Insight Generation and Sensemaking

Research generates information.

Observation generates evidence.

Interviews generate stories.

Surveys generate data.

However, collecting information is not the same as generating insight.

Many organisations gather enormous amounts of data yet struggle to create meaningful understanding. Teams conduct interviews, administer surveys, generate analytics reports and facilitate workshops, only to find themselves overwhelmed by information and uncertain about what it all means.

This is where sensemaking becomes essential.

Sensemaking helps innovators move from information to understanding. It enables teams to uncover patterns, identify themes, challenge assumptions and generate insights that inform decisions and inspire innovation.

Within the Evaluate stage of the 5E Design Thinking Framework, insight generation and sensemaking transform research findings into opportunities for action.

The goal is not simply to collect information.

The goal is to uncover meaning.

An insight is a meaningful understanding about people, systems, behaviours or situations that can inform action.

Insights are not:

• Raw data
• Individual observations
• Isolated quotes
• Simple facts

Instead, insights reveal something important about why people behave as they do.

For example:

Observation

Students frequently access course materials late at night.

Insight

Many postgraduate students balance study with work and family commitments, creating a preference for flexible, self-paced learning experiences.

The insight explains the behaviour and suggests opportunities for innovation.

These terms are often confused.

They represent different stages of understanding.

Data

Raw observations.

Examples:

• Survey responses
• Interview transcripts
• Website analytics

Data alone rarely creates meaning.

Information

Organised data.

Examples:

• Summary statistics
• Categorised interview themes
• Visualised trends

Information provides structure.

Insight

Understanding that explains behaviour, motivations or opportunities.

Insights create direction.

Wisdom

Judgement about what should happen next.

Wisdom creates action.

Without sensemaking, research can become overwhelming.

Teams may experience:

• Information overload
• Conflicting interpretations
• Uncertainty
• Analysis paralysis

Sensemaking helps teams:

• Identify patterns
• Clarify meaning
• Recognise opportunities
• Generate direction
• Build shared understanding

The objective is not more data.

The objective is better understanding.

Sensemaking is the process of interpreting information to create meaning.

It involves:

• Exploring evidence
• Looking for patterns
• Asking questions
• Comparing perspectives
• Identifying relationships
• Constructing explanations

Sensemaking transforms scattered observations into coherent understanding.

Most sensemaking activities involve a repeating cycle.

Gather Information

Collect evidence through research.

Examples:

• Interviews
• Observation
• Surveys
• Analytics

Organise Information

Structure data so it can be explored.

Examples:

• Notes
• Tags
• Categories
• Clusters

Identify Patterns

Look for repetition and relationships.

Questions include:

• What appears repeatedly?
• What surprises us?
• What themes are emerging?

Generate Insights

Interpret patterns and explore meaning.

Ask:

• Why is this happening?
• What does this reveal?
• What opportunity exists?

Take Action

Use insights to inform innovation and decision-making.

Pattern recognition sits at the heart of insight generation.

Patterns help innovators identify recurring relationships within information.

Examples include:

Behavioural Patterns

What people repeatedly do.

Emotional Patterns

How people consistently feel.

Communication Patterns

Common language and themes.

System Patterns

Relationships and interactions.

Patterns often reveal deeper truths about experiences.

Affinity Mapping is one of the most widely used sensemaking methods.

It helps teams organise large amounts of qualitative information.

How It Works

Step 1

Capture observations individually.

Step 2

Group similar observations.

Step 3

Create categories and themes.

Step 4

Identify patterns.

Step 5

Discuss emerging insights.

Benefits

Affinity Mapping helps:

• Organise complexity
• Encourage collaboration
• Reveal relationships
• Generate shared understanding

Clustering involves grouping related information.

Examples include grouping comments about:

• Motivation
• Frustration
• Engagement
• Communication
• Learning

Patterns become easier to identify when related information is organised together.

Themes represent recurring areas of significance.

Example interview findings may reveal themes such as:

Flexibility

Participants value adaptable experiences.

Confidence

Participants seek reassurance and support.

Simplicity

Participants prefer intuitive systems.

Belonging

Participants value community and connection.

Themes help researchers focus attention on meaningful areas.

Strong insights often emerge from contradictions.

Examples:

Participants say they enjoy collaboration.

However:

Participation rates remain low.

This tension invites further exploration.

Questions include:

• Why does the contradiction exist?
• What barriers remain hidden?
• What assumptions need testing?

Contradictions often reveal opportunities.

Not every insight emerges through majority patterns.

Sometimes unusual behaviours reveal important opportunities.

Examples:

• Unexpected use cases
• Alternative workflows
• Innovative workarounds
• Extreme users

Outliers challenge assumptions and expand understanding.

A useful insight statement often follows a simple structure:

Users need [need] because [underlying reason].

Example:

Students need opportunities for timely reflection because delayed feedback reduces their confidence and ability to improve.

Strong insight statements connect behaviour to motivation.

Insights become valuable when they create opportunities.

Example:

Insight

Students seek feedback earlier in projects.

Opportunity

Design a system that supports ongoing formative feedback throughout project development.

Insights should inspire action.

Research interpretation is influenced by perspective.

Different people may notice:

• Different patterns
• Different priorities
• Different concerns

This is one reason collaborative sensemaking is valuable.

Diverse perspectives create richer understanding.

Many organisations conduct sensemaking collaboratively through:

• Workshops
• Affinity Mapping Sessions
• Data Review Meetings
• Reflection Activities
• Insight Generation Workshops

Collaboration reduces individual bias while creating shared ownership of findings.

Educational researchers often explore:

• Student engagement
• Learning challenges
• Assessment experiences
• Digital learning environments

Insights help educators improve learning design and student outcomes.

Examples include:

• The importance of belonging
• Flexibility in learning pathways
• Timely feedback
• Authentic assessment

Entrepreneurs use insights to:

• Identify customer needs
• Discover opportunities
• Refine products
• Improve services

Many startup failures occur because founders act on assumptions rather than insights.

Deep understanding improves decision-making.

Insights become more powerful when linked to systems.

Rather than viewing observations in isolation, innovators examine:

• Causes
• Relationships
• Structures
• Feedback loops

Systems perspectives create deeper understanding.

Confusing Data With Insight

Data describes.

Insights explain.

Seeking Validation

Research should reveal understanding, not simply confirm existing beliefs.

Ignoring Contradictions

Tensions often create valuable opportunities.

Focusing Only on Majority Views

Outliers can reveal important insights.

Jumping to Solutions Too Quickly

Insights should be explored before solutions are developed.

Strong insights should be:

Clear

Easy to understand.

Actionable

Relevant for decision-making.

Evidence-Based

Grounded in research.

Human-Centred

Connected to real experiences.

The best insights help people see challenges differently.

Stories often communicate insights more effectively than statistics.

Combining:

• Quotes
• Observations
• Themes
• Context

creates richer understanding.

Stories help stakeholders connect emotionally with insights.

Insight Clustering Workshop

Objective

Transform research findings into meaningful insights.

Instructions

• Collect observations.
• Write each observation separately.
• Group related observations.
• Create themes.
• Develop insight statements.
• Identify opportunity areas.

Expected Outcome

A set of actionable insights from research findings.

Estimated Time

60–90 minutes.

Review a recent project.

Identify:

• Ten observations
• Five patterns
• Three themes
• Two insights
• One opportunity statement

Notice how information becomes increasingly meaningful as it is synthesised.

• Are you collecting information or generating understanding?
• What themes appear repeatedly in your research?
• What contradictions exist?
• What opportunities emerge from your insights?
• How might others interpret the same evidence differently?

• Data and insight are not the same.
• Insights explain behaviour and reveal opportunity.
• Sensemaking transforms information into understanding.
• Pattern recognition is central to analysis.
• Affinity Mapping helps organise complexity.
• Contradictions often reveal valuable opportunities.
• Collaboration improves insight generation.
• Research creates value when it informs action.

Review the most recent research, feedback or observations from your project. Rather than looking for answers, look for patterns. Ask yourself: What does this information reveal about people's needs, behaviours and experiences? The most valuable innovations often emerge from insights that others fail to notice.