An African proverb says, if you wish to go far, go together. 0:00 I couldn't agree more, especially when it comes to user research, 0:05 analysis, and synthesis. 0:08 Figuring out what happened during research and 0:10 what it means is a team effort for two excellent reasons. 0:13 One, a shared understanding clarifies what 0:18 different people in the team learn from research. 0:21 Sharing those learning bits increases everyone's understanding. 0:24 Two, a shared understanding also means everyone in 0:29 the team is committed to what's coming after reasearch is done. 0:32 If people agree on what's next, they are more committed to making it happen. 0:37 Jeff Patton is visually explaining the concept of 0:42 shared understanding in a brilliant way. 0:45 Let's apply his concept to making sense out of research data. 0:48 First, different team members have different insights from research. 0:52 Yet, since they were all involved in research as moderators, observers, or 0:57 people who go over the data, they think they agree on what they saw and 1:01 what it means. 1:05 As soon as each person's understanding is put into the sketch, or 1:07 some kind of a written form, everybody understands they were seeing, thinking or 1:10 concluding different things. 1:16 The team then shapes their various learnings, understanding and 1:18 insights through a process of analysis and synthesis. 1:22 In the end of the process, the team has reached a shared understanding about what 1:26 happened during research and what should be done about it. 1:31 Shared understanding is the collective knowledge of the team that builds up 1:35 over time as the team works together. 1:39 Shared understanding is the currency of user research. 1:42 The more a team collectively understand what it's doing and why, the less it 1:46 has to depend on second-hand reports and detailed documents to continue its work. 1:50 Your key takeaway is to involve as many team members as 1:56 possible in research in general, and specifically in 1:59 the process of coming to conclusions after data collection has ended. 2:02 Next, we'll introduce the first analysis and 2:07 synthesis technique, the experience sampling classification. 2:10