Using a sensemaking process to guide research and drive meaningful insights

Karl Weick the inventor of the term ‘sensemaking’ simply defined it as “the making of sense”, and the process of “structuring the unknown”. As Deborah Ancona of the MIT-Sloan School of Management describes, “sensemaking is most often needed when our understanding of the world becomes unintelligible in some way. This occurs when the environment is changing rapidly, presenting us with surprises for which we are unprepared or confronting us with adaptive rather than technical problems to solve.” In the world of market research and data analytics however, sensemaking can be thought of as the means of transforming raw data into meaningful insights and actionable knowledge. It involves making sense of the vast and often complex arrangement of information gathered from various sources to understand more clearly, for example, market dynamics, consumer behaviour, and competitive landscapes.

The idea of sensemaking in research has gained prominence as we seek effective ways to interpret and utilise data, leveraging information to uncover connections and insights that inspire innovative products and services.

With reports showing that “74% of [organisations] say they want to be ‘data-driven,’ but only 29% are actually successful at connecting analytics to action”, sensemaking can often be thought of as the missing link in providing context and actionable insights from data. As Jeff Humble of The Fountain Institute concurs:

“[Research] is a messy undertaking. Any field that attempts to study how humans act in the messy real world will run into chaos. [Researchers] have many methods for dealing with the disorder of humans and the data that these studies generate. Sensemaking allows you to apply data in a way that will inform, inspire, and align the project.”

Jan Chipchase author of ‘The Field Study Handbook’ crafted a process for sensemaking that structures the understanding and synthesis of data.


As shown in the graphic, the process involves 6 steps, with a “higher level of understanding” being created as the model moves from left to right.

Identify Hypothesis – Where the problem is identified and assumptions are made, often when the mission is defined and key objectives for the project are set.

Collect Data – Information is gathered and can be from a range of sources/ methodologies, including but not limited to surveys, focus groups, in-depth interviews, desk research and ethnography.

 Structure Information – Data is organised, analysed and cross-checked, with a focus on areas of importance.

 Cluster Knowledge – Patterns and relationships are uncovered across and within data sets, creating a narrative and greater sense of what the data is ‘actually saying’.

 Synthesise Insights – The most crucial step, linking patterns and information that have been uncovered into key findings that address initial assumptions and the mission defined in the first stage of the sensemaking process.

The below example from Brent Dykes of Forbes offers a great example as to how the collection of data can move through to the synthesis of insights:

“My Fitbit watch gives me all kinds of activity data: steps, BPM (heart beats per minute), miles, calories and so on [collect data]. At this moment, my watch says I’ve taken 7,442 steps today [structure information]. This single fact is fairly useless, especially without more context. My Fitbit app includes a number of data tables and visualizations such as a weekly steps report where I can see how many steps I completed each day [cluster knowledge]. When I analyse this trended information I can see my current trajectory is ahead of what I accomplished the past few days and with a little extra effort in the evening I can reach my daily target [insight]: I only need 2,558 more steps in next 5 hours).”

Evaluate Wisdom – The final and most often overlooked stage, using the insights developed in Step 5 in broader contexts and with outside knowledge to test the accuracy and impact of the research undertaken.

Sensemaking offers a powerful framework for navigating the complexities of data and transforming it into actionable insights. By following structured processes like Jan Chipchase’s model, researchers can effectively identify patterns, synthesise information, and evaluate insights to drive meaningful decision-making. As organisations strive to bridge the gap between data analysis and actionable outcomes, sensemaking emerges as a powerful tool for unlocking the value inherent in raw data and guiding research towards impactful results.

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