Mybus721_v8_wk5_data_analysis_worksheet1.docx

BUS/721 v8

Data Analysis Worksheet

BUS/721v8

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C:UsersdjshireyOneDrive - University of PhoenixF_DriveStyle GuidesUPX LogosHorizontal formatUOPX_Sig_Hor_Black_Medium.pngData Analysis Worksheet

Write a response to each prompt below.

Ensure each response is

thorough and complete

supported with rationale

edited carefully for grammar, punctuation, and spelling errors

formatted according to course-level APA guidelines (where applicable)

cited correctly (where applicable)

A. Data related to People:

To support the Leadership Optimization Business Plan, data related to people should include market trends, measurement of performance marketing campaigns, employee engagement levels, workflows to improve employee engagement, turnover rates, skill and knowledge gaps to streamline operations, performance metrics, and diversity and inclusion statistics (Stedman, 2023). This data will provide insight into the organization's workforce, including strengths and areas of improvement, and help identify opportunities for employee development and retention (Falletta & Combs, 2021).

B. Data related to Processes:

The data related to processes should include key performance indicators (KPIs) such as cycle time, process efficiency, and process effectiveness. Additionally, the data must identify and communicate the business value by measuring the value of data assets, determine and sustain a data catalog, and act as a catalyst for changes to the business model (Goasduff, 2016). It should also include data on process defects, quality metrics, and customer satisfaction levels. This data will help in identifying areas for process improvement, bottlenecks, and waste reduction.

C. Data related to Systems:

Data related to systems should include prescriptive analytics that recommends conceivable outcomes and results in actions that are probable to maximize key business metrics to optimize and supports to achieve the best outcomes (Project Pro.io, 2023). Additional recommendations should include system uptime, availability, utilization, and maintenance information. This data will provide insights into the organization's technological infrastructure, including strengths and areas of improvement, and help identify opportunities for system optimization and efficiency. By employing the correct reporting tools and comprehending how to analyze and measure the data precisely, the business can make the decisions that will take the business forward (Calzon, 2022).

The PPT framework regards how the three aspects interrelate. Processes make these aspects work more efficiently. Technology supports workforces in doing their tasks and also assists in automating processes (Plutora.com, 2022). Equally, data and analytics are fundamental tools for optimizing functional performance in any company. Likewise, the business must adjust the people and processes to adapt to the new tools if the technology adaptations. Thus, businesses can realize organizational efficiency by corresponding to the three and optimizing the associations between people, processes, and technology. Businesses must apply robust processes to ensure that people's performance is effectively maintained. Utilizing the data related to people, processes, and systems will assist in optimizing operations by providing insights into the organization's strengths and areas of needed improvement. For instance, analyzing employee engagement levels and turnover rates will help identify opportunities for employee development and retention (Patil, 2022). Analyzing KPIs related to processes will help identify areas for process improvement and reduce waste, while analyzing data related to systems will help optimize technological infrastructure and reduce downtime (Sjödin et al., 2018). The organization can increase efficiency, reduce costs, and improve overall performance by utilizing data to optimize people, processes, and systems.

Prompt 3: Explain how this data will provide a competitive edge to the organization.

Big data analytics (BDA) collected and analyzed for strategic purposes to support the Leadership Optimization Business Plan will provide a competitive edge to the organization by enabling it to make data-driven decisions and its impact on different parameters of firm performance (Shah, 2022). By utilizing data to optimize people, processes, and systems, the organization can increase efficiency, reduce costs, system health monitoring consumer experience management, and improve overall performance, giving it a competitive advantage. For instance, if the corporation has lower turnover rates and higher employee engagement levels than its competitors, it can attract and retain top talent (Athira, 2022). Additionally, suppose the organization has streamlined processes and a more efficient technological infrastructure than its competitors. In that case, it can reduce costs and improve customer satisfaction, giving it a competitive edge in the marketplace (Majdalawieh & Khan, 2022). Utilizing data to optimize operations will enable the organization to remain agile and responsive to changing market conditions, providing a competitive advantage (Diskiene et al., 2019).

References

Athira, M. K. (2022). TALENT MANAGEMENT PRACTICES, EMPLOYEE ENGAGEMENT AND EMPLOYEE RETENTION: A SYSTEMATIC LITERATURE REVIEW.  Organising Secretary, 63.

Calzon, B. (2022). Why Data Driven Decision Making is Your Path To Business Success. https://www.datapine.com/blog/data-driven-decision-making-in-businesses/

Diskiene, D., Pauliene, R., & Ramanauskaite, D. (2019). Relationships between Leadership Competencies and Employees’ Motivation, Initiative and Interest to Work. Montenegrin Journal of Economics, 15(1).

Falletta, S. V., & Combs, W. L. (2021). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1), 51-68.

Goasduff, L. (2016). 3 Key Steps to a Data-Driven Business.

Majdalawieh, M., & Khan, S. (2022). Building an Integrated Digital Transformation System Framework: A Design Science Research, the Case of FedUni.  Sustainability14(10), 6121.

Patil, K. (2022). 5 WAYS DATA CAN INCREASE OPERATIONAL EFFICIENCY AND PRODUCTIVITY IN ECONOMIC VOLATILITY. https://community.nasscom.in/communities/analytics/5-ways-data-can-increase-operational-efficiency-and-productivity-economic#:~:text=Tracking%20progress%20and%20productivity&text=In%20addition%2C%20data%20can%20be,as%20well%20as%20upskill%20employees.

Project Pro.io. (2023). Types of Analytics: Descriptive, Predictive, Prescriptive Analytics. .

Shah, T. R. (2022). Can big data analytics help organisations achieve sustainable competitive advantage? A developmental enquiry.  Technology in Society68, 101801.

Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing Moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies.  Research-technology management61(5), 22-31.

Stedman, C. (2023). Business Intelligence (BI). https://www.techtarget.com/searchbusinessanalytics/definition/business-intelligence-BI#:~:text=Business%20intelligence%20(BI)%20is%20a,workers%20make%20informed%20business%20decisi

Copyright 2021 by University of Phoenix. All rights reserved.

Copyright 2021 by University of Phoenix. All rights reserved.

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