Big Data Analysis: History, Challenges, Techniques, and Business Support

This poster and paper provide an overview of Big Data Analysis, including its history, challenges, techniques, and business support. It explains the characteristics of Big Data, such as volume, variety, velocity, and variability, and the available techniques for its analysis, such as A/B testing and data mining. It also discusses the challenges of storing and managing Big Data, and how Big Data technology can support businesses. Examples of Big Data usage in Royal Bank of Scotland are also provided.

History on big Data
The Big Data terms has been used since the early 1990s but it is not exactly known that who was the
first person to use this term. Where as most people give credit to John R. Mashy for making this
term popular to the world. the term Big Data has been around 2005, when it was launched by
OReilly Media in 2005. However, the usage of Big Data and the need to understand all available
data has been around significantly longer. The primary major data project is created in 1937 and was
ordered by the Franklin D. Roosevelts administration in the USA. Big data history can be majorly
divided into three main phases such as Phase 1.0, 2.0 and 3.0. In the Big Data phase 1.0 data
analysis, big data and data analytics was originated from the longstanding domain of data base
management. The Big data phase 2.0 includes the beginning of internet and the web to offer unique
data collection as well as data analysis opportunities. Where as in the Big data phase 3.0 includes
that web based unstructured content is still the main focus for many organization in data analysis or
big data and it consist of current possibilities to retrieves valuable information. After the Social
Security Act became law in 1937, the government had to keep track of contribution from 26 million
Americans and more than 3 million employers. The principal data-processing machine appeared in
1943 and was developed by the British to decipher Nazi codes during World War II. In 2005 Roger
Mougalas from OReilly Media coined the term Big Data for the initial time, just a year after they
created the term Web 2.0. It refers to a large set of data that is almost impossible to manage and
process utilizing traditional business intelligence apparatuses. After the Social Security Act became
law in 1937, the government had to keep track of contribution from 26 million Americans and more
than 3 million employers. The principal data-processing machine appeared in 1943 and was
developed by the British to decipher Nazi codes during World War II. In 2005 Roger Mougalas from
OReilly Media coined the term Big Data for the initial time, just a year after they created the term
Web 2.0. It refers to a large set of data that is almost impossible to manage and process utilizing
traditional business intelligence tools.
Information Systems and Big Data Analysis
Name of the Student
The challenges of big data analytics
Big data can be describe as the collection of data that includes huge in volume as well as it is yet growing exponentially
with the time. This consist of the data which is large in size as well as is more complexity which can not be processed
by the traditional data management tools and this could be store effectively and efficiently.
Characteristics of Big data
There are different challenges of big data analytics that will lead to affect the way it is
performing and functioning. It is essential that different challenges could be correctly analysed in
order to timely deal with the issues. Although new technologies have been developed for data
storage, data volumes are doubling in size about every two years. Organizations actually struggle
to keep pace with their data and find ways to effectively store it. Companies often get confused
while selecting the simplest apparatus for giant Data analysis and storage. It has been analysed
that lack of knowledge professional is the crucial issues which will affect big data analytics. It is
very challenging to find right and skilled professional person who have appropriate information
to successfully run these modern technologies and large data tools. One of the foremost pressing
challenges of massive Data is storing these huge sets of knowledge properly. the quantity of
knowledge being stored in data centers and databases of companies is increasing rapidly.
The challenges of big data analytics
There are different characteristics of big data that is mention below –
Volume – This can be consider as the crucial characteristics of big data as this includes that amount
of data matters. The name Big Data itself is related to a size which is enormous. Size of data plays a
very crucial role in determining value out of data. Also, whether a particular data can actually be
considered as a Big Data or not, is dependent upon the volume of data. Through the support of big
data individual will be able to process high volumes of low density or unstructured data.
Variety – This characteristics of big data can be describe as the many types of data that are available.
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured.
During earlier days, spreadsheets and databases were the main sources of data considered by the
greater part of the applications. Nowadays, data as emails, photos, videos, monitoring devices,
PDFs, audio, etc. are also being considered in the analysis applications. This could be classified into
unstructured and semi structured data types such as video, text, audio and so on.
Velocity – This can be explain as the characteristics of big data as the fast rate at which data could
be received as well as acted on. The term velocity refers to the speed of generation of data. How fast
the data is generated and processed to meet the demands, determines real potential in the data. It has
been determined that highest velocity of data streams directly into the memory versus being written
to disk.
Variability – This characteristic of big data can be explain as the inconsistency of the data which
could be shown during the data at times and this will lead to hampering the process that is being
able to successfully handle data in proper and appropriate manner. This refers to the inconsistency
which can be shown by the data at times, accordingly hampering the process of being able to handle
and manage the data effectively.
References
Richardson, S.M., Petter, S. and Carter, M., 2021. Five ethical issues in the big data analytics
age. Communications of the Association for Information Systems, 49(1), p.18.
Fathi, M. and et.al., 2021. Big data analytics in weather forecasting: A systematic
review. Archives of Computational Methods in Engineering, pp.1-29.
Chang, V., 2021. An ethical framework for big data and smart cities. Technological Forecasting
and Social Change, 165, p.120559.
How Big Data technology could support business & Examples
It has been identified that big data technology is one of the effective and efficient technology that is
supporting business to successfully and correctly functioning of data.
Big Data gives businesses the devices they need to make smarter decisions that are based on data,
not assumptions or hunch. But for this to happen, everyone in the company should have access to
the data they need to improve decision making. For example Royal Bank of Scotland is also
utilizing Big Data to deliver a better service to its customers. The average bank knows a great deal
about its customers, from what they like to buy, to where they go on holiday. RBS is beginning to
harness the potential of this knowledge to better meet customers needs. Big data technology is very
supportive to this organisation in order to successfully identify and analyse the needs or wants of
customers in order to take right decisions. This will help in successfully influencing performance of
Royal Bank of Scotland by providing high quality services to its customers.
Techniques that are currently available to analysis big data
There are different types of techniques which are currently available to analysis big data that will assist in taking
right and appropriate decision for successfully conducting task. It has been analysed that right technique will lead to
increase overall performance and functioning of organisation.
A/B testing This data analysis technique involves comparing a benchmark group with a variety of test gatherings, to
discern what treatments or changes will improve a given objective variable. This can be explain as one of the
common big data techniques which will lead to affect performance and functioning of business. Through this
technique of big data it has been analysed that once again fits into this model it can be test different numbers. This
could be successfully achieved if the groups are of big enough size in order to gain meaningful differences.
McKinsey gives the example of analyzing what copy, text, images, or layout will improve conversion rates on an e-
commerce site.
Data mining - A typical instrument used inside big data analytics, data mining extracts patterns from large data sets
by combining methods from statistics and machine learning, inside database management. An example would be
when customer data is mined to determine which segments are probably going to react to an offer. This is one of the
effective and significant techniques that helps in successfully and correctly functioning of data in order to gain
desired information.
The definition of big data will be data that contains greater variety,
arriving in increasing volumes and with more velocity. big data is
larger, more complex data sets, especially from new data sources. The
big data is refer as the great quantity of the diverse information that
could be arrives with the increasing volumes as well as higher
velocity. This is considered to be more structured or unstructured way
to keep information in proper and appropriate manner. These data sets
are entirely voluminous, to the point that traditional data processing
software simply cant manage them. But these massive volumes of
data can be used to address business problems you wouldnt have been
able to tackle before. Big data is a combination of structured, semi
structured and unstructured data collected by organizations that can be
mined for information and used in machine learning projects,
predictive modeling and other advanced analytics applications. It has
been analysed that big data is most often stored in computer database
and it is analyzing using software specifically designed to handle
large, complex data sets with the motive of gaining information is
desired time frame.