MGT602 Business Decision Analytics: Research Analysis Report, Analysis

This report provides a detailed analysis of two scenarios related to business decision analytics. Scenario A explores the use of clustering methods to identify key members for meetings within an Australian company, focusing on research, development, and design (RD&D) employees and their communication patterns. The analysis utilizes hierarchical clustering to group employees based on their interconnectedness in email communication networks. Scenario B utilizes Leximancer concept mapping to illustrate the relationships between different concepts within a wide service-based company, highlighting themes such as communication, management, and customer focus. The Leximancer analysis reveals the significance of communication and customer support in achieving organizational goals, and it underscores the importance of effective communication between field staff, management, and customers. The report concludes with recommendations for the management, emphasizing the use of clustering techniques and the importance of strong connections between concepts in the Leximancer map to improve decision-making and achieve organizational objectives. The report also suggests the implementation of soft clustering and the importance of customer-service communication to improve decision-making.

Running Head: MGT602
Business Decision Analytics
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Research Analysis
Executive Summary
In order to bring the group of people together, organizations are using innovative or
creative techniques. Formation of virtual teams helps the organizations to decrease the demand
of globalization within the organizations. To interact with each other and the customers instead
of physical presence the virtual teams are using various technologies. Making an efficient use of
communication service strategy has proved to be useful for most of the organizations. The paper
discusses two scenarios based on business decision analytics. Scenario A uses a clustering
method that selects the nodes which are connected and nearer to each other. This method has
been used to select the key members who will attend the meeting. While scenario B utilizes the
Leximancer Concept map that discusses the processes used within the organization. It also
discusses the role of managers within the organization to achieve the organizational goals.

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Research Analysis
Table of Contents
Introduction.................................................................................................................................................3
Discussion of Scenario A............................................................................................................................3
Discussion of Scenario B.............................................................................................................................5
Recommendations.......................................................................................................................................7
Conclusion...................................................................................................................................................8
References...................................................................................................................................................9
Appendices................................................................................................................................................11

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Research Analysis
Introduction
Presently organizations are constantly making use of innovative or creative techniques so
that more and number of people could work together. The challenges faced due to globalization
could help in the formation of virtual teams. The concept of communication is adopted by
various organizations to execute business activities effectively. Understanding communication
challenges, psychological costs and use of technology that helps the virtual managers to execute
the work systematically. The paper provides two scenarios A and B where scenario A has
utilized the clustering method and scenario B has utilized Leximancer concept analysis for
showing the connection between the concepts.
Discussion of Scenario A
A mid-sized Australian company where 800 people are employed, out of that 800 people
80 employees were involved in Research, Development, and Design (RD&D) work. The
company has a huge interest in recognizing the innovative as well as creative capabilities of their
team members who are engaged in R, D & D work (Aguwa, Olya and Monplaisir, 2017). The
firm seeks to make a wise use of its capabilities to segregate its business strategies from its
opponents. Thus, the company has organized a research project that will help to examine the
innovative capabilities of R, D & D that are organized by the employees to manage their
unstructured communication. Within a period of one week the direction, rates, and emails of
connectivity of the R, D & D employees have been mapped systematically (Ayed, Halima and
Alimi, 2015). On the attached map of unstructured information of RD&D available in emails, the
results based on this particular research audit have been provided.
The specific project requires an understanding of the decision made that is combined
together to cooperate in making tacit unstructured information. The employees of RD&D will be
provided with different meeting rooms along with a facilitator, visual aids and a group of
decision support system (Chan, Choi, and Yue, 2016). A group based communication technology
system will be used by the project to organize the meetings of the employees which will help
them to gather explicit data and implicit information (Cheng, 2016). Along with the resources
and participation of each members, it will also support the group members present in the other
meeting rooms to generate explicit data.

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Research Analysis
The following table indicates the nodes and clusters of six members of RD&D who are
chosen to participate in the meeting.
SR/No. Clusters Nodes identified per cluster
1 19 4
2 31 3
3 45 4
4 38 3
5 61 5
6 75 4
The key members of the clusters are shown in the following table who have been chosen
to participate in the meetings. It shows RD&D employees according to their meeting room and
cluster.
S
R/No.
Meetin
g Rooms
Clusters Key Members chose to participate in
the Meetings
1 A 1 Technical Staff member
2 B 2 Assistant staff member
3 C 3 Technician
4 D 4 Production Team leader
5 E 5 Project Team leader
6 F 6 Research staff member
Clusters are usually helpful in connecting more number of objects together to collect a
higher number of clusters of different elements (Choi, Chan and Yue, 2017). Every cluster was
chosen through grouping a set of numbers in the same group who are similar in nature. It is
entirely based on segmenting the portfolios of the customers based on demographics, transaction
behavior, and behavioral attributes. Hierarchical clustering is the standard technique that is being

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Research Analysis
used for the clustering of the nodes. In the hierarchical method of clustering, the data-point close
to the base-point will be similar as compared to the data-point far ahead from the base-point
(Duan, and Xiong, 2015). In this method, the numbers present in each cluster are similar to each
other whereas the end point of the clusters varies from one another (Elgendy and Elragal, 2016).
The capabilities of the staff members possess unstructured information that is provided in their
emails. Communication technology system will be used by the project to arrange the meeting
with the staff members of RD&D.
In the analysis dangling nodes has not been utilized as it presents the nodes without
outgoing links. Basically, dangling nodes is used to remove a node from the cluster, but here no
such clusters have been removed (Güçdemir and Selim, 2015). Removing a node would become
critical to select the key members for the purpose of the meeting. Therefore, instead of dangling
nodes, unconnected nodes have been used to execute the business analysis. The two unconnected
nodes present the members those who are not engaged in the RD&D work, thus they will not
participate in the meeting. Hence, their capabilities based on innovative or creative skills will not
be recognized. In this scenario using unconnected nodes will make the task easy to select the key
members to attend the meeting.
The primary purpose of selecting the clusters is completely based on combining the
objects which are nearer to each other. In the analysis, two RD&D staff member like technical
staff member and research staff member are chosen as they are nearer to one another and they
possess similar work (Hazen, Skipper, Boone and Hill, 2018). Hence, they have been chosen to
participate in the meeting. Therefore, these particular staff members are chosen as they are
interlinked with one another in comparison with the other RD&D staff member.
Discussion of Scenario B
The Leximancer Concept map shows the importance of the concepts and the significant
relationship between the concepts based on its lines and placement. The map presents six themes
such as communication, focused, management, customer, open and results. Each balloon is
linked with another balloon which shows that every theme has some connections with others in
operating a business (Huang, Wang, Zhang and Zhang, 2018).

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Research Analysis
The Leximancer concept analysis is related to wide service based company in Australia
that has 200 service staff all over 10 service centers that report to 40 managers. The firm
recognizes the support of service level that is provided by the staff members. Thus, to implement
this process the organization have developed a project that will identify the responsibilities and
role of the employee and the support provided by the management (Khan and Vorley, 2017).
From the Leximancer concept analysis, it is observed that except focus all the themes are
connected with one another. This shows that the organizational members are not less focused on
achieving their goals. This may affect the organization to execute its business effectively
(Menzel, Ranjan, Wang, Khan and Chen, 2015). Hence, according to the map the theme focused
is less important as compared to communication and other concepts. Thus, by observing the
concept analysis it is observed that communication helps in formulating the organizational
activities effectively.
With the help of using simple words, communicating through training, developing a
receptive environment, one to one interaction, sending emails and open meeting the field staff
member communicates with each other. All these techniques are helpful in making appropriate
decisions to get better output. To execute the work smoothly it is essential to have effective
communication among the staff members. There is not a high level of communication among the
managers and customers, where usually the customers communicate with the other staff
members discuss their issues or requirements (Sivarajah, Kamal, Irani, and Weerakkody, 2017).
Thus, the customers are not connected directly with the managers to provide feedback in order to
present their demands.
Support availability plays a significant role to execute a business systematically. It helps
in providing the customers with various technologies according to their needs. Providing
customers with better resources could be advantageous for the company as it fulfills the
requirements of the customers (Systems, 2018). Therefore the map presents a high level of
support that is available to the customers. In addition, the map also presents the fact that the field
staff and management is quite open. The management and field staff freely discusses the
significant issues of the company to make an appropriate decision (Yazici, Beyca, and Zaim,
2017). In order to make an adequate decision related to the project, effective communication is
necessary. If there is no proper communication between the management and the field staff than

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Research Analysis
it affects the process of decision-making (Analysis, 2018). As per the map, the communication
strategy is well linked with the customers that will be beneficial for the firm to operate its
business. Based on the results it is observed that managers are constantly focusing on satisfying
customer needs. The results also present the fact that the management must work together with
the field service team to reach to its customers to fulfill their needs. It is highly important for
both the team to participate in the activities within the organization to operate its business
efficiently (Yazici et al., 2017). Therefore, if a company seeks to operate its business efficiently
than it must make a wise use of the concepts and ideas of field service teams and management.
According to the results supporting availability has been proved to be useful that provides
the customers with all the resources. To execute the business systematically it is necessary for a
company to have the superior support system. As per the results, not only customers but also the
staff members are facilitated with superior support to maximize its profitability. It can be seen
that customer-service communication must be changed to have greater openness (Khan and
Vorley, 2017). This change effectively helps in making adequate decisions to get better results.
Therefore, in order to make decisions related to the needs of the customers, it is necessary to
implement the customer-service strategy.
Recommendations
In an organization, it is highly important to execute the work properly to attain the
organizational goals. Proper execution of work entirely depends upon the decision making
process. The decisions are made by the management of the organization to have a systematic
work. Hence, it is highly recommended to the management to make adequate decisions to
execute the work effectively to achieve the target. Clusters must be chosen by observing which
nodes are connected and close to one another. The company may execute its process of choosing
the key members who will participate in the meeting with the help of soft clustering. The
purpose of the soft clustering method is to gather all the data into a single node for all the
customers. The method helps in recognizing the employees who are chosen to attend the
meeting. Furthermore, it is considered to be an appropriate method to execute the process of
clustering the nodes.
It is recommended to the management team to have a strong connection with the concepts
provided in the Leximancer Concept map. All the concepts must be connected directly with each

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Research Analysis
other execute the work effectively and fulfills the requirements of the customers. It is essential
that there must be a good connection between all the concepts like field staff, management,
communication, customers and results. Satisfying the demand of the customers is important or
else it will put a negative effect on the organization. The Leximancer concept analysis facilitates
with the concepts as well as the relationship among each concept that is formulated in the
company. Thus, it is the responsibility of the management is to have a strong connection with all
the concepts provided in the Leximancer Concept map.
Conclusion
The virtual team within the organizations work mutually to facilitate better results to
achieve the target. It is important for the organizations to make proper decision to develop its
organizational activities. The managers within the organization must make an appropriate use of
the tools to enhance appropriate change management and decision-making skills. Hence, the
paper concluded by evaluating the technologies that help in decision making. It is observed that
managers make a wise use of emerging and available technologies that enhance the
organizational activities. It is also observed that clustering method has been used by scenario A
and Leximancer Concept map has been used by scenario B for the analysis.

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Research Analysis
References
Aguwa, C., Olya, M.H. and Monplaisir, L., 2017. Modeling of the fuzzy-based voice of the
customer for business decision analytics. Knowledge-Based Systems, 125, pp.136-
145.
Chan, H.K., Choi, T.M. and Yue, X., 2016. Guest editorial big data analytics: Risk and
operations management for industrial applications. IEEE Transactions on Industrial
Informatics, 12(3), pp.1214-1218.
Cheng, M., 2016. Sharing economy: A review and agenda for future research. International
Journal of Hospitality Management, 57, pp.60-70.
Choi, T.M., Chan, H.K. and Yue, X., 2017. Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1),
pp.81-92.
Duan, L. and Xiong, Y., 2015. Big data analytics and business analytics. Journal of
Management Analytics, 2(1), pp.1-21.
Elgendy, N. and Elragal, A., 2016. Big data analytics in support of the decision making
process. Procedia Computer Science, 100, pp.1071-1084.
Güçdemir, H. and Selim, H., 2015. Integrating multi-criteria decision making and clustering
for business customer segmentation. Industrial Management & Data Systems, 115(6),
pp.1022-1040.
Hazen, B.T., Skipper, J.B., Boone, C.A. and Hill, R.R., 2018. Back in business: Operations
research in support of big data analytics for operations and supply chain
management. Annals of Operations Research, 270(1-2), pp.201-211.
Huang, W., Wang, H., Zhang, Y. and Zhang, S., 2017. A novel cluster computing technique
based on signal clustering and analytic hierarchy model using Hadoop. Cluster
Computing, pp.1-8.

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Research Analysis
Khan, Z. and Vorley, T., 2017. Big data text analytics: an enabler of knowledge
management. Journal of Knowledge Management, 21(1), pp.18-34.
Menzel, M., Ranjan, R., Wang, L., Khan, S.U. and Chen, J., 2015. CloudGenius: a hybrid
decision support method for automating the migration of web application clusters to
public clouds. IEEE Transactions on Computers, 64(5), pp.1336-1348.
Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big
Data challenges and analytical methods. Journal of Business Research, 70, pp.263-
286.
Systems, D. (2018). Decision Support Systems. Journals.elsevier.com. Retrieved 1 December
2018, from https://www.journals.elsevier.com/decision-support-systems
Analysis, B. (2018). Business Decision Analysis (MGMT20005) — The University of
Melbourne Handbook. The University of Melbourne Handbook. Retrieved 1
December 2018, from https://handbook.unimelb.edu.au/subjects/mgmt20005
Yazici, İ., Beyca, Ö.F. and Zaim, S., 2017. Clustering Techniques Within Service Sector.
In Applying Predictive Analytics Within the Service Sector (pp. 74-87). IGI Global.
Ayed, A.B., Halima, M.B. and Alimi, A.M., 2015, May. Big data analytics for logistics and
transportation. In Advanced Logistics and Transport (ICALT), 2015 4th International
Conference on (pp. 311-316). IEEE.

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Research Analysis
Appendices
Appendix 1: Scenario A
Figure: MGT602 Assessment 3 Scenario A Data
(Source: Khan, Z. and Vorley, 2017)

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Research Analysis
Appendix 2: Scenario B
Figure: MGT602 Assessment 3 Scenario B Data
(Source: Cheng, 2016)