Big Data: History, Characteristics, Challenges, and Techniques for Analysis

This presentation provides an overview of Big Data, including its history, characteristics, and challenges. It also discusses techniques for analyzing Big Data and how Big Data technology can support businesses with examples. The presentation references studies on Big Data knowledge management and context awareness in Big Data analytics for business applications.

History on big Data
A big data is defines about the variety data which contains
greater variety, arriving in increasing volumes and with
more velocity. The big data has been described with some
management of data with huge overwhelming amount of
information. As exact evolution of big data includes number
of preliminary steps of foundation.
Through providing the big data used by the multinational
companies on which evolution of modern technology is
interwoven with the evolution of big data. The foundation of
big data become a issue for the US census bureau in 1880.
They estimated it would take eight years to handle process
of data collected during 1880 census and predicated the
data from 1890 more than 10 years of process.
Big Data
What is big Data
Big Data is term that used to describe large, hard to manage volumes of
data. This is specify about both structured and unstructured that inundate
business on day to day basis. This big data as been analyzed for insight
that improve their decision and give confidence for making strategic
business moves.
Characteristics of Big data
Volume: This is first feature of Big data, it depicts about that
companies manage skyrocketed around 2012. As they began
collecting more than three millions pieces of every
individual data.
Velocity: It is second feature of big data where the
companies need that information to flow quickly as to close
real time possible. As the Velocity can be more important
than volume because it gives us bigger competitive
advantages. There is sometimes to having better limited data
in real time that has lots of data in low speed.
Veracity: This characteristic of Big data is linked with the
value characteristics as it refers to the accuracy relative to
collected massive and massive data as majority information
get encountered is unorganized. It makes crucial to separate
essential and necessary data for effective processing. This
ensures that all data is collected for analysis that makes
relevant and effective
The challenges of big data
analytics
• Lack of proper understanding of massive data: There
is lack of understanding the data and how where it
proceed, stored and it overall value have been challenged
for various organization.
• Securing Data: It has been a huge challenge in big data
analysis as the better process, storing and analyzing the
data through organization miss out with crucial elements
of security. This is the type of unprotected data can be
References
Saide, S. and Sheng, M.L., 2020. Toward Business Process
Innovation in the Big Data Era: A Mediating Role of Big Data
Knowledge Management. Big Data, 8(6), pp.464-477.
Dinh, L.T.N., Karmakar, G. and Kamruzzaman, J., 2020. A
survey on context awareness in big data analytics for
business applications. Knowledge and Information Systems,
62(9), pp.3387-3415
How Big Data technology could
support business & Examples
For Example: The Asda Stores company utilized Big Data
technology provides assistant to the organization through
determine their competition and how they can improve their
products and services. Moreover, effectively utilizing
customer data such a purchase transaction and buying
pattern.
Techniques that are currently
available to analysis big data
• A/B test: This technique involves that determines for effective
comparison of a various test group with controlled group of data in
order to discrete suitable or alteration which would enhance more
provided target available. It helps in deriving best course of action for
an organization.
• Data fusion and data integration: This techniques focus on
combining set of techniques in order to analysis along with integrate
massive data from various origin from solutions. This would provide
successful insights to collect the accurate data which are relative
collected from individual origin