I keep saying the sexy job in the next ten years will be statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the 1990s? The
ability to take data—to be able to understand it, to process it,
to extract value from it, to visualize it, to communicate
it—that’s going to be a hugely important skill in the next
decades, not only at the professional level but even at the educational
level for elementary school kids, for high school kids, for college
kids. Because now we really do have essentially free and ubiquitous
data. So the complimentary scarce factor is the ability to understand
that data and extract value from it.

Well…maybe there’s hope!

HBR:  Does randomness have a role to play in birding?

Sibley:

Even then these vagrant birds (ones not expected to be in a certain location) tend to show up in places that have a pattern of rare birds turning up.  So they’re probably not random occurrence but a pattern we haven’t worked out yet.

ThinkingAnalytically discussion starters:

• Can you think of a time that an apparent set of random data turned out to be an undiscovered pattern?  What was it?
• How do you avoid claiming a random situation too early?

Use the comments to contribute.

Purchase full the HBR article here.

HBR: One of the toughest challenges in pattern recognition is knowing when you’ve looked at enough information to make a reliable judgment.  At what point can you be certain that the pattern you’ve identified is real?

Sibley:

Identification is never 100% certain.  Even field marks aren’t completely dependable, so you have to have an idea of how reliable each mark is or how reliable your judgment of it is, how well you saw it.   [Example:]  OK, there are three 90% characteristics that point this way and a couple of 50% or 70% characteristics that point that way, so I’ll go to the 90% side.  The identification may or may not be correct, but that’s the more likely choice.

ThinkingAnalytically discussion starters:

• How do you know when enough is enough?
• How do you assign probabilities to the factors used to recognize patters?

Use the comments to contribute.

Purchase full the HBR article here.

HBR:  What’s the most common mistake a beginner makes in trying to identify a pattern – or, in this case, a bird?

Sibley:

Beginners usually latch on to common characteristics; they focus on similarities rather than differences…Once you’ve mastered common patterns, the real trick is to educate yourself about where discrepancies are most likely to appear — and to concentrate your attention on those areas.

ThinkingAnalytically discussion starters:

• What similarities can through you off-track when examining a pattern?
• How have you focused on areas that are most likely to show discrepancies?

Use the comments to contribute.

Purchase full the HBR article here.

HBR: One of the goals of pattern recognition is to quickly narrow down your set of possibilities.  As birders, is there a particular thing you look for first?

Sibley (paraphrased):

[By answering the following,] you can narrow down the likely possibiliites to a handful of species.

1. Where am I and what time of year give a broad idea of which species may be present.
2. Type of habitat the bird is in and what it is doing.
3. The overall size of the bird.

ThinkingAnalytically discussion starters:

• What are the two or three things you look at first when examining a business pattern?
• How do you avoid wearing “blinders” that take you down the wrong path?

Use the comments to contribute.

Purchase full the HBR article here.

HBR: Can you describe how you begin to identify patterns? In business pattern recognition still seems to be more of an art than a science.

Yoshida:

Birders are also using new information from molecular biology and field ornithology.  This information expands the repertoire of possible patterns…As new ways to process data emerge, new patterns are discovered.

ThinkingAnalytically discussion starters:

• What new patterns have emerged in your business as a result of new ways of processing the data?
• What patterns are evident now but perhaps not visible ten or twenty years ago?

Use the comments to contribute.

Purchase full the HBR article here.

HBR: Can you describe how you begin to identify patterns? In business pattern recognition still seems to be more of an art than a science.

Sibley:

Pattern finding in birding is scientific in the sense that it is very deductive. I’m aware that there are patterns out there…and I try to fit my observations to patterns. It’s like making hypotheses and then testing them.

ThinkingAnalytically discussion starters:

• How is pattern finding in your business more like an art or more like a science?
• Do you tend to create a hypothesis then look for patterns to prove it…or…do you tend to gather patterns then develop a hypothesis that explains the patterns?

Use the comments to contribute.

Purchase full the HBR article here.

• There’s no bad time to innovate.
• Frugality drives innovation. One of the only ways to get out of a tight box is to invent your way out.
• You have to be willing to be misunderstood if you’re going to innovate.
• Skills-focused companies focus on what they can do which puts a finite lifetime on a company. A more stable strategy is to start with “what do my customers need?”
• You know that when your harshest critics are among your best customers, you can’t be doing that badly.

He includes examples of innovation at Amazon.com such as the Kindle e-book reader. Which, by the way, is on my wish list. I’d love to review one for you but first I’d need to get one in my own hands. Check it out at Kindle: Amazon’s New Wireless Reading Device.

Leaders should get out ahead of this trend to ensure that information makes organizations more rather than less effective. Information is often power; broadening access and increasing transparency will inevitably influence organizational politics and power structures. Environments that celebrate making choices on a factual basis must beware of analysis paralysis.

The close by suggesting the following books:

• Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Boston: Harvard Business School Press, 2007. (see my entry on wikiBookNotes.org)
• John Riedl and Joseph Konstan with Eric Vrooman, Word of Mouse: The Marketing Power of Collaborative Filtering, New York: Warner Books, 2002.
• Stefan H. Thomke, Experimentation Matters: Unlocking the Potential of New Technologies for Innovation, Boston: Harvard Business School Press, 2003.
• David Weinberger, Everything Is Miscellaneous: The Power of the New Digital Disorder, New York: Times Books, 2007.

I’ve only read the first.  I’m interested in any reviews of the others.

In today’s world, litigation typically involves the review of thousands, if not millions, of e-docs. Though shredders can permanently destroy a paper document, past e-mails and e-docs hide in all types of digital nooks and crannies. Unfortunately, many companies have not kept up with the proliferation of e-documents. But that’s no excuse when in the middle of litigation. If there’s a chance that a relevant e-doc exists, it typically must be found, reviewed and deemed relevant or not.

Sean McNee describes a recent case where a small legal team representing a small time inventor was able to use visual analytics to review and analyze the contents of 50 million pages of electronic documentation in just a few months. (Yes, 50 million pages.) He states,

“Visual analytics tools identify nouns and noun phrases in a series of messages, then visually cluster the documents together according to similarities in subject matter”

Apparently some Fortune 1000 companies have seen three-fold productivity increases using visual tools. According to the article, “U.S. corporations spend nearly $5 billion a year analyzing emails for litigation, regulatory requests and investigations.” A three-fold increase in productivity yields signficant benefits!

Read more at BI Review Online.