Contribution of Students

This document discusses the contribution of students in a group report on big data privacy. It includes their research findings, discussions, and recommendations for future issues related to big data privacy.

Contribution of Students

Student 1
In this group report, I have started with Introduction part. After researching
about big data, I understood that big data is a technology, which deals with complex
data sets and it helps in reducing data management complexity. I have also learnt
about safety and security of big data.
For discussion part, I had to note down definition of big data privacy. After
researching about such details from “Competitive Benchmarking: An IS Research
Approach to Address Wicked Problems with Big Data and Analytics” by Ketter ei al.,
2013 and, it was much easier for me to complete this part. I have also got idea from
few other peer reviewed articles by McAfee et al., 2012, Manyika et al., 2011,
Kallinikos & Constantiou, 2015, Sharma, Mithas & Kankanhalli, 2014, Wu et al., 2014
and Xu et al., 2014.

Student 2
I started with second part of discussion, which is “Importance of big data”.
This particular technology is extremely important for completing analysis on a priority
basis. I have researched a number of articles and extracted secondary data from
websites so that I get clear idea about data breaching and how to maintain
confidentiality of data. I even understood that external data might lead to interference
of new factors and thus it is required to maintain integrity and confidentiality. Data
sensitivity was understood by me that sensitive data requires to be stored and
processed properly and big data is responsible for providing this advantage (Lu et
al., 2014).
I also learnt about the mechanisms to protect data privacy and reason for
restricting data access with the help of data protection techniques. After getting idea
about few such techniques, I understood that encryption based technique would be
the best for securing sensitive information (Patil & Seshadri, 2014). Moreover, few
issues like outsourcing are also highlighted in this section, which is quite common for
increasing big data costs.

Student 3
After two of my team members completed until importance of big data, I
started with advantages with big data technologies and privacy. Although, my team
member had already mentioned about importance of this technology, I decided to
emphasize on the benefits that this technology provide to its users. I got help from
few journal articles and understood that this technology is effective for both
structured and unstructured data and it provides better support as well as enhances
decision making process.
By researching secondary data, I knew about cost effectiveness, time
reductions, helps in developing new product, understanding market conditions,
controlling online reputation, easier data management, real time data forecasting and
effective decision making (Patil & Seshadri, 2014). Few other journal articles and
conference papers were also used. After completing the advantages, I also started
noting about issues faced due to big data privacy. The issues highlighted by me
were concern about security and privacy, challenges and needs for technology,
problem with big data value and handling with voluminous data (Kallinikos &
Constantiou, 2015).

Student 4
Since, three of my team members have covered all the points of introduction,
importance of big data privacy, advantages of big data technologies and privacy and
issues faced by big data privacy, I decided to focus on conclusion by providing a
summary of all points. After analysing the entire research report, I understood
although there are few issues in big data privacy, this particular technology is also
responsible for providing some of the most significant advantages to its users and
hence increasing chance for better data security.
I even provided few significant recommendations for future issues related to
this big data privacy. The first and the foremost recommendation for such issue that I
have provided is implementing machine learning. It would be helpful for big data
preparation and conduction of predictive analysis. The other recommendations
include involvement of data scientists, buying of algorithms and good investments. I
analysed after inclusion of these recommendations, it would be easier to deal with
the future issues.