Researchers: from Data Gatherers to Knowledge Architects

During World War II, the Allies wanted to reinforce the armor of their planes to reduce combat losses. They analyzed the planes returning from missions and identified the areas with the most bullet impacts. The initial conclusion was that those areas should be reinforced.

However, a mathematician named Abraham Wald realized a crucial error: the data came only from the planes that had managed to return, not from those that had been shot down. Wald interpreted the data from another perspective: the planes that did not return were likely hit in critical areas such as the engine or cockpit, and therefore, never returned. The solution was not to reinforce the parts with the most visible impacts, but precisely those that had no damage on the planes that managed to return, because those were the areas where an impact meant total destruction.

This analysis is a classic example of survivorship bias, a common error in decision-making by focusing only on the cases that have "survived" a process, ignoring those that have not.

These types of errors do not only occur in military conflicts. It is common for a misinterpretation of data to lead us to make wrong decisions. Data, by itself, does not give us answers; the key is to ask the right questions. This was the focus of the recent Runroom LAB: Data + Product = Effective Strategy. In the LAB, Manuel Maffé, Product Research Manager at HP, and Pablo Andrés Margara, Senior UX Researcher at Glovo, discussed how to interpret data strategically to avoid deceiving ourselves and make better decisions.

The trap of accumulating data without purpose

Many companies are obsessed with accumulating data without a clear purpose, hoping that at some point it will become useful. Others chase metrics as the ultimate goal, without understanding that metrics are just tools to understand the context. When a metric becomes a goal, it loses its purpose. Without a research strategy, data is just noise.

In theory, making data-driven decisions sounds like the most rational and objective way to act. But in practice, being 100% rational is impossible. There will always be variables we cannot measure, information we do not know, and biases that influence our interpretation of data. Intuition is not an obstacle to data-driven decision-making. It is what allows us to act when data does not give us a definitive answer.

For years it has been suggested that intuition and data are in constant opposition, but in reality, well-used intuition is accumulated experience. It is what allows us to recognize patterns. It is not about choosing between data or intuition, but understanding that informed intuition is what truly drives innovation. To achieve this, we must wear different hats:

• 🔍 Archaeologists: We discover hidden patterns in the data.
• 📖 Historians: We understand how the past shapes the present.
• 🔭 Astronomers: We anticipate what is to come.

The key is to combine these three perspectives to not only collect data but turn it into actionable knowledge.

Take the case of Steve Jobs and the launch of the iPhone. In 2007, the most popular phones were BlackBerrys with physical keyboards, and most market data indicated that users valued physical keyboards above all. If Apple had relied solely on existing data, it would never have bet on a touchscreen without buttons. However, Jobs and his team intuited that touch interaction could change the way people used mobile devices. Their intuition was not unfounded: it was backed by the observation of technological trends and changes in user behavior.

This is the key point: data can tell us what is happening, but it cannot always tell us what to do. This is where informed intuition comes in, allowing us to take strategic risks based on a future vision.

From data gatherers to strategic partners

Research and data analytics are evolving. Previously, research teams were mere data gatherers. Then they became knowledge providers. But the next step is for researchers to become strategic partners in decision-making.

To achieve this, we must stop obsessing over superficial metrics and start building relevant data:

• Select: Not everything that can be measured matters. We must identify what information is truly useful for business objectives.
• Validate: Data can be biased. It is crucial to question it and ensure it reflects reality.
• Connect: Relate different sources of information (market, brand, product) to obtain a complete view.

This approach allows data to be truly useful and not just an accumulation of information in dashboards that no one consults. Data is not answers, but tools to make better decisions. It is not enough to collect, store, or analyze them; the real challenge is to make sense of them and turn them into strategic actions.

The story of the British bombers taught us that analyzing only what we see can lead to wrong decisions. Intuition showed us that sometimes, data is not enough to make the leap. The evolution of research is not just about improving analysis techniques, but about changing our mindset: from being data gatherers to becoming knowledge architects.

And in that change, the best researchers are not those who have more data, but those who ask better questions.