The path to natural insights—those that are simple, provocative, feeling fundamental and obvious—is only clear in retrospect. It is more often the case that you will just barely afloat in the sea of possibility. 

You need to take time to steep in the breadth of data collected, understanding its broad shape as well as where conceptual pockets of nuance and insight lie. The need becomes especially difficult and urgent as the distance from actual observation or interviews, and the weight of collected data grows. 

Feeling blocked or stuck is either a sign of incomplete data coverage, or a lack of deeper connection and conceptual view of the data on hand. Building dedicated time to methodically review the data is crucial to identify incomplete coverage and building that connection.

Consider how to make sense of smaller units of data, and move from the bottom back up. You can evaluate qualitative data from a number of frames: sentiment, topic, theme, or pre-existing frameworks like AEIOU. Investigate how time, sequence, and context perspectives might help reframe the data. These tools will provide a structured approach but not a prescriptive output. 

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Therefore, carve out time to work with the data and review it from a number of perspectives. Always start by reviewing the nature of data collected, its potential bias or conflict, and the range of sources it comes from. Chart out the conceptual landscape the data covers while pulling on interesting threads. Apply existing tools whose models fit the nature of insights you expect to work with. Generate testable frameworks and identify meaningful questions about the data.