What is Big Data? Characteristics, Challenges, Techniques, and Business Applications

Big Data is a concept that involves analyzing large and complex data sets to uncover hidden facts and market trends. This article discusses the characteristics of Big Data, its challenges, techniques to analyze it, and how it supports businesses. It also provides examples of Big Data applications in healthcare, education, and marketing.

What is
BIG DATA

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
1. WHAT IS BIG DATA ...........................................................................................................3
2. CHARACTERISTICS OF BIG DATA..................................................................................3
3. CHALLENGES OF BIG DATA............................................................................................4
4. TECHNIQUES TO ANALYSE BIG DATA..........................................................................5
5. HOW BIG DATA TECHNOLOGY COULD SUPPORT BUSINESSES.............................6
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................8

INTRODUCTION
Big Data is a great process of analysing big data in order to uncover hidden
facts ,references, Uncertainties of market in order to make balanced decisions. It may be revealed
as advanced level of analytics where big models, statistical algorithms are involved, co-powered
by big analytics systems (Ahamad and et.al., 2018). Big data analysis involves professionals
from different areas likewise Statistics, Modellers which collect all the relevant data and process
it for further usage.
TASK
1. WHAT IS BIG DATA
Big data may be referred as larger data sets which may only be analysed with the help of
big data tools in order to reveal human behaviour, marketplace trends and its patterns and much
more. It is a concept implies larger and complexing data sets which are processed up with data
processing applications & software. Data is a term refers to raw material, i.e., unprocessed data.
It involves steps explained below:
Firstly, data hunters collect a data mix from all different provided sources. It is then filtered and
then reclassified for further processing.
Secondly, it is further processed up and then stored in a data warehouse. Data is rechecked for
further errors and mistakes in it with the help of analytics software. Tools like data
mining,machine learning are used to forecast patterns,consumer trends and relationships. AI
(artificial intelligence) is also an emerging tool to analyse it (benoit and et.al., 2020).
2. CHARACTERISTICS OF BIG DATA
1. Volume- The quantity of data provides an insight that the data must be considered big
data or not. If such data is large enough and involves much complexities require a
number of data analytics techniques to process it, then it is classified as big data. Big data
usually contained in larger volumes in terabytes and petabytes.
2. Variety- Traditionally, RDBMS was enough to handle the stored larger data easily. But
as changes occurred in data types and their structures, traditional data analytics
techniques were challenged as then big data came into play. Frequent produced data of
different varieties and formats require various special processing capabilities. An
example of CCTV may be included within this as various types of audio & video files

generated within different places. Variety of data incoming from both internally and
externally to organisation may be classified as structured, semi-structured and
unstructured data.
3. Velocity: The velocity of big data generation is higher than the smaller and personal data
as it is evenly and frequently produced at real time basis (choi and et.al., 2018). Its rate of
generation is continuous and hence also requires regular and frequent handling, recording
and publishing techniques. As an example we can consider regular facebook posts.
4. Veracity: Big data must be reliable referred to its quality and data values. Higher the
veracity higher the meaningful data, lower the veracity higher the amount of meaningless
data. Big data must be valuable to such extent so that it may conclude such results which
contribute to the way to overall results.
5. Value: Big data will be of no value, till its values are taken out or extracted from it. It is
important to get something of it to prove it as valuable. So it is most important of all 5v's.
3. CHALLENGES OF BIG DATA
1. Lack of understanding: The demand for the analysts and scientists are ever increasing
due to continuous rise in the data that is being created. It has now became a crucial
responsibility of the business to appoint a data analysts who acquires the skills of
properly analysing the data of the firm (Pramanik and et.al., 2021). Due to such increased
demand, many businesses can face the challenge of acquiring such professionals as there
is a shortage of people having skills of analysing the data.
2. Incorrect analytics: If the firm has wrong and defective data, the analysis done on such
data would result in defective results which eventually impact the validity and
authenticity of the data. Sometimes the system has some errors due to the omission at the
time of development and testing of it which increases the problems related to processing
of the data.
3. Security issues: The tools that are being used for the analysis uses various sources to
extract the data that could make the data vulnerable. Hence, increase in the data would
increase the security issues related to the data.
4. Storage of data: The data that is being produced is increasing at rapid pace due to the
growth of the organizations and businesses. Such rapid increase in the amount of data
demands more storage capacity of the system which is a challenging task.

4. TECHNIQUES TO ANALYSE BIG DATA
There are a lot of big data analysis techniques to analyse the datasets of the businesses. Some of
these techniques are as below:
1. A/B Testing: It is a technique that compares the control group with the different test
groups to identify what changes can be adopted to improve the main variable for which
the analysis is done. Example would be identifying what changes in the colour, layout,
new images can be done on online website of the firm to enhance its rate of conversion.
2. Data Mining: It is a technique used on large amount of data to draw out a pattern with
the help of combination of two techniques, i.e., machine learning & statistics within the
framework of management of database. Example includes when a firm need to identify
that if it introduces new offer, which customer segment is going to react more or would
be attracted most and to identify such change the firm will do the mining of data of the
customers (Seo and et.al., 2018).
3. Machine Learning: It is a software that helps the computer to learn new programs and it
makes predictions of the data or make assumptions based on the entered data. Some of
its uses includes: to find out the best idea to be adopted to attract the customers, to
analyse the data and produce recommendation on the data, to differentiate between the
unsolicited email and other types of messages.
4. Statistics: It is an another technique to analyse the data by collecting, organizing and
making interpretation on the data being entered. This technique also helps in designing
the surveys and conducting experiments. The techniques is also being used in forming
judgements on what relationship could have formed by chance among variables, also
what relationship would be formed among variables if there is any casual relationship
between them.
5. Associate Rule Learning: This technique is used in big databases to determine the
correlation among variables. It identifies interesting relations among the variables. To
produce rules and to test them, associate rule learning have diverse sets of algorithm.
Some of its examples includes; when a firm wants to increase its sales by placing the
products together who are frequently purchased by the customers to maximize the sales
of the firm, to draw out the information that are related to the frequency of website being
viewed or browsed by the visitors.

5. HOW BIG DATA TECHNOLOGY COULD SUPPORT BUSINESSES
Big data technologies are not less than an unlocked treasure for all category of
businesses. Every type of business, during research need smaller data insights, or may be bulk of
data in order to understanding their external environment, their consumers and their preferences,
big data plays important role in it (shoumy and et.al., 2020).
In business usage terms, big data may be referred to processes and tools for utilization and
management of a large data set. It can be very helpful to create new services and products for a
grand consumer experiences. It also provides a competitive edge to businesses over rivalry firms
in their particular marketplace within their country or globally. Being much useful, it is used
now being in around all the sectors of businesses. Some of their examples provided below:
Big data in healthcare industry: healthcare is multiple dimensional established with aim of health
diagnosis. Under this companies then on basis of trial data frame risks and beneficial outcome to
its users. Big data also included patient records, information from testing machine, medical exam
results etc.
Big data in education industry: education industry makes best of the use of big data as it has lots
of data to store likewise course material, results and much more. Education industry is highly
transformed by customized and (e-programs) dynamic learning, grading systems and student
progress records.
Big data in marketing: traditional techniques of marketing now overlapped by modern digital
marketing techniques where big data is used to collect info about consumer preferences in short
duration. Big data analysis run advertisement campaigns, improvise the products and remove
errors and hindrances to reach desired end points(taleb serhani and et.al., 2018).
CONCLUSION
Big data and its analytics is now becoming the regular activity for all the businesses
under all the sectors. Small businesses and new startups are becoming unicorns with the help of
big data analytics techniques. Big data not only provides the basic information about the
dynamic environment but also helps to predict uncertain future. Large businesses follow data
analytics for getting a competitive edge.

REFERENCES
Ahamad, M.V. and et.al., 2018. Insight Into Big Data Analytics: Challenges, Recent Trends, and
Future Prospects. In Handbook of Research on Pattern Engineering System Development
for Big Data Analytics. (pp. 67-79). IGI Global.
Benoit, D.F. and et.al., 2020. On realising the utopian potential of big data analytics for
maximising return on marketing investments. Journal of Marketing Management. 36(3-
4). pp.233-247.
Choi, T.M. and et.al., 2018. Big data analytics in operations management. Production and
Operations Management. 27(10). pp.1868-1883.
Pramanik, M.I. and et.al., 2021. Privacy preserving big data analytics: A critical analysis of
state‐of‐the‐art. Wiley Interdisciplinary Reviews: Data Mining and Knowledge
Discovery. 11(1). p.e1387.
Seo, C.K. and et.al., 2018, December. A study on modeling using big data and deep learning
method for failure diagnosis of system. In 2018 IEEE International Conference on Big
Data (Big Data). (pp. 4747-4751). IEEE.
Shoumy, N.J. and et.al., 2020. Multimodal big data affective analytics: A comprehensive survey
using text, audio, visual and physiological signals. Journal of Network and Computer
Applications. 149. p.102447.
Taleb, I., Serhani and et.al., 2018, July. Big data quality: A survey. In 2018 IEEE International
Congress on Big Data (BigData Congress). (pp. 166-173). IEEE.
2021)(Seo and et.al., 2018)(Shoumy and et.al., 2020)(Taleb Serhani and et.al., 2018)