Big Data Analysis: Characteristics, Challenges, Techniques and Business Applications

This report describes big data and its characteristics that are used by the companies to enhance customer experience. It also describes the challenges that are faced while using big data analytics and various techniques that are available for using it effectively. In the end, it describes the use of big data in businesses and its importance for them.

Information System
and Big Data Analysis

Table of Contents
POSTER...........................................................................................................................................1
INTRODUCTION...........................................................................................................................2
BIG DATA.......................................................................................................................................2
CHARACTERSTICS OF BIG DATA............................................................................................2
CHALLENGES OF BIG DATA ANALYTICS.............................................................................3
TECHNIQUES AVAILABLE FOR BIG DATA ANALYTICS....................................................4
HOW BIG DATA TECHNOLOGIES COULD SUPPORT BUSINESSES...................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

1
POSTER
INTRODUCTION
The use of technology has intensified in the previous few years now it has advanced to
the use of digital technologies. The advancement of digital technology and IT has
increased the use of data driven tools and applications in the daily lives of the people to
enhance their experience (Qi, 2020). This report will describe big data and its
characteristics that are used by the companies to enhance customer experience. In this
context it will also describe the challenges that are faced while using big data analytics
and various techniques that are available for using it effectively. In the end it will
describe the use of big data in the businesses and its importance for them.
BIG DATA
Big Data can be explained as an integration of unstructured, semi-structured and
systematic data that is obtained by the company to achieve information and using them
for enhancing the development of advanced analytics apps, Market Trend Analysis and
machine learning programmes. In simple words, big data consists of effectively
handling sets of enormous and complex data in a very short time that cannot be
processed by using traditional tools, methods or frameworks.
CHARACTERSTICS OF BIG DATA
Big data is the collection, clarification, presentation and analysis of information from
various sources, and it is often considered by 5 Major characteristics that it has.
 Volume
 Variety
 Veracity
 Value
 Velocity
 Validity
CHALLENGES OF BIG DATA ANALYTICS
The major challenges of big data analytics involve the problems related to
intelligence and appropriate use of big data.
Business analytics solutions lack timely updated information
 Lack of information
 Long data responses
 Old approaches applied to new systems
Inaccurate analysis
 Bad quality of sourced information
 Improper flow of data
Complications of using data analytics
 Time consuming process
 Big data is highly advanced
Increased Response time of big data
 Improper data organisation
 Improper infrastructure and resource utilisation
Increased costs for continuous upgradation
 Outdated technology
 Infrastructure improvement
HOW BIG DATA TECHNOLOGIES COULD SUPPORT BUSINESSES
According to a survey conducted by Bloomberg Businessweek Research
Services, it is found that nearly 97% of respondents reported their companies had
adopted big data analytics. With Big Data, business associations can utilize
examination, and sort out the most significant clients. Each business association, little
or enormous, needs important information and bits of knowledge.
 Identify Customer Trends
 Redevelop products
 Increments competitiveness
 Opportunities for growth
 Improve Products and services
 Enhances Data Safety
 Customer Insights
 More options for revenue
TECHNIQUES AVAILABLE FOR BIG DATA ANALYTICS
Following are various techniques for big data analytics that are elaborated below.
A/B testing and analysis procedure includes contrasting a benchmark group and an
assortment of information, to observe what treating or changes will be effective for a
given objective.
Data Fusion and Data integration: By combining a bunch of procedures that examine
and incorporate information from various sources.
Data mining separates designs from enormous information data by joining techniques
from insights and artificial intelligence, in managing databases.
AI and Machine learning is commonly utilized for examining enormous amounts of
data or information.
Natural Language Processing (NLP) is known as a sub-set of computer sciences,
linguistics and artificial intelligence, this tool for analysing data utilizes computerized
algorithms to investigate human (regular) language.
Data statistics collection, this procedure attempts to gather, classify, and anticipate
large sources of data, inside overviews and examinations.
CONCLUSION
It has been concluded from the above report that technology has been involved in
almost all the parts of our daily life. Big data is one of the most crucial technologies
that are used in the era of information technology. Moreover, it has described certain
challenges that are faced while using big data analytics along with the techniques
available for improving the big data analytics. Finally, it has been discussed that big
data analytics may help the businesses in today's world for enhancing competitiveness
and gaining various advantages.

INTRODUCTION
The use of technology has intensified in the previous few years now it has advanced to the
use of digital technologies. The advancement of digital technology and IT has increased the use
of data driven tools and applications in the daily lives of the people to enhance their experience
(Qi, 2020). This report will describe big data and its characteristics that are used by the
companies to enhance customer experience. In this context it will also describe the challenges
that are faced while using big data analytics and various techniques that are available for using it
effectively. In the end it will describe the use of big data in the businesses and its importance for
them.
BIG DATA
Big Data can be explained as an integration of unstructured, semi-structured and systematic
data that is obtained by the company to achieve information and using them for enhancing the
development of advanced analytics apps, Market Trend Analysis and machine learning
programmes. In simple words, big data consists of effectively handling sets of enormous and
complex data in a very short time that cannot be processed by using traditional tools, methods or
frameworks (Ghani and et. al., 2019). Some of the examples of the use of big data can be seen in
the use of social media networks, mobile phone applications, online platforms, clickstream logs
on the internet, electronic mails, customer databases, and payment processing systems etc.
CHARACTERSTICS OF BIG DATA
Big data is the collection, clarification, presentation and analysis of information from various
sources, and it is often considered by 5 Major characteristics that it has. These characteristics of
big data involve veracity, velocity, variety, value and volume that are further elaborated.
 Volume: The name big data represents the use of vast volume and enormous information.
It refers to the use of enormous amounts or vast volumes of information that are created
by many users using social media platforms, internet networks, mobile phone
applications and many other tools and technologies. An example of Instagram can be
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given that produces enormous amounts of data with almost 300 million posts uploaded
each day. Such amounts of data can be handled by use of big data technologies.
 Variety: The information in big data may be obtained from a variety of sources and it
may be structured, semi-structured, and unstructured. It may be obtained from the
websites, government data, social media, etc. and these sources are generally secondary
as well as primary sources. It may be in various forms like videos, mails, audits,
feedbacks, photos, data sheets etc. Examples may include the web server logs that are
generated and managed by servers that include the series of steps.
 Veracity: It can be explained as the authenticity/reliability of data (Baig, Shuib and
Yadegaridehkordi, 2019). Big data has many ways to filter or translate data that help it to
manage and segregate the data in optimum manner. Examples of it can be the post on
Facebook with a hashtag.
 Value: This can be considered as one of the important aspects of big data. It is not only
the information that is collected, classified, stored and managed by big data, but it
involves reliable and valuable information that is stored processed and analysed properly.
 Velocity: It is also an important aspect in comparison to other characteristics of big data
because it is the rate at which the data is processed in real time. It includes the
interconnectivity of data exchange, speed of data sets, etc. It deals with the page at which
the information is processed from various sources of data.
 Validity: Big data is also valid because the data obtained from various sources are
precisely undertaken for classification, organisation and sorting the information. Such as
search engines always provide the same results that we ask for and do not include the
invalid information. For example Amazon shows the most preferable things that are
continuously preferred by the person.
CHALLENGES OF BIG DATA ANALYTICS
The major challenges of big data analytics involve the problems related to intelligence and
appropriate use of big data (Saggi and Jain, 2018).
Business analytics solutions lack timely updated information
 Lack of information is the major problem due to which it is difficult for the organisation
to obtain newly generated data on time.
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 Long data responses in the real time can lead to delay in processing of the new
information of the customers.
 Old approaches applied to new systems lead to difficulty in obtaining new information
because of clashes with the previously feed information.
Inaccurate analysis
 Bad quality of sourced information may lead to defect or inaccurate results in the search.
 Improper flow of data caused by system defects lead to reduction in the data processing
systems and lead to inaccuracy of results.
Complications of using data analytics
 Time consuming process and hectic data processing requires specialised skills for
handling the big data.
 Big data is highly advanced and it requires an individual or an organisation to understand
its use and perseverance for achieving optimum results.
Increased Response time of big data
 Improper data organisation makes it difficult for the big data analytics to analyse
information quickly (Mariani and Wamba, 2020).
 Improper infrastructure and resource utilisation lead to the limited efficiency and
effectiveness of big data, due to which it provides information in lengthy responses.
Increased costs for continuous upgradation
 Outdated technology may lead to the inefficiency to handle big data effectively therefore
constant upgradation is required.
 Infrastructure improvement requires enormous costs on a consistent basis for supporting
the use of big data effectively and efficiently.
 Re engineering is required in the tools and technology so that the infrastructure can be
optimised for big data processing.
TECHNIQUES AVAILABLE FOR BIG DATA ANALYTICS
Following are various techniques for big data analytics that are elaborated below.
 A/B testing and analysis procedure includes contrasting a benchmark group and an
assortment of information, to observe what treating or changes will be effective for a
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given objective. McKinsey gives the case of examining what texts, pictures, or format
will further develop transformation rates on big data.
 Data Fusion and Data integration: By combining a bunch of procedures that examine
and incorporate information from various sources and arrangements, the data/information
are more productive and possibly more precise than if created through a single source of
information.
 Data mining separates designs from enormous information data by joining techniques
from insights and artificial intelligence, in managing databases (Ardito and et. al., 2018).
 AI and Machine learning is commonly utilized for examining enormous amounts of data
or information. Arising out of software engineering, it works with computerized
algorithms to create assumptions for given information.
 Natural Language Processing (NLP) is known as a sub-set of computer sciences,
linguistics and artificial intelligence, this tool for analysing data utilizes computerized
algorithms to investigate human (regular) language.
 Data statistics collection, this procedure attempts to gather, classify, and anticipate large
sources of data, inside overviews and examinations.
Different data analysis strategies incorporate spatial examination, prescient displaying,
affiliation rule learning, network investigation and many more (Choi and et. al., 2018). The
advancements in technology that analyses, organizes, anticipates and integrates large scale data
are of unique and expansive field that are constantly evolving.
HOW BIG DATA TECHNOLOGIES COULD SUPPORT BUSINESSES
According to a survey conducted by Bloomberg Businessweek Research Services, it is
found that nearly 97% of respondents reported their companies had adopted big data
analytics. With Big Data, business associations can utilize examination, and sort out the most
significant clients. Each business association, little or enormous, needs important information
and bits of knowledge.
 Identify Customer Trends: When it comes to understanding your ideal interest group and
client's inclinations, Big Data assumes a vital part. It even assists you with expecting their
necessities. The right information should be actually introduced and appropriately
investigated. It can assist a business association with accomplishing different objectives.
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 Increments competitiveness: Using Big Data has been vital for the overwhelming
majority driving organizations to beat the opposition (Ferraris and et. al., 2018). In
numerous businesses, new participants and laid out contenders use information driven
techniques to contend, catch and enhance.
 Opportunities for growth: Big Data can make a ton of new learning experiences. It might
in fact lead to another classification of organizations, for example, the ones that break
down and total industry information. The vast majority of these organizations will be
sitting in enormous data streams about administrations and items, providers and
purchasers, shopper goal and inclinations, and that's only the tip of the iceberg.
 Improve Products and services: Big Data is one of the most amazing ways of gathering
and use criticism. It assists you with understanding how clients see your administrations
and items. Accordingly, you're ready to roll out the fundamental improvements and yet
again foster your items.
 Enhances Data Safety: The use of big data helps the organisation to examine a wide range
of data security thefts and reduce their effects on the business. With this data, you can
guard touchy data. It's safeguarded in a fitting way and put away as per administrative
necessities.
CONCLUSION
It has been concluded from the above report that technology has been involved in almost all
the parts of our daily life. Big data is one of the most crucial technologies that are used in the era
of information technology. The report has been describing characteristics of big data such as
large volume, variety, velocity, etc. Moreover, it has described certain challenges that are faced
while using big data analytics along with the techniques available for improving the big data
analytics. Finally, it has been discussed that big data analytics may help the businesses in today's
world for enhancing competitiveness and gaining various advantages.
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REFERENCES
Books and Journals
Ardito, L. and et. al., 2018. A bibliometric analysis of research on Big Data analytics for
business and management. Management Decision.
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2019. Big data adoption: State of the art and
research challenges. Information Processing & Management, 56(6), p.102095.
Choi, T.M. and et. al., 2018. Big data analytics in operations management. Production and
Operations Management, 27(10), pp.1868-1883.
Ferraris, A. and et. al., 2018. Big data analytics capabilities and knowledge management: impact
on firm performance. Management Decision.
Ghani, N.A. and et. al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Mariani, M.M. and Wamba, S.F., 2020. Exploring how consumer goods companies innovate in
the digital age: The role of big data analytics companies. Journal of Business
Research, 121, pp.338-352.
Qi, C.C., 2020. Big data management in the mining industry. International Journal of Minerals,
Metallurgy and Materials, 27(2), pp.131-139.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big
insights for value-creation. Information Processing & Management, 54(5), pp.758-790.
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