writing journal -- 2
Budget: $10 – $30 AUD
Assignment: Measuring Service Productivity using Data (Individual 40 marks)
Task 1: Course Completion (15%)
Objective:
In this assessment, you will develop the foundational skills around Data Analytics.
Task:
Access Lynda.com or LinkedIn Learning platform and enrol in the "Data Analytics FoundationsLinks to an external site." course.
Complete all the modules and lessons within the course, including watching the video lectures, engaging with practical exercises, and reviewing any supplementary materials provided.
Take notes or summarize the key concepts, techniques, and tools covered in each module.
Upload your notes and Lynda.com certificate to Canvas.
Evaluation Criteria:
Successful completion of the Data Analytics Foundations course (on Lynda.com) (15%)
Task 2: Service productivity data integration plan (25%)
Objective:
In this assessment, you will demonstrate your ability to develop a data integration plan that incorporates a customer journey map to measure service productivity. This assessment is designed to demonstrate your ability to source, structure and format and analyse data to measure service productivity using DEA analysis (as shown in 21741 – Operations and Quality Management).
Please follow the instructions and answer the questions provided below.
Task:
You are required to develop a data integration plan that outlines the steps required to integrate various data sources and create a unified dataset for measuring service productivity.
Consider the following components when developing your plan:
Identify Key Performance Indicators
Determine the KPIs that are relevant to measuring service productivity aligned to the customer journey map from assignment 1. Examples of KPIs could include customer satisfaction ratings, average response time, number of service requests handled per day, employee productivity metrics, or any other indicators that align with the organization's goals and objectives.
Data Sources:
Identify at least three relevant data sources that can provide insights into service productivity and customer journey. These sources could include transactional data, customer feedback, social media data, etc. Explain why each data source is valuable for measuring service productivity.
Data Collection:
Describe the methods and techniques you would use to collect data from the identified sources. Consider both internal and external data collection methods. Explain how you would ensure data quality, integrity, and compliance with relevant regulations.
Data Transformation and Cleaning:
Outline the steps you would take to transform and clean the collected data. Consider techniques such as data normalization, outlier detection, missing value imputation, etc. Explain how these steps would contribute to the accuracy and reliability of the final dataset.
Data Integration and Unification:
Explain how you would integrate the transformed and cleaned data into a unified dataset. Discuss any challenges you anticipate during the integration process and propose potential solutions.
Data Storage and Management:
Discuss your approach to storing and managing the integrated dataset. Consider factors such as data security, scalability, and accessibility for analysis purposes.
Ethical use of Data
Discuss your approach to ensuring the ethical collection and use of data.
Data file
Using the data file provided, prepare the data file discussing the inputs and outputs, the mode of analysis.
Submission Guidelines:
Submit a written document that includes your data integration plan and customer journey map.
Ensure your document is well-structured, includes headings/subheadings, and is free of spelling and grammatical errors.
If applicable, include any assumptions or limitations in your plan.
Clearly reference any external sources used for your analysis and provide proper citations.
Task 1: Course Completion (15%)
Objective:
In this assessment, you will develop the foundational skills around Data Analytics.
Task:
Access Lynda.com or LinkedIn Learning platform and enrol in the "Data Analytics FoundationsLinks to an external site." course.
Complete all the modules and lessons within the course, including watching the video lectures, engaging with practical exercises, and reviewing any supplementary materials provided.
Take notes or summarize the key concepts, techniques, and tools covered in each module.
Upload your notes and Lynda.com certificate to Canvas.
Evaluation Criteria:
Successful completion of the Data Analytics Foundations course (on Lynda.com) (15%)
Task 2: Service productivity data integration plan (25%)
Objective:
In this assessment, you will demonstrate your ability to develop a data integration plan that incorporates a customer journey map to measure service productivity. This assessment is designed to demonstrate your ability to source, structure and format and analyse data to measure service productivity using DEA analysis (as shown in 21741 – Operations and Quality Management).
Please follow the instructions and answer the questions provided below.
Task:
You are required to develop a data integration plan that outlines the steps required to integrate various data sources and create a unified dataset for measuring service productivity.
Consider the following components when developing your plan:
Identify Key Performance Indicators
Determine the KPIs that are relevant to measuring service productivity aligned to the customer journey map from assignment 1. Examples of KPIs could include customer satisfaction ratings, average response time, number of service requests handled per day, employee productivity metrics, or any other indicators that align with the organization's goals and objectives.
Data Sources:
Identify at least three relevant data sources that can provide insights into service productivity and customer journey. These sources could include transactional data, customer feedback, social media data, etc. Explain why each data source is valuable for measuring service productivity.
Data Collection:
Describe the methods and techniques you would use to collect data from the identified sources. Consider both internal and external data collection methods. Explain how you would ensure data quality, integrity, and compliance with relevant regulations.
Data Transformation and Cleaning:
Outline the steps you would take to transform and clean the collected data. Consider techniques such as data normalization, outlier detection, missing value imputation, etc. Explain how these steps would contribute to the accuracy and reliability of the final dataset.
Data Integration and Unification:
Explain how you would integrate the transformed and cleaned data into a unified dataset. Discuss any challenges you anticipate during the integration process and propose potential solutions.
Data Storage and Management:
Discuss your approach to storing and managing the integrated dataset. Consider factors such as data security, scalability, and accessibility for analysis purposes.
Ethical use of Data
Discuss your approach to ensuring the ethical collection and use of data.
Data file
Using the data file provided, prepare the data file discussing the inputs and outputs, the mode of analysis.
Submission Guidelines:
Submit a written document that includes your data integration plan and customer journey map.
Ensure your document is well-structured, includes headings/subheadings, and is free of spelling and grammatical errors.
If applicable, include any assumptions or limitations in your plan.
Clearly reference any external sources used for your analysis and provide proper citations.