Big Data Analytics: Characteristics, Challenges, Techniques and Business Support

This report discusses the characteristics of Big Data, challenges faced in Big Data analytics, techniques available to analyze Big Data, and how Big Data technology could support businesses with examples. The report also includes a poster and references. The subject is Business Management, course code BMP4005, Information Systems and Big Data Analysis.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
Contents
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Introduction p
What big data is and the characteristics of big data p
The challenges of big data analytics p
The techniques that are currently available to analyse big data
p
How Big Data technology could support business, an explanation
with examples p
Poster p
References p
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Introduction
Big Data is the field that analyses and extracts the information which helps in
dealing with the data at large scale (Favaretto and et.al., 2020).
The report will analyze characteristics of big data and what it signifies. Challenges
of big data analytics will also be identified and described appropriately. Along with this,
techniques available to analyze big data will also be known and explained at large scale.
And how big data supports business with explanation of examples will be provided. This
will help in evaluating all essential information which will be identified and then will lay
importance to big data and its analytics.
What big data is and the characteristics of big data
Big data is the field that idealizes the ways to systematically evaluate and extract
information and dealing with sets of data which are too large or complex in dealing with
data processing software traditional in nature. Big data helps in analyzing and evaluating
the information at large scale. Big data is the data which is of huge size.
Characteristics of big data defined are –
Volume – Size of the data plays very important role and big data is itself very big in size.
Volume of big data helps in determining the value out of data. Volume is the main
characteristic which help in dealing with the data while having solutions at large scale
and this helps in knowing major scale and basis through which the big data can be
analyzed effectively and significantly (Sun and et.al., 2018).
Variety – Variety refers to sources which are heterogeneous in nature along with the
nature of data defined as unstructured and structured. Data nowadays is send through
emails, PDF’s, monitoring devices, videos, audio etc. The unstructured data incurs some
certain issues for mining, analyzing data and storage which is being evaluated at large
scale and this helps in knowing variety through which type of data is being analyzed.
Unstructured data possess issues which are known effectively.
Velocity – Velocity refers to generation of data through speed. It means that data is runs
fast and is being generated by fulfills demands, determines potential of data for its
evaluation. Velocity deals with data flows from sources like social media sites, mobile
devices, application logs, etc (Ghasemaghaei, 2019). The flow of data is continuous and
massive and is evaluated at large scale. This helps in analyzing basis of data through
which all aspects are being considered.
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Variability – There can be inconsistencies shown by data many times and this hampers
the process of data in handling and managing the data effectively at large scale. This
helps in knowing that the basis through which data can be evaluated can be driven by
many problems and issues which are shown through variability at large scale. For
managing the data effectively, the variability of data should be analyzed (Elragal and
et.al., 2017).
The challenges of big data analytics
There are various challenges which are being faced by big data analytics and this
helps in knowing that how the data is affecting the information at large scale.
Challenges of big data analytics are -
Lack of Knowledgeable Professionals – To run large data tools and companies are
needed professionals which are skilled and they include data analysts, data engineers,
data scientists which help in managing data at large scale. But it seems that there are no
skilled and knowledgeable professionals which become major challenge for managing
the big data (Dai and et.al., 2020).
Lack of Proper Understanding of Massive Data – This is the major challenge for the
companies. There are employees within companies which might not know type of data
which is being used and this creates major problem while understanding about massive
data. This creates problem in which there is insufficient understanding for data.
Data Growth Issues – There are growth issues in data which creates problem for
analysis and knowing the form and information which the data has been gathered at
large scale. The data growth issues are concerned with databases which are being
affected at times. Most of the information regarding the data is unstructured (Al-Abassi
and et.al., 2020).
Confusion in Selecting Big Data Tool – Companies fall into confusion while they select
the big data tool and this creates problem and becomes major challenge for them at
large scale. Due to this, poor decisions are taken and inappropriate technology selection
is done which creates problem at large scale and creates confusion adversely.
Integration of Data from Sources Spread – Data in an organization includes various
sources like customers logs, social media pages, e-mails, financial reports, ERP
applications, etc. These become a challenging aspect in which the management of these
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tasks is crated and the data within these sources is affected at large scale
inappropriately.
Poor Security of Data – One of the major challenges of data are in securing the data
which becomes high as understanding, storing and analyzing the data becomes difficult
which makes big problem for the companies to evaluate and identify the major basis
through which the data is being gathered inappropriately and poorly (Berman and et.al.,
2018).
The techniques that are currently available to analyze big data
There are various techniques which analyzes big data which are described as –
A/B Testing – Control group are compared with variety of test groups to know the
changes and treatments which will help in knowing the given objective variable. The
companies through this technique are able to achieve big data through the size and gain
meaningful differences at large scale effectively (Hwang and et.al., 2017).
Data Integration and Data Fusion – There are multiple solutions and insights which are
made available for analyzing and integrating the data so that the sources of data are
more ccurate and insight is provided by them. This helps in managing the scale through
which all aspects are concerned for integrating and managing the data.
Data Mining – This technique helps in analyzing data mining extract patterns from sets
which are large in size and are combined through the statistics, database management
and machine learning. For example - customer data is mined which helps in determining
the segment to react to an offer at large scale and in appropriate manner effectively
(Sedkaoui, 2018).
Machine Learning – Machine learning is used within the field of artificial intelligence
which helps data in knowing basis through which data analysis can be done effectively. It
works on computer algorithms to produce assumptions and provides the predictions that
are impossible for human analysts. This is how the machine learning is helpful in
analyzing big data.
Natural Language Learning (NLP) – It is the sub specialty of how the computer science
is being done and this helps in evaluating and knowing artificial intelligence, linguistic,
data analysis tools to analyze human language. This helps in making and framing the
scale trough which natural language learning is known as best technique to analyze big
data.
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Statistics – This technique collects, organizes and interprets data within experiments
and surveys which helps in analyzing and evaluating the big data and the basis of this is
considered as how effectively and in appropriate manner the aspects of statistics are
concerned (Wright and et.al., 2019). This is the best technique of how statistics helps in
analyzing the big data.
How Big Data technology could support business, an
explanation with examples
Big data technology is the best ways to collect the data and information which
helps in analyzing and evaluating the basis through which data can be managed and this
helps in knowing the aspects through which services within the business are identified
and analyzed as how the big data is being managed. Big Data is the combination of all
tools and processes which are related to utilizing and managing the large sets of data. It
helps in understanding the needs, preferences and patterns through which big data is
being analyzed and evaluated at large scale within the business. This also helps in
evaluating the aspects through which the needs and demands of the customers are
being identified at large scale (Singh, 2019). With big data, business organizations uses
data analytics to value the views and perspectives of customers The businesses helps in
creating new experience of products and services and this helps in creating value for the
business at large scale. The main aim of the business is that the businesses help in
valuing the aspects in which the big data is being analyzed and evaluated at large scale.
The big data technology helps in creating value for the business by re –
developing and developing of the products and services which are being served at large
scale. It also helps in analyzing the risk analysis within the business due to the changes
in the technology. The up gradation in the technology helps in describing how effectively
the business can change the patterns according to how the business is being conducted
effectively (Singh, 2019). The changes and modifications in the technology impacts the
business along with its products and services and while creating new revenue streams
for increasing the scale of business. For example – Sigma Data Systems is the
company which uses big data technology by having pre – determined workshop patterns
to understand the problems in the business and to analyze them effectively and
efficiently. Light IT Company provides innovative web and mobile software solutions and
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this helps in creating the value trough which all essentials of technology are attained and
utilized at large scale.
References
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Al-Abassi, A. and et.al., 2020. Industrial big data analytics: challenges and
opportunities. In Handbook of big data privacy (pp. 37-61). Springer, Cham.
Berman, E. and et.al., 2018. Small Wars, Big Data. Princeton University Press.
Dai, H.N. and et.al., 2020. Big data analytics for manufacturing internet of things:
opportunities, challenges and enabling technologies. Enterprise Information
Systems.14(9-10).pp.1279-1303.
Elragal, A. and et.al., 2017. Theory-driven or process-driven prediction?
Epistemological challenges of big data analytics. Journal of Big
Data.4(1).pp.1-20.
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.
Ghasemaghaei, M., 2019. Understanding the impact of big data on firm
performance: The necessity of conceptually differentiating among big data
characteristics. International Journal of Information Management.p.102055.
Hwang, K. and et.al., 2017. Big-data analytics for cloud, IoT and cognitive
computing. John Wiley & Sons.
Sedkaoui, S., 2018. Data analytics and big data. John Wiley & Sons.
Singh, N., 2019. Big data technology: developments in current research and
emerging landscape. Enterprise Information Systems.13(6).pp.801-831.
Sun, Z. and et.al., 2018, October. Big data with ten big characteristics.
In Proceedings of the 2nd International Conference on Big Data
Research (pp. 56-61).
Wright, L.T. and et.al., 2019. Adoption of Big Data technology for innovation in B2B
marketing. Journal of Business-to-Business Marketing.26(3-4).pp.281-293.
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