​Overview
This activity introduces participants to the foundational ideas behind data collection, structure and interpretation by having them collaboratively build a large paper spreadsheet.
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Through hands-on participation they experience how data is created, what choices shape a dataset, and how inconsistencies and missing information influence analysis.
Paper Spreadsheet
Learnings
Data is a simplified representation of the world.
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Different data types require different approaches to organisational analysis.
Real-world data is often messy, incomplete or inconsistent.
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Data cleaning is essential before meaningful intrepretation.
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Ethical and privacy considerations apply even in simple data collection contexts.
​Overview
This activity uses a deliberately skewed deck of cards to give participants a live experience of how data behaves in the real world.
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Each card becomes a "data point" and participants physically sort themselves into categories based on the attributes of their card.
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As they move, observe patterns, and form conclusions, they notice that the dataset behaves strangely: some categories are unexpectedly large, other nearly empty, and the overall distribution feels "off."
Game Of Cards
Learnings
How sampling bias distorts conclusions.
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How hidden structures affects interpretation.
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Why small samples can mislead.
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How assumptions can shape analysis.
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The importance of metadata and documentation
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The limits of forecasting when data quality is uncertain.
​Overview
Increasingly, data arrives on our doorstep in for form of a spreadsheet.
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In this activity you will work with colleagues to ask good questions of a spreadsheet so that you can take it from data points to data story.
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We will be using the Titanic Passengers data set which is based on the Kaggle Machine Learning Challenge.
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We will be searching the data for hidden truths and investigate some of the stories onboard the ship.
Titanic
Learnings
Understand your data by exploring its structure, context, and quality before analysis.
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Ask deeper comparative questions to uncover meaningful patterns and insights beyond basic counts.
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Use historical and social context to add depth and relevance to your data story.
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Present findings with clear visuals and focus on percentages for better storytelling impact.
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Humanize data by highlighting individual stories and connecting then to broader social factors.