The Second Digitalization: From “Know-Know” to “Unknown-Unknown”
In businesses, digital transformation has long been perceived as simply a matter of technology, data, and processes. Yet, a fundamental blind spot persists: understanding one's own teams, customers, or competitors within one's ecosystem. This is where an old model, the Johari Window, finds unexpected relevance for mapping missing, dormant, or unknown data.
The 4 states of data: The Manufacture Thinking method
To structure this new era of data, our method is based on four zones, each requiring a specific approach:
1. Know what you know (Data Know-Know / Public Area)
This involves taking stock of formalized, organized, and accessible data (customer databases, KPIs, financial reports). Mapping what the company already uses is not about being satisfied with it, but about precisely identifying blind spots and missing connections, where latent value begins to be hidden.
2. Awakening what we had forgotten to know (Data Unknown-Knowledge / Blind Spot)
This area embodies the quintessential industrial paradox: data is collected and stored faithfully (sensor history, old server logs), but is never analyzed. This passive data literally lies dormant within the information systems.
3. Unleash what we know collectively without knowing it (Data Known-Unknown / Hidden Zone)
This is the most insidious state. The company possesses the knowledge, but it remains confined within silos, "Shadow IT" documents, local notes, or the minds of experts. This information is often critical, but it doesn't exist on a collective scale.
4. Explore what we don't yet know (Data Unknown-Unknown / Potential Zone)
This is the expedition phase: seeking value where no one else thinks to look. These repositories (raw audio and video streams, weak market signals) are the Holy Grail of the second wave of digitalization. Finding this data will open up unexplored economic fields, like a company managing to detect the smell emitted by a machine tool just before it breaks down.
AI as a value revealer
Generative artificial intelligence tools (such as Gemini or Claude) represent a true game-changer. By simulating viewpoints, reformulating reasoning, and highlighting inconsistencies, these models act as powerful indicators of blind spots. AI enables the industrialization of feedback and puts human judgment under pressure. Consequently, reducing one's blind spot is no longer just a technical issue, but a major strategic governance challenge.
Navigating this matrix effectively and leveraging your dormant data becomes possible thanks to La Fonderie , which we developed as part of the NPR 575 project, and which performs this diagnostic in just a few minutes. The hidden resources within your organization finally become visible.
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