Big Data Analytics in Business Management: Challenges, Techniques, and Applications

This project discusses the role of Big Data Analytics in Business Management, including its features, challenges, and available techniques for analysis. It also explains how Big Data technology can support businesses with communication, risk analysis, and revenue generation. The poster and accompanying paper provide a comprehensive overview of the topic, with references to relevant books and journals.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:

Contents
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

INTRODUCTION
The above project will mention the business management by the big data analytics which
is needed for the running of business in a effective manner by incorporating the significant
information from software to hardware having knowledge of business internal factors. The
main aspect of big data features is helping in evaluation of business according to the current
scenario(Aljawarneh and Lara Torralbo, 2021). Stating the challenges which big data faces in
businesses with tools and techniques which are presently available for the analytical approach
for conquering challenges for good understanding of market and generate profits by having info
about requirements and needs of consumers according to current condition of market with it it
will bes significant info about big data technology which assists the business for having source
of information about significance of big data and how is it needed for looking ahead current
conditions.
MAIN BODY
Big data and its features
Big data refers to the huge complicated unstructured and structured sets of data which are
generated quickly and transferred from the vast sources variety. Data today is regularly generated
any time and result in huge collections of significant informations which the firms and companies
require for management, storing, visualizing and analysing(Grant, 2021). Conventional tools of data
are not made for handling the volume and complexity, which has lead to slow down of specifics big
data software and solutions of architecture designed for managing load. Platforms of big data are
designed specially for handling the large data volumes which come in system at high velocities and
vast varieties. These platforms usually include of various servers, tools of business intelligence and
databases which permits the data scientists for manipulating the data for finding patterns and trends.
Features

 Volume- The big data name itself is related to the size which is huge. Size of the data
plays a vital role in determination of data's value. Along with it whether the big data is
considered as Big data or not, is dependent on the data's volume. It is the feature which is
required for being considered while dealing with the solutions of big data(Kunanets
Vasiuta and Boiko, 2019).
 Variety- It refers to the heterogeneous source and data nature structured and
unstructured both. Earlier the databases and spreadsheets were the source of data used by
most applications. But today data is in form of photos, emails, videos, monitoring
devices, PDFs, audio and others are considered in the applications analysis. This
unstructured data variety poses the challenges for the mining, storage and data analysing.
 Velocity- It is the data generating speed, how fast the speed is generated and processed
for meeting the demands, determination of real potential in data. Velocity of big data
deals with speed at which data runs from various sources such as applications logs,
process of business, sites of social media, mobile devices and networks. Data flow is big
and constant.
 Variability- It refers to the incompatibility which is been showed by data at the times.
obstructing the process of being able for handling and managing the data in a effective
way. Able to pull the value from data is important as value of the data is increased
depending on insights which may be gained by them.
 Veracity- It is the accuracy and quality of data. Collected data may have some missing
pieces, which can be inaccurate or not for providing real and valuable insights. It is the
trust level which is in the gathered data(Lee and Huh, 2019). Data may be sometimes
very difficult and mesy for use and create confusion instead of insights.
Big Data challenges
There are many challenges of big data analytics-
 Messy visualization of data- Reports complexity level is very high and time consuming
or hard for finding the essential information. It can be solved by the engagement of UI
or UX specialist who will aids for creating the user interface which is flexible and it is
easy for navigating and work with.

 Inefficient organisation of data- Data organizing is what makes it difficult for working.
It is good to check if the data warehouse is designed in accordance to the use of case
scenarios.
 Data sharing- By having the data access from public depository results in having
different issues and lead to considerable issues as it is big in terms of size.
 Outdated technologies- New technologies which process more data volumes in quick
way and inexpensive way emerge each day. So later or sooner technologies of big data
analytics will be outdated and need more resources.
 Non optimal infrastructure- It is the cost variable which always has some room for
changes. With the cloud solution you pay for use for decreasing the costs. If the
restrictions of security are there, chances of mitigating the private cloud.
 Poor quality of data source- If the system depends on the data which have defects or
errors and is incomplete the results will be poor. Process of obligatory data validation
and management of data quality covering the stage of ET process may aid for ensuring
quality of incoming data.
Techniques presently available for analysing the big data
Here are some of the techniques which are available for the big data analytics-
 A/B TESTING- Various kinds of groups are compared by the primary controlling group
intended for treatment determination which will improve the goal element and basically
the experiment where the more than two elements are compared to get the tool for
analysing which is better for achievement of objectives(Novak, Bennett and Kliestik,
2021).
 CLASSIFICATION- There are different number of techniques which are used for
determination of categories which new data belongs and supervise the learning which
aids in decision-making by the contribution of different categories amount.
 ANALYSIS OF SENTIMENT- It aids in understanding the need or expectations of
consumers from the selected brand which results in having improvement in business

services which aids in emotion interpreting with data is providing as it aids in
multiplying peoples sentiment .
 ANALYSIS OF SOCIAL NETWORK- It is used for the improvement in relationship
among the business and consumers for understanding social structure of consumers and
first was industry of telecommunication for using its and after which used by many
scientists for finding the social concept and evaluate the bond among individuals and
activities of merchants and along with it modes for determination of individuals who are
in system while having ties determining relationship among individuals for analysing the
social network use and evaluate connections among consumers and individuals.
of the business and evaluating the connections between individuals or the customers.
How the Big Data technology can assist business,
explanation with examples
 COMMUNICATION WITH CONSUMERS- Consumers are smart enough for
understanding priorities according to the current time for the exploration everything
before buying. But communication with the business enterprises via the various platforms
of social media. Business is able for reaching the consumers by the help of big data and
interact with them for the requirements and desires due to the large firm in market
business firms have to treat the consumers according to the needs(Soares, 2022).
 REMANUFACTURING GOODS- Big data is helpful in analysing the requirements
and needs of consumers by the systems of feedbacks for consumers by which they can
update the qualities and characteristics the consumers needs in good. Firm understands
the reviews and make alterations in goods.
 RISK ANALYSIS- It refers to the firms analysis which will affect the business
operations. It aids in the scanning of social media feed, reports of newspapers for the
knowing about present scenario of market as it will aids the firm to understand the

present market competition and have growth in product in accordance to competiton of
market.
 SAFETY OF DATA- Analytics of big data aids in finding of whole data base in the
firm and have knowledge of each type of internal threats by which the firm will be able
in removing and resolving threats and keep the important info safe.
 NEW REVENUE SYSTEM- Big data aids in knowing about the generating of more
sales for the firm and provide information about wants and need of consumers as per the
need of consumers(Zhang and et, al., 2021).

POSTER

REFERENCES
Books and Journals
Aljawarneh, S. and Lara Torralbo, J.A., 2021, April. Meteorological forecasting based on big
data analysis. In International Conference on Data Science, E-learning and Information
Systems 2021 (pp. 9-11).
Grant, E., 2021. Big Data-driven Innovation, Deep Learning-assisted Smart Process Planning,
and Product Decision-Making Information Systems in Sustainable Industry
4.0. Economics, Management, and Financial Markets, 16(1), pp.9-20.
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.
Lee, S. and Huh, J.H., 2019. An effective security measures for nuclear power plant using big
data analysis approach. The Journal of Supercomputing, 75(8), pp.4267-4294.
Novak, A., Bennett, D. and Kliestik, T., 2021. Product Decision-Making Information Systems,
Real-Time Sensor Networks, and Artificial Intelligencedriven Big Data Analytics in
Sustainable Industry 4.0. Economics, Management & Financial Markets, 16(2).
Soares, R.R., 2022. The evolving field of Big Data: understanding geographic information
systems analysis and its transformative potential in ophthalmic research. Current
Opinion in Ophthalmology, 33(3), pp.188-194.
Zhang, J.Z., and et, al., 2021. Big data analytics and machine learning: A retrospective overview
and bibliometric analysis. Expert Systems with Applications, 184, p.115561.