The 7 habits of highly successful intelligence analysts

I just love these kinds of lists, that boil things down to the essentials: In a September 11 post in the Digimind blog, Orlaith Finnegan let Monica Nixon of NICS and “Bob A.”, ex Navy intelligence, put down the following 7 habits of highly successful intelligence analysts:

1) Be Organized and Disciplined
2) Communicate with Confidence, Clarity and Credibility
3) Find Meaningful Patterns in Meaningless Noise
4) Adopt a Patient, Methodical Approach
5) See the Bigger Picture
6) Be Flexible and Responsive to Change
7) Learn from Mistakes

For each point in this list, read the full description in the original article:
http://digimind.com/blog/market-industries/the-7-habits-of-highly-successful-intelligence-analysts/

High math school grade correlated to overall high grades

On May 21, 2010, the Swedish university professor Staffan Stenhag at the University of Uppsala defended his Ph D dissertation “Betyget i matematik: Vad ger grundskolans matematikbetyg för information?”, which in english would be “Mathematics Grade: What information is provided by the school grades in mathematics?”.

The findings and conclusions presented in the dissertation are another strong argument for giving a high weighting to mathematic ability when recruiting people for intelligence work. My own personal reflection is that mathematic and logic ability is positively correlated to mental intelligence – or IQ if you wish.

The following is an abbreviated translation of an interview with Staffan Stenhag made by Susanne Sawander, published on http://www.skolporten.com/art.aspx?id=CkaUG

Pupils with high math grades also succeed in other school subjects. A possible explanation is that math studies develop the general learning ability. Stenhag got interested in the subject by observing during 25 years as a college professor how students with successful math studies would also succeed in other subjects. The dissertation starts off with the question about why pupils should study maths at junior high school level. Stenhag reviewed existing arguments. Among those arguments was the claim that mathematics serve to develop the general intellectual ability. Another argument was that mathematics serves as a selection tool when identifying the pupils most apt for higher education. In order to assess the validity of these arguments, Stenhag checked for correlations between school grades in mathematics and school grades in other subjects. His research material consisted of the grades of 124 000 Swedish pupils graduating from junior high school in 2006.  He also investigated the correlation between math grades and results in the national school exam in reading comprehension. Stenhag found that a top grade in mathematics is positively correlated to top grades overall, in other subjects as well. On the other hand, that correlation is not as strong between a top grade in Swedish, English or  social science, and the overall grades. Moreover,  Stenhag aslo found that 83 percent of pupils with a top grade in maths also got a top grade in reading comprehension. Stenhag says that it isn’t necessarily so that math studies as such result in high achievements in school. It could for example be so that the grade in math is an indicator for motivation, learning techniques, logic ability, and social conditions.  He also finds it exciting to think that studies in math might develop the general intellectual ability.

Dissertation abstract in English:

The aim of this study is to investigate what the grade in mathematics tells us about the pupil’s general academic success in other school subjects in Sweden’s compulsory school. What proficiency, except mathematical skills, does a high grade in mathematics indicate? First an inventory of the official arguments for school mathematics was conducted. The inventory shows that the arguments generally can be classified into two main classes: i) utilitarian arguments and ii) cultural arguments. In addition to these two main groups the debate also includes more remote and indirect arguments: iii) the transfer argument and iv) the selection argument .If the two last arguments are valid it is assumed that the so called indication hypothesis should be true: that pupils who succeed in mathematics also will achieve high grades in other school subjects. A statistical analysis was conducted of the grades data for the approximately 124,000 pupils who completed compulsory school in Sweden 2006. The analyses provide support for the indication hypothesis. Those pupils who manage to achieve the highest grades in mathematics often achieve high grades in other school subjects as well. This applies to both the purely theoretical and to the more practically oriented subjects. In the last phase of the study it was assumed that a possible explanation for the results could lie in the reading comprehension hypothesis; that pupils who are successful in mathematics in their ninth year of compulsory school also have good reading comprehension. This hypothesis was tested with data from the pupils’ results in the reading comprehension test that was included in the national exam in Swedish in 2006. The results provided strong support for the hypothesis. Pupils with high final grades in mathematics also have good reading comprehension. However the reverse did not apply. A good result in the reading comprehension test was not a reliable predictor of a high final grade in mathematics.

The full dissertation is available here in PDF format. It also contains an extended, 6-page abstract in english: http://www.diva-portal.org/smash/get/diva2:305754/FULLTEXT01

Learn statistics or stay stupid, misinformed and foolish

Clive Thompson of WIRED published an excellent article on April 19, 2010, on the importance of understanding probability, coincidence, correlation, causation, snap-shot samples versus trendlines, anecdotal information vs statistically valid samples, and that it is just as important as literacy.

I’m quoting some highlights form the text:

“If you don’t understand statistics, you don’t know what’s going on — and you can’t tell when you’re being lied to. Statistics should now be a core part of general education.”

“Of course, as anyone with any exposure to statistics knows, correlation is not causation. And individual stories don’t prove anything; when you examine data on the millions of vaccinated kids, even the correlation vanishes.”

“There are oodles of other examples of how our inability to grasp statistics — and the mother of it all, probability — makes us believe stupid things. Gamblers think their number is more likely to come up this time because it didn’t come up last time. Political polls are touted by the media even when their samples are laughably skewed.”

“Granted, thinking statistically is tricky. We like to construct simple cause-and-effect stories to explain the world as we experience it. “You need to train in this way of thinking. It’s not easy,” says John Allen Paulos, a Temple University mathematician.”

“That’s precisely the point. We often say, rightly, that literacy is crucial to public life: If you can’t write, you can’t think. The same is now true in math. Statistics is the new grammar.”

http://www.wired.com/magazine/2010/04/st_thompson_statistics/

Unstructured data analysis by SAS Institute

The statistician Christopher Broxe at SAS Institute worked for ten years with structured data before he took an interest in unstructured data. He finds his raw data in discussion forums, sites where consumers rate products and services, in blogs, and in online newspapers.

Using a tool from SAS Institute called TextAnalytics, he evaluates unstructured text statements about for example hotel experiences, and turns it into statistics.

The end result can be displayed as a visualization, in this case a treemap, which is described in the following award-winning way by the Swedish computer industry daily “Computer Sweden”: “The result of the sifting process is displayed as a ‘heatmap’, a color-coded rectangle which looks almost like an aerial photo of the crops fields in the Skane region, but with colors of your own choice.”

The article writer (Anders Lotsson) also says that the software from SAS Institute is not meant for consumer clients, which the price clearly indicates. It also requires software developer skills.

http://computersweden.idg.se/2.2683/1.312915/mer-an-tusen-ord

(April 25, 2010)

An illustration from the article. Bread-crumb legend says: “Purpose of travel > Age group > Co-traveller > City > Hotel name”

What it takes to be a good analyst

Steve Miller, co-founder and president of OpenBI (www.OpenBI.com) wrote a blog post for Information-management.com on April 19, 2010, titled “BI, Analytics and Statistical Science“. He writes that “I think the list provides a foundation of what it takes to succeed in BI” (Business Intelligence). Steve holds degrees in Quantitative Methods and Statistics from Johns Hopkins, the University of Wisconsin, and the University of Illinois, and he acknowledges that his list is statistics/research-centric. I my view, that is not a disadvantage.

I think the list is also very valid for somebody working in an analyst function within for example competitive intelligence or governmental open source intelligence analysis:

General Skills

• Strong interpersonal and communication skills,
• Customer-facing personality,
• Ability to work productively as an individual or in collaboration with others,
• Ability to write/communicate clearly, accurately, and effectively,
• Ability to think analytically,
• Data centricity – obsession with evidence-based problem resolution,
• An understanding of the scientific method – theory, hypotheses, testing and learning,
• Ability to use the scientific method to conceptualize business problems,
• Orientation to business and one or more business processes – either vertical or horizontal,
• Commitment to life-long learning.

Technical Skills

• Intermediate programming and computation skills,
• Facility with logical and physical relational databases (SQL),
• Understanding of the economic approach – “the allocation of scarce means to satisfy competing ends” – to problem solving,
• Facility with standard statistical/BI packages to perform analytic calculations,
• Ability to interpret the the results obtained from these packages,
• Facility with a variety of graphical/visualization techniques for exploring and presenting analytic data,
• Understanding of the principles of management, accounting, finance and marketing,
• Understanding of the meaning of business optimization,
• Ability to recognize the nature of, and to model, the random variation underlying given business data,
• Understanding the nature of statistical inference – its scope, limitations and proper role in the process of business analytical investigation,
• Ability to express a generally-posed business problem in a statistical context; ability to translate business concepts for measurement.
• Understanding how to obtain a suitable sample from a population and how to make inferences from that sample,
• Understanding of experimental and quasi-experimental designs for BI,
• Ability to provide advice on the design of business analytic investigations,
• Understanding of a variety of commonly-used analytic techniques and the models underlying them,
• Conversance with the mathematical underpinnings of often-used analytics techniques to facilitate simple modifications in appropriate situations,
• Understanding of alternatives to traditional statistical modeling from computer and mathematical sciences,
• Comfort with Internet research,
• Obsession to stay current with the latest analytic methods/techniques.

BI, Analytics and Statistical Science.

Innumeracy – your employees can’t do math

In his book Innumeracy: mathematical illiteracy and its consequences from 1990, John Allen Paulos writes about the common inability among people – even in important positions – to do simple math. While society looks upon illiteracy as a big problem, and inability to spell correctly is shameful for the individual, nobody seems to be troubled by innumeracy. For example: Nobody says “corporation with a C or Korporation with a K, I don’t care how you spell it in the report as long as you have it done in time”. As a contrast, quotes similar to the following is not unheard of: “A billion or a trillion, I don’t care how many of them you have detected, just file the report in time”. Just ask your self – are you fully aware of the difference between a “billion” and “trillion”? If you are not, make sure you become so.

James Taylor writes about exactly this on SmartDataCollective.com (a TeraData community site) on April 4, 2010, in a post called Don’t rely on your staff’s ability to do math:

I often tell folks that one of the benefits of decision management is that it enables analytic decision making – that is decisions based on accurate analysis of data about what works and what does not – even by people who don’t have any analytic skill.[…] And this is important because most people don’t have these skills! Presenting them with data and expecting them to accurately use it is just not reasonable. […] Please, embed the analytics, don’t rely on your staff’s ability to do math.

http://smartdatacollective.com/Home/25961

Predicting the Future With Social Media

Sitaram Asur and Bernardo A. Huberman at the Social Computing Lab at HP Labs in Palo Alto, California, have demonstrated how social media content can be used to predict real-world outcomes. They used content from Twitter.com to forecast box-office revenues for movies. With a simple model built from the rate at which tweets are created about particular topics, they outperformed market-based predictors. They extracted 2.89 million tweets referring to 24 different movies released over a period of three months. According to the  researchers’ prediction, the movie ”The Crazies” was going to generate 16,8 million dollars in ticket sales during its first weekend.  The true number showed to be very close –  16,06 million dollars. The drama ”Dear John” generated 30,46 million dollars worth of tickets sold, compared to a prediction of 30,71 million dollars.

Reported by British BBC: http://news.bbc.co.uk/2/hi/8612292.stm

Reported by SiliconValleyWatcher: http://www.siliconvalleywatcher.com/mt/archives/2010/04/twitter_study_i.php

The research report: http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf

Previous related iOSINT posts:

https://iosint.wordpress.com/2010/03/29/ted-com-sean-gourley-on-the-mathematics-of-war/

https://iosint.wordpress.com/2010/03/17/social-media-intelligence-output/