The importance of warm data in change and optimization processes

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In the age of AI, automation, and the obsession with what can be quantified, we still face a fundamental unresolved problem: the models we build do not explain the reality we experience. Why? Because representing data and processes is not enough—they fail to capture the complexity of living systems and interactions.

One of Sociology’s core pursuits is precisely this: getting closer to an accurate representation of reality, and therefore of complexity (because reality is always complex).

This is where warm data comes into play: relational and transcontextual information that allows us to understand how a system is sustained (or blocked) beyond KPIs.

Cold data vs. warm data (a useful distinction)

Cold data (Hard, Cold Data): what is measurable and comparable. Time, costs, throughput, errors, queues, deviations.
Warm data: what connects and gives meaning to the above. Trust, implicit norms, tensions, commitments, “what is left unsaid,” fears, shared stories, social and cultural context—what Sociology refers to as “interactions.”

Both are necessary. The problem begins when we try to optimize only what can be “easily” measured.

Three theses for viewing organizations as living systems

This topic was a fundamental part of the work of Gregory Bateson, philosopher, sociologist, and cyberneticist, and later of his daughters Cathy and Nora Bateson. The Bateson family’s theses on what to consider when analyzing ecosystems such as organizations are as follows:

1 The transcontextuality thesis

“No process exists in a single context.”

In traditional process design (Business Process Model and Notation - BPMN), we tend to isolate processes (e.g., “credit approval”). For the Batesons, this is a functional simplification error. Warm data provides that transcontextual information: the approval process is “marinated” in economic context, workplace climate, the employee’s personal life, and the company’s technological culture.

Application in processes

Process Mining may detect technical inefficiencies such as delays (cold data). Warm data may explain that the delay occurs because the employee prioritizes the client relationship over system metrics to avoid cultural conflict.

2 The relationality thesis

“Intelligence is not in the parts, but in the relationships between them.”

Gregory Bateson argued that to understand a system, we must look not at the subjects but at the messages they exchange. Nora extends this idea, stating that warm data describes the interdependencies that keep the system alive.

Cold data measures silo performance; warm data measures the health of the connections between silos.

Application in processes

In a DTO (Digital Twin of the Organization), it is not enough to model tasks. You must also model “adhesion” and trust in the process. A process may be technically optimal but relationally toxic, ensuring long-term failure.

3 The coalescence thesis

“Real changes in complex systems are invisible processes of coalescence.”

This thesis states that before a change becomes visible in a KPI (cold data), an “Aphanipoiesis” occurs: an accumulation of small variations in relationships and perceptions (warm data), subtle but system-shaping. They “coalesce” (like raindrops merging into a larger drop). If we only manage what is measured (cold data) and ignore this coalescence, we will react too late to crises or opportunities.

Application in processes

For example, in their work, an Agile Coach does not only look for “burn rate velocity,” but for changes in team communication that precede efficiency gains. Before a team becomes formally “agile” (events, artifacts… cold data), there is a “coalescence of trust, shared language, and tacit rule understanding.” Without seeing this, a consultant risks deploying processes on unstable ground.

Warm data enables proactive orchestration, detecting system fatigue before logs reflect performance drops.

Warm data in a process: a practical example enriching VSM

Imagine you map a process using a Value Stream Map: you extract a SIPOC matrix, measure activity times, classify inefficiencies, and validate hypotheses. You detect that an administrative process has 30% waste in waiting time. Cold data may suggest automation, but warm data may reveal that delays stem from lack of trust between teams, fear of mistakes, underused tools, or implicit norms slowing agility.

Without relational data, any optimization is superficial—and often counterproductive.

For an optimization expert, ignoring warm data is like trying to understand a forest by analyzing only the wood (cold data), without considering root symbiosis and climate (warm data).

A comprehensive orchestration in 2026 requires both. This approach can mean the difference between a solution that works on paper and a real, sustainable transformation.

Warm data has traditionally been dismissed as “soft,” subjective, or unscientific. Relational sociology proves otherwise. It captures relationships, not isolated variables. Combined with cold data (costs, time, performance), it helps explain how human and cultural dynamics shape those numbers, including interactions with processes and machines—as anticipated by Lean TPS autonomation (Jidoka).

How do we integrate warm data into process analysis?

Detecting and measuring warm data is challenging, which is why it is often ignored. As a starting point:

Observe interactions, not just outcomes. How are decisions made? What conversations never happen? Why are certain tools chosen? How do teams relate? Are there implicit rules?
Analyze behavioral patterns, not just outputs. Why do inefficiencies persist despite good solutions? Are we automating hidden inefficiencies?
Include multiple perspectives. Operational data is not enough; we must understand lived experiences, emotions, and leadership dynamics. #diversitymatters
Finally, challenge your own optimization efforts. Does optimization reduce cognitive load or just redistribute it? Is change adopted or merely tolerated?

Warm data and agility: a natural connection

Agile and Lean frameworks emphasize adaptation and iterative improvement. Defining a framework is defining a way of working together, and agility has invested heavily in this. However, without understanding invisible relationships and contexts, improvement remains superficial.

Warm data reveals resistance to change, cultural values, and systemic impacts of interventions.

At Paradigma Lean Papers, we have discussed the importance of BA (context) to understand systems, daily team life, and the observation of Gen-ba as a living ecosystem.

Conclusion

In a world obsessed with numerical precision, we must remember that organizations and their processes are living systems.

If you work in optimization, remember: complexity is an experience. If your models do not explain the reality teams live, you are not optimizing—you are dangerously simplifying.

How do you integrate relational data into your agile projects? I’ll read you in the comments 👇