From Quotes to Conclusions: A Hands-On Workshop in Making Sense of Qualitative Data

Our workshop follows the talk that we have proposed. It expands on the ideas we are putting forward around how teams can think about making sense of qualitative data coming in from various sources, and how one goes from disparate quotes, transcripts, NPS feedback to answers and insights to important questions. It is targeted towards those who work in teams to help structure, analyse and present insights from various datasets especially for internal team decision making.

The workshop would unfold in 3 parts: 1/ Initial introduction to the ideas in the talk so that everyone has a shared understanding and there’s time for resolving doubts, understanding any specific experiences to assimilate into the workshop design & flow

2/ Analog exploration of qualitative data

• We would present the case study, along with the decision that the company is trying to make
• We would then share bite-sized versions of the synthetic data in physical forms (exact formats to be decided), and setup the more detailed questions the company needs to answer using the data.
• The participants would then have some time to go through the datasets, and pull out the data, both qualitative and quantitative, to gain insight and put forward recommendations for the decisions
• Most importantly, we will explore how they can put forward their ideas with the backing of quotes, and patterns from the datasets

3/ Digital Exploration

• We would then onboard the participants onto a version of the Dots platform that is already setup with the various datasets
• The groups would then use the platform to work with a much more expanded dataset, and use various systems to filter and analyze the data along relevant variables and attributes

Learning outcomes:

• Learn how insights emerge when qualitative data is structured across sources, variables, and attributes
• Experience the difference between anecdotal reasoning and evidence-backed conclusions
• Practice tracing insights back to supporting quotes, patterns, and datasets
• Gain a transferable mental model for synthesising qualitative information into decision-grade insight
• See how visual and structural organisation of data enables sense-making, not just summarisation
• Reflect on the limits of AI-generated summaries when working with complex qualitative inputs