DWMA Assignment 1Guidance and Template Assessment

Assignment Guidance: Data Warehouse Design (Integrated Health and Social Care Data System)

Important:

• Always check the marking schema for each task.
• This guidance is not an assessment specification, but offers suggestions and best practices for each task.
• The order of your submission may vary depending on your case study and the narrative you wish to present.

Task 1: Evidence of Tutorial Completion 

• Submit evidence for each tutorial task as listed in Appendix B of the Module Handbook.
• Locate each tutorial in the relevant weekly folder (see the appendix for details).
• Tip: Clearly label and organise your evidence for easy reference.

Task 2: Introduction to the Case Study & Data

• Introduce your case study: Briefly describe the context and purpose of your integrated health and social care data system.
• Data Dictionary (DD) for the current system:
  For each dataset, provide:
  • Data source (file/table name)
  • Column name
  • Data type
  • Description (explain what the data in each column represents)
  • Example data value
• Template: Use the OLTP Data Dictionary template (available in Week 3).

Task 3: Problem Definition, Architecture & Reporting

• Problem Statement:
  • What current issue(s) will your centralized database solution address?
  • What is the rationale and aim of your project?
• KPIs:
  • Identify and justify the key performance indicators (KPIs) your system will support.
• Stakeholders:
  • Who are the users of your proposed system?
• System Architecture:
  • Propose and justify your chosen architecture (e.g., centralized, two/three-tier, data marts).
  • Briefly evaluate alternative architectures and explain why they were not chosen.
  • Include a diagram if possible.
• Reporting Objectives:
  • List 3–5 reports your system will generate.
  • Use the LA Shop tutorial (Week 2) for inspiration.
  • Think creatively—consider correlations or trends relevant to your data (e.g., impact of weather on complaints, COVID-19 effects, etc.).
• Business Drivers & Strategy:
  • Discuss internal/external drivers, management needs, competitors, and market context.
  • Outline the data strategy for your case study organization.
  • Explain how your BI solution supports short- and long-term strategy.
  • Is there management buy-in?
• Implementation Approach:
  • Describe the methodology (e.g., SDLC) and project management approach.
  • Identify key people involved.
  • Support your arguments with literature and in-text citations.

Task 4: Star Schema Design

• Design a Star Schema (SS):
  • Support your chosen KPIs and reports.
  • Include at least three measures.
• Documentation:
  • Provide a full table structure (using DD1 or similar) for all tables in your SS.
  • Include actual columns, measures, and any new columns (e.g., surrogate keys).
  • Use the SS Data Dictionary template (available in Week 3).
• Diagram:
  • Create and include your Star Schema diagram (e.g., using QSEE).

Task 5: ETL Design

• ETL Process:
  • Design the ETL process for your system.
  • Provide a detailed ETL Data Dictionary.
  • Include a sample data table (Excel) for each SS table.
• ETL Diagram:
  • Illustrate the flow from OLTP sources to the Staging Area and Star Schema.
• Data Quality & Transformation:
  • Discuss data quality issues, transformation needs, and cleaning strategies relevant to your datasets.
  • Address issues such as anomalies, inconsistencies, missing data, use of nulls, primary keys, metadata, and data granularity (e.g., date, location).
• Documentation:
  • Use the ETL Data Dictionary template (available in Week 3).

Task 6: Reflection and Evaluation (Individual)

Purpose:
This task asks you to critically reflect on your personal contribution and experience working within your project team. Your reflection should demonstrate self-awareness, learning, and insight into team dynamics and your own development.

What to Include:

• Structure your reflection around each stage of the project and key group meetings.
  • Consider using subheadings for each stage (e.g., “Initial Planning,” “Data Modelling,” “ETL Design,” etc.).
• Discuss your individual contribution at each stage:
  • What specific tasks or roles did you take on?
  • How did you support the team’s progress?
• Evaluate your experience of teamwork:
  • How did you communicate and collaborate with others?
  • What challenges did you face, and how did you address them?
  • How did group meetings influence your understanding or approach?
• Reflect on your learning and development:
  • What skills or knowledge did you gain?
  • What would you do differently in future team projects?
  • How has this experience influenced your approach to group work or data projects?
• Reference group meeting minutes where relevant (these should be submitted separately and are not included in the word count).

Word Count:

• Aim for approximately 300 words for your reflective discussion (excluding referenced meeting minutes).

Tips for Success:

• Be honest and specific—focus on your own actions, thoughts, and feelings.
• Go beyond description—analyse what worked well, what didn’t, and why.
• Link your reflection to relevant teamwork or project management theories if appropriate.
• Use evidence from group meetings or project milestones to support your points.
• Check the marking schema to ensure you address all assessment criteria.

Remember

Reflection is about learning from experience. Show how you have grown as a team member and a data professional through this project.

General Tips

• Support all arguments and design choices with academic literature and in-text citations.
• Reference all templates and resources provided in the module.
• Check the marking schema regularly to ensure you meet all criteria.
• Structure your work logically, but adapt the order to best fit your case study and narrative.

Summary of Assessment Requirements

This assessment focuses on designing a comprehensive Data Warehouse and Business Intelligence (BI) solution for an integrated health and social care data system. Students must complete six key tasks:

1. Tutorial Evidence – Submit all tutorial outputs listed in Appendix B as proof of practical skill development.

2. Case Study & Data Introduction – Present the case context and prepare a structured Data Dictionary describing the existing datasets.

3. Problem Definition, Architecture & Reporting – Identify system problems, propose KPIs, outline stakeholders, justify a suitable architecture, list reporting objectives, and explain business drivers and implementation methodology.

4. Star Schema Design – Develop and document a star schema supporting your KPIs and reporting needs, including dimension/fact tables and schema diagrams.

5. ETL Design – Design the complete ETL process, document transformations, address data quality issues, and provide example tables and ETL diagrams.

6. Reflection & Evaluation – Produce an individual reflective evaluation discussing contributions, teamwork, challenges, learning, and professional development.

Academic literature, in-text citations, proper templates, and adherence to the marking criteria are mandatory.

How the Academic Mentor Guided the Student 

The academic mentor provided structured guidance to ensure the student understood the expectations, aligned their work with the module requirements, and maintained clarity across all tasks.

Step 1: Clarifying Requirements & Reviewing the Marking Schema

The mentor began by breaking down each task in the assessment brief, emphasising the importance of template usage, evidence submission, academic sourcing, and logical narrative flow. The student was guided to first review Appendix B, weekly tutorials, and tool folders to organise all required materials.

Step 2: Developing the Case Study Context & Data Dictionary

The mentor instructed the student to:

• Clearly describe the purpose of their integrated health and social care system.

• Extract dataset details systematically.

• Use the OLTP Data Dictionary template to record source files, columns, data types, descriptions and examples.

This ensured accuracy, consistency, and professional presentation of all data.

Step 3: Structuring the Problem Statement, KPIs, Architecture & Reporting

For Task 3, the mentor guided the student by:

• Helping formulate a precise problem statement grounded in operational challenges.

• Explaining how to define measurable KPIs linked to data-driven decision-making.

• Comparing architectural options and selecting the most appropriate model with justification.

• Encouraging creative yet relevant reporting ideas inspired by Week 2 examples.

• Strengthening arguments with academic sources on BI strategy and architecture.

This step ensured all analytical and strategic components were coherently integrated.

Step 4: Designing the Star Schema

The mentor supported the student in identifying facts, dimensions, measures, and hierarchies that align with the established KPIs and reporting needs. The student was instructed to:

• Use the SS Data Dictionary template.

• Create a well-labelled star schema diagram.

• Ensure surrogate keys, grain definitions, and table relationships were clearly documented.

This ensured the schema was technically robust and academically justified.

Step 5: Building the ETL Framework

The mentor helped the student outline each transformation from OLTP → Staging → Star Schema. Guidance covered:

• Using the ETL template.

• Listing each source column, transformation rule, and target field.

• Identifying data quality issues like missing values, inconsistencies, granularity variations, and anomalies.

• Preparing ETL diagrams to visualise the data flow.

The focus was on accuracy, traceability, and alignment with BI best practices.

Step 6: Writing the Individual Reflection

The mentor encouraged the student to produce a reflective, analytical, and self-aware evaluation. Support was given on:

• Structuring reflection around project stages.

• Discussing contributions, communication, teamwork, and challenges.

• Linking experiences to personal development and project management concepts.

• Referring to meeting minutes for evidence.

This ensured the reflective component demonstrated growth, critical analysis, and professional insight.

Final Outcome & Learning Objectives Achieved

Through structured mentoring and step-by-step guidance, the student produced a well-organised submission that:

• Demonstrated practical skills in data modelling, ETL design, BI architecture, and analytical documentation.

• Applied academic research to justify design choices and methodologies.

• Showed competence in using templates, diagrams, and structured data practices.

• Reflected deep understanding of teamwork, planning, and critical evaluation.

• Addressed all marking criteria with clarity, coherence, and evidence-based reasoning.

The student successfully met the learning objectives related to data warehouse design, BI strategy, ETL implementation, stakeholder analysis, and reflective professional practice.

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