DATA4000: Case Study Analysis on Big Data Applications and Challenges

This case study analysis examines the application of big data analytics in two distinct industries: advertising and firefighting, as well as the application of data analytics tools in environmental conservation. The analysis begins by exploring how big data can be leveraged in the advertising industry to optimize marketing strategies and in firefighting to predict fire outbreaks, including the challenges associated with data management, interpretation, and stakeholder adaptation. It then delves into a real-life case study of the Nisqually River Foundation, demonstrating how data visualization and predictive analysis tools were used to monitor fish species and improve watershed stewardship. The analysis highlights the benefits of these tools and the importance of data adaptability and effective data interpretation for stakeholders like designers, firefighters and environmental conservationists. The assignment concludes by emphasizing the adaptability of data analytics tools in practical activities and the need for training and skill development to ensure effective implementation and maximize outcomes.

Running Head: CASE STUDY ANALYSIS
Case Study Analysis
Name of the Student:
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Author Note:

1CASE STUDY ANALYSIS
Part A
Case Study Analysis
Big Data Analytics and Art
The first case study dealt with the big data analytics and tried to find out the possibilities
whether big data analytics can be used in replacing the artworks of Gaudi, the great Catalonian
artist.
a. The industry where the big data analytics can be best applied is the Advertisement and
publishing industry. They big data analytics will find its huge application in this industry.
The application of big data can be used in the advertisement and marketing sectors as it
involves the work of art and designs. Through the big data, the marketers can fund out the
relevant details and the shopping patterns along with the interests, consumer habits and
the trends in the current market scenario for enhancing the effectiveness of the
advertisement sector.
b. The application of the big data analytics in solving the challenges in the advertisement
industry are the optimization of data for the real time interpretation that can be used to
effectively address the market demands in order to maximize the satisfaction of the
consumers. The big data will be helpful in the classical scheduling according to the time
slots of the conveyance mode. For the airports especially big data can be really helpful in
providing with the real time information for the enhancement of its application (Shahid
et.al 2016).
c. The type of big data analytics that is used in the advertisement and marketing industry are
the open street map that provides with an excellent data source for the purpose of

2CASE STUDY ANALYSIS
computation and analytics. Based on this analysis, one can easily conduct the
stimulations in order to figure out the different underlying mechanisms for the marketing
activities. Other applications include the sensors that will effectively measure the product
demand and availability and the individual sensors that will be helpful in the
determination of the trajectories of the availability of products in the market scenario
(Mosavi et.al 2017).
d. The main challenges of applying the big data analytics in attaining the business
objectives of the transport sector lies in the management of the unprecedented amount of
data that gets constantly generated from the different transactions along with the data
pricing levels, the customer feedback mechanism and similar other challenges.
e. In order to increase the adaptability of the big data analytics among the stakeholders like
designers and artists by raising the fundamental concepts of the sophisticated data
intelligence tool along with the machine learning techniques. The designers should be
provided with the basis skill training activities in order to adapt themselves to the big data
analytics tool (Magi et.al 2018).
Big Data Analytics and Firefighting
The second case study deals with the use and the application of the big data analytics in the
Amsterdam fire department. This case study deals with the application of data analytics in the
firefighting and the rescue operations sector:
a. The officials of the fire department of many nations can use the big data analytics tool in
order to predict the occurrence and the most important areas and reasons for the
outbreaks of fire. In few areas the fire departments has applied the big data analytics in
order to determine the distribution process of the free smoke detectors where the data

3CASE STUDY ANALYSIS
scientists developed a particular tool that could predict the vulnerable city blocks that
face the high fire risks. The tool is known as the Smoke Signals This tool was first used
in Atlanta by the New Orleans (Wu et.al 2017).
b. The challenges that are involved in applying the data analytics in solving the fire risks are
first the risks associated in analyzing the past data and applying it in both commercial and
residential structures can provide with wrong information that will lead to misleading
actions. Sometimes the challenge also lies in the misinterpretation of data by the local
officials due to the lack of proper training and technical knowhow in this field.
c. The main types of data analytics that is used in the firefighting sector are the predictive
modelling tool that can be used to construct a risk profile of the area under consideration
where the outbreak of the fire takes place. For the data modeling measures, the data is
collected from the fire sensors along with the equipment of personal protection measures
in order to build those specific models that can be applied to assess the risks due to the
outbreak of fire (Ajala et.al 2018).
d. The main challenges in yielding an increasing outcome in the firefighting sector includes
the lack or the gap in the knowledge of the data that is used for the purpose of
firefighting. Lack of skills and technical knowhow among the people leading to the
misinterpretation of the data leads to lower level of outcomes of application of data in
firefighting.
e. The recommendations for increasing the outcome increasing the knowledge and the
technical tools for the proper interpretation of the data that will assess the fire risk
assessment scenarios. This will result in the efficient assessment of the risks associated
with outbreaks of fire (Xu et.al 2018).

4CASE STUDY ANALYSIS
Part B
The Role of Analytics in solving the main Business Problems
Adaptability of two data analytics tool in one real life case study
Two different data analytics tool from the above workshop are data visualization and
predictive analysis tools. The real life case study where these two tools were applied was by the
conservation organization of Nisqually River Foundation.
By using the predictive analysis and the data visualization tools, the organization sought
to implement a plan for watershed stewardship in order to measure and monitor the species of
fish that are present in the Nisqually River. In order to effectively apply the tools, the
organization installed a video camera along with the infrared sensors in the water. The camera
was installed with the purpose to record the video for approximately 30 seconds whenever the
fish appeared in the water bodies. Later the members of the organization kept reviewing in order
to manually identify the fish species. The entire process was very time consuming and became an
inefficient process so the organization later took up the process of data visualization tool for
deriving an automated and tech driven solution (Murray 2017).
The organization used the data visualization tool of Gramener for the predictive analysis
of the company which implemented a specific web based program related to the artificial
intelligence tool. The entire AI based program will be projected to deliver the Nisqually river
foundation savings at a rate of approximately 80%.
The main objective for this case study analysis is to analyze the adaptability of the data
analytics tool in practical activities. The segment categorizes the adaptability between the
collection and the availability of the data that is addressed by the researchers in this field. The

5CASE STUDY ANALYSIS
above chosen case study highlights the processing of data in order to extract the required
information and the features, to promote the data visualization techniques, to promote the
clustering of the different sections of data collected into a unit and finally to effectively analyze
the features associated with the collected data (Cardno et.al 2018).

6CASE STUDY ANALYSIS
References
Magi, A., Semeraro, R., Mingrino, A., Giusti, B. and D’Aurizio, R., 2018. Nanopore sequencing
data analysis: state of the art, applications and challenges. Briefings in bioinformatics, 19(6),
pp.1256-1272.
Shahid, M.B., Sheikh, U., Raza, B., Shah, M.A., Kamran, A., Anjum, A. and Javaid, Q., 2016.
Application of data warehouse in real life: State-of-the-art survey from user preferences’
perspective. International Journal of Advanced Computer Science and Applications, 7(4),
pp.415-426.
Ajala, M.T., Khan, M.R., Shafie, A.A., Salami, M.J.E., Nor, M.M. and Oladokun, M.O., 2018.
Optimization of CO2 production rate for firefighting robot applications using response surface
methodology. Cogent Engineering, 5(1), p.1555744.
Xu, Y.W., Su, G.F., Chen, J.G. and Du, P., 2017. Research on management and application of
smart firefighting technology in urban city. Университета гражданской защиты МЧС
Беларуси, 1(4), p.469.
Murray, S., 2017. Interactive data visualization for the web: an introduction to designing with. "
O'Reilly Media, Inc.".
Cardno, A.J., Ingham, P.S., Lewin, B.A. and Singh, A.K., New BIS Safe Luxco SARL, 2018.
Methods, apparatus and systems for data visualization and related applications. U.S. Patent
9,870,629.
Mosavi, A., Lopez, A. and Varkonyi-Koczy, A.R., 2017, September. Industrial applications of
big data: State of the art survey. In International Conference on Global Research and Education
(pp. 225-232). Springer, Cham.

7CASE STUDY ANALYSIS
Wu, X., Dunne, R., Zhang, Q. and Shi, W., 2017, October. Edge computing enabled smart
firefighting: opportunities and challenges. In Proceedings of the fifth ACM/IEEE Workshop on
Hot Topics in Web Systems and Technologies (pp. 1-6).
Bibliography
marr, b., 2020. Big Data And Art: Can Machine Learning Technology Recreate The Work Of
Gaudi?. [online] Bernard Marr. Available at: <https://www.bernardmarr.com/default.asp?
contentID=715> [Accessed 20 April 2020].
Bernard Marr. 2020. Amsterdam Fire Department: The Use Of Big Data Analytics In Fighting
Fires. [online] Available at: <https://www.bernardmarr.com/default.asp?contentID=1082>
[Accessed 20 April 2020].