Big Data Report: Characteristics, Challenges, Techniques, and Business

This report provides a comprehensive analysis of Big Data, defining it as a technology for storing, analyzing, and managing large datasets to identify patterns for informed decision-making. It explores the core characteristics of Big Data, represented by the 5 Vs: Volume, Veracity, Variety, Value, and Velocity, highlighting the challenges associated with each. The report discusses the challenges in big data analytics, including failure to obtain timely insights, inaccuracy, complicated processes, delayed response times, and high costs. It also outlines various techniques for efficient big data analysis, such as A/B testing, data fusion and integration, data mining, machine learning, natural language processing, and statistics. Furthermore, the report examines how big data technology supports businesses through effective customer insight, enhanced market intelligence, agile supply chain management, better audience targeting, data-driven innovation, diversified cases, and efficient business operations. The conclusion emphasizes the value of big data analysis for organizations to understand their customers and improve business processes.

INFORMATION SYSTEMS AND BIG DATA
AnalysisBig Data can be referred to as a technology for storing, analysing and
managing the bulky data for identification of an appropriate pattern in
such a large pool of data for decision-making. Thus, the following
report will explain the concept of big data along with its
characteristics. Then the challenges involved will be discussed also
explaining the techniques for such analysis. And finally, how big data
technology supports businesses will be analysed.
Thus, it can be said that the characteristics of Big Data can be
explained by the 5 Vs which are explained as follows:
VOLUME – As the name suggests Big Data is a data which is big or
enormous and volume. Such a data is generated in large volumes on
daily basis from the variety of sources like processes of businesses,
machines, platforms of social media, networks, interactions from
humans, etc. For example, more than a billion messages are generated
daily on Facebook.
VERACITY – The reliability of data can be also referred to as
veracity. There are various ways through which the data can be
filtered or translated. Therefore, it can be defined as a process of
handling and managing the data efficiently (Wang and et.al., 2020).
Business development also requires the effective and efficient
handling and managing of Big Data. Example of this are the posts on
Facebook with hashtags.
VARIETY – On the basis of its nature, Big Data can be structured,
unstructured or even semi – structured which is obtained from variety
of sources. Earlier, data used to be obtained from only from databases
and sheets but current trends has shown various other sources like
PDFs, audios, photos, emails, SM posts, videos, etc.
VALUE – It is one of the most essential characteristic of Big Data. It
can be very reliably said that it is not the data that is processed and
stored which matters but the value and reliance which can be placed
on such data that is stored, analysed and processed.
VELOCITY – It is the speed of creation and recording of data in real
– time (Naeem and et.al., 2022). It involves speed of data sets, change
rate and activity bursts for providing the required data on time.
In simple words, the data or information which is larger, much more complicated than the usual data sets and which is obtained
exclusively from newer data sources is called as Big Data. There are three Vs of Big Data i.e., volume, velocity and variety.
Volume means the amount of data. Here, the data will be of high volume and of low-density along with being unstructured.
Velocity is the speed at which such data is received and acted thereon (Hancock and Khoshgoftaar, 2020). This includes
operating of some smart products which are internet enabled and thus, will be requiring evaluation and action in real-time.
Variety denotes the types of data. Relational database is utilized for processing of traditional data type whereas the data which is
unstructured or semi – structured will be requiring additional pre-processing like text, video and audio.
Data sets of this Big Data is so bulky that software used for processing of traditional data cannot manage this data but it is also
possible to handle the complicated business problems by analysis and processing of this bulky data (Hariri, Fredericks and
Bowers, 2019). Over the last few years two more Vs have emerged namely, value and veracity. Naturally, every data set has its
intrinsic value but it is necessary to discover that value. Also, the level of reliance that can be put on this data is also important
i.e., truthfulness and integrity of the data is very crucial.
Cui, Y., Kara, S. and Chan, K. C., 2020. Manufacturing big data ecosystem: A systematic literature review. Robotics and computer-
integrated Manufacturing. 62. p.101861.
Ghasemaghaei, M. and Calic, G., 2020. Assessing the impact of big data on firm innovation performance: Big data is not always
better data. Journal of Business Research. 108. pp.147-162.
Hancock, J. T. and Khoshgoftaar, T. M., 2020. CatBoost for big data: an interdisciplinary review. Journal of big data. 7(1). pp.1-45.
Hariri, R. H., Fredericks, E. M. and Bowers, K. M., 2019. Uncertainty in big data analytics: survey, opportunities, and
challenges. Journal of Big Data. 6(1). pp.1-16.
Kolajo, T., Daramola, O. and Adebiyi, A., 2019. Big data stream analysis: a systematic literature review. Journal of Big Data. 6(1).
pp.1-30.
Concluding the above report, it can be said that big data analysis has
proven to be a very helpful tool for the organizations to know and
understand their customers on the basis of the large volume of data
and information obtained, analysed and interpreted. Thus, the above
report defines big data and discusses its characteristics. Also, the
challenges faced and the techniques applied in such big data analytics
is explained. Lastly, how the big data technology will support the
businesses is discussed.
There are certain challenges associated with the Big Data analytics which are discussed below:
FAILURE TO OBTAIN NEW OR TIMELY INSIGHTS - There are chances that the user may not be able to obtain new and
timely insights which are not available. This may be due to lack of proper data available as a result of inefficient organization of
data.
INACCURACY - It is one of the major concerns of a business using big data. This inaccuracy is due to detrimental quality of
the source of data or data itself (Kolajo, Daramola and Adebiyi, 2019). It means that the data is defective, erroneous or even
incomplete. It can also happen due to defects in the system or omission of requirements of the system.
COMPLICATED PROCESS - Highly complicated data analytics will result in difficulty for extraction of value from the data.
This complicatedness will arise either on the part of users navigating the system or certain technical issues involved.
DELAYED RESPONSE TIME - When the analysis of data is taking unusually longer period and the reports on such analysis
is needed now then such analysis will be of no use apparently. This is a major issue in the real-time systems.
EXPENSIVE - It is obvious that every system is needed to be maintained and infrastructural improvements. Thus, it is always
advisable to assess the system and ensure that the costs are not overpaid.
INTRODUCTION BIG DATA
CHARACTERISTICS CHALLENGES
CONCLUSION
REFERENCES
Technology of Big Data is very beneficial for supporting the
businesses which can be explained as follows:
EFFECTIVE CUSTOMER INSIGHT - Understanding and
analysis of customers whether individually or even categorically
becomes easy through the technology of big data. The sources of
this big data includes traditional sources like purchases and calls,
external sources like financial transactions and reports on credit,
activities on social media, surveys and also from computer
cookies.
ENHANCED MARKET INTELLIGENCE - The complex
market dynamics and market intelligence can be understood
effectively through this big data technology by analysis of the
complicated shopping behaviour of the consumers. One of the
most common source of such market intelligence is social media.
AGILE SUPPLY CHAIN MANAGEMENT - Managing the
fragile and sensitive supply chain becomes easier with the big
data technology which provides analytics for the prediction. This
keeps the network of production, distribution and demand at the
global level operating well.
BETTER TARGETING OF AUDIENCE AND
RECOMMENDATIONS - Newer and latest systems of
recommendations are much more advanced than ever as they are
based on the sophisticated insights of the customers which
results in increased sensitivity of the data to the behaviour of the
customers and demographics.
DATA – DRIVEN INNOVATION - Innovation and
developing the novel goods and services can be done through big
data technology and tools which assist in enhancing the research
and development phase (Cui, Kara and Chan, 2020). This
technology results into data which is more cleansed and better
governed & prepared to be shared and thus, is a product in itself.
DIVERSIFIED CASES - Big data technology has made it
possible for storing of the raw data in a data lake which can be
utilized and processed by application of appropriate data models
for using it in a specific analytics application. This provides
flexibility with respect to the analysis of a data set.
EFFICIENT OPERATIONS OF BUSINESS - Optimization
of the business process is possible by utilization of big data
technology. Such optimization will result in the generation of
savings of cost, boosting of productivity and increasing the
customer satisfaction. Human resource management becomes
more efficient along with improved detection of fraud, better risk
management, etc.
Analysis of this big data needs to be very efficient to get
the desired results therefore, certain techniques which
can be followed are explained as follows:
A/B TESTING - It involves comparison of a control
group with the different test groups to ascertain the
treatments and changes that will be required for
improvement of the given objective variable.
DATA FUSION AND DATA INTEGRATION - It
involves combination of various techniques for
analysing and integration of data via multiple solutions
and sources. This will allow obtaining of the insights
which will be more efficient and potentially more
precise as compared to the process of development by a
single data source.
DATA MINING - It a very common tool which
includes extracting relevant patterns from huge sets of
data by combination of database management with the
statistics and machine learning (Ghasemaghaei and
Calic, 2020). It can be explained through knowing of
which customer segment will most like be reacting to a
specific offer.
MACHINE LEARNING - Referring to the field of AI
(artificial intelligence), data analysis can also be done
through machine learning. It involves setting up and
production of various assumptions on the basis of the
data to work with the computerized algorithms.
NATURAL LANGUAGE PROCESSING - It utilizes
the algorithms for analysing the human language
(natural language) and is also commonly referred to as
subspecialty of artificial intelligence, linguistics and
computer science.
STATISTICS - As the name suggests it includes
collection, organizing and interpretation of data with the
help of various surveys and experiments.
TECHNIQUES SUPPORT