INFO260 Data Management Project: Optimizing Emergency Response Data 2025-26 | UCNZ

Academic Year	2025/2026

INFO260: Data Management Project: Optimizing Emergency Response Data

Case Study:

Managing Emergency Incident Data for Enhanced Performance As a newly appointed Data Manager for a regional Emergency Services organization, your primary responsibility is to transform how critical operational data is managed. Your organization’s current data practices, which involve collecting and storing incident response data in disparate basic ﬁles (like ﬂat CSVs and ad-hoc spreadsheets), are leading to signiﬁcant challenges. These challenges include inconsistencies, difficulties in understanding incident patterns, inefficient resource allocation, and delays in response to critical events.

Your leadership team recognizes that a robust and scalable approach to data management is not just an IT problem, but a strategic imperative for improving current operations and future-prooﬁng the system. They envision leveraging advanced analytics and potentially Artiﬁcial Intelligence (AI) to enhance emergency service delivery, reduce operational costs, and ultimately improve public safety.

You have been provided with a sample of their current incident response data: INFO260_Assignment_Data_v2.csv. This dataset contains anonymized details about emergency calls and responses. Your task is to analyse this current data situation and propose a comprehensive Data Management Plan and Proposal. This plan should outline better ways to manage this data, ensuring it meets the organization’s strategic goals for enhanced work performance, is scalable, and supports future data-driven initiatives.

Provided Data File:

INFO260_Assignment_Data_v2.csv (Main incident response data)
INFO260_Assignment_v2_Data_Details (Data Dictionary)

INFO260 Assignment Tasks

Your Role: You are the Data Manager. Your goal is to develop a comprehensive Data Management Plan and Proposal that addresses the challenges of the current data, aligns with the organization’s strategic goals, and prepares the data for advanced use.

1. Current Data Landscape & Strategic Imperative (Data Management Principles, Data as an Asset)

Objective: To analyse the current state of data management within the Emergency Services organization and justify the strategic necessity of a robust data management plan.
Instructions:

1. Analyse the Current State: Based on your understanding of how data is typically managed in “basic ﬁles” (like CSVs) and your exploration of INFO260_Assignment_Data_v2.csv:

Describe the current data situation for the Emergency Services organization. What are the inherent limitations and risks of managing this critical incident data in its current format (e.g., redundancy, lack of integrity, difficulty in analysis)?
Identify at least three speciﬁc business challenges (e.g., inefficient resource allocation, slow response time analysis, inability to track long-term trends) that arise directly from the current data management practices.

2. Strategic Justiﬁcation: Explain why a comprehensive Data Management Plan is a strategic imperative for the Emergency Services organization. How will better data management directly contribute to their core mission of enhancing emergency work performance and improving public safety?

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2. Data Governance & Metadata Management Framework (Data Governance, Metadata Management)

Objective: To propose a foundational data governance structure and a strategy for managing metadata for the Emergency Services data.
Instructions:

1. Data as a Critical Asset: Identify two speciﬁc data elements from the INFO260_Assignment_Data_v2.csv dataset that you would classify as “critical data” for the Emergency Services organization (e.g., Call_District, CAD_Final_Priority, DateOfCall). Justify why each is critical, considering its impact on operations or safety.

2. Proposed Data Governance Model: Based on DAMA principles, propose a suitable Data Governance Operating Model (e.g., Centralized, Hybrid, or Federated) for this Emergency Services organization. Justify your choice based on the need for consistency, control, and scalability across different emergency service units.

3. Key Governance Roles: Identify and describe at least two speciﬁc data governance roles (e.g., a “Data Owner” for Call_District data, a “Data Steward” for CAD_Final_Priority data) that would be essential in your proposed model. Outline their primary responsibilities in ensuring the quality and ethical use of the incident data.

4. Metadata Management Plan: Explain why metadata (data about data) is crucial for managing this incident response dataset. Outline a basic plan for how the Emergency Services organization would manage metadata for this data, including:

Identifying two types of metadata (e.g., business, technical, operational) that would be essential.
Providing a speciﬁc example for each type from the columns available in INFO260_Assignment_Data_v2.csv.

3. Database Design for Scalability & Data Independence (Data Modeling & Design, Data Storage)

Objective: To design a relational database schema that is scalable and promotes data independence for the incident response data.
Instructions:

1. Conceptual Entities & Relationships: Based on INFO260_Assignment_Data_v2.csv and your understanding of emergency response, identify the main entities (tables) and their relationships for a relational database.

2. Logical Database Design (Simpliﬁed): For each identiﬁed entity, list the attributes (columns) that would belong to it. Deﬁne a Primary Key for each entity.

3. Normalization for Scalability & Independence: Explain, in simple terms, how converting this ﬂat CSV data into a normalized relational database structure (e.g., up to Third Normal Form – 3NF) contributes to:

Scalability: How does it help manage growing volumes of incident data?
Data Independence: How does it separate the logical view of data from its physical storage, making future changes easier?

4. ER Diagram: Create a clear Entity-Relationship (ER) diagram representing your proposed database schema. Use a standard, easy-to-understand notation (e.g., Crow’s Foot).

4. Data Quality Assessment & Remediation Strategy (Data Quality)

Objective: To identify potential data quality issues in the dataset and propose managerial strategies for their assessment and remediation, without complex SQL.
Instructions:

1. Identify Potential Issues: Choose three variables (columns) from INFO260_Assignment_Data_v2.csv that you think are most likely to have data quality issues (e.g., Age, DateOnset, CAD_Final_Priority). For each, brieﬂy explain why you anticipate issues in that column (e.g., missing values, inconsistent formats, invalid entries).

2. Data Quality Dimensions: For each of your three chosen variables, identify the most relevant Data Quality Dimension (e.g., Completeness, Validity, Accuracy, Timeliness, Uniqueness) that would be impacted by the issue you identiﬁed.

3. Managerial Remediation Strategy: For each of your three chosen variables, propose a managerial strategy (not a SQL query) for how the Emergency Services organization could:

Assess the extent of the data quality issue (e.g., a process for data proﬁling or auditing).
Remediate the issue (e.g., a process for data cleansing, training for data entry personnel, system changes).
Explain why addressing this speciﬁc data quality issue is important for emergency response performance.

5. Data Integration & Data Environment Proposal (Data Integration & Interoperability, Data Warehousing & Business Intelligence)

Objective: To propose how data will be integrated into a new data environment (like a data warehouse) to support enhanced analysis.
Instructions:

1. Data Warehouse Relevance: Discuss the concept of a data warehouse and its speciﬁc relevance to managing emergency incident data for long-term analytical trend analysis, historical reporting, and supporting future AI model training.

2. ETL Process & Managerial Oversight: Explain the purpose of Extract, Transform, Load (ETL) for preparing this incident data for a data warehouse. From a managerial perspective, what are two key oversight considerations for ensuring the ETL process is effective and maintains data quality?

3. Future Data Environment (Conceptual): Brieﬂy describe how the Emergency Services organization’s data environment could evolve to support advanced analytics and AI.
This might include:

How would different types of data (e.g., real-time dispatch data, historical incident data) be integrated?
What role might a Data Lake or other big data concepts play in this future environment?

6. Data Utilization for AI & Ethical Data Handling (Big Data & Data Science, Data Handling Ethics)

Objective: To propose how the managed data can be utilized for AI and to articulate key ethical considerations.
Instructions:

1. AI/Predictive Analytics Use Case: Propose one speciﬁc AI/predictive analytics use case for this emergency incident data (e.g., “predicting high-risk incident zones,” “optimizing ambulance dispatch routes,” “forecasting seasonal demand for speciﬁc emergency services”).

Explain what strategic business problem this AI aims to solve for the Emergency Services organization.
Identify what key data elements (from your cleaned and managed dataset) would be crucial for training and operating this AI model.

2. Ethical Data Handling for AI: Given the sensitive nature of emergency services and patient-related data, identify at least one important ethical consideration when using this data for AI (e.g., patient privacy, potential for algorithmic bias in resource allocation, data sovereignty).

7. Conclusion & Proposal Summary

Objective: To summarize the comprehensive data management plan and its proposed beneﬁts.
Instructions:

o Provide an effective summary of the key ﬁndings and insights from your data management plan.
o Conclude with a persuasive argument to the Emergency Services leadership, highlighting how your proposed plan will enhance emergency work performance and position the organization for future data-driven success.

Submission Guidelines:

Deadline:

o Week 4: Tasks 1, 2, & 3
o Week 12: Tasks 4, 5, 6, & 7

Format:

o Submit a single PDF document for your written report. Clearly structure it with headings for each task.
o Include your ER diagram (as an image) within the report.
o If any SQL is used for exploration in Task 4 (though the task is designed to be less technical on SQL, students may use it for proﬁling), include it as plain text or screenshots with results.

Clarity: Ensure your submission is well-organized, clearly written, and uses simple, direct language. Avoid overly technical jargon without explanation.
Justiﬁcation: For all decisions, analyses, and proposals, provide clear explanations and justiﬁcations based on DAMA principles and the assignment scenario.
Word Count: Aim for approximately 2500-3500 words for the full report (excluding diagrams). Reference – APA 7th edition.