We live in a time when data is seen as the new gold. Every organization collects, measures, models and stores - as if more data automatically leads to more knowledge. But data is not a goal. It is merely the raw material for understanding. Only when we know the creator of data and can organize it, interpret it and place it in context, does real information emerge. And when that information becomes meaningful to the user, intelligence is created - insight on which you can act.
Suppose five unknown ships appear off the coast on radar. That's data: five dots on a screen. When it turns out that they are naval vessels moving in formation, it becomes information. This information is then analyzed in conjunction with previous reports and communications: it turns out to be a pre-announced exercise. And only then does intelligence arise: not a threat, but routine.
Based on that, the decision follows: no alarm, do observe. That is the OODA loop in action: Observe - Orient - Decide - Act. A constantly turning cycle of observe, understand, decide and act. Each phase feeds the next - and the outcome in turn becomes new data.
In the world of defense and security, we speak of the intelligence cycle: collection - processing - analysis - dissemination. Data is collected, collated, interpreted and shared. It is a continuous process in which raw observations are transformed into actionable insights. The goal is not to have more data, but to understand faster and better what that data means - and what to do with it. And to know that that data is original and reliable.
American fighter pilot John Boyd developed his OODA run during the Korean War. In air combat, whoever went through his observation, orientation and decision cycle faster than the opponent won the fight. Boyd's model was later applied more broadly - from military strategy to operations. It is all about situational awareness: knowing what is happening, understanding why and responding appropriately.
The intelligence cycle feeds that OODA loop. Without intelligence no orientation, without orientation no decision. The two circles run side by side, reinforcing each other in a continuous movement of perception and action.
Where Boyd focused on speed and adaptation, W. Edwards Deming's Plan - Do - Check - Act emphasized improvement and learning. Both models are mirrors of each other. One accelerates response, the other deepens understanding. Together, they make clear that intelligence is not an endpoint, but a means of continuous adaptation and growth.
In our time, artificial intelligence (AI) is playing a new role in this dynamic. AI helps process vast amounts of data, recognize patterns and discover connections that humans would miss. But AI does not create intelligence - humans do that. AI can analyze, but not interpret; it can predict, but not understand. Real value is created only when human experience and technological power complement each other. Intelligence ultimately remains a human skill: the ability to give meaning to what we see. And then to act on what we understand and (instinctively) trust.
Deming's check-act phase is becoming increasingly important in the world of AI. AI results are largely statistical estimates - calculated probabilities, not truths. Therefore, feedback is crucial: the constant testing, correction and improvement of models. What AI learns depends entirely on the quality of its data and feedback. And that's where "data trust" comes in: the assurance that source data is and was reliable, traceable and with integrity. Without that reliability, false intelligence emerges - pretty graphs without meaning. Consider the nitrogen affair. With reliable data, the circle closes: the outcome of intelligence feeds the next observation, the check feeds the next act, and learning becomes a continuous process of improvement and understanding.
As Jozua van der Deijl of Digicorp Labs so eloquently stated recently in the article "The Digital War: The real gold rush and the forgotten truth of ownership.
'Data trust is the moral core of the digital age.'
After all, the true digital hero trusts no one but the original creator of the data. Everything that happened and happens to that data after that is almost impossible to control. The difference between truth & fake.
In this continuous circle, data and intelligence together form the yin and yang of insight. Data is the objective, the measurable; intelligence is the subjective, the interpretive. One cannot exist without the other. Too much data without interpretation leads to blindness; too much interpretation without facts leads to illusion. In their balance lies wisdom - the closed circle in which perception, understanding and action turn in harmony.
And this brings us to the core: data is necessary, but never the goal. Value arises only when trusted data is translated into meaning. And meaning into action. The real power is not in what we measure, but in what we trust, understand - and do.
