February 27, 2014
A recent article in Smithsonian magazine, “Kasparov’s Gambit”, resurrects the age-old linkage between chess and global politics, in this case adding computer intelligence to the mix. We all know the archetype to which this refers – the chess wizard, usually but not always Russian, calculating out intricate moves on the world stage using the skills developed in playing chess.
That is all well and good, but I do not think the chess is the appropriate game analogy for people to look to for intelligence, particularly for competitive intelligence.
What do I say that? Because in chess, both sides start out equally positioned and empowered. Because in chess, there are known limits to what you can do and what you cannot do. Because in chess, often there is a third-party judges whose decisions are final. And, more importantly, in chess we know who our opponent is, even if it is a computer, and we often know all of the opponent’s past games, and knowing any more about our opponent rarely if ever increases our ability to defeat that opponent.
In competitive intelligence, it is extremely unlikely that your competitor is equal to you. In competitive intelligence, you are governed by the rules that you and your employer (or client) set, in addition to rules established by law and custom. It is never clear whether your opponent is governed by the same rules and if so, whether or not a competitor complies with them (but, usually they do). In competitive intelligence, there are no third-party judges make final decisions. And in competitive intelligence, the more you learn about your competitor and, conversely the less you can keep your competitor from learning about you, the better you will ultimately do.
So what is a good game analogy for CI? Poker, scrabble, paint ball, baseball, mahjong? Any suggestions?
 Smithsonian, March 2014, 21-5.
Within the community of intelligence educators, there is a discussion about how to establish who are really good analysts. That is, how do we test for it, interview for it, or something else?
With this in mind, I found a recent article in Smithsonian Online, “Elegant Mathematical Formulas Activate the Same Brain Region As Music And Art”, very interesting. The general thesis of the article is that certain portions of the brain are stimulated, in people with a mathematical bent, by viewing elegant mathematical equations, the same way the same region is stimulated by fine art for those of that bent.
What does it have to do with an analyst? Think of the statement by a Supreme Court justice, who when struggling to define hard-core pornography, is credited with saying “I know it when I see it”.
Is it possible that we can determine who is a good analyst only by seeing how well they respond to succeeding at or reviewing a good piece of analysis? I know that one thing that I always enjoy is the “gotcha” moment, that moment when a piece of data that you suspected was there came into your possession and enables you to complete the puzzle that is competitive intelligence analysis. Does that make me a good analyst?
Perhaps what we can do is to test potential analysts and see how happy they are or how much they enjoy completing a difficult analysis or reviewing the analysis of someone else.
Does all this mean that analysts are born and not trained? Maybe, to some degree.
February 12, 2014
Just because you can see a trend coming does not mean that you – or anyone else – can or will act on it.
Let me give you an example. I was listening to the radio where the audience was discussing a walk-out at a local school. The reason is unimportant. But what followed was a discussion of when, if at all, the listeners had been involved in something similar.
Of course, most of them were angels – never, never, did such a thing.
But one caller admitted that, in 1963, she and some high school classmates had staged a walk out over the failure of the high school to establish – ready – a smoking room for seniors. Wow! Things have really changed. And that is the point.
In the past week, the Acting Surgeon General of the US called for a drop in the adult smoking rate from its current low of 18% to under 10% in the next decade. And smoking rates for US teenagers are at record lows. How did we get from there to here?
That question provides an interesting look at early warning. Consider the following partial time-line:
1963 – A group of Pennsylvania high school seniors calls for a smoking room for them. According to the Robert Wood Johnson Foundation, per capita consumption of cigarettes, which was rising during the 1950s, reaches its peak.
1964 – The US Surgeon General reports that cigarette smoking is a “health hazard”. Per capita cigarette consumption falls by 15% following the report.
1970 – Cigarette ads are banned from US television and radio.
1984 – The US Surgeon General calls for a “smoke-free” society by 2000.
1994 – Prices of cigarettes begin a two-decade long increase, driven by higher taxes. The Associated Press reports that “on average a pack of cigarettes that would have sold for about $1.75 20 years ago [in 1994] would cost more than triple that now [in 2014].”
1995 – California becomes the first state to ban smoking in public buildings.
1998 – The major US tobacco companies settle a case brought by 40+ states seeking compensation for the costs of treating smoking-related illnesses; smoking is banned on domestic US airline flights.
2014 – The US Surgeon General links smoking to diabetes, colorectal cancer and other problems.
OK – cigarettes in the US are in trouble. When should the early warning have been sounded? 1964, 1970, 1994, this year?
Probably in 1964. But, if you have sounded that alarm, who would have cared. Who would have acted? And what, if anything would they have done? In fact, why would they do anything? The eventual, inevitable decline would not take hold until well after they all had retired or died. So why should they act?
What I am trying to indicate is that an early warning system has to view the future in a time frame when you (and your firm) can and will take action. Look too far out and maybe you can see the future clearly. But no one else may care.
February 6, 2014
I was playing an online game the other day, Taptiles, which is a three-dimensional version of Mahjong. And I found that I played better when I didn’t first play it.
What? Sounds a little Zen-like.
In this game, the tiles will come tumbling down to a screen, and you have to match them up when they stop. The temptation is to watch them as they fall, and then began to search for matching tiles.
I found that if I do that, it takes me longer because I first spent time watching the falling and attempted to remember what I’ve seen, rather than looking away from everything, or better, just closing my eyes and then looking at the entire picture when it is settled. In doing that, I am seeing potential patterns without first looking for them. What is happening is that I’m just trying to get a general sense of where everything is and then get started.
What does this have to do with CI analysis? Sometimes, we are dragged too quickly into analysis because of the details that we have at the start. Then we then tend to dwell on these early details, giving them too much weight, just because we found them first.
To break free from that mentality, somehow stand back and away from your data before you go into your final stage analysis, or, better, before you begin any analysis whatsoever. By being dragged into the data before you have mastered it and sorted it out, you run the danger of missing the big picture. What you have to do in such cases is to see the data, but not look at it.