A few days ago, I was looking for some books to read this mid-summer week-end. I ended up picking Travels, a book from Michael Crichton. Crichton, of Jurassic Park fame, was a highly readable author with many techno-thrillers on the best-seller list. I particularly like Timeline, a science-fiction novel about parallel worlds, so I thought Travels would be similar. It turns out that the book is a cross between an autobiography and a travelogue of exotic locations that he has visited as well as more common places that enticed him to make his “inner travels”. For Crichton, there was little distinction between inner and outer travels and they complement each other. He often felt that “I go to some distant region of the world to be reminded of who I really am.”
Anyway, I read through his accounts of trips to Shangri-La, Virunga, Kilimanjaro … and at the very end of the book, I found an interesting piece that is relevant to the subject of this post: Knowledge, or to be more specific, the peril of Scientific Knowledge. In the Postcripts, Crichton reproduced the text that he has prepared for the speech to the Pasadena chapter of CSICOP (Committee for the Scientific Investigation of Claims Of the Paranormal) at Cal Tech. He talked about the narrow view of Science and the fanatical attitude of many scientists against anything non-rational.
“This, in essence, is the problem with the scientific view of reality. Science is a kind of glorified tailoring enterprise, a method for taking measurements that describe something – reality – that may not be understood at all.”
“This” is the scientific way to interpret and understand life, not unlike the measuring approach of the tailor to a fictitious person named George. But I am getting ahead of myself.
The story was about the challenge of describing George as a person. Through Crichton, we found that it was not easy to describe George accurately in a statement. For example, consider a simple statement borne out of some casual observation:
“George is an even-tempered man.”
In closer look, we found that he sometimes lost his temper. So we modified the statement to read:
“George is often an even-tempered man.”
But the word “often” is vague, because it didn’t tell when and why he lost his temper. To be more explicit, we would then say something like:
“George is usually an even-tempered man, except on Mondays when his favorite football team lost the day before, or when his wife had a fight with him, or when … or when …”
We could go on and on for pages and still not being able to completely describe George in an accurate manner. But his tailor could, in a way. He could describe George in exact terms:
“George is a forty-four long”
because he has measured George and the suits that he cut for George fit him perfectly. So armed with this confidence, the tailor claimed that he could describe exactly who George is.
If we replace Science by Information Technology (IT), Business Intelligence (BI) or any data-driven disciplines (in the same way that Crichton substituted Science for the tailor), we would find ourselves on similar ground. Automated data that are systematically captured, processed, measured and “mined” give way to Information (connected Data) and Knowledge (a collection of useful Information). When presented as Knowledge, it represents the truth of the Past. Knowledge is thus looked upon as the penultimate goal, just a short step away from Wisdom which is described as the truth of the Future (1). And just like the scientists at CSICOP, some IT or BI practitioners believe in the absolute truth of their Knowledge and in the power of using the past to predict the future.
But what sort of Knowledge is acquired in such a way? The least useful, according to Crichton when he wrote about the measuring exercise of the tailor:
“… statements [about George] that are most securely held are also the least interesting.”
Similarly, there are too many performance measurements that inundate the executive dashboards today without providing any insight about how these pieces of Data/Information/Knowledge may help the business moving forward. They are known as the lag indicators by the initiated (2), the least interesting pieces of Knowledge. So the next time that you hear someone expounds the virtue of Knowledge, ask yourself whether or not that kind of Knowledge is relevant to the business at hand or just a bunch of gobbledygooks to impress people.
Postcripts.- Crichton was never invited to speak at CSICOP. Maybe the scientists there have known all along about his heretic view of Science. If you are interested, go pick up the book and spend a lazy afternoon. It will be worth your time.
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(1) Known by its acronym DIKW, the Data-Information-Knowledge-Wisdom relationship (also known as the Information or Knowledge Hierarchy) was ubiquitous in IT literature. Although not universally endorsed, it is widely used to purportedly demonstrate the added value of the next layer in the hierarchy. Thus, Information is more valuable than Data, Knowledge is better than Information, etc. The model also implies that beyond Knowledge, there is nothing else but Wisdom, the ultimate prize to acquire in both business and life.
(2) There are many write-ups on the subject but one of my favorites is a 2006 article Lead vs. lag indicators in an Australian HR publication.
