There are a couple of things recently that brought my attention back to the topic of knowledge, information and data. At a recent presentation about TOGAF certification it was mentioned that
there was still some disagreement about the distinction between data and
information. It seems to me that the next step, processing information into knowledge is at least as important and is still an unrecognised gap in the conversation.
I can see an obvious link to the current focus on "Big Data" and the extraction of useful correlations from the enormous and diverse records collected by entities such as Facebook or Google. To me, this appears to be in the same category as trawling through mine tailings in search of missing nuggets. This is not necessarily a pointless activity - many mines are becoming profitable as new extraction techniques allow finer traces of material to be recovered. However, more intelligent application of tools at the time of collection would seem to provide a more efficient (and timely) method of collecting useful artefacts.
But, to stretch the analogy, even as the rock is only useful for the ore it contains, the ore cannot be used without further processing. Data is only useful because of the meaning/information that may be extracted. But that information is only valuable because of the knowledge that may be gained. Knowledge that may then be used to direct an enterprise and create value; or just to create pretty things.
One known 'gotcha' when dealing with "Big Data" is the possibility of false positives - patterns in the data which appear simply because there is so much data that some of it must be linked in some way. The best way to avoid this problem is to have a discerning review of the findings by an experienced person (not machine). In other words to connect the information extracted to the framework of knowledge held in someone's head. Similarly low-yield ore is discovered and discarded during processing and only the pure metal is extracted (at least in principle).
