Following on from recent posts on the OODA loop and information warfare, lets examine the DIKW Hierarchy a little closer.
As noted in Observations on Observability, Metrics are a key source of measurement data and are the base input into the Observe portion of the OODA loop but operating just on data does not lead to significant observability or control over the situation. We continuously hear about big data and interesting, large, complex data sets but in all cases the important focus should be what is done with that data once you have access to it.
The figure above, attempts to depict the nuances of how data in its plainest form is elevated to mythical wisdom.
As show in the figure, Information (another term that is loosely thrown around) is derived from processing data. Information is data with attributes and relationships associated with the data.
A data string of “70,71,63,70,71,72,71” is just a serial string of numbers we can only derive statistical properties such as min, mean, max etc from the numbers and those in itself are pretty boring.
If for instance we added the attributes to this data string of numbers that they are temperature measurements in degrees F and that each entry represents a day of the week starting with Sunday. These attributes immediately change that raw data into information that has more value, we see that they temperatures are pleasant spring or summer temps.
If we not take the information string, and begin to infer or examine it in terms of relationships, an immediate question that comes to mind is what was happening on Tuesday? as the data measurement (63) is not well aligned with the other measurements for the weeks, this inference results in an idea that maybe Tuesday was a cloudy day and that is how that data measurement is lower then the rest.
Wisdom and understanding in this contrived example may be that one knows that, no Tuesday was not a cloudy day. Instead the gardener knocked the waterproof temperature sensor off the ledge where it was sitting into some overgrown plants which had just been watered and were not in the direct sun. When the sensor captured its daily temperature measurement it was not sitting its normal location and for reasons not directly related to weather was 10 degrees F outside expectations. Sometime Tuesday the dislodged sensor was found and returned to its original position and this change of position provides the understanding and intuition (aka Wisdom) for the sensor owner to decide to firmly attach the sensor to its desired position so as to remove these anomalous measurements from the data collection process.