Big Data and Society: An Analysis Report - Course Name

This report examines the significant influence of Big Data on contemporary society. It highlights how Big Data is revolutionizing decision-making processes across various sectors, including healthcare and risk management. The report explores the utilization of predictive analysis in crime prevention and healthcare, emphasizing the benefits of proactive interventions. Furthermore, it analyzes how companies leverage Big Data to manage risks effectively, such as identifying potential financial troubles among customers. The report underscores the importance of data-driven insights in enhancing efficiency and responsiveness. The author also shares personal observations regarding the implementation of Big Data in banking, illustrating how it reduces the likelihood of loan defaults. Overall, the report provides a comprehensive overview of Big Data's transformative impact on society, emphasizing its potential to improve processes and drive innovation.

Big Data and Society 1
BIG DATA AND SOCIETY
By (Student’s Name)
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BIG DATA AND SOCIETY
The society has sought to make good use of Big Data on efficient decision making.
Whereas data analytics have usually been utilized in the improvement of decision-making
process efficiency and quality, the Big Data advent implies that areas of people live whereby
decision-making drive by big data plays a central role is dramatically outpouring; both
government and business become increasingly better able to exploit novel flows of data.
Moreover, the real-time alongside predictive feature of decision-making facilitated by Big Data,
are growing permitting automation of such decisions. Consequently, Big Data is giving business
and governments a unique opportunity to establish novel solutions and insights thereby
becoming increasingly responsive to novel opportunities alongside effectively able to act swiftly
and preemptively to tackle emerging threats.
The society is already starting to exploit the ability of Big Data to facilitate and enhance
processes of decision making. This is being applied to each sector from healthcare to transport
and is usually cited within the literature as a key Big Data pros. For example, the police forces
have increasingly used data-propellant predictive analysis to assist them in forecasting both times
and geographical destinations in which crimes are highly probably to take place. This move has
permitted the police force to effectively lower rates of crime (Bennett Moses and Chan 2016).
Another example is where Bid Data is helping the healthcare system through predictive
modeling propelled by Big Data. It is being used in this sector to identify patients in a proactive
manner especially those patients that might benefit from preventive care as well as lifestyle
changes (Chan and Bennett-Moses 2017).
Further, Big Data is innovatively having a significant impact on decision-making
capabilities in the area of risk management. For example, Big Data currently permits the

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companies to map respective whole data landscape. This subsequently assists in the detection of
sensitive info like sixteen-digit numbers. This is potential data on credit card that are never
stored in accordance with the regulatory framework or requirements for effective intervention. In
a similar manner, comprehensive data analysis held regarding customers and suppliers have
helped firms in the identification of the people in financial trouble, permitting them to swiftly act
to minimize any potential default exposure (Chan and Bennett-Moses 2016).
Personally, I have seen this innovation being used in the banks I run an account with and
the bank has increasingly lowered the possibility of potential default. This is because the loans
are now being given after assessing all the required information about the person. This means
that the credit-worthiness of an individual is fully ascertained alongside any other risk-
predisposition factors that can lead to default. Thus, only those that the bank is certain will make
schedule payment are approved for loans.

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References
Bennett Moses, L. and Chan, J., 2016. Algorithmic prediction in policing: assumptions,
evaluation, and accountability. Policing and Society, pp.1-17.
Chan, J. and Bennett Moses, L., 2016. Is big data challenging criminology?. Theoretical
criminology, 20(1), pp.21-39.
Chan, J. and Bennett Moses, L., 2017. Making sense of big data for security. The British Journal
of Criminology, 57(2), pp.299-319.