Information Systems and Big Data Analysis - Challenges, Techniques and Business Support

This report discusses the concept of big data, its characteristics, challenges in big data analytics, techniques to analyze big data, and how big data technology could support businesses. The report also includes a poster and accompanying paper. The course code for this report is BMP4005.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
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Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 4
The techniques that are currently available to analyse big data
4
How Big Data technology could support business, an explanation
with examples 5
Poster 6
References 7
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Introduction
Big data is defined as the perception that helps in describing high volume data which is
unstructured and structured (Kunanets, Vasiuta and Boiko, 2019). The big data is fast,
complicated and large which is a very complex to be procedure utilizing traditional
approaches. In the first year of 2000s the perception of big data has achieved
widespread fame and after by the well-known big data analyst named Doug Laney
provided the novel description about big data was expressed which describes the Big
data while utilizing three V’s which are Velocity, Volume and Variety. For this conception
the new perception was elevated called as big data analytics that is defined as approach
which utilizes the advanced analytic tools and techniques against complex, large and
diverse data that composed of different sizes. The following report focus on concept
associated with big data along with its characteristics. Furthermore, the challenges
experienced in big data analytics that are presently accessible to evaluate big data.
Lastly, the submissions by which big data expertise could sustenance corporate has also
been debated in the report.
What big data is and the characteristics of big data
Big Data: The computer science has accomplished success in developing new software
and techniques that with data that could derive values for others. The big data is
considered as such technology which has data in high volume, but is growing in recent
years continuously (Aljumah, Nuseir and Alam, 2021). Basically, the big data is broad in
size, complicated and have diverse data that is not be collected by old-style data
management techniques. There are mainly three kinds of big data which is organised,
unorganised and semi structured. The data is collected, assessed and processed in
particular format than it is called as structured data.
Characteristics of Big data
The Big data possess four V’s which are defined as four features of big data which
are explained below:
Volume: The volume of facts is constantly concerned with big data. In the big data
there are featured of dispensation a big amount of data that is little in
concentration as well as is unorganised. This characteristic of big data makes it no
minimum than corporate intelligence but it becomes large in arrangement
(Orlovska, Wickman and Söderberg, 2018). Therefore, this simplifies that big data
is large in size and thus volume is needed to allocated with while occupied with big
data solutions.
Variety: This is considered as additional feature of big data by which
organizations could increase broad range of data concluded big data. Within big
data the data which is achieved is in overall arrangements that include organised,
semi-structured and unstructured in various diverse sizes. In big data there are
diversified diversity of structured data that involves videos, e-mails, audios and
text credentials. This feature of the big data within unstructured data kind have
some problems associated with collection and mining of data through big data
analytics.
Veracity: This feature involves obtainability and quality of data or evidence within
big data. The big data possess superiority of data due to appearance of the data is
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from several sources that makes the data complicated to relate, transform and
cleanse through various systems (Arolfo and Vaisman, 2018).
Velocity: The big data possess wide velocity at which data is developed and
controlled. In context to velocity with information technology the data is handled
with appropriate method and also data is gained along with acted in terms of
output at continuous rates. Usually these specific states that Big data is issued at
uppermost velocity straight into the recollection in contradiction of to inscribed to
disk. This delivers real time assessment and achievement.
The challenges of big data analytics
There are several challenged in big data analytics at the level of strategic because of
which issues occur. Some of the important key challenges associated with big data
analytics are provided below:
 Inaccurate analytics: There are time periods when data evaluated through
big data analytical system which is addressed properly that is considered as
serious issues. Because of poor quality of source system because of which
data flow develops an inaccurate evaluation and further developing
challenges for organizations.
 Fails to deliver new and timely insights: The large analytic resolutions fail
to provide exceptional insights associated organization that could assist
companies in providing smart decisions. But it has been observed that the big
data analytics is not able to providing equal level of insights as before
systems utilized to perform (Biancone, Secinaro and Brescia, 2018).
 Expensive maintenance: This is a kind of technique that need an existing
speculation to maintain and repair the arrangement of software. When the
technology become old than it carries the requirement of establishment of
novel technology that could develop more data at high volume in cheaper and
faster rate that indirectly enhance the cost of preservation.
 Using big data analytics is complicated: This is one more challenge
associated with difficulty that could develop overall invested in creating this
system into ineffectiveness. This complex utilization of big data analytics
would also create difficulties in extracting data. This generally occurs while
complicated data visualization.
The techniques that are currently available to analyse big data
The big data analytics is defined as approach to determine the large capacity data
established so that results and solutions could be drawn as a section of reaction about
the information they have. This is generally done by more particular systems,
approaches. Software and techniques. The big data analytic along with important
features has broadened itself into form of fresh merging technologies portion of artifical
intelligence and machine learning (Day, Cheng and Li, 2018). There are various
techniques to evaluate big data that are mentioned below in detail:
 A/B testing: This is a technique that is used to evaluate the big data under
which a regulator team is distinguished with diversified type of test teams that
helps in evaluating the modifications or conduct that could recover objectives.
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The popular instance of this technique is E-commerce websites so that
conversion rates of color, images and other could be improved.
 Machine learning: This method is highly popular to evaluate the big data.
This method could be used to distinguish and identify the outcomes of certain
data of big data. It is the technique which possess potential for determining
the expected results of certain set of big data. Hence, this method is
browbeaten in prognostic analytical which indicates that it is not achievable for
humanoid analyst.
 Natural language processing (NLP): For this method of big data analysis
there is utilisation of set of practises generated from sub section of computer
science that originates from history related with artificial intelligence. In this
kind of techniques of big data analytics there are also utilisation of linguistics
which include algorithms and computer coding so that intereptions could be
developed in human language. For instance, within social media there are
applications of sentiment evaluation which is one of the section related to NLP
that assist in identifying the reaction of people at real time while promoting
brand.
How Big Data technology could support business, an
explanation with examples
Big data consist of several techniques and tools that are important to use as well as
organise the big volume data established in the organisations. The application of big data
is important in several corporate segments and their popular companies to minimise
competition. The big data also assist various organisations, their rivals and new entrants
to compete through developing different technology related to big data. The support of
Big data technology in corporate are highlighted below:
Dialogue with customers: In today's time when companies such as Zara, H&M
and others are adopting modern strategies through implementing advanced
technologies. As the clients of this industrial sector compare products of different
companies on website and social media before shopping (Lee, Hyun and Lee,
2019). The big data technology helps companies in including their consumers in
face-to-face discussion.
Evaluate risk and Ensure Data Safety: The big data technology has a tool of
prognostic analytic that assist companies such as Sainsbury, Telco and others in
analysing the challenges associated with current modifications of financial data. It also
assist companies to keep updating themselves with modifications and development in
the sector.
Poster
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References
Kunanets, N., Vasiuta, O. and Boiko, N., 2019, September. Advanced technologies of big
data research in distributed information systems. In 2019 IEEE 14th International
Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp.
71-76). IEEE.
Aljumah, A.I., Nuseir, M.T. and Alam, M.M., 2021. Traditional marketing analytics, big data
analytics and big data system quality and the success of new product development.
Business Process Management Journal.
Biancone, P., Secinaro, S.F. and Brescia, V., 2018. A review of big data quality and an
assessment method and features of data quality for public health information
systems.
Day, M.Y., Cheng, T.K. and Li, J.G., 2018, August. AI robo-advisor with big data analytics
for financial services. In 2018 IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining (ASONAM) (pp. 1027-1031). IEEE.
Arolfo, F. and Vaisman, A., 2018, September. Data quality in a big data context. In European
Conference on Advances in Databases and Information Systems (pp. 159-172).
Springer, Cham.
Lee, S., Hyun, Y. and Lee, M.J., 2019. Groundwater potential mapping using data mining
models of big data analysis in Goyang-si, South Korea. Sustainability, 11(6), p.1678.
Orlovska, J., Wickman, C. and Söderberg, R., 2018. Big data analysis as a new approach for
usability attributes evaluation of user interfaces: an automotive industry context. In
DS 92: Proceedings of the DESIGN 2018 15th International Design Conference (pp.
1651-1662).
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