Data Analysis

Collecting and examining information to discover patterns, behaviours, trends, and areas for improvement

Project Stage:

Define

Who:

Experts

Decision makers

Service Providers

Design Principles:

06. We will challenge assumptions & design with evidence

08. We will design to deliver value in all its forms

Data analysis involves collecting and examining information to discover patterns, behaviours, trends, and areas for improvement. It helps make informed decisions by turning raw data into useful insights. It helps you understand patterns, trends, and relationships within the data, allowing you to make informed decision or draw conclusions. It can be both qualitative (e.g., coding interviews) and quantitative (e.g., analysing survey results or metrics).

Use it to

Identify user needs, preferences, and pain points that emerge through the data.
Validate assumptions and decisions with real evidence.
Uncover trends and patterns that might influence service design choices.

Top Tips

Start by clearly defining the question you are trying to answer or the problem you’re investigating.
Combine quantitative and qualitative data where possible for richer insights.
Be mindful of biases – ensure the analysis process does not lead to misinterpretation or over-generalisation.

How – to

Collect: Gather the relevant data (qualitative, quantitative, or both).
Organise: Sort and structure the data, making it easier to work with
Analyse: Use tools or methods, such as affinity diagrams to find patterns, or themes.
Interpret: Draw conclusions based on the data. What does it tell you about user needs or service efficiency?
Visualise: Present the data using charts, tables, or infographics to communicate findings effectively.
Act: Use the insights gained to inform decision-making, service design iterations, or improvements.