Information Systems and Big Data Analysis

This report explains big data analytics, its characteristics, challenges, and available tools and techniques. It also discusses how big data technologies can support businesses with examples. The report is useful for understanding the importance of big data in making predictions related to business execution and for taking monetary and functional decisions.

INFORMATION
SYSTEM AND BIG
ANALYTICS

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
Explain big data and state characteristics of big data..................................................................3
Describe challenges of big data analytics and the tools and techniques which are currently
available for examination of big data..........................................................................................4
Define big data technologies would support business with the help of examples.......................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
The report prepared as under takes in account Big data which is a type of data or information
which is brief, complex and massive as well. In general times, there was a problematic situation
while handling situations that involved handling of data without any innovative strategies. This
report considers the fundamental base and idea of elements such as big data. There are certain
difficulties and complexities which are being expected to settle down for better functioning and
working of large-scale data and innovation (Liang and Liu, 2018). At last, there are many
processes which are being helpful in assessment of information and data. The new innovation of
large scale data would be useful in giving and rendering support the companies or businesses
which would help in carrying out operational work on giving advantages of the business
enterprise.
TASK
Explain big data and state characteristics of big data.
Big data can be explained as a term which is being used for providing suggestions towards a
huge measure of data being ascertained and assessed with the help of information collected
which would be used by investigators for being able to reach at a certain solution. The
information collected would be accounted as huge which would lead to issue in sorting, handling
and even interpreting the results as well (Qi and Tao, 2018). The data can be collected and
gathered from various sources, for instance perceptions, records and online entertainment locals.
Companies are expected to attain bits of knowledge about the alteration in interest of consumers.
There are some attributes of big data which can be explained as under:
 Variety: The data is observed to have a different sort for instance semi organised,
organised and unstructured as well. The organised data can be said to be more organised
in a coordinated manner, for example with the help of tables, and would take the
guidance of information-based administration framework. The unstructured data is said to
be not so coordinated in a more appropriate manner and accurate way as well and it
doesn’t follow a suggested design (Shi, 2022). The semi organised data is stated to some
extent being organised and which won’t be utilising formal designs. The information
could be either comprising of homogenous data or heterogenous data.

 Velocity: Such term can be explained such as which would mention the speed of
information at which it is being developed, made or created. It would mirror the speed at
which the information is being handled. It would prompt administration to use for giving
maximum satisfaction the request of consumers or client (Xu and Duan, 2019).
 Veracity: This term would specify towards maintaining the honesty towards information
being collected. It denotes and depicts the sincerity or accuracy of the data being
gathered. Numerous information is in form of unstructured structure that would make it
troubling to arrange the data. The base of legitimacy would reflect and show the degree
of exact information being collected.
 Volume: The big data consists of various information and data which is bigger in size and
on scale. The enterprises are required to deal with the data which is large in size and
would be demanding a method for giving meaning towards the information (Bibri, 2018).
 Value: The significant value of enormous data would come from viable operations and
activities, strong connections and links with clients and few business related advantages
would help in examination of connections.
Describe challenges of big data analytics and the tools and techniques which are currently
available for examination of big data.
There are certain challenges which big data analytics come across which can be explained as
under:
 Incorporation of information from various sources and medium: The big data can be
explained as a mix variety of information that is collected from various sources and is
considered to be the best method and tool for coordinating and assembling the
information (Wachter and Mittelstadt, 2019). The sources are different types and are
defined as online entertainment, reports, email, monetary reports and introductions. The
assembling of the information collected from various sources is pivotal in examining and
deciphering the collected and interpreted information.
 Lack of knowledge specialists: The idea of vast information and data generally requires
various experts such as information examiners, information specialists and information
researchers in order to work with the fresh and new developed innovation and creativity.
The faculty needs various different courses related to instructional subject so that huge
data innovation can be chip away easily (Shirdastian, Laroche and Richard, 2019). It

might also helps in expanding of the prescribed association hence the selected
representatives are furnished with the preparation of the strategic meetings.
 Issues in information development: The size of the data is developing in a fast manner
and it is quite common to deal with such massive data. The collected data is generally is
not in a proper structure and comes in the form of pictures and PDF (Kiselev, Andrews
and Hemberg, 2019). For collecting the data in an effective manner it is important to have
different programming structures which develop with time.
 Lack of knowledge about large information: There are some type of data that creates
difficulty in selecting the right classification and sort of information about the particular
topic (Raguseo, 2018). The data is quite large and mainly requires straightforward and
clear image about the touchy data. Thus, it is important to held military preparation
program for the prescribed workers.
Define big data technologies would support business with the help of examples.
There are different type of data technologies that help the business in gaining growth and success
in the market and they are defined as under:
 Natural processing of language: It is considered as an important data technology in the
business which utilises in software engineering and artificial intelligence. It generally
includes the calculation of the figures and facts in order to handle the particular issue.
Calculations are more helpful in taking care of the whole in an easier manner (Vassakis,
Petrakis and Kopanakis, 2018). Hence, the flowcharts are also used for deriving the flow
of certain calculations at the time of graphical configuration. It also utilises the use of
language which generally looks like English language and is explainable by the
individuals without any more problem.
 A/B testing method: It is also known as bucket or split testing which is a strategy to
analyse two different variables with different calculation and helps in concluding the
following result in an effective manner. The A/B testing is defined as type of structure
which involves some stages such as collecting information in which less knowledge is
provided for improving the start (Brinch, 2018). There are some objectives also of this
method which are defined as like clicking a connection or button to acquire an item or for
signing up the mail in an appropriate manner. Whenever the right objective is selected the
age of the theory is finished and accomplished. Data integration and data fusion –

integration of data means mainly helps in organising the data in the single database while
on the other hand data fusion mainly involves analysing the data by breaking it into
different parts.
 Data mining method: It is defined as a course of tracking down the relationships and
examples with countless and huge data type (Hopkins and Hawking, 2018). The data is
extracted and mainly broken down into deriving it into the methodology of data mining in
an effective manner. There are different types of data mining such as descriptive data
mining and perceptive data mining.
 Statistics method: This is an important method of data technology that generally helps in
collecting, sorting and dissecting the data. It is utilised in the changing interaction and is
useful in deriving accurate data. The collection of data by including the important and
optional sources helps in making the choices in a more appropriate manner (Wang, Kung
and Byrd, 2018). The important information is also known as direct data on the ground
from where the real scientists gather and analyse data about a particular topic and then
derive result on the basis of that information so that more growth and efficiency can be
attained. It is important to collect and interpret information on the basis of right and
reliable information in order to avoid confusion.
CONCLUSION
From the above prepared it can be asserted and concluded which would enormously or
immensely information is huge for organisations. It is being helpful and useful for making
predictions or expectations related to business execution. There are certain types of the data
which can be explained such as semi organised, organised and unstructured as well. In any sort
of case structuring of information is important for taking the monetary and functional choices of
the business enterprise. The enormous data innovation additionally would utilise and adapt
programming as a shield from the threat of marketplaces. The enormous data would help in
providing assistance to companies with fulfilling it in a powerful manner. Generally, the advance
should be taken in account with absolute attention for detailing and determined preparation
which is ought to be given to the new representatives for utilising the innovation base data in a
effective and efficient manner. Such reports are useful for serving as a proof in the eye of law
and understanding where a business is lacking and lagging behind.

REFERENCES
Books and Journals
Bibri, S.E., 2018. The IoT for smart sustainable cities of the future: An analytical framework for
sensor-based big data applications for environmental sustainability. Sustainable cities and
society, 38, pp.230-253.
Brinch, M., 2018. Understanding the value of big data in supply chain management and its
business processes: Towards a conceptual framework. International Journal of Operations
& Production Management.
Hopkins, J. and Hawking, P., 2018. Big Data Analytics and IoT in logistics: a case study. The
International Journal of Logistics Management.
Kiselev, V.Y., Andrews, T.S. and Hemberg, M., 2019. Challenges in unsupervised clustering of
single-cell RNA-seq data. Nature Reviews Genetics, 20(5), pp.273-282.
Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data
analytics: A bibliometrics study. Expert Systems with Applications, 111, pp.2-10.
Qi, Q. and Tao, F., 2018. Digital twin and big data towards smart manufacturing and industry
4.0: 360 degree comparison. Ieee Access, 6, pp.3585-3593.
Raguseo, E., 2018. Big data technologies: An empirical investigation on their adoption, benefits
and risks for companies. International Journal of Information Management, 38(1),
pp.187-195.
Shi, Y., 2022. Advances in big data analytics: theory, algorithms and practices. Springer Nature.
Shirdastian, H., Laroche, M. and Richard, M.O., 2019. Using big data analytics to study brand
authenticity sentiments: The case of Starbucks on Twitter. International Journal of
Information Management, 48, pp.291-307.
Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and
challenges. In Mobile big data (pp. 3-20). Springer, Cham.
Wachter, S. and Mittelstadt, B., 2019. A right to reasonable inferences: re-thinking data
protection law in the age of big data and AI. Colum. Bus. L. Rev., p.494.
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and Social
Change, 126, pp.3-13.
Xu, L.D. and Duan, L., 2019. Big data for cyber physical systems in industry 4.0: a
survey. Enterprise Information Systems, 13(2), pp.148-169.