What are Synthesis & Insights?

Theory of Design Sythesis Design Synthesis is an abductive sensemaking process of manipulating, organizing, pruning, & filtering data in effort to produce info and knowledge. Abductive “When I do A, B occurs, I’ve done smth like A before but w/ different circumstances, so I can abduct that C is the reason B is occurring. It’s logic in which you embrace your bias. Pruning Making a judgement Produce Generate Ideas

Insight Combination

Reframing See things in new ways

Purpose To expose the underlying purpose of common methods of design analysis and synthesis. This paper presents a framework for organising the design analysis and synthesis process, and more easily identifying the purpose and function of the activities and methods designers’ use.

Design Analysis The process of breaking down data and observations to understand a problem better. Design Synthesis Combining analyzed information to create new ideas, solutions, or frameworks.

Key Takeaways on Design Analysis Purpose: Understanding existing situations by identifying patterns, breaking down data, and uncovering insights. Steps in Analysis: Organisation: Preparing raw data for use. Aggregation: Combining diverse data into one format. Deconstruction: Breaking complex data into smaller, meaningful parts. Categorization: Grouping data based on similarity or relevance.

Key Takeaways on Design Analysis Exploration: "Playing" with data to find patterns. Immersion: Engaging with the data to understand its context and quality. Manipulation: Rearranging data (e.g., sticky notes) to identify relationships. Association: Finding connections and meaningful groupings. Interpretation: Turning data into meaningful insights. Abstraction: Simplifying data to its key characteristics. Visualisation: Representing data visually to uncover relationships.

Key Takeaways on Design Synthesis Purpose: Combining insights to form new solutions, concepts, or directions. Steps in Synthesis: Framing: Selecting important insights and explaining them. Summarization: Highlighting key themes. Curation: Choosing examples that represent data effectively. Narration: Telling a story to make the data relatable. Projection: Imagining future possibilities based on insights. Situated Solutions: Direct responses to identified needs. Prediction: Anticipating future scenarios and designing accordingly. Abduction: Using creative leaps to generate new ideas. Ekphrasis: Creating a new medium (e.g., a model or prototype) to communicate insights.

Data vs. Findings vs. Insights What’s the difference?

Data Data refers to an unanalyzed collection of observations about users that may include transcripts, notes, metrics, or survey output. Quantitative vs. Qualitative User quotes or behaviors are qualitative data. Task time, success, analytics metrics, or responses to certain survey questions are quantitative.

Findings Findings describe patterns in collected data or summaries across it. To extract findings, we look across the captured data, looking for patterns across comparable things. However, context is required to be able to interpret a finding. We can understand what happened but not why. What Happened, Not Why

Insights Insights are focused explanations of opportunities, based on other user research and business context. Insights connect specific opportunities to user needs. When we do research, there is inevitably some bias inherent to an insight. Relying on multiple sources of data, multiple approaches to analyzing the data, and multiple researchers doing the analysis.

But not all insights are equal Validated vs. inspiring Simple vs. Complex