Big Data Analysis Report: Business Support, Techniques, and Challenges

This report delves into the realm of big data, tracing its historical roots back to the 1960s and 1970s, and highlighting the evolution of data storage and analysis techniques. It defines big data as datasets characterized by high volume, velocity, and variety, and explores its five key characteristics: volume, value, variety, veracity, and velocity. The report outlines the challenges associated with big data analytics, including lack of understandability, poor data quality, issues in scaling, and real-time problems. It then examines various analytical techniques such as ensemble analysis, association analysis, and deep analysis. Furthermore, the report illustrates how big data technologies support businesses through customer interaction analysis, risk analysis, data safety measures, and revenue stream generation. The report provides a comprehensive overview of big data, its applications, and its significance in modern business operations.

INDIVIDUAL POSTER AND
SUMMARY PAPER
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TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
History of big data.......................................................................................................................3
What is big data...........................................................................................................................3
Characteristics of big data analysis..............................................................................................4
The challenges of big data analytics;...........................................................................................4
The techniques that are currently available to analysis big data -...............................................5
How Big Data technology could support business, and examples..............................................6
REFERENCES................................................................................................................................8
APPENDIX......................................................................................................................................9
Poster in Power point...................................................................................................................9
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INTRODUCTION
MAIN BODY
History of big data.
The big data concept sounds like a new concept, but actual origin of the large sets of data
got started since the 1960s and 1970s, when world of the data was just taking place with its very
first centres of data and relational database development. In 2005, it came into notice that there
were many data users through the platforms like youtube, facebook and few other online
platforms. One framework naming Hadoop, was an open source which was created to analyse
and store the sets of big data. Another framework, NoSQL was also starting to get popular during
this period. Development of frameworks like Hadoop and Spark was very important for the
growth, as they make the storing of the big data much cheaper and easier. Since then, the big
data volume has skyrocketed (Gutmann, Merchant and Roberts, 2018). (IOT) internet of things
has also connected more devices and objectives to the net by collecting data on the usage pattern
of customers and performance of the products. Also, the machine learning emergence have
started to produce more data. The big data is existing form a long time but the use of it has just
began. The possibilities of big data have been expanded by the cloud computing even more. It
offers and elastic scalability which help the developers to test the subsets of data by simply
spinning up hoc clusters. Also, with the quality of displaying huge amount of data which makes
the analytics comprehensive and fast, the graph databases have become very important recently.
What is big data
The big data is generally referred to the data that contains wide range of variety, arrive in
increased volumes and with greater velocity. This wide variety of database allows access to it in
real time manner (Favaretto and et.al., 2020). These are the large and tangled data sets from the
new sources of data and are very voluminous in nature, making it very difficult for the traditional
data software to manage them. With this massive voluminous nature of the data, problems of the
business can easily be addressed and even in much better way. It also helps to make the decisions
of the business in much precise way.
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Characteristics of big data analysis
Big data contains large and good amount of data which is not used by the traditional storage of
data and is used in the big multinational companies in order to process the business data of
number of organizations. There are 5 characteristics of big data, which are as follows-
Volume- The size of big data is enormous because it contains huge amount of data. The name of
big data itself defines that it contains a huge volume of data which is mainly generated from the
sources such as machines, networks, social media, business process, human interactions etc.
Value- The big data with no value is not good for any company. Big Data holds a good value
and is very reliable in nature so that it can be stored processed and analysed. It is a data which
helps the business to extract money from it. This value also comes from the pattern recognition
that enables more effective operations.
Variety- This refers to the nature of the data, as it can be unstructured which includes all the
files such log files, image files, audio files etc., the semi-structured which is stored in tables or
the structured data which is stored in management system of relational database. It is collected
from various sources such as Emails, PDFs, photos, audio, videos etc.
Veracity- This is referred to the reliability of the data. It is a process that helps to manage and
handle the data efficiently and is a must for the business development.
Velocity- Velocity is defined as the high speed accumulation of data. This is the massive and
continuous flow of the data that is generated to meet demands. It is engaged with linking the rate
of change, activity bursts and the data set speeds.
The challenges of big data analytics;
Despite big data being voluminous and used by the number of big companies, it has some sides
where the big data is not able to serve the companies after a certain extent, as the data generated
these days is getting more and more in number.
Lack of understand ability- Lack of knowledge which is required to use and store the data is
the very first challenge which is faced by the companies using big data. Multinational companies
and many other big companies are always looking for the people who can use the big data
efficiently (Suoniemi and et.al., 2020). But as the technology is evolving, the employees and
workers are not able to use the data efficiently and therefore, the work processes are slowed and
there are number of disruptions which keeps taking place in the business.
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Poor data silos and quality- This is a big challenge that stands ahead of the companies which
have large datasets. As the big data also involves unstructured data which means duplicate files,
records, different formats etc. making it very difficult for the businesses to store such
information. So, it is said that as the software grows the count of the errors will also grow.
Issues in scaling- Big data is always growing, it is never less but is always increasing rapidly
(Hariri, Fredericks and Bowers, 2019). The enhanced use of data all around the world is making
the companies and industries to incur a huge cost on the cloud based hardware for storing the
data.
Real time problems- There are numerous changes that takes place in the environment of the
economy which also influences the businesses to adopt these changes. Therefore, in order to
cope up with the trends and latest developments, no business which has a big data storage can
rely on the historical data as, by the recent times the data becomes obsolete and is of no use for
the businesses in the present times.
The techniques that are currently available to analysis big data -
There are 6 types of techniques namely,
Ensemble analysis- This is the analysis which is done for the cause of improving accuracy of
applications of data mining and predictive analysis by simply including related but various
analytical models that synthesize results into one score card.
Association analysis- It refers to the determination of the interesting relationships in great
datasets. These relationships are of two forms namely, association rules or frequent sets of
items.
High dimensional analysis- It means the count of dimensions are extremely high in this analysis
which makes the calculations difficult (Reddy And et.al., 2020). With this high dimensions count
of observations can be easily exceeded by the counts of features.
Deep analysis- It refers to the complex queries and precisely targeted queries on the datasets
which can be measured in either in exabytes and petabytes with the requirements for near-real-
time or real time responses.
Precision analysis- This analysis is used in refining the present definition of the precision
metadata for the selective columns which are based on the real data values.
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Divide-and-conquer analysis- This kind of analysis involves breaking down of the problem into
pieces or sub- problems until the problems become easier to solve. These solutions are then
eventually combined and presented.
How Big Data technology could support business, and examples
The data collection in the present world is increasing day by day and is also turning out to be a
huge problem for the companies, as the data is generated from almost every online platform and
is going out of the hands of the traditional storage. So it is becoming important for the businesses
to adopt big data as it support the businesses in following ways-
Customer interactions- Big data helps the businesses to understand the customers taste and
preferences by looking at their profiles. It provides the companies to profile their customers from
a far reach and gives a benefit to the businesses to come in contact with customers in a real time
(Grover and et.al., 2018). For example, when a customer enters a salon, the assistant can open
the profile of the customer and find out the services and product for which the customer is
looking for advice the relevant services accordingly.
Risk analysis- The environment is all uncertain and there are various risks which keep on
emerging with time. Big data helps the businesses to analyse the latest developments and trends
with its predictive analysis.
Data safety- This tool helps the companies to keep a track on the of data which flow
continuously in the business and helps the business to determine all the threats. In short, big data
helps the businesses to keep their highly sensitive information’s safe.
Generates new streams for revenues- The big data enables an organization to analyse the
insights from the market (Akhtar and et.al., 2019). This data can be used by various businesses
by simply training the employees to use the big data and with the information’s collected from
this data, the businesses can do wonders.
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REFERENCES
Books and journals
Akhtar, P. and et.al., 2019. Big data‐savvy teams’ skills, big data‐driven actions and business
performance. British Journal of Management. 30(2). pp.252-271.
Favaretto, M. and et.al., 2020. What is your definition of Big Data? Researchers’ understanding
of the phenomenon of the decade. PloS one. 15(2). p.e0228987.
Grover, V. and et.al., 2018. Creating strategic business value from big data analytics: A research
framework. Journal of management information systems. 35(2) pp.388-423.
Gutmann, M.P., Merchant, E.K. and Roberts, E., 2018. “Big data” in economic history. The
Journal of Economic History. 78(1). pp.268-299.
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data analytics: survey,
opportunities, and challenges. Journal of Big Data. 6(1). pp.1-16.
Reddy, G.T. And et.al., 2020. Analysis of dimensionality reduction techniques on big data. IEEE
Access. 8. pp.54776-54788.
Suoniemi, S. and et.al., 2020. Big data and firm performance: The roles of market-directed
capabilities and business strategy. Information & Management. 57(7). p.103365.
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APPENDIX
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