There's one particular and notable step in the path that has led me here to the world of analysis and pattern recognition that I always look back and kind of laugh at. Once upon a time I had a university class assignment centered around selecting & analyzing a "self-help" book. While browsing the bookstore I couldn't help but pick out Reading People: How to Understand People and Predict Their Behavior - Anytime, Anyplace, primarily because of it's absurdly ridiculous title. 

As it turns out, the book is very much in the same camp as the "building layers and layers of understanding from thin slices of experience" idea from Malcom Gladwell's Blink. So it makes sense that a detailed analysis of the book and how it applies to pattern recognition has popped up on LessWrong.org, calling attention to the author's primary charge:
 
If this book could deliver but one message, it would be that to read people effectively you must gather enough information about them to establish a consistent pattern. Without that pattern, your conclusions will be about as reliable as a tarot card reading.
 
The author of the article relates the key points of the book to an earlier post of theirs, What is Bayesianism?. I've selected some highlights below that you might find helpful when thinking about identifying patterns; they are primarily written through the lens of observing individuals, but for the most part the ideas behind them apply to larger trends as well. 

1. Start with the person's most striking traits, and as you gather more information see if his other traits are consistent or inconsistent.

As computationally bounded agents, we can't simply take in all the available data at once: we have to start off some particularly striking traits and start building a picture from there. However, humans are notorious about anchoring too much (Anchoring and Adjustment), so we are reminded to actively seek disconfirmation to any initial theory we have.

2. Consider each characteristic in light of the circumstances, not in isolation.

The second core tenet in What is What is Bayesianism was "How we interpret any event, and the new information we get from anything, depends on information we already had."

A Bayesian translation of this might read roughly as follows. "Suppose you told me simply that a young man wears a large hoop earring. You are asking me to suggest some personality trait that's causing him to wear them, but there is not enough evidence to locate a hypothesis. If we knew that the man is from a culture where most young men wear large earrings, we might know that conformists would be even more likely to wear earrings. If the number of conformists was sufficiently large, then a young man from that culture, chosen randomly on the basis of wearing earrings, might very likely be a conformist, simply because conformist earring-wearers make up such a large part of the earring-wearer population.

3. Look for extremes. The importance of a trait or characteristic may be a matter of degree.

4. Identify deviations from the pattern.

5. Ask yourself if what you're seeing reflects a temporary state or a permanent quality.

6. Distinguish between elective and nonelective traits [events]. Some things you control; other things control you.

7. Give special attention to certain highly predictive traits.