MIS609 Case Study Report: Data Analytics for Decision-Making

This case study report examines the application of data analytics for organizational decision-making, focusing on Kellogg Company. It covers data analysis concepts like business intelligence, machine learning, AI, data mining, and data warehousing. The report identifies the revenue challenges faced by Kellogg's, the implementation of data analytics solutions, and the problems encountered during the transition. It analyzes the benefits of these changes, including increased revenue and cost reduction. The conclusion highlights the usefulness of data analytics tools, the findings emphasize the positive impact of AI and machine learning on overcoming challenges, and the recommendations suggest proactive problem identification and effective communication during change implementation. The report utilizes various data sources such as online sites, company websites, published articles, journals, books, and case studies. Desklib offers a platform for students to access similar solved assignments and study resources.

Case study report Data
Analytics for organizational
Decision making

TABLE OF CONTENTS
INTRODUCTION TO THE ORGANIZATION.......................................................................3
INTRODUCTION TO THE CONCEPT OF DATA ANALYSIS............................................3
DATA SOURCES......................................................................................................................4
ANALYSIS................................................................................................................................4
Problem being faced by the organisation...............................................................................4
Organisation implement the concept......................................................................................4
Problems faced by the organisation in implementing the new change..................................5
Analysing the change beneficial for the organisation............................................................5
CONCLUSIONS, FINDINGS AND RECOMMENDATIONS................................................5
REFERENCES...........................................................................................................................7

INTRODUCTION TO THE ORGANIZATION
In this report, Kellogg Company is taken as an organization which is an American
multinational organization having it headquarter in Battle Creek, Michigan, US. The reason
behind choosing this organization for the study is that the organization is into food
manufacturing which makes it having a strong market position all across the world. It is into
various product segments which makes it right choice of company to be studied upon which
involves making use of data analytics. Kellogg's is into manufacturing food products which
incorporates crackers, pastries and is also having its brand such as Corn Flakes, Frosted
Flakes, Pringles, Eggo, and Cheez-It. It is having its largest factory in Manchester, UK which
is also the location of its UK headquarters.
INTRODUCTION TO THE CONCEPT OF DATA ANALYSIS
Business intelligence: It refers to the procedural and the technical system which
gathers, stores and also analysis the data which is produced through the business activities. It
is much bigger term and it encompasses data mining, performance benchmarking along with
the descriptive analysis (Rodrigues, Santos and Bernardino, 2018). Through BI all the
company related data is gathered and is presented in an easy form or reports which helps in
determining the trends and patterns resulting into undertaking informed decisions.
Machine learning: It is referred to as the branch of AI and the computer science
which is mainly concentrated in making use of data and algorithms with the objective of
imitating the way which helps in making humans understand and thus gradually improvise
the accuracy. It is useful in making predictions along with taking decisions without any
explicit programming. It is mainly useful in the areas of telecommunications, marketing,
sentiment analysis and computing. Cybersecurity mainly utilizes machine learning for the
purpose of filtering and spam identification.
Artificial intelligence (AI): It accounts for the simulation of the human intelligence
processes through the use of machines and the computer systems. Some of the specific
application of AI is expert systems, speech recognition and the machine vision. It is strongly
supported in the various business areas such as manufacturing, military, economics etc.
Data mining: It refers to the process in which the anomalies, patterns and the
correlation within the data set is being identified which is set to predict the outcomes. It
involves making use of the broad range of techniques which helps in gathering relevant
information which assists the companies in increasing its revenue, minimising the costs,
along with enhancing the relationship with the customers and reducing risk (Ghasemaghaei,

Ebrahimi and Hassanein, 2018). In addition to this, it helps in discovering the structures
which are hidden within the large data set which are otherwise, remain hidden and it is also
helpful in discovering structures and summarising the data sets.
Data Warehousing: It refers to the data management system which is being
designed to enable and assist the BI activities of the organization mainly the analytics. It is
mainly intended to carry out the queries and involves huge amount of historical data. It can
be considered as a large data repository which involves integrating data from one source to
another in one place. It involves making use of the technical methods such as the extraction
of data, integration of data. It helps in improving the operational efficiency of the business.
DATA SOURCES
In order to effectively complete this assessment, the data has been gathered by
taking into consideration, online sites, company website, published articles, journals, books
and case study related to the selected organization. These sources are highly reliable and
valid as they are collected from the authentic sources.
ANALYSIS
Problem being faced by the organisation
The organization was confronting the greatest issue of revenue, as there was not a
high profit margin in the item, and the 33% parts of the produced income was spent on the
limited time expenses of the organization such as the special discounts, sponsorships, and so
forth. The revenue growth of the company was saturated in respect to some of the product
categories. In addition to this, the company had to spends a lot of amounts over the traditional
infrastructure which was not up to the mark in attaining desired outcomes.
Organisation implement the concept
The company has made use of the all the above discussed concepts in order to
overcome the challenge it faced. The company employed highly reliable BI system which
helped in effectively gathering and storing the business information which is later analysed
based upon which reliable and accurate business-related decisions are being undertaken. With
the use of machine learning and AI the company was able to effectively predict and
understand the changing trends and patterns of the consumer perception, marketing
requirements and in conducting sentimental analysis (Jarrahi, 2018). Effective AI system
were also implemented which helped in providing better customer services and effectively
meeting with their needs. Kellogg’s also introduced data warehouse system and data mining
which helps in storing data at higher security and creating reports by making use of the

historical data resulting to determining the information hidden in the large data, this has
helped it in determining the core factors affecting the revenue of the company. In addition,
these concepts help in identifying the areas where cost can be further reduced.
Problems faced by the organisation in implementing the new change
In regard to implementing various changes there were certain problems faced by
Kellogg’s like dealing with the issue of resistance to change as initially it was seen as a threat
by the employees. In addition to this, providing training to the employees which will help in
keeping up with the technical changes (Top 10 technology challenges businesses face. 2020).
This was a difficult, time consuming and costly step as convincing staff how it will be helpful
for them was tough. Resource and infrastructure management was also another issue in
regard to the change.
Analysing the change beneficial for the organisation
By implementing changes, Kellogg’s was in the position to overcome the main
challenge it was facing. The revenue of the company was increasing as it was able to serve
large group of target customers as these tools helped in analysing and serving the different
customer groups for the variety of its products. It helped in changing needs of the customer
and in addition to this, the change helped in eliminating unnecessary cost in manufacturing
the items leading to reducing the price of the goods and increasing the profit margin of some
of its products. The company also reducing irrelevant traditional advertising methods of
promotions.
CONCLUSIONS, FINDINGS AND RECOMMENDATIONS
Conclusion
From the above analysis, it can be stated that, data analytical tools and techniques
are very useful to the business a sit helps in deriving in-depth information from the raw data
which might not to possible to determine with the human eye. The various data analysis
concepts have supported the organization in undertaking effective business-related decisions
leading to mitigating the challenges or the issues faced by the organization.
Findings
Based on the analysis of the challenge faced by Kellogg’s, it was found that earlier
the organization was having problem in increasing its revenue even though it was incurring
the significant amount for the advertisement and promotion. After introduction of the
machine learning, AI, data warehouses and mining and business intelligence system into the
organization has overcoming the challenge of the Kellogg’s (Hung, He and Shen, 2020). It

helped the company in better analyzing the changing trend of the customer perception and
need, and consumption pattern along with this, determining the ways through which
unnecessary cost can be eliminated. This has resulted in reducing the cost of product of the
products which lead to reducing the selling price and enhancing the profit margin of the
products which consequently lead to increasing the overall profitability from the various
product categories.
Recommendations
There are certain recommendations if implemented by the company can help in overcoming
various issues related to the stated challenge.
 The company should identify the cause behind the problem at the initial stage and
based upon the proper evaluation of the problem should look for the proper solution to
it. Finding solution will involve identifying the data analytics methods which help in
meeting with the desired objectives in a better and effective manner. This is very
important before undertaking decision as it is a costly affair which is needed to be
accounted for in order to accomplish the desired objectives.
 After finalizing to bring in the changes, the company should communicate the same to
the employees and the staff so that they can mentally prepare themselves with the
same leading to avoiding the situation of resistance to change by employees. Along
with this, proper training schedules should eb initiated before the change
implementation process is completed which will help in ensuring that till the time new
system or processes is introduced every employee or the relevant people are in the
position to carry out the activities.

REFERENCES
Books and Journals
Ghasemaghaei, M., Ebrahimi, S. and Hassanein, K. (2018). Data analytics competency for
improving firm decision making performance. The Journal of Strategic Information
Systems. 27(1). pp.101-113.
Hung, J. L., He, W., & Shen, J. (2020). Big data analytics for supply chain relationship in
banking. Industrial Marketing Management. 86. 144-153.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in
organizational decision making. Business Horizons. 61(4). pp.577-586.
Rodrigues, M., Santos, M. Y. and Bernardino, J. (2018, October). Experimental evaluation of
big data analytical tools. In European, Mediterranean, and Middle Eastern
Conference on Information Systems (pp. 121-127). Springer, Cham.
Online
Top 10 technology challenges businesses face. 2020. [Online]. Available Through:<
https://channeldailynews.com/news/top-10-technology-challenges-businesses-face/
7570>.