“Warm Data” can be defined as: Transcontextual information about the interrelationships that integrate a complex system.
Information can come in many forms, depending on what is being studied. There is a need now for a way to gather and impart relational information when what we need to study is relational in nature.
Warm Data is a category of information to develop in tandem to existing forms of data. This kind of information is a slippery mess of variables, changes, and ambiguities. It does not sit nicely in graphs or models, and it takes longer to produce.
Since Warm Data describes relational interdependencies it must also include the necessary contradictions, binds (double-binds and more), and inconsistencies that occur in inter-relational processes over time.
Simply using analytic methods focused on parsing statistical (cold) data will often point to conclusions that disregard the complexity of the situation at hand. Moreover, information that does not take into account the full scope of interrelationality in a system is likely to inspire misguided decision-making, which compounds already “wicked” problems.
Warm Data is not meant to replace or in any way diminish other data, but rather it is meant to keep data of certain sorts “warm” — with a nest of relations intact.