Information

Famously, the concept of data has been contrasted with that of information and then onto other supposedly superior terms in a hierarchy. Here, only the distinction between noise and data is important. Data that is not noise can be utilized for answering questions, interpretation, inference, analysis, systems development, and so forth. That is the domain of information: systematization of data into useful outcomes.

Of course, having access to such information can be very helpful for the development of knowledge regarding a given entity. But the DIKW pyramid model suggests a connection between knowledge and information that bears no parallel to the highly cohesive relation between data and information so that the progression it suggests from there onwards is a non sequitur.

Information when taken outside of the computing context in which it is cohesive with the concept of data is extremely abstract and semantically impoverished. To try and wedge it next to a loaded term such as "knowledge", in particular as if it were a natural progression or even a requirement to get to it, is quite a stretch -- not to mention wisdom.

There are many avenues to knowledge, and a lot of false information too: tidy, beautifully structured, metadata-rich, completely false information. The matter of its truthfulness is important from the perspective of the philosopher in particular, and of any scientist who looks at information as just another entity that may or may not inform knowledge, that may or may not carry a portion of truth or useful pointers to it.

Data is a raw material that must be made meaningful by interpretation, and scientists of all fields are specialists in doing precisely that. If it can't be made meaningful to be interpreted, if it's just noise, its truthfulness can't be determined either. A negative regarding its truthfulness in this regard is positive, because it qualifies the method used to obtain it. Computing practictioners should welcome that, not attempt to create machines that never lie, because error is a staple of correctness.