Big Data Analytics: History, Challenges, Techniques, Characteristics and Business Support

History on big Data
The concept of big data was introduced by John Graunt 1663. At that point of
time, he was dealing with the large amount of information when he was
studying Bubonic Plague, it was haunting Europe. He was the first person
who started using the analysis of statistical data. At the time of early 1800s,
the study of area of statistics expanded to the strategical collection and
analysing of big data. In the year 1880, the world first time saw the problem
of large amount of data. There was the estimation done by US Census Bureau
that it has taken approximately eight years to handle and process the large
information at the time of census program conducted. Then in the year 1881,
there was a man named Herman Hollerith from Bureau invented the Hollerith
tabulating machine which helped in the reduction of calculation work. Then
at the earlier phases of 20th Century, data evolution was seen by the
organizations at the very unexpected speed. The term big data became the
core of evolution. Then the machines were developed or build up in order to
store data or information in a magnetic manner. Such machines scans the
patterns in the form of messages. In the year of 1965, first data centre was
built by the US government with the motive of storing the sets of millions of
fingerprints and also the tax returns (Batistič and Van Der Laken, 2019).
Information Systems and Big Data Analysis
What is big Data Characteristics of Big data
There are ten V's of big data which are basically the characteristics of it.
Volume is one of the characteristic. It describes the size of the data.
Velocity is an another characteristic. It describes the speed of big data at
which it is being generated. Variety is also one of the characteristic. It
describes the various of data collected for storage and processing. Veracity
is also an another characteristic. It describes the accuracy of big data
management. Value is one of the characteristic. It describes the use of data
that where it can be used in a more effective and efficient manner. Validity
is an another characteristic. It describes the quality of data and governance
along with the data management on a massive basis. Variability is also one
of the characteristic. It describes the dynamic and evolving behaviour in
the sources of data. Venue is also an another characteristic. It describes the
distributed and heterogeneous data from more than one platforms from
where the data is generated. Vocabulary is one of the characteristic. It
describes the data models and semantics which explains the structure of
data. Vagueness is an another characteristic. It describes the confusion on
the tools and meaning used in terms of big data that what exactly are
required to be used (Tabesh, Mousavidin and Hasani, 2019).
The challenges of big data analytics
The are various challenges of big data analytics. Lack of professional
knowledge is one of the challenge. This is because there are less people who
have gained expertise in big data analytics. This has resulted in the lack of
professionals and the accurate knowledge about how the big data can be
managed with an appropriate tool. Lack of understanding of massive data is
an another challenge. This is because large data is difficult to understand as it
hard to read and identify about the relevant nature of the date. Data growth
issues is also one of the challenge. This is because big data has the feature of
continuously growing that leads in the complications and issues of
administrations and monitoring of data. Confusion while the selection of big
data tool is also an another challenge. This is because there are various tools
for managing the big data bur selecting the most suitable one as per the needs
and requirements is a challenge. Data integration is one of the challenge. This
is because collaboration of big data is problematic as it requires the expertise
knowledge of management and that is why integration is still the challenge of
big data analytics. Securing data is an another challenge. This is because
cyber crimes are increasing day by day and the risk of data management is
also increasing that is why security of big data is a challenge (Al-Sai and
Abdullah, 2019).
How Big Data technology could support business &
Examples
Big data technology could support business by making the
decisions in a more better way. For example, Walmart renders the
example of democratisation of data in action. Understanding the
customers is an another support given by big data technology. For
example, Disney got support of understanding the behaviour of the
visitors. Delivering smart services and products is also one of the
support given by big data technology. For example, Royal bank of
Scotland is using the big data and providing good experience and
services to their customers. Improving business operations is also
an another support provided by the technology of big data. For
example, PeopleDoc company has introduced the Robotic process
automation platform for enhancing the business operations.
Generating an income is one of the support to business using big
data technology. For example, Amex is leveraging the data and got
assisted in generating the large revenues and profits by
management of the big data technologies (lyverbom, Deibert and
Matten, 2019).
Techniques that are currently
available to analysis big data
There are various techniques that are currently available to analyse the big data. A/B
Testing is one of the technique. It involves the comparison of the entire control group
with the test groups so that the treatment and changes can improve the objective variable.
Data fusion and data integration is an another technique. It helps in analysing the data in
order to integrate from more than one sources so that solutions can be generated in an
accurate manner. Data mining is also one of the technique. It helps in extracting the
different patterns from the big data sets in order to combine the methods from machine
learning and statistics in the database management. Machine learning is also an another
technique. It works with the computer algorithms so that the assumptions can be produced
which is based on data and also it provides predictions which is impossible for the human
analyst to perform manually. Natural language processing is one of the technique. It also
known as the sub speciality of computer science and artificial intelligence which uses
algorithms in order to analyse the human natural language. Statistics is an another
technique. It supports in collecting and organising along with the interpretation of data
with the help of surveys and experiments. Other techniques includes spatial analysis and
predictive modelling along with the association rule learning and network analysis
(Karimi, 2019).
Big data is defined as the large and massive amount of data which are collected in huge
volumes and continuously grow exponentially as time moves. It the data which is so big in size
that the manual system or traditional ways cannot help to manage it in order to store or process
the information with such large amount. It is also the normal data but in ample of size and
needs to be processed in an efficient and effective manner. There are various examples of big
data analytics such as it includes the stock exchanges and social media sites along with the jet
engines and many more. Big data is available in majorly three forms that are the structured and
unstructured along with the semi structured. Structured data is in the readable format,
unstructured needs to be converted into structured so that it can be read out and semi structured
creates confusion to read and understand. Big data management has several advantages where it
provides the easy administrations to the organizations who handles the big data. Such as it
improved the customer servicing and better operational efficiency along with the better decision
making capabilities. The current poster covers the information regarding characteristics,
challenges, techniques and support of big data analytics with examples (Leonelli, 2020).