All data ultimately has a human source—it is not collected, but created. Data-savvy leaders understand this nuance.
Decision infrastructures are often built on the premise that data is objective, definitive, and value-neutral. This leads organizations to treat data as an infallible compass.
However, every byte of information springs from human actions, decisions, interactions, goals, and biases. Customer data, for example, doesn't just show behavior but reflects how people navigate interfaces we've designed, within constraints we've established. Even pristine financial data carries the imprint of human judgment—from revenue recognition timing to expense categorization—codified in vast accounting guidelines, but human-made nonetheless.
Does this mean data is just subjective figures open to any conclusion? Of course not! It means that for proper understanding and interpretation, data's context is vital. All that metadata and methodology documentation isn't a footnote, but a crucial user's manual. Even the most carefully constructed dataset can be misinterpreted without proper context.
This demands a targeted response. Implementing the following five specific structural changes can help address this reality:
1️⃣ Make the documentation of collection methods, decision points, known biases, and limitations a part of your data quality metrics.
2️⃣ For major decisions, require stakeholders to articulate which assumptions the data implicitly reflects and how changes would affect conclusions.
3️⃣ Pair data specialists with subject matter experts who understand the contexts generating the data. Formalize this collaboration for critical insights.
4️⃣ Integrate behavioral variables into risk assessment by testing how human motivations could invalidate data patterns. Create alternate scenarios for more robust strategies.
5️⃣ Establish mechanisms to test data-derived insights against lived experiences, where frontline observations can challenge or validate data-based conclusions.
When businesses acknowledge that humans shape every piece of data, they gain insights that others miss and avoid misinterpretations, strategic missteps and compliance failures (like algorithmic bias). Success comes not from making data more human-friendly, but from recognizing data as fundamentally human in the first place.