Leaders reviewing a draft article agreed on several things. The data itself was sound. No protected information was included. No departments were named. No policies were violated. And yet, there was discomfort about publishing the piece. The worry wasn’t about confidentiality. It was about whether someone might read the story and recognize themselves in it.
Even without names, could it feel like airing internal disagreements? Could a unit see its challenges reflected in the narrative and interpret the article as exposing “dirty laundry,” despite the care taken to generalize and anonymize the example?
This kind of sensitivity sits in a different space than traditional data governance. It isn’t about whether data is restricted. It’s about whether a story feels identifiable, even when it is technically anonymous.
True data classification is far more controlled. Certain information cannot be shared because it is protected by law, policy, or ethical obligation. When that line is crossed, the answer is clear: the data should not be published.
Narrative sensitivity is murkier.
Stories about data often resonate precisely because they reflect real experiences. That resonance is their strength and their risk. A well-told, anonymized story can still feel personal to those who lived it. Recognition does not require naming; it only requires familiarity.
In these moments, hesitation is not a sign that the data is unsafe. It is a signal that the organization is navigating how openly it is willing to embody reflective learning.
That does not mean the concern is misplaced. Publishing narratives about internal challenges requires care. Without sufficient framing, even a well-intentioned article can be read as critique rather than reflection, or as exposure rather than learning.
The question, then, is not simply whether a department is named. It is whether the purpose of the story is clear. Is it meant to assign blame, or to surface a pattern others can learn from? Is the focus on individuals and units, or systems, language, and alignment?
When stories stay anchored at the level of systems and shared processes, they are less likely to feel personal, even when they are recognizable. When they drift toward implied judgments, recognition can quickly turn into defensiveness.
Leadership judgment plays a critical role here. Not to silence stories that are uncomfortable, but to ensure they are told with intent and care. Sometimes that means adding more context. Sometimes it means delaying publication until relationships are stronger or understanding is more widely shared.
Data classification asks, is this information allowed to be shared?
Narrative sensitivity asks, will this story be experienced as exposure rather than learning?
They are related, but not the same.
Healthy data cultures learn to navigate both. They protect what must be protected. And they find ways to tell honest, anonymized stories that help the organization grow without turning lived experience into a spotlight.
Knowing the difference allows leaders to move forward thoughtfully, balancing transparency with trust, and learning with respect.
Don Bailey
Director of Data Solutions
Office of Data and Strategic Analytics