Business Decision Analytics Case Study

This case study focuses on Business Decision Analytics and critically evaluates the sources of data, decision making systems and techniques, and emerging tools and technologies for decision making. It includes a situational analysis and sense making for a business that needs to decrease its wage and salary costs. The report provides potential solutions and recommendations for keeping profits up without firing a large percentage of staff.

University
MGT602 Business Decision Analytics
Case Study
Student Name:
Register Number:
Submission Date:

Executive Summary
The spot light of this research is Business Decision Analytics. The report ensures to
compare, contrast and critically evaluate the sources of data, to take right decisions in a range of
business contexts. The decision making systems and techniques are examined and evaluated, for
analyzing the sustainable outcomes. The emerging tools and technologies for decision making
are also critically examined. Subsequently, a case study is considered to review all these business
decision analytics’ aspects. The current situation in the case study includes a business which has
come to a conclusion of instantly decreasing its wage and the salary costs, in order to survive the
next period. To take this action, the executive management has taken a decision of setting up a
panel for advising on methods to instantly decrease its wage and salary costs. Decreasing the
number of staff is considered as the solution, to positively address the issues depending on the
business’s current P&L statement. Thus, the team is asked to use the available data set and
analyze it for providing a list of people for redundancy. Finally, appropriate justifications are
given for how to keep the recommended people to be made redundant.profits up, without firing
60 percentage of staff or by firing small percentage of staff.

Table of Contents
1. Introduction............................................................................................................................1
2. Main Discussion......................................................................................................................2
2.1 Problem Definition..............................................................................................................2
2.2 Situational Analysis............................................................................................................2
2.3 Sense Making......................................................................................................................3
2.4 Formulation of potential solutions....................................................................................4
2.5 Selection between solution...............................................................................................13
2.6 Decision making based on solutions................................................................................13
2.7 Implementation of decisions............................................................................................14
3. Recommendations................................................................................................................14
4. Conclusion.............................................................................................................................15
References.....................................................................................................................................17

1. Introduction
Business Analytics refers to a study of data based on the statistical analysis and
operations analysis, which helps to form the predictive models, helps the companies in
automating and optimizing their business processes and communicates the results with the
customers and the business partners. Additionally, the data-driven companies make use of
Business Analytics for achieving competitive advantage, with its insight for supporting the
evidence-based decision making and performance management.
Here is a case study which ensures to enlighten about the Business Decision Analytics,
where I am a member of the team with three line mangers for forming a panel with the task of
business cost containment in an organisation which contains 300 people. The importance and
urgency of the panel’s work relates to the business which has recently lost two key products from
its product line. The reason behind this is considered as the changes in the government policy,
which needs immediate step to stop the falling business profitability. Thus, this case requires
comparing, contrasting and critically evaluating the sources of data, for making the right
decisions in a range of business contexts. The decision making systems and techniques should be
examined and evaluated, to analyze the sustainable outcomes. The emerging tools and
technologies for decision making must also be examined. Because, the business has come to a
conclusion that it instantly requires to decrease its wage and salary costs, for surviving the next
period. Hence, the executive management has taken a decision of setting up a panel for advising
on methods to instantly decrease its wage and the salary costs, by decreasing the number of staff,
to positively address the issues with the business’s current P&L statement.
On the other hand, the business is well aware of the amount of staff required for the
business, to run the business, i.e., it believes that it needs 275 staff for operating its business well.
Strong recommendations are demanded by the team with appropriate justifications on the
recommended peoplegiven for how to be removed from the job.keep the profits up, without
firing 60 percentage of staff or by firing small percentage of staff.
Objective
The objective of this report includes, the sources of data will be compared, contrasted and
critically evaluated. The decision making systems and techniques will be examined and
evaluated. The emerging tools and technologies for decision making will be critically examined.
1

To recommend and justify how to keep the reason for selecting the people to be made redundant.
profits up, without firing 60 percentage of staff or by firing small percentage of staff.
2. Main Discussion
2.1 Problem Definition
The importance and urgency of the panel’s work relates to the business which has
recently lost two key products from its product line. The reason behind this is considered as the
changes in the government policy, which needs immediate step, to stop the falling business
profitability. The decided solution is decreasing the number of staff in the company. Here, the
problem is to look for the right staff to be terminated based on positive addressing, instead of
randomly removing the employees. It is a serious and complex task to carry out. Thus, the team
requires analyzing the provided data set to list the people recommended for redundancy. Totally,
out of 60 people, the team needs to select the people and provide appropriate recommendations
on the type and number of people to be made redundant (Dasgupta, 2015).
The main problem here is, how to keep the profits up, without firing 60 percentage of
staff or by firing small percentage of staff. Then what else can be the solution.
2.2 Situational Analysis
The current situation of the business is that it has come to a conclusion that, it instantly
requires to decrease its wage and the salary costs, for surviving the next period. The executive
management has taken a decision of setting up a panel for advising on methods to instantly
decrease its wage and salary costs, by decreasing the number of staff, to positively address the
issues with the business’s current P&L statement. The team is asked to use the available data set
and analyse it for providing a list of people for redundancy (Delen, 2015).
The case study provides the exposure of experiencing to work at both the individual level
as well as a team member, for identifying the data from different sources and contexts to process
the information and help in decision making. On the other hand, it also helps to form a group for
sharing the MBTI results, decision preferences and other types of psychometric tests that could
support the team in identifying and acting on the differences between each individual’s
information preferences, abilities, decision styles and willingness to work in the team.
Additionally, to work as a team member to recognize the differences in the preferences of
2

information processing, ways of working together and decision styles, by engaging the team in
data evaluation, information processing, utilizing the systems of decision-making and techniques
for instant and high quality group decisions.
2.3 Sense Making
To make the decisions for the provided case study problem by using the business analytics tool
like Weka. So, user needs to download and install the weka. After, open the provided dataset like
access data. Then, perform the business analysis (Getting started with business analytics:
insightful decision-making, 2013).
The provided data set is successfully opened and it is illustrated as below.
After, choose classify to analysis the provided the data. Here, we are choose the years of
experience to predicted the employee’s information to reduce the employee salary or removed
from the job.
3

2.4 Formulation of potential solutions
Practical Business Analysis one by using the Random Tree for Years of experience
To make the decisions for the provided case study problem by using the business analytics tool
like Weka. So, user needs to download and install the weka. After, open the provided dataset like
access data. Then, perform the business analysis.
The provided data set is successfully opened and it is illustrated as below (Isson and Harriott,
2013).
After, choose classify to analysis the provided the data. Here, we are choose the years of
experience to predicted the employee’s information to reduce the employee salary or removed
from the job.
The below screenshots is used to display the random tree analysis on years of experience
variables (Liebowitz, 2014).
4

Output is shown below.
RandomTree
==========
Name = Jacob : 10 (1/0)
Name = Michael : 2 (1/0)
Name = Mary : 7 (1/0)
Name = Joshua : 6 (1/0)
Name = Youseff : 1 (1/0)
Name = Emma : 3 (1/0)
Name = Abe : 2 (1/0)
Name = Kelvin : 7 (1/0)
Name = Lillian : 11 (1/0)
Name = Terry : 10 (1/0)
Name = Rubin : 6 (1/0)
5

Name = Hassan : 7 (1/0)
Name = Fred : 6 (1/0)
Name = Shavonne : 8 (1/0)
Name = Sibyl : 9 (1/0)
Name = Larry : 1 (1/0)
Name = Zane : 5 (1/0)
Name = Rudolph : 1 (1/0)
Name = Angelo : 3 (1/0)
Name = Lee : 5 (1/0)
Name = Marc : 5 (1/0)
Name = Chad : 7 (1/0)
Name = Anton : 12 (1/0)
Name = Rick : 11 (1/0)
Name = Keith : 1 (1/0)
Name = Torri : 1 (1/0)
Name = Mica : 3 (1/0)
Name = Kim : 4 (1/0)
Name = Earle : 3 (1/0)
Name = Hester : 8 (1/0)
Name = Lindsey : 7 (1/0)
Name = Mario : 8 (1/0)
Name = Simon : 8 (1/0)
Name = Kathrine : 9 (1/0)
Name = Stephen : 4 (1/0)
Name = Blake : 3 (1/0)
Name = Fraser : 13 (1/0)
Name = Gemma : 5 (1/0)
Name = Heath : 6 (1/0)
Name = Rashid : 7 (1/0)
Name = Mohammad : 9 (1/0)
Name = Nada : 9 (1/0)
6

Name = Paul : 6 (1/0)
Name = Sarah : 4 (1/0)
Name = Nima : 5 (1/0)
Name = David : 5 (1/0)
Name = Ali : 9 (1/0)
Name = Maria : 8 (1/0)
Name = Nicola : 8 (1/0)
Name = Lorenzo : 2 (1/0)
Name = Colin : 1 (1/0)
Name = Bryan : 1 (1/0)
Name = Vito : 9 (1/0)
Name = Rosa : 4 (1/0)
Name = Yvette : 6 (1/0)
Name = Marcel : 9 (1/0)
Name = Claude : 13 (1/0)
Name = Louis : 2 (1/0)
Name = Pam : 1 (1/0)
Name = Percy : 7 (1/0)
Size of the tree : 61
Time taken to build model: 0 seconds
=== Cross-validation ===
=== Summary ===
Correlation coefficient 0.1425
Mean absolute error 2.7234
Root mean squared error 3.411
Relative absolute error 98.3241 %
Root relative squared error 102.126 %
Total Number of Instances 60
Ignored Class Unknown Instances 6
The years of experience has the 60 instances and Ignored class unknown instances are 6.
7

Practical Business Analysis one by using the Random Tree for Employees Position
Size of the tree : 204
Time taken to build model: 0 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 28 44.4444 %
Incorrectly Classified Instances 35 55.5556 %
Kappa statistic 0.009
Mean absolute error 0.2097
Root mean squared error 0.3548
Relative absolute error 96.1205 %
Root relative squared error 108.6119 %
Total Number of Instances 63
8

Ignored Class Unknown Instances 3
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area
Class
0.071 0.163 0.111 0.071 0.087 -0.109 0.580 0.260 S
0.727 0.767 0.511 0.727 0.600 -0.045 0.470 0.494 M
0.231 0.040 0.600 0.231 0.333 0.286 0.717 0.373 J
0.000 0.000 ? 0.000 ? ? 0.069 0.015 Safe no accidents
0.000 0.016 0.000 0.000 0.000 -0.016 0.100 0.015 Moderately
safe:1 near miss reported
0.000 0.016 0.000 0.000 0.000 -0.016 0.108 0.015 Safety Risk - Has
been in 1 accident event
Weighted Avg. 0.444 0.447 ? 0.444 ? ? 0.527 0.394
=== Confusion Matrix ===
a b c d e f <-- classified as
1 13 0 0 0 0 | a = S
5 24 2 0 1 1 | b = M
3 7 3 0 0 0 | c = J
0 1 0 0 0 0 | d = Safe no accidents
0 1 0 0 0 0 | e = Moderately safe:1 near miss reported
0 1 0 0 0 0 | f = Safety Risk - Has been in 1 accident event
9

Practical Business Analysis one by using the Gaussian Processes for Years of experience
Kernel used:
Linear Kernel: K(x,y) = <x,y>
All values shown based on: Normalize training data
Average Target Value : 0.40694444444444444
Inverted Covariance Matrix:
Lowest Value = -0.14609951610422994
Highest Value = 0.35795999121679684
Inverted Covariance Matrix * Target-value Vector:
Lowest Value = -0.13502653684508972
Highest Value = 0.16806340316705135
Time taken to build model: 0.04 seconds
=== Cross-validation ===
10

=== Summary ===
Correlation coefficient 0.4637
Mean absolute error 2.416
Root mean squared error 2.96
Relative absolute error 87.2256 %
Root relative squared error 88.6244 %
Total Number of Instances 60
Ignored Class Unknown Instances 6
Annual Profits
11

Annual Salaries for Employees
Usage of utilities in the business
12

2.5 Selection between solution
Based on experience and position analysis on Weka, it is used to predict the employee’s
information to remove from the job. In years of experience, the below two years are needs to
remove from the job because these does not have any knowledge about the business process and
also verifies the position level as junior level with below two years of experiences (Miller, 2015).
2.6 Decision making based on solutions
Decision making based on the solutions is to remove the junior level with below two years of
experiences. Because, junior specialists can some of the time saddle their vitality to impel the
association forward; in any case, they as a rule need both the expert and educational experience
expected to roll out a positive improvement. More seasoned laborers have this experience and
are frequently overlooked resources for some organizations. More youthful laborers regularly
battle to push ahead a reliable way (Ohri, 2013). Despite their insight and brilliant thoughts, they
were not able make their dreams work out as intended. The issue was they continually altered
their opinions and were not able propel the business a reliable way. While a few representatives
might have the capacity to instantly re-organize their time and undertakings, some may at first
experience troubles getting balanced with their new duties. The expansion in work can make
representatives get baffled, wore out and bring down their general profitability while others
perform ineffectively, because of absence of preparing, absence of intrigue or absence of
clearness about their new assignments. Poor execution can make representatives feel a feeling of
inadequacy or as though they've you thumped. Poor execution can likewise make workers get
deprived of their new obligations, which can cause humiliation (Power and Heavin, 2017).
13

J M S
0
50
100
150
200
250
Sum o f Y e a r s o f Se r v ic e by Po s it io n S= Se nio r ; M=
Middle t e ir ; J = J unio ur
J M S
0
50
100
150
200
250
Sum o f Y e a r s o f Se r v ic e by Po s it io n S= Se nio r ; M=
Middle t e ir ; J = J unio ur
2.7 Implementation of decisions
Implementations of decisions is used to increase the productivity. The business urgently
requires a board, for talking about the business which has as of late lost two key items from its
product offering. The purpose for the misfortune is the adjustments in the administration
approach, which needs quick advance, to stop the falling business gainfulness. Henceforth,
diminishing the quantity of staff is considered as the arrangement, to emphatically address the
issues relying upon the business' present P&L declaration. Along these lines, the group is
requested to utilize the accessible informational index and break down it for giving a rundown of
14

individuals to repetition. From now on, in this report, the wellsprings of information are thought
about, differentiated and fundamentally assessed, for taking right choices in a scope of business
settings, then, the basic leadership frameworks and methods are inspected and assessed, for
breaking down the practical results. Further, developing apparatuses and innovations for basic
leadership are fundamentally inspected (PUTLER, 2017).
3. Recommendations
A standout amongst the most significant advantages of employee’s analysis is that you're
ready to robotize such huge numbers of procedures that can uncover essential insights about the
work propensities for your group. These procedures may appear to be minor when watched
independently, however exhaustively they can have a colossal effect. Accept timecard
management for instance. In isolated conditions, taking a look at a solitary worker's timecard
information and efficiency levels might possibly give experiences into how that individual could
best be used (Surma, 2011). Be that as it may, when this information is gathered progressively
for a whole office, examination can show imperative patterns concerning how time
administration impacts profitability. This process is used to improve the business productivity.
4. Conclusion
A case study is considered in this report which reviews the aspects of business decision
analytics. The current situation in the case study includes a business which has come to a
conclusion of instantly decreasing its wage and the salary costs, in order to survive the coming
year. To take this action, the executive management has taken a decision of setting up a panel for
advising on methods to instantly decrease its wage and salary costs.
The case study has helped to understand the importance of Business Decision Analytics,
where I am a member of the team with three line mangers for forming a panel with the task of
business cost containment in an organisation which contains 300 people. The business urgently
requires a panel, for discussing the business which has recently lost two key products from its
product line. The reason behind the loss is the changes in the government policy, which needs
immediate step, to stop the falling business profitability.
Hence, decreasing the number of staff is considered as the solution, to positively address
the issues depending on the business’s current P&L statement. Thus, the team is asked to use the
15

available data set and analyze it for providing a list of people for redundancy.
Henceforth, in this report, the sources of data are compared, contrasted and critically
evaluated, for taking right decisions in a range of business contexts, Then, the decision making
systems and techniques are examined and evaluated, for analyzing the sustainable outcomes.
Further, emerging tools and technologies for decision making are critically examined.
To accomplish this, the cases study gives the exposure of experiencing to work at both
the individual level as well as a team member, for identifying the data from different sources and
contexts to process the information and help in decision making. On the other hand, it also helps
to form a group for sharing the MBTI results, decision preferences and other types of
psychometric tests that could support the team in identifying and acting on the differences
between each individual’s information preferences, abilities, decision styles and willingness to
work in the team. Additionally, to work as a team member to recognize the differences in the
preferences of information processing, ways of working together and decision styles, by
engaging the team in data evaluation, information processing, utilizing the systems of decision-
making and techniques for instant and high quality group decisions.
Therefore, appropriate justifications are given for how to keep the recommended people
to be made redundant.profits up, without firing 60 percentage of staff or by firing small
percentage of staff.
16

References
Dasgupta, M. (2015). Analytics for Decision Making at Ports. International Journal of Business
Analytics and Intelligence, 3(2).
Delen, D. (2015). Real-world data mining. Upper Saddle River, NJ: Pearson Education.
Getting started with business analytics: insightful decision-making. (2013). Choice Reviews
Online, 50(12), pp.50-6856-50-6856.
Isson, J. and Harriott, J. (2013). Advanced business analytics. Hoboken, N.J.: John Wiley &
Sons.
Liebowitz, J. (2014). Business analytics. Boca Raton, Fla.: CRC Press.
Miller, T. (2015). Modeling techniques in predictive analytics. Upper Saddle River, NJ: Pearson
Education.
Ohri, A. (2013). R for Business Analytics. New York, NY: Springer New York.
Power, D. and Heavin, C. (2017). Decision Support, Analytics, and Business Intelligence, Third
Edition. New York: Business Expert Press.
PUTLER, D. (2017). CUSTOMER AND BUSINESS ANALYTICS. [S.l.]: CRC PRESS.
17

Surma, J. (2011). Business intelligence. [New York, N.Y.] (222 East 46th Street, New York, NY
10017): Business Expert Press.
18