University Business Analytics: Case Study Analysis and Recommendations

This assignment presents a comprehensive analysis of business analytics through two case studies. The first case study examines IBM Watson's application of machine learning to enhance design and creativity, highlighting the use of descriptive and predictive analytics. The second case study focuses on the Amsterdam Fire Department's implementation of big data analytics for firefighting, emphasizing the importance of data visualization and predictive modeling. The assignment further explores the role of analytics in resolving business difficulties, differentiating between descriptive and predictive analytics, and discussing various analytics professionals, including data analysts, data scientists, and analytics translators. The analysis covers the challenges of implementing these analytics types and provides recommendations for stakeholders to adapt to these applications, demonstrating the broad applicability of data science and big data across different sectors.

Running head: BUSINESS ANALYTICS
BUSINESS ANALYTICS
Name of the Student:
Name of the University:
Author’s Note:

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Table of Contents
Part A:-.............................................................................................................................................2
Case Study Analysis:-......................................................................................................................2
Case study 1:-..............................................................................................................................2
Case Study 2:-..............................................................................................................................3
Part B:-.............................................................................................................................................5
The Role of Analytics in Resolving Business Difficulties..............................................................5
Descriptive analytics:-.................................................................................................................5
Predictive Analytics:-..................................................................................................................6
Part C:-.............................................................................................................................................7
Sourcing Analysts Professionalism:-...............................................................................................7
Data visualization:-......................................................................................................................7
Data analyst:-...............................................................................................................................7
Data scientists:-............................................................................................................................8
Analytics translators:-..................................................................................................................9
Case Study:-.................................................................................................................................9
References:-...................................................................................................................................11

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Part A:-
Case Study Analysis:-
Case study 1:-
In this instance, the study is relating to the machine learning concepts which are applied
to the organization named IBM Watson. This organization recommended a subject of natural
organic structures and shape, like crabs, beehives, and shellfish, which had not directly been
ostensible to them.
Analytics is the detection, understanding, and statement of expressive designs in data. It
also involves applying information patterns near active decision making. In other terms, analytics
can be assumed as the construction between documents and active decision making within a
business (Big Data and Art: Can machine learning technology recreate the work of Gaudi?,
2020). In this case, study the business authority which is useful for their business structure. The
machine learning method of IBM was feeding vast numbers of pictures of Gaudi’s effort, as well
as images connected to Spanish art, music, and literature. With the help of these approaches, the
authority can solve their business problems.
IBM Watson’s natural language handling, visual recognition and colour-matching
implements were then applied to classify themes, substances and philosophies – Antoni Gaudi’s,
as well as those that encouraged him – and apply them as the basis of its specific effort. The
outcome is a construction which is “obviously suggestive” of Gaudi’s work, however at a similar
time very idiosyncratic (Big Data and Art: Can machine learning technology recreate the work of
Gaudi?, 2020). Big data delivers this organization with the understanding that can be applied to

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progress planning. Better planning denotes more precise budget approximations and a better
accepting of timelines and budgets.
There is a good relationship between IoT, Artificial intelligence and machine learning
techniques. In the current time, people are trying to evolve this knowledge efficiently. In this
scenario, humans and machine learning came together to generate something that else may never
have happened. These displays how AI can increase human imagination, not mostly replaced.
That is a beneficial and very timely experience.
Analyzing the information accessible to them, IBM Watson can forecast the threat of
upcoming accidents. These workforces are frequently weak relations in company security
training programs. Apart from this stakeholder designer architect are also accepting this
knowledge because they want to improve their creativity in a computerized way (Big Data and
Art: Can machine learning technology recreate the work of Gaudi?, 2020). Businesses can make
variations ahead of time to remove safety threats, chiefly for temporary workers and contract
workforces. So this report writer recommends that this approach are very much beneficial to the
designer and architect for better work.
Case Study 2:-
In this case, a study is related to the Amsterdam fire department who are trying to evolve
a big data concept for firefighting. The perception is that authority must gather as much
information as possible over the knowledge, which is becoming accessible – for instance, IoT
sensors linked to pumps and fire engines (Amsterdam Fire Department: The use of Big Data
analytics in fighting fires, 2020). However, the conception also uses to create as far of that
information as possible accessible in a readable and open arrangement to the community, so
inventive attentions can develop innovative life-saving presentations.

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The fire authority uses some firefighting term and prediction, which can be distributed
between divisions to confirm they are precisely talking a similar language. These were a
dynamic primary stage, because else, no matter how thrilling ad huge their information set
becomes, everyone has to be understanding it over the similar lens, or understandings and
expectations could be constructed on a defective investigation (Amsterdam Fire Department: The
use of Big Data analytics in fighting fires, 2020). The assumed predictive structure could be
applied to construct a representation of the risk profile of the extent where an occurrence is
taking place. Information taken from sensors and individual protection apparatus can be applied
to construct prototypes for measuring risk.
There have been difficulties of course – as fire authority puts it, no one needs to realize
firefighters arriving at a fire and then understanding their iPads for some times before they get
out of the engine. These are where the perception of connected information becomes so
undoubtedly valuable. Firefighters want to have a commanding, reunited stream of evidence,
telling them accurately what they want to distinguish at the correct time, in a way that can be
consumed as rapidly as possible.
Sharing and responsive documents is unquestionably a vital part of the procedure when it
derives to applying Big Data and analytics to create the world an enhanced place. Firefighting is
just one of the thousands of areas where Big Data can undoubtedly make a variance (Amsterdam
Fire Department: The use of Big Data analytics in fighting fires, 2020). The information is out
there, and the user has the apparatuses to gather it, so currently the race is on to discover the best
means to put it to use, before more human death. The report writer recommended that in this
significant data evaluation are very much beneficial for all shareholder. The government can
quickly mitigate human death risks and the firefighting team also effectively complete their task.

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Part B:-
The Role of Analytics in Resolving Business Difficulties
In a commercial background, the significance of analytics cannot be excessive. It is
applied to scrutinize and recognize traditional designs and to forecast and progress upcoming
business performs. As such, it is essential to the victory of any business, hence the high demand
for analytics specialists (Appelbaum et al. 2017). The four types of analytics are very much
common in solving any business problems. The types of analytics are descriptive, diagnostics,
predictive and perspective analytic. In this report, the writer is describing two types of analytics
which can be helpful for business.
Descriptive analytics:-
It is an initial phase of data handling that makes a summary of antique data to produce
valuable evidence and perhaps organize the records for supplementary analysis. Descriptive
analytics applies a full range of information to contribute an exact picture of what has occurred
in an industry and how that fluctuates from other equivalent periods (Lismont et al. 2017). These
presentation metrics can be applied to flag areas of weakness and strength to notify management
approaches. The Coca-Cola case study is one of the most prominent examples of this descriptive
analytics. Innovation and the subsequent key landmark in the journey for Coca-cola Company
were when they devoted in an "out of the box" structure which delivered them with usual metrics
and actions and permitted rapid and unpretentious descriptive analytics (Menaria 2015). This
business applied a solution that incorporates information from several resources, whether it is
their Human resource system, the case management arrangement for the facility centre, or their

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recruitment tools. The CCE carried all that information into one central area and established a
considerable number of shares and processes. That took it to the subsequent level.
Predictive Analytics:-
Predictive analytics is the preparation of removing evidence from current data sets to
regulate decorations and forecast future consequences and developments. Predictive analytics
does not express the user what will occur in the upcoming. As an alternative, it estimates what
might occur in the prospect with a satisfactory level of dependability, and contains what-if
situations and risk valuation. Businesses gather massive amounts of real-time client documents,
and predictive analytics applies this historical information, shared with client insight, to forecast
future actions. Predictive analytics permit administrations to apply big data to transfer from a
historical opinion to a forward-looking view of the client (He and Chua 2017). For instance,
stores that apply information from loyalty plans can examine past buying performance to
forecast the tickets or elevations a client is most to contribute in or buy in the forthcoming.
Predictive analytics might also be useful to client website browsing activities to distribute
a personalized website involvement for the client. U.S. cellular case study is the example of the
predictive analysis. Fundamental Path led a determined acquisition analysis for U.S. Cellular and
exposed the website activities that are most predictive of future procurements (Schoenherr and
Speier‐Pero 2015). Armed with these visions, preliminary testing has already determined a 23%
rise in visitors to the "Offers" division of the website and a 61% improvement in client
generation value by developing the combination of higher-value clients in the mix and growing
reaction ratios.

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Part C:-
Sourcing Analysts Professionalism:-
Data visualization:-
Data visualization is a graphical demonstration of evidence and information. By applying
visual foundations like graphs, charts and maps, these tools deliver an easy technique to see and
understand outliers, trends and designs in data. Data Visualization Experts are persons who are
experts at interpreting statistical information in ways that are beneficial for both subject matter
SME as well as commercial users. The report writer though that descriptive analytics is most
likely to assign data visualization roles (Williamson 2016). The reason behind that most of the
descriptive analyst provides an analysis of any business sales, operation and financial
department. Every role is not possible without the evaluation of data, diagram and graphical
representation. So it is a fact that the data visualization technique is very much crucial for the
specialist of descriptive analysis.
Data analyst:-
A data analyst gathers and stocks information on market research, sales numbers,
logistics other behaviors. They carry technical knowledge to confirm the accuracy and quality of
those documents, then method, strategy, and present it in techniques to help persons, companies,
and administrations make better quality judgments (Fiaz et al. 2016). So the data analyst must
use descriptive analytic methods and also follow predictive analytics techniques. Predictive
analytics is a classification of data analytics expected at creating predictions about upcoming
consequences based on historical information and analytics methods.

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Data scientists:-
Data scientists are analyzing vast sets of organized and unstructured documents. A data
scientist’s role combines statistics, computer science and mathematics. They examine, route, and
model data then understand the consequences to make actionable strategies for businesses and
other administrations. In request data scientists characteristically specify in a specific industry or
improve strong abilities in areas like as AI, machine learning, database management or research.
Specialization is a correct way to rise any organization’s earning prospective and do work that is
expressive to them. More usually, a data scientist is somebody who distinguishes how to excerpt
meaning from and understand data, which needs both methods and tools from machine learning
and statistics, as well as being human.
The data scientist expends a considerable time in the procedure of cleaning, collecting
and managing data because the information is never clean (Hauser et al. 2015). This procedure
needs perseverance, measurements, and software engineering abilities which are also essential
for understanding references in the documents, and for correcting logging output from code.
Prescriptive Analytics is the part of data analytics that concentrates on searching the best action
course in a situation given the accessible data. It has connected to both predictive analytics and
powerful analytics but highlights actionable understandings instead of data observing.
Prescriptive analytics help industries classify the best sequence of action, so they realize
organizational objectives like customer satisfaction, cost reduction, profitability. Whereas
figuring out what they should do is a crucial characteristic of any industry, the importance of
prescriptive analytics is frequently missed. There is still a disposition to “go with the gut” when
observing at an array of probable scenarios.

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Analytics translators:-
Analytics translators implement some of the most exceptional vital functions for
incorporating analytics abilities in business. They define economic difficulties that analytics can
help to explain, guide functional teams in the formation of analytics-driven explanations to these
difficulties, and implant solutions into commercial operations (Ali et al. 2016). As these
creativities evolve, different roles arise in the business. The latest of these analysis-related
responsibilities is the Analytics Translator. As the enterprise considers the significance of this
different role within the industry, it is significant to realize the accountabilities of an Analytics
Translator, and how this part might assist the business to accomplish its goals. They follow the
clear analytic rules where they add resources to a group that contains Data Scientists, IT, and
Data Architects.
Case Study:-
In the first case study is related to the big data and work recreation process of Spanish
architect Antoni Gaudi. In this scenario, the IBM authority is trying to evolve the big data
concept, which can be helpful to recreate the architecture. According to this case study, the data
analyst can collect all the buildings information as well as collect several architectural methods
for data analyses. When they collect all evidence, then the data analyst easily navigates this
information through the data visualized process. So it is defined that in the first case study are
very much similar to the descriptive-analytic method.
On the other hand, the Amsterdam firefighting team can launch big data tactics to
decrease death, not only that they want to evolve modern techniques which can be beneficial for
them. That is the reason the data operation department first calculate some error reports and also

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predict some outcome when the incident is occurring. So, in this case, study, the data scientist
can follow predictive analytic approach to navigate upcoming errors.

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References:-
Ali, S.M., Gupta, N., Nayak, G.K. and Lenka, R.K., 2016, December. Big data visualization:
Tools and challenges. In 2016 2nd International Conference on Contemporary Computing and
Informatics (IC3I) (pp. 656-660). IEEE.
Appelbaum, D., Kogan, A., Vasarhelyi, M. and Yan, Z., 2017. Impact of business analytics and
enterprise systems on managerial accounting. International Journal of Accounting Information
Systems, 25, pp.29-44.
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 14 April 2020].
Bernard Marr. 2020. Big Data And Art: Can Machine Learning Technology Recreate The Work
Of Gaudi?. [online] Available at: <https://www.bernardmarr.com/default.asp?contentID=715>
[Accessed 14 April 2020].
Fiaz, A.S., Asha, N., Sumathi, D. and Navaz, A.S., 2016. Data visualization: Enhancing big data
more adaptable and valuable. International Journal of Applied Engineering Research, 11(4),
pp.2801-2804.
Hauser, M., Zügner, D., Flath, C. and Thiesse, F., 2015. Pushing the limits of RFID: empowering
RFID-based electronic article surveillance with data analytics techniques.
He, X. and Chua, T.S., 2017, August. Neural factorization machines for sparse predictive
analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and
Development in Information Retrieval (pp. 355-364).