Information Systems: Big Data Analysis, Business Support & Challenges

This essay provides an overview of big data analysis, starting with its historical roots in John Graunt's statistical analysis of the bubonic plague. It defines big data by its key characteristics: variety, volume, and velocity, emphasizing its complexity and potential to drive organizational growth and efficiency. The essay discusses techniques used to analyze big data, such as association rule learning, statistical classification using classification tree analysis, genetic algorithms, and machine learning. It also addresses the challenges associated with big data analytics, including a lack of skilled professionals, underutilization of big data's full potential, and high costs. Furthermore, it highlights the application of big data in various industries like energy, finance, manufacturing, and government, illustrating its role in optimizing operations, managing risks, improving supply chains, and enabling smart city initiatives. The essay concludes by referencing text mining in big data analytics, underscoring the importance of continuous learning and adaptation in this evolving field.

History on big Data
he big data come in by ohn raunt dealt with theT 1663 J G
big amount of data while they study in the bubonic
plague raunt is the fi rst person which deals with statistical. G
data analysis.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
ig data is contain a greater variety and increases the volume andB
velocity in the organisation ig data is a more comple data set it. B x
increases the company volume and speed he data is help the. T
company to grow and increase its efficiency level of the organisation.
Characteristics of Big data
• he big data manage the company size and workT .
• he big data insights discover the opportunity in theT
market.
• ig data work on structured data semi structuredB -
data
• t increases the speed of the business and managesI
all the work in the organisation
• t stores a huge amount of data and increases theI
ability of the business.
The challenges of big data
analytics
• he challenges of big data analytics the big data is veryT
useful for the government as well as the companies
but the companies face the challenges he company. T
don t have professionals he professional lack of’ . T
knowledge.
• hat big data is a very big term Companies use onlyT
those parts which they required so they do not use the
full big data hey dint understand the big data. T
process.
• he company does not understand the function of bigT
data so they are not able to reach their growth hich. W
company wanted to go?
• ig data is very e pensive so every company does notB x
buy this.
References
assani and et al e t mining in big dataH , H., . ., 2020. T x
analytics . ig ata and Cognitive ComputingB D , 4 p(1), .1.
How Big Data technology could
support business & Examples
• he energy industry uses eth big data for fi nding theT
location of drilling oil and gas t also monitors the. I
pipeline operation and also tracks the electronic grid .
• he fi nancial services organisation use big data for riskT
management and also analysis the real ti me analysis- .
• he manufacturer uses big data to manage theT
supply chain and optimise the delivery route.
• overnment use the big data for emergencyG
response he government use big data to make. T
smart city and prevention of crime.
Techniques that are currently
available to analysis big data
• he company use the association rule learning the company useT
to discover the correlation between the large databases .
• Statically classification in every company so the company use the
classification tree analysis.
• he company also use genetic algorithm and machine learningT
which increases system productivity in the organisation.