Big Data Analysis: Meaning, Characteristics, Challenges, Techniques and Business Support

This report discusses the meaning and characteristics of big data, challenges and techniques for analysis, and how big data technology supports businesses. The report includes a case study of Tesco and covers topics such as volume, velocity, variety, veracity, and value. It also discusses techniques such as regression analysis, Monte Carlo simulation, data mining, data fusion and integration, A/B testing, and cluster analysis. The report concludes with the benefits of implementing big data analysis tools, such as cost minimization, increased sales, revenue, effective marketing, risk management, and product development. Subject: Big Data. Course Code: NA. College/University: NA.

BIG DATA

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Meaning and characteristic of big data........................................................................................3
Challenges and techniques that are currently available to analysis big data...............................4
Big Data technology supports the business.................................................................................5
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................1

INTRODUCTION
Big data is the way through which all database is to be analysed to extract the useful
information for the benefit of organization success. This report is prepared on the case study of
Tesco. This report will cover meaning and characteristic of big data (Wu and et.al., 2018).
Moreover, it will also analyse challenges and techniques of big data analytics. In addition to this,
it will also evaluate how big technology supports the business with an example.
MAIN BODY
Meaning and characteristic of big data
Big data analysis is the complex process to evaluate the useful information such as hidden
pattern, market trend, customer preferences etc. which help the organization to take informed or
accurate decision. Tesco use the big data analytics to reduce the cost, duplication of work and
analyse needs of the consumer. Big data can also improve the operational efficiency of the
company. A raw data is to be processed to get valuable information. In addition to this, in Tesco,
it deals with the complex problem to resolve with data processing software.
Characteristics
Volume: Volume is stated that big data analysis tool deal with high range or bulk data. It is
required to process in the data to convert into simpler form for taking useful decision. In Tesco,
there is large number of customer data are to be recorded with data analysis tool.
Velocity: Velocity can be defined as the speed of data processing such as how fast the system is
processing all the data (Wu and et.al., 2018). Velocity is more important than volume because it
will give the competitive advantage to the company. Tesco is applying this tool for analysing the
market trend of consumer taste.
Variety: Big data can be unstructured, structured and semi structured which are to be collected
from different sources. Moreover, it has collected from the data base and sheet where past
information has been recorded. Likewise, Tesco records all the data in structured form so the
useful information can be generated.
Veracity: Veracity is defined the reliability of data by applying many filters to translate the data.
It is the process of managing and handling data efficiently. It is essential for business
development. Tesco use many filters while taking useful decision.

Value: Value is important characteristic of big data. It is not just an amount which need to store
or analyse but this data represented as valuable, trustworthy, reliable data which required to
store, processes and analyse.
Challenges and techniques that are currently available to analysis big data
Challenges
Lack of knowledge professionals: Tesco need appoint professionals to analyse or implement
data tool into the business. Moreover, these professionals include data analyst, scientist and
engineer who has an experience to work with the tool and make informational data-set. In
addition to this, Tesco is facing the problem due to the lack of data professionals.
Lack of proper understanding: Due to the lack of understanding while analysing the massive
data, Tesco has to face the challenges of accuracy, reliability and interpretation. It is required for
the company to provide efficient training and development programme to their employees to
understand the data analytical tool for the growth of the company.
Data growth issue: it is one of the most important issue which is being faced by the Tesco. Due
to huge range of data stored on the centre, company is unable to get quality of information
(Delgado and et.al., 2019). Moreover, as data is growing with the time so it becomes difficult or
challenging to handle the large amount of data.
Confusion while selecting the best data tool: Management of Tesco is getting confused while
selecting the data tool as per the requirement. Moreover, company required to take decision like
hasoop is ok or need to replace it with spark. Therefore, company has to face challenges to hire
the professional who has better knowledge about analyses tools.
Integrating data from a spread of sources: Tesco receive data from social media pages,
consumer logs, ERP application, financial reports and presentation etc. and on the basis of that
employees need to prepare the report. Combining all the data and preparing reliable report is a
challenging task.
Security data: Securing needs to be provide to the large set of knowledge, is one of the doubting
challenges for massive data (Izquierdo and et.al., 2021). This is not sensible move to protect the
data. For that company need to hire cybersecurity professional who can safely guard the data.
Data analyse techniques

Regression analysis: it is used to analyse the relationship between two variables. Before
analysing the regression, it is required to analyse, is there any correlation exist between two
depended or independent variable. The main aim of regression is to analyse or predict how one
or more variables might impacted the dependent variable. For example: How much Tesco is
spending on the social media marketing and sales. Here sales is the dependent variable which get
affected due to the marketing strategies.
Monte carlo simulation: While taking any decision there is huge range of difference in
outcomes. It is customized technique which is used to generate the models of possible outcomes.
It is used to analyse the advance the risk, allowing the better forecast for the future (Lin and
et.al., 2017). Under this techniques, company pre determine their data of sales, revenues by
applying this model by creating random situation. Tesco is applying this technique by applying
itself in different circumstances to know the batter position.
Data mining: It is the common tool which is being used to analyse the data. It is an extract
pattern in which there are different tools such as statistics and machine learning to analyse the
data base management. Tesco use the graphs and charts of available data so that useful
information can be produce.
Data fusion and integration: By combining a set of techniques which analyse and integrated
form the multiple sources. The insights are the more efficient and more accurate if developed the
single set of data.
A/B testing: It analyse the techniques which involve a comparing the control groups with the
help of variety of test groups. It is more concern about what treatment and changes improve the
performance of the company. It is applied by Tesco to improve their productivity.
Cluster analysis: It is an exploratory technique which is looking forward to identify the structure
of data base. The main goal of this technique is to analyse the different sort of data points into
the homogenous internal and external group (Lv and Singh, 2021). Moreover, data point in the
cluster are divided into similarity and dissimilarity to each other’s. It becomes easily to analyse
the data for making decisions.
Big Data technology supports the business
Big data technology supports the business in terms of analysing revenue, fashion and
market trend, budget preparation, product line analyses etc. with the help of big data analysis tool

company can take reliable and accurate decision for the future growth. In the finance industry,
big data tool has been used to fraud detection, credit ranking, risk assessment and block chain
technology etc. Moreover, in health care sector, hospital and pharmaceutical companies use the
big data analysis tool to improve and advance the health care sector. In addition to this, it can
access large amount of patient data, performing the efficient research on disease such as cancer,
Alzheimer's for developing new drug (Cheung, Leung and Seto, 2019). Moreover, in the media
sector, it helps in analysing data through reading and listening, viewing and building individual’s
experience. Further, in FMCG sector, Tesco can take competitive advantage by applying the data
analysing tool. Moreover, Tesco is also using these tools to maintain their positive image in the
market and also update their product with the help taste and preference of the customer.
There are some benefits which can be provided by implementing this tool into the business, are
as follows:
Cost minimization: Big data tool helps to prepare standard budget this will automatically reduce
the cost of the company. Moreover, business can properly analyse which operation needs high
cost, how much raw material is required to produce the goods etc. decision can be taken.
Increase the sales: Business can increase the sales by properly analysing the taste and preference
of the customer (Mahmud and et.al., 2020). In addition to this, with the help of past data,
company can develop customize product on the basis of demand which will helps to take
competitive advantage.
Increases revenue: By implementing big data tool company can analyse or forecast their sales,
revenue and cost which will automatically help in the growth. Moreover, by applying different
techniques of big data, company can take an advantage of different situation occurred in the
market.
Effective Marketing: With the help of big data analysis tool, company can identify the taste and
preference of the consumer, their feedback related to product and also ensures that marketing
campaign is powerful. Tesco get millions of data from the social sites and also analyse the
feedback which will help to determine which product line is performing well.
Risk management: Before risk occur, company can address it with the help of data miming tool.
organization can predict or evaluate the risk beforehand and adopt appropriate practices which
can help to reduce it (Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017). Moreover, company
has set of data base to analyse (past record) or take appropriate decision to mitigate the risk.

Product development: with the help of big data management tool, company can analyse which
product has performed well in the past year. Moreover, on the basis of this, company can take
necessary step to shut down (which is not performing upto the expected level) or continue. In
addition, it also gives opportunity to the company to focus on the area which is demanding high
efforts to grow more.
CONCLUSION
Big data analysis tool is necessary to evaluate the business performance. A business can
take competitive advantage by implementing different techniques of data analysis. Moreover, it
speeds up the process of decision-making, helps in preparing a budget, cost minimization etc. In
today area, many companies are implementing big data analyse tool to analyse or interpret
demand of the consumer in order to increase the revenue.

REFERENCES
Books and journals
Cheung, K.S., Leung, W.K. and Seto, W.K., 2019. Application of Big Data analysis in
gastrointestinal research. World journal of gastroenterology. 25(24). p.2990.
Delgado, J.A. and et.al., 2019. Big data analysis for sustainable agriculture on a geospatial cloud
framework. Frontiers in Sustainable Food Systems. 3. p.54.
Izquierdo, J.L. and et.al., 2021. Clinical management of COPD in a real-world setting. A big data
analysis. Archivos de Bronconeumología (English Edition). 57(2). pp.94-100.
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X., 2017. A review on the practice of big
data analysis in agriculture. Computers and Electronics in Agriculture. 143. pp.23-37.
Lin, W. and et.al., 2017. An ensemble random forest algorithm for insurance big data
analysis. Ieee access. 5. pp.16568-16575.
Lv, Z. and Singh, A.K., 2021. Big data analysis of Internet of Things system. ACM Transactions
on Internet Technology. 21(2). pp.1-15.
Mahmud, M.S. and et.al., 2020. A survey of data partitioning and sampling methods to support
big data analysis. Big Data Mining and Analytics. 3(2). pp.85-101.
Wu, J. and et.al., 2018. Big data analysis-based secure cluster management for optimized control
plane in software-defined networks. IEEE Transactions on Network and Service
Management. 15(1). pp.27-38.
Online references
Big Data Analysis Techniques, 2021. [Online]. Available through <
https://www.getsmarter.com/blog/career-advice/big-data-analysis-techniques/>
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