Exploring Big Data: Concepts, Challenges & Business Applications

This report provides a comprehensive overview of big data, starting with its definition and key characteristics such as volume, variety, velocity, value, and veracity. It delves into the obstacles encountered during big data analysis, including a deficiency of skilled professionals, a lack of understanding of massive information, issues in data growth, information security concerns, and confusion during the selection and promotion of big data techniques. The report further explores the tools available for analyzing big data, such as data fusion, A/B testing, technical learnings, data mining, and measurements. It also highlights how big data technology can support business structure by increasing rivalry benefits, improving goods and services, enhancing information security, and operating risk analysis. The report concludes that effective utilization of big data technology enhances business competitiveness and success.

Big Data

Table of Contents
Big Data...........................................................................................................................................1
INTRODUCTION .......................................................................................................................1
MAIN BODY ..................................................................................................................................1
Explain the concept of big data and its characteristics :- .......................................................1
The obstacles of huge information analysis :- .......................................................................2
The tools that are recently available to analyse big data :- ....................................................3
How big data technology could support business structure :- ...............................................4
CONCLUSION ............................................................................................................................4
References:.......................................................................................................................................5

INTRODUCTION
Big data is the utilisation of advance analytic tools against very huge collected
information that considers system, unorganized data and many others. With this the
management can make development with in the organisation, outlining the system
structure and assuming the upcoming life. Source of massive data become more complex
than those for traditional information due to they are being driven by artificial
intelligence , mobile device. Digital media and many others. In this report will cover the
concept of big data and its characteristics further it will explain the challenges faced during
the gathering of details and in last it will show the effect of collected details in an organisation.
MAIN BODY
Explain the concept of big data and its characteristics :-
This is refers to huge collection of precious information which is organized or
unorganized. The information are gather by the individual, details from internet and
transactions just as buying, order and accounting transmissions with records of workers.
The understanding of details gives aid in making of systematic records in the details which
gives help in whole action of business activity operations. Here are some characteristics of
massive details which is listed in below:-
 Volume :- This refers to the huge amount of details which is collected and
gathered by big companies at a large scale. The big data is surveyed fro different
sources as digital media, images, visualisation and transaction of accounting with
client records. This is important its value as it analyse if the collected information
is relevant.
 Variety :- It is most important factor as it refers to collection of information from
different origins and their attributes. The base root of big data have been changed
in current years and In present it is available through images, sound record and data
file and others.
 Velocity :- This is refers to the involvement of total velocity at which the big data
is being introduced or furnish. This is creation of big data production majorly
connected with the style of collected data is active to be refined which is important
for impressive analytic thoughts and improves in gathering request of the ruin.
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 Value :- it is important of massive data consider the total velocity at which the big
data is being introduced or rendered. It is essential for the raw data to be
gathered effectively and is explained of irrelevant data in regards to determine for
the steps to be achievable.
 Veracity :- this is interlink with the accuracy related to gathered information as
details encountered is unorganized. This creates an important to divide necessary
details for impressive processing. This assures every detail is gathered for
understanding and analysis is appropriate and effective.
The obstacles of huge information analysis :-
This analysis is mandatory for an undertaken uncertainty as it assist to develop the
procedure of making decision making. It betters the accountability, bring financial health
and assist workers to analyse operations and assume financial reduction. Hence there are
different issues within this big data analysis and some of those issues are listed in below:-
1. deficiency of professional education :- it is essential to have ability and talented
professional in relation to have achievable process of recent technology and big data
techniques. The company needs skilled data collectors and analyst with data
reformers. This is been big issue an because of deficiency of such expertise in the
company.
2. Deficiency of appropriate understanding of massive information :- The lack of
the information and how it is to be processed , saved and its total value has been a
issues for various company. The deficiency of getting of difficulty information
with an industry finally leads to inefficiency of activity of important data. This is not
only deduct the activity of an industry but also finds it decisions making process.
3. Issues in data growth :- The foremost issue of big data has been the storage of
large amount of unit data and knowledge appropraitely. The overall quality of
details which is stored at information centers and database of company is inclinizing
at a massive path. This increase in set growth has established the issues of managing
the collected details in an effective and efficient way.
4. Saving information :- it has been a huge issue in big data analysis as the process,
saving and understanding the details, company forgets on the important factor of
security of users and the organization because of leak of personal and vital details.
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5. Confusion during selecting big data techniques :- The factor of huge data is
massive and promotes organization to use different instruments to effective
procedure the details gather by the person to ensure their decision making ability
system and stable. Hence, it is a typical task as organization are certain times ables
the determination the techniques which leads to wastage of capital and operation
duration.
The tools that are recently available to analyse big data :-
The tools to collect huge data to be understand analysis and saving in an effective
ways. In extent to perform task, it is important to utilise suitable tools which will help in
serving wanted results of best decision making procedure and reducing uncertainty.
Here listed in below are tools for analysing of big data.
1. Data fusion and data integration :- This tool concentrate on sets of tools in
regards to analyse with integrated big data from different roots and resolutions.
This gives insight for an impressive and faithful information comparative to data
amassed from a particular area.
2. A/B testing :- This tools concentrate on comparing of a assorted units to analyse the
integrated massive data related from different regions and resolutions. This gives
attainable results for an impressive data relatedness to data being collected from an
individual area.
3. Technical learnings :- this techniques is well recognised in big data as it uses
artificial intelligence and the equipment knowledge and process the collected data.
This delivers accurate and structured data connected with deducted reduced value
and time used, which creates it an effective manner with big data analysis.
4. Mining data :- Refereed as a common tools and techniques used in big data
analysis . it refers to mining of details and excluding system from sizeable units
of combined systems of valuable and reliable information in an effective ways.
5. Measurements :- this tool concentrate on collecting, managing and interpreting
the details through survey and experiments. It Is effective in giving in details
understanding of the collected information and operations. This show of liableness
and organized data gives assistance to the users to add an interpretation relative
to the information in relation to take necessary action for making decisions.
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How big data technology could support business structure :-
It is important for all kind of enterprise of its size to have valuable data and insight
in order to understand their users and posses to mark a particular market and users with
analysing their taste. Here are some points that shows effect of this details on business:-
1. Increase rivalry benefits :- the big data analysis gives to organizations by giving
them help in understanding where they are lacking relative to this business
operations. This enables an industry to take essential benefits in the market
relative to rivals.
2. Improvements in goods and services :- the effectiveness of big data gives help
to an organizations where their goods are lacking by taking valuable data like the
cost of their services in comparison of their challengers, users buying method. This
able the company makes essential modification of their goods for re development.
3. Information security :- this helps an industry to navigate their Image of details to
understand different internal threats. This allows an industry to keep its details
secure as patents safety. It is even more essential for financial organization to keep
the data such as credit and debit card detail safe.
4. Operate risk analysis - this support an industry by assisting them in determine
upcoming and current levels of threat. The collective data takes into account the
present performance of the company. Related to their rivalry to analyse risk
components that can effect the total functioning and growth of a company.
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CONCLUSION
It is concluded from above report that big data technology makes the business more
effective and impressive as it gives the way to presents the company by having
competitiveness and success In the organisation. In the above report it is explained that
what Is big data and its characteristics. Further It is challenges that has been faced during the
analysing the big data. In last the techniques that is used to measure the big data and how it
effect the business organisation.
References:
Books and Journals
Khalid, Z.M. and Zeebaree, S.R., 2021. Big data analysis for data visualization: A review.
International Journal of Science and Business, 5(2), pp.64-75.
Zhang, L., Guan, Y. and Jiang, S.C., 2021. Investigations of soil autotrophic ammonia oxidizers
in farmlands through genetics and big data analysis. Science of the Total Environment,
777, p.146091.
Guedea-Noriega, H.H. and García-Sánchez, F., 2019. Semantic (big) data analysis: an extensive
literature review. IEEE Latin America Transactions, 17(05), pp.796-806.
Nateghi, R. and Aven, T., 2021. Risk analysis in the age of big data: the promises and pitfalls.
Risk Analysis, 41(10), pp.1751-1758.
Wei, C., and et., al., 2018, June. A two-stage data processing algorithm to generate random
sample partitions for big data analysis. In International Conference on Cloud
Computing (pp. 347-364). Springer, Cham.
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Naqvi, R., and et., al., 2021, June. The nexus between big data and decision-making: A study of
big data techniques and technologies. In The International Conference on Artificial
Intelligence and Computer Vision (pp. 838-853). Springer, Cham.
Kashef, R., 2020. Adopting Big Data Analysis in the Agricultural Sector: Financial and Societal
Impacts. In Internet of Things and Analytics for Agriculture, Volume 2 (pp. 131-154).
Springer, Singapore.
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