DATA4000: Individual Case Study Analysis in Business Analytics

This assignment presents a comprehensive analysis of business analytics through individual case studies. Part A examines the application of analytics in the art industry, specifically using IBM Watson to enhance artistic creativity, and in the Amsterdam Fire Department, where data analytics aids in managing fire hazards. Each case study details the industry, business problems, analytics types used (machine learning and linked data analytics), challenges faced, and recommendations for stakeholder adaptation. Part B delves into the role of prescriptive and automation analytics in solving business issues, using elderly care as a real-world example. Part C focuses on sourcing analytics professionals, matching job roles (data visualization analyst, business analyst, data scientist, analytics translator) with suitable analytics types. The analysis highlights how different analytics approaches are best suited for various roles and business objectives, emphasizing the importance of data-driven decision-making and strategic implementation.

Running head: BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
Name of the Student
Name of the University
Author Note

1BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
Table of Contents
Part-A: Case Study Analysis...........................................................................................................2
Big Data and Art..........................................................................................................................2
Amsterdam Fire Department.......................................................................................................2
Part-B: The role of Analytics in solving business issues.................................................................3
Part-C Sourcing Analytics professionals.........................................................................................4
References........................................................................................................................................6

2BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
Part-A: Case Study Analysis
Big Data and Art
The analytics has been applied to the art industry in this case study. The focus is on using
the IBM Watson computer to enhance the creative scope of Artists. The business problem that it
solves is enhancing the creativity of artists. It helps artists to get inspiration from the right kind
of sources and utilize the same to improve their creative scope. The problems related to learning
artistic forms are also solved through the analytics. The analytics can be used to create physical
sculptures. Data from various locations are captured after a piece of art is fed to the computer.
The computer then utilizes big data analytics to search similar images from various cities across
the globe or any particular city that has been mentioned. The type of analytics used was the
machine learning system. Big data analytics was also used to enhance the learning system. The
data concerning the original works of Gaudi, a renowned architect, were fed into the machine
learning system. The system then captured various information that were related to life in
Barcelona (Big Data and Art: Can machine learning technology recreate the work of
Gaudi? 2020). These were then used to create art forms that were also helped by the image
recognition technology of the analytics system. It is a type of system that can be used to create
newer but inspired art forms. One of the main challenges of using this system was that it can fail
to provide just the right inspirations to the artists. It can provide objective information by
scouring all the relevant data. However, an artist’s inspiration is subjective. It can be drawn from
various things. Hence, if this kind of technology is not used judiciously it can affect the natural
creativity of the artists. Moreover, as this has also been used to recreate art, it can falter at times.
The stakeholders need to find better ways to utilize the analytics. They need to be more familiar
with the ways in which it works. There need to be judicious development of strategies in regard
to the utilization of AIs in the various fields of art. The artists need to understand how they can
optimize art and creativity through the analytics.
Amsterdam Fire Department
In this case study the data analytics has been applied to the fire fighting services. The
analytics is being used by the fire department of Amsterdam. The business problem that the
application of data analytics focuses on solving is the increase in fire issues across the city. The
usage of the analytics systems is focused on finding the most important information related to
any fire incident across the city. Fire is a significant problem faced by the city of Amsterdam as
it is one of the major cities of Europe. Hence, the fire hazard problems that can affect the city are
in focus for using the big data analytics techniques (Amsterdam Fire Department: 2020). The
type of analytics used to solve the identified problem is linked data analytics. The fire fighting
devices and the fire fighting vehicles were fitted with special data gathering technology. As
much data as possible is gathered through the utilization of various types of technologies. The
data gathered from these devices are then presented to the public in a readable format. The data
consist of information about the potential fire hazards that might be present at various places
across the city. Moreover, an e-library was created from the gathered data to make all the fire
departments aware of the fire-fighting terms to make the communications process more
effective. The information gathered and presented can help to make people more aware of the
fire hazards that are present across the city and reduce fire incidents. It can also enhance the
functionality of the department. One of the main challenges is that all the vehicles might not be

3BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
fitted with the data gathering devices. Another challenge is gathering data from fire incidents that
are more critical than the others. Getting the public interested in such an activity is also a major
challenge. It can be recommended that the usage of these devices are made officially mandatory
for all the fire fighters. Moreover, the training functions can be enhanced train the fire fighters
about using the analytics from the beginning of their career. The fire fighters can be assisted
further is technologies such as AI or drones can be integrated into the process.
Part-B: The role of Analytics in solving business issues
Two important types of analytics are prescriptive and automation analytics. Prescriptive
analytics is a data analytics type that focuses on the best courses of actions that can be taken
from the data available in the context of a given scenario. Automation analytics helps to find
various trends and build effective models in consideration of the same. Algorithm for machine
learning is an important example of the prescriptive analytics type (Wang, Kung and Byrd 2018).
Chatbots or robotic vacuum cleaners are examples of automated analytics. It is important to
consider real-world case studies to evaluate the significance of both the analytics types.
One of the significant real-life areas where analytics can be utilized is Elderly care. In
real life situations, caring for the health and welfare of elderly citizens is an area where data
analytics are being used presently. Elderly care is a significant problem in the health and care
giving industry. Elderly people often tend to suffer from multiple health conditions that need to
be regularly monitored (Baik 2019). Hence, this becomes a significant problem across the
industry. Already, predictive analytics have been used to address the problem. However, the
prospects of prescriptive and automation analytics can also be said to be good in this field.
Prescriptive analytics can be used to solve the issues that are faced by the elderly.
Prescriptive analytics is a data analytics type that focuses on the best courses of actions
considering the available data. This can be important in this case as the health data of the elderly
can be used to develop the best courses of actions that can be taken to improve their conditions
of the elderly. Prescriptive research techniques can be used to develop systems through which
the health oriented data of different people can be found. The risks factors can be separated from
the positive factors. Hence, proper care can be provided to the elderly through the better
understanding of the exact negative aspects that affect the health of the elderly. Actionable
insights can be provided through the usage of prescriptive analytics. Care givers for the elderly
can be sure about the do’s and don’ts concerning each patients.
Automated analytics can help to address the same problem. Automated analytics can
create automated reports for the elderly without human intervention. AIs can be integrated within
diagnostic tools that are used to monitor the health of the elderly. It is important to understand
that this can help in gaining better understanding of the patient history. Chatbots can be designed
to interact with the elderly. The elderly care patients can feed their exact symptoms and weak
areas to the chatbots. This can then be used by the automated analytics to create better data in
regards to the elderly. It can help to make the work of the care giving professionals easier.
Moreover, it would also create positive values for the elderly. The information of the symptoms
can be tallied with similar symptoms that have been reported by the other patients across the
world. This way the data analytics can help to improve the situation of the suffering elderly.

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Part-C Sourcing Analytics professionals
1. Data Visualization Analyst
Descriptive analytics is more suited for this job role. Hence, this form of analytics is
likely to be assigned to the role of a data visualization analyst. The data visualization analysts
have to present data in ways that can facilitate the better understanding of data. This can include
data that is either quantitative, qualitative or data presented in any other form. The job requires
the effective designing and presentation of accurate data. Descriptive analytics refers to the
analytics that help in the better interpretation of historic data. The job role of a Data
Visualization Analyst would require the person to effectively present historic data. This can be
made possible through the effective implementation of the descriptive analytics. The
interpretation of data and its effective presentation can help in learning facts better.
2. Business Analyst
A business analyst is engaged in providing profitability and development solutions to
various businesses. The type of analytics that can be best suited for this job role is predictive
analytics. Predictive analytics evaluate the past circumstances to develop better judgements
about the future decisions that need to be taken. It involves techniques such as statistical
modelling and effective machine learning. The job of a business analyst is to help companies
make the best possible strategies to overcome business challenges. The job also requires
providing insights to businesses that can ensure their maximum profitability. In this case
predictive analytics can create a better understanding of the present business environments to
ensure that effective strategies are taken to address the future business opportunities. Moreover,
analytics can help to understand the best suited technological tools that can help businesses grow
and develop further. Hence, predictive analysis can help to achieve the most important goals for
this profession.
3. Data Scientist
The work of a data scientist requires the application of effective data analytics
mechanisms to develop solutions to large data problem. In the present world, the availability of
Big Data Analytics systems have made the position of the data scientist even more important.
Some of the important tasks for data scientists include finding the data analytics issues that
provide the organization with the best opportunities and determining the data sets (What Do
Data Scientists Do? 2020). Additionally, collecting large sets of data, cleaning data and applying
models become very important tasks for the data scientists. In this case automated analytics can
be most effective in helping the data scientist’s role. This form of analytics makes it easier to
find out the most effective data automatically. AIs are able to efficiently tack and find data that
can be effective in finding the right problems that need to be solved. The data scientists can then
concentrate on the other important functional areas that they need to manage. This analytics type
can efficiently improve their functional effectiveness.
4. Analytics Translator
Analytics translators help to develop better operational values from the inputs that are
provided by the data scientists. This role is important as this helps to actually implement the
strategies that are found to be the most important for the organization by consulting the right

5BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
data. This is an important part of analytics role as this helps to create better scope for the
implementation feasibility of the requisite strategies. The translators use AI systems and
analytics elements to set achievable goals before the company. This makes the application of
prescriptive analytics more important in consideration of this job role. Prescriptive analytics is a
data analytics type that focuses on the best courses of actions that can be taken from the data
available in the context of a given scenario. Hence, the role of the translator can become easier.
The prescriptive analytics system can help to enhance decision making among the translators.
The above analysis has helped to understand that the job descriptions that would be the
best to address the case study issues discussed in part A are Data Visualization analyst and data
scientist. Data visualization analyst can help in the first scenario as the artistic elements can be
improved by the participation of a data visualization expert to increase the creativity factors of
the Watson system. In the second case the most important roles can be played by data scientists.
Data scientists can help to interpret the data that is found through the devices installed on the fire
engines. This data can then be used to create hotspot zones for fire hazards in the entire city of
Amsterdam. This would improve the usability of the systems for solving the most important
problems.

6BUSINESS ANALYTICS: INDIVIDUAL CASE STUDY
References
Amsterdam Fire Department: The use of Big Data analytics in fighting fires (2020). Available at:
https://www.bernardmarr.com/default.asp?contentID=1082 (Accessed: 7 April 2020).
Analytics translator: The new must-have role (2020). Available at:
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-
translator (Accessed: 7 April 2020).
Baik (2019) How Analytics Transforms Senior Living, Technology Solutions That Drive
Healthcare. Available at: https://healthtechmagazine.net/article/2017/06/how-analytics-
transforms-senior-living (Accessed: 7 April 2020).
Baik (2019) How Analytics Transforms Senior Living, Technology Solutions That Drive
Healthcare. Available at: https://healthtechmagazine.net/article/2017/06/how-analytics-
transforms-senior-living (Accessed: 7 April 2020).
Big Data and Art: Can machine learning technology recreate the work of Gaudi? (2020).
Available at: https://www.bernardmarr.com/default.asp?contentID=715 (Accessed: 7 April
2020).
Villanovau.com. (2020) Villanovau.com. Available at:
https://www.villanovau.com/resources/business-analysis/business-analyst-job-description/
#.W2g1vvZuJjo (Accessed: 7 April 2020).
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and
potential benefits for healthcare organizations. Technological Forecasting and Social
Change, 126, pp.3-13.
What Do Data Scientists Do? | University of Wisconsin Data Science (2020). Available at:
https://datasciencedegree.wisconsin.edu/data-science/what-do-data-scientists-do/ (Accessed: 7
April 2020).