of business was also studied. Data is important for any business and helps to
determine consumer preferences.
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References
Bag, S.,and et. al.,2021. Role of institutional pressures and resources in the adoption of big
data analytics powered artificial intelligence, sustainable manufacturing practices
and circular economy capabilities. Technological Forecasting and Social
Change, 163, p.120420.
Dey, N., an et, al., 2019. Big data analytics for intelligent healthcare management. Academic
Press.
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.
Ghasemaghaei, M., 2020. The role of positive and negative valence factors on the impact of
bigness of data on big data analytics usage. International Journal of Information
Management, 50, pp.395- 404.
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.
Wamba, S.F.,and et, al.,2020. The performance effects of big data analytics and supply chain
ambidexterity: The moderating effect of environmental dynamism. International
Journal of Production Economics, 222, p.107498.
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Appendix 1: Poster
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