We live in overlapping layers of meaning: personal, cultural, emotional, and historical. The challenge is that most data ignores this complexity, offering facts without context. Warm data provides a different approach: attending to the subtle, relational patterns that shape how things work and what truly matters in any situation.
We live in a world where everything happens in context, not just one context but many, layered, and entangled contexts. A conversation occurs not just between two people but within a culture, a family history, a workplace, a mood, and a moment in time. Decisions aren’t made in isolation; they’re shaped by relationships, habits, systems, and environments. That’s where warm data comes in.Nora Bateson coined the term warm data
to describe the information in these overlapping contexts. It’s the opposite of cold, detached data, which consists of numbers or facts stripped from the situations that give them meaning. Warm data pays attention to the relationships between things, not just the things themselves.
1. Data
A raw, context-free measurement.
- Example:
10°C - A number without context. It could refer to the temperature of air, water, or soil. On its own, it tells us nothing about its significance or implications.
2. Information
Data placed in a specific time and context.
- Example: The outdoor temperature in London at 7 AM this morning was 10°C.
- The data becomes useful. It may help someone decide how to dress, whether to open a window, or what to expect from the day. It’s now functional, even actionable.
3. Warm Data
A relational understanding that reflects how the data is lived, remembered, and connected across multiple systems.
- Example: The early morning chill of 10°C in London in June reminded a local grandmother of how the climate has shifted over her lifetime. Her granddaughter, visiting from Nigeria, found it unexpectedly cold and wrapped herself in two sweaters. The sudden drop in temperature also affected a local bee population, delaying pollination patterns and disrupting a nearby community garden’s flowering cycle.
- This is not just temperature. It is experience, ecology, memory, and adaptation. Warm data reveals how information is entangled in the lives and systems it touches.
In Brief
- Data:
10°C - Information: Temperature in London at a specific time
- Warm Data: Interwoven experiences, memories, and systemic effects shaped by and shaping that 10°C
In conversation, warm data is about storytelling, intuition, and observation. It’s how we understand one another when we say, “You had to be there.” It’s the difference between reading a recipe and tasting a dish someone made with love.
Warm data isn’t easy to measure or define, but it’s essential for understanding complexity. It helps us see the bigger picture, not as a static snapshot but as a shifting web of interdependence. We often miss what matters most when we make decisions or try to solve problems without it.
So, to work with warm data, you need to ask: What’s really going on here? Not just what’s visible, but what’s underneath, around, and in between.
Warm Data | Nora BatesonImagine a mid-sized city where a public school begins to experience a sharp rise in youth violence—more fights, more suspensions, more fear among students and staff. In response, local authorities implement the usual responses: more security around the school, stricter disciplinary policies, and public messaging aimed at encouraging parental responsibility.
Despite these efforts, the violence continues.
If we examine only disciplinary records or crime statistics, we might conclude that this is a behavioral or safety issue. But consider approaching this situation through the lens of warm data.
A group of educators, youth workers, and community members comes together to explore what is happening. Instead of isolating the issue, they begin looking at it in context. They speak with students, parents, teachers, and neighborhood residents. They visit homes, playgrounds, and local shops. What they gather is not just measurable data, but stories, experiences, observations, and patterns of relationships.
What begins to emerge:
- Several of the students involved live in households where a parent recently lost a job or the family was evicted.
- Many families are new to the area, having relocated from other regions or countries, carrying trauma and struggling with language barriers and limited trust in institutions.
- Teachers describe feeling overwhelmed and unsupported, with little time to build relationships or respond to the emotional needs of students.
- Community centers that once offered after-school activities have closed due to a lack of funding.
- Local shop owners and neighbors describe a growing sense of disconnection and concern, not just among young people, but across generations.
Seen this way, youth violence is not simply about misbehavior, parenting gaps, or ineffective schools. It’s connected to a set of overlapping pressures—economic strain, social fragmentation, cultural isolation, and weakened connections between people and institutions.
From this understanding, a different kind of community response begins to take shape. Not a single program, but small coordinated actions to rebuild trust and cooperation:
- A neighborhood space is reopened to provide after-school activities centered around food and art.
- Local elders and youth start a storytelling project that brings different generations together.
- Teachers are given regular time to meet and reflect together.
- A pilot group of parents, school staff, and local organizations come together to design new ways to support families.
These efforts do not offer immediate metrics. But slowly, trust returns. Students feel seen. Parents get involved. Conflicts begin to ease. The community starts to mend.
Why this example illustrates warm data
- It moves beyond isolated causes to consider the relationships between different pressures.
- It gathers information across different areas—education, economy, health, and belonging.
- It values lived experience and multiple perspectives, not just statistics.
- It supports community members in responding together rather than through disconnected programs or external instructions.
Although this scenario is fictional, it illustrates the kind of change that warm data encourages: paying attention to how different aspects of life intersect, and using that understanding to inform shared responses and learning.
Warm Data and Conversational Leadership
“Warm data” is a widely accepted term popularized by Nora Bateson. It’s a compelling phrase that has helped open up meaningful conversations about how we understand meaning within living systems.
That said, here are three alternative, more descriptive terms that capture key aspects of warm data and may suit different contexts:
- Context-rich information: Data that only makes sense when considering its surroundings, background, and circumstances. It draws attention to the layered nature of meaning in real-life situations.
- Contextual information: A more familiar term highlighting the importance of setting and situation, reminding us that data detached from its context is often misleading or incomplete.
- Relational information: Emphasizes connections. Meaning emerges not from isolated elements but from the relationships between people, ideas, or parts of a system.
These alternatives don’t replace “warm data,” but they can help clarify what it means, especially if encountering the idea for the first time.
Leadership is often imagined as a role: someone with authority making decisions from the top. However, Conversational Leadership sees leadership as a practice, something anyone can engage in, not through control but through conversation, care, and close attention to what is unfolding between people. This is also where warm data becomes essential.
Warm data is information that exists within context. It is subtle, layered, and relational. It is not the kind of data you find in a spreadsheet. It is the glance that speaks more than words, the shift in tone when something feels off, the quiet that follows a difficult question.
Warm data helps us navigate complexity, not by simplifying it but by noticing how things are connected. It moves through relationships and is shaped by emotion, culture, history, power, and meaning.
In Conversational Leadership, this type of information is not a side note. It is central.
Imagine a team discussion about a significant change. Everyone agrees aloud, but you notice someone who usually contributes is unusually quiet. You pause and check in. She says she is uncomfortable with the direction, but does not feel safe enough to speak up. That discomfort was not on the agenda or in the meeting notes. But it was present: in the silence, the posture, and the tension in the room. Noticing it made space for a more honest conversation.
Warm data helps us notice what might otherwise be missed. Conversational Leadership allows it to matter.
As Nora Bateson writes: “To observe a system without including yourself in the observation is to miss the whole point.”
This way of leading includes us: our presence, our listening, and our awareness of the relationships around us. It asks us to notice tone and texture and pay attention to what is unspoken. It invites us to slow down, ask different questions, and let what others share influence us.
We do not need a title to do this. We just need to pay attention to what is said, how it is said, why it is said, and in what context. When we do, we begin to notice leadership already happening around us, in the connections, in the shared understanding, and in the warm data that moves through it all.
Two Ways to Think About Information
What is information? It seems like a simple question, but the answer depends entirely on how and why you’re asking it. At the heart of this inquiry lie two influential yet different conceptions of information: one by Claude Shannon, the father of modern information theory, and the other by Gregory Bateson (father of Nora Bateson), an interdisciplinary thinker who wove together ideas from cybernetics, ecology, and anthropology.
At first glance, their views seem to diverge sharply. Shannon treats information as abstract and mathematical. Bateson treats it as contextual and meaningful. But look closer, and you’ll see that their perspectives aren’t contradictory but complementary. Together, they reveal a richer picture of information.
Shannon: Information as Transmission
Claude Shannon’s groundbreaking 1948 work on information theory laid the foundation for digital communication. His goal was clear and technical: to understand how to efficiently transmit data over a noisy channel. In Shannon’s model, information is defined in terms of entropy, a measure of surprise or uncertainty. The more unpredictable a message, the more information it contains.Importantly, Shannon’s theory makes no claims about meaning. A random string of characters can be rich in Shannon-style information, even if it’s utterly meaningless to a human. This abstraction was deliberate. By stripping meaning out of the equation, Shannon built a universal, mathematical framework that powers everything from your mobile phone to the internet.
In this view, information is cold: it’s structured, static, and stripped of context. However, it allows signals to be stored and transmitted faithfully and efficiently from one place to another.
Bateson: Information as Difference That Makes a Difference
Enter Gregory Bateson. Writing in a very different context, systems theory, psychology, ecology, Bateson famously defined information as
“A difference that makes a difference.”
What did he mean?
For Bateson, information is not just about patterns in data. It’s about the effects those patterns have within a system. A flashing light only becomes information if it is perceived and leads to some change in thought, feeling, behavior, or systemic response. In other words, meaning arises from context and relationship.
Where Shannon focused on efficient transmission, Bateson was concerned with how information affects minds, ecosystems, and cultures. His view of information is dynamic, relational, and alive.
This leads to a different kind of information: warm data. Nora Bateson’s “warm” information lives in context, carries ambiguity, and can’t be reduced to bits or bytes. It’s the kind of information that can’t be decoded by a machine alone, because its significance depends on the system that receives it.
Are They in Conflict?
It’s tempting to see Shannon and Bateson offering incompatible definitions of information. But they’re not in conflict; they’re focused on different levels of the informational process.
- Shannon asks: How much information is being sent? Can it be transmitted with fidelity?
- Bateson asks: What kind of difference does this information make and to whom, and in what context?
Shannon gives us the tools to measure and optimize the flow of signals. Bateson helps us understand how those signals become meaningful, impactful, even transformative. One is about form; the other is about function. One addresses the mechanics of communication; the other, its meaningfulness.
An Analogy: Paint and Canvas
If you’re looking for an analogy, imagine Shannon’s information as a bucket of paint, measurable, quantifiable, transmittable. Bateson’s information is what happens when that paint hits a canvas, a wall, or a person’s skin. Depending on the context, the same paint can become a masterpiece, a protest, or a mess. The difference it makes depends on the system it enters.
Why This Matters
In today’s world, we are inundated with information. Shannon’s cold data is abundantly streamed, stored, and shared across global networks. But without Bateson’s lens, we risk forgetting what really matters: whether that information means anything and whether it changes anything in the systems we care about.
Understanding both views is essential for technologists, theorists, and anyone navigating an information-rich world. The challenge is to move data efficiently, make sense of it, and act on it wisely.
To work with warm data, we must slow down and pay attention to what lies beneath the surface. We can listen for what isn’t said, notice relationships and context, and ask better questions. By doing this, we begin to make decisions and build understanding in more meaningful ways.
Resources
- Website: Warm Data
- Blog Post: Warm Data
- Blog Post: Warm Data to Better Meet the Complex Risks of This Era
- Blog Post: Eating Sand: Tasting Textures of Communication in Warm Data
Posts that link to this post
- Information Theory Information is surprise
- Interaction as the Primary Unit of Change Is not the individual, it’s the interaction
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