“Don’t bother me with petty distinctions between BI, analytics and decision support. I want meaning, not tools for their own sake – and here I see glimmers.”

Read it here.

Twenty tweets the first day!

Just out of the frame of that now-famous photo of Barack Obama, Hillary Clinton, and others as they watched the operation against Osama bin Laden was the CIA analyst who led the way to that moment.

From the Associated Press story about it:

He had been a standout in the agency’s Russian and Balkan departments. When Vladimir Putin was coming to power in Russia, for instance, John pulled together details overlooked by others and wrote what some colleagues considered the definitive profile of Putin. He challenged some of the agency’s conventional wisdom about Putin’s KGB background and painted a much fuller portrait of the man who would come to dominate Russian politics.

That ability to spot the importance of seemingly insignificant details, to weave disparate strands of information into a meaningful story, gave him a particular knack for hunting terrorists.

Though I don’t hear analysts in business identifying with intelligence analysts, the two have similarities worth observing. This story illustrates a few obvious ones.

Read the whole Associated Press story here.

Just by chance, two ostensibly unrelated items arrived here at Datadoodle headquarters on the same day last week.

First, the Department of Here and There received notice of a comment on a post by Information Management editorial director Jim Ericson about his video interview of eLearning Curve director of education Dave Wells. In the interview, Dave described a convergence of forces, a “perfect storm,” that will overwhelm IT shops. Comments ensued, including this one:

… I only hope those who refuse to be intimidated by “IT buzzwords” understand that it’s not going to happen by magic … someone still needs to know what they are doing … and it ain’t glamorous.

Mere hours later came this from the editor on duty at the Foreign Desk. It’s an excerpt of a conversation intercepted in early February between then-ruler of Egypt Hosni Mubarak and an unnamed fellow Middle East dictator presumed to be Muammar Qaddafi:

“I’m tellin’ ya, Mu [Muammar], those kids [the protesters] have no idea what it takes to run a country. Look at ‘em down there [in Tahrir Square]. What a mess! They can’t even pick up after themselves and they think the garbage will still be picked up if I take early retirement!”

I assume the IT person who wrote the blog comment would have nothing to do with the former Egyptian dictator, and vice versa. But the two do have things in common.

First, both have a point. They know that running their vast machinery, whether it’s an IT infrastructure or a medium-size country, is a task for which the barbarians at the door are under-qualified.

Second, some among their peers feel sorry for themselves. Oh, poor us, no one appreciates us! It’s a dirty job, etc. You’ll appreciate us when we’re gone!

But the smarter IT people, like the more sophisticated military professionals of the world, will see this “storm” for what it is: merely a transfer to civilian authority. The smart ones don’t want to run the show. Advanced organizations and countries have managed to shed that spooky awe of power that the less advanced project onto things like the military and technology. This is a step up.

IT budgets may decline and come under more scrutiny. Business people will take more responsibility for some traditional IT tasks, and things may get messy for a while. But isn’t that as it should be?

In any case, the Datadoodle editorial council met overtime on this one. As they say, it’s “a breaking story.”

What drove 39 tweeters to endorse CIO Insight’s latest monument to redundancy? I wonder how many actually read it all. I could hardly reach the first period before fatigue set in.

The blog post titled “Gartner: CIO as Business Transformation Leader,” dated November 1 and promoted on CIO Insight’s email blast last week, begins with an admonishment to CIOs: “it’s time” that they plan to emerge as a “change leader.” OK, but shouldn’t any CIO know that already?

Ten questions follow, including “What type of change is happening?,” “What is the deadline for the change?,” and “What are the constraints …?” followed by explanation and the most basic of advice. Anyone but undergraduate business students should have heard it all before.

It all takes up 1,312 words when the same ideas could have been expressed in half that. What makes people think this stuff is worth a tweet?

Some of the books Stephen Few reviews may at first glance to have little to do with data analysis. On second glance, though, they have everything to do with it. He often goes into the essence of thinking, insight, and decision making — core knowledge for BI practitioners.

See his latest, posted yesterday afternoon, on Gary Klein’s Streetlights and Shadows.

Wolf Blitzer calls for heads to roll after the Christmas Day attack. But Jill Dychè is a data pro, and she’d rather let the heads think.

“Who should get fired?” is the same conversation as after screwups in corporations, writes Dychè, principal at Baseline Consulting.

Instead, the government should be addressing process issues. Indeed, the real conversation should be how to move forward. These questions should be asked now: “How should we bring identifying data together? What are the key sources? How should integration, access, and usage policies be formulated? What would a sustainable process look like?” Those questions aren’t “who” questions, they’re “how” questions, and they should be front-and-center in the national security conversation.

Read the full blog post.

George Packer wrote in the New Yorker (quoted in Taegan Goddard’s Political Wire) on the books that Obama and his generals are reading. He wrote this about making public policy, about the difficulty of basing decisions on the past.

Making policy is about making choices, and they are never the same twice. Over the past few years, I’ve come to believe that doing it well is hard—almost impossible. It takes imagination, a knowledge of history, a certain analytical coldness, an ability to hold contradictory ideas in one’s head at the same time, intellectual courage, and prolonged immersion in staggering depths of facts. Few leaders are capable of more than one or two of these, let alone all. And given the complexities, there’s only so much policymakers can learn from their predecessors.

There’s only so much that decision makers can learn from the data.

Stephen Few gave a snappy name to his new book, Now You See It, and a cover that signals a gem — all black with a slice of sunset that highlights the “see.”

Inside, many charts are so beautiful — at least to a visual analysis fan — that they rival the waterfalls and trees of old Sierra Club coffee table books. It’s on paper so thick — and even smoother than Few’s first book, Show Me the Numbers — that you might feel like you’re flipping postcards.

While Show Me the Numbers was about how to present visualized data, he explains, Now You See It: Simple Visualization Techniques
for Quantitative Analysis is about understanding them, which apparently includes how to make them.

Few’s a natural teacher. He reviews, again, the basis of visual analysis and how the brain is better wired for visualization than for row-and-column thinking. In each chapter, he takes the reader through by the hand from the most basic concepts of visual data analysis through complex ones. Toward the end, he points the student to the horizon to imagine a future state of visualization. I especially liked the interesting chapter “Prerequisites for Enlightening Analysis.”

My question after a while, though, was who Few’s “you” is. What kind of student does he imagine? If the student really requires the kind of patient explanation found throughout the book, shouldn’t that student do some other kind of work instead? Besides, isn’t visualized data supposed to be understood easily?

I’m a mere beginner at visual data analysis, comfortable with Excel charts but not fully competent with Tableau. Even so, the out-of-the-box brain function that visualization scholars call “pre-attentive cognition” seems to have guided me well through visualized data so far.

I can’t help but skim past some parts of the book. In the chapter on part-to-whole and ranking analysis, for example, he spends four luxurious pages on what I thought would have been too basic for adults. That includes nearly one whole page on the meaning of trend lines: some go up, some go down, and some stay the same.

Do these basic explanations bore me because I’ve seen at least parts of Few’s basic presentation too many times? Or did I learn enough from Show Me the Numbers, from Edward Tufte’s books and presentations, and from a stint with a Tufte-admiring market research boss?

Or could it be that, like so many other kinds of work, visual analysis is a lot easier to observe than to practice? Would I appreciate the explanations if I were up to my elbows every day in visual analysis? Hard to know.

Too basic for some, but the book could be just right for others. This book could be the bridge from the early adopters to mass adoption. Also, longtime analysts could appreciate the refresher — for that slap-on-your-own-forehead moment when reminded of basics.

Few conducts the book like a classroom, narrating with statements like, “We’ll look at the following techniques and best practices” as if coaxing freshmen through a long, hot afternoon session.

He does best when he illustrates points with a story. The chapter on distribution analysis begins with Stephen Jay Gould — diagnosed with a kind of cancer with a median mortality of only eight months after discovery. Gould thought about that statistic and discovered that he was a prime candidate for longer survival. He went on to live another 20 years, and helped this chapter seem shorter.

But there’s got to be more to visual analysis than the simple charts in this book. I wish Now You See It skewed toward more sophisticated visual analysis, the kind I hear about but have not yet reached.

In fact, the phrase “now you see it” has another half, ignored here: “now you don’t.” The two halves suggest a cycle of knowing and then asking again — what Tableau calls the cycle of visual analysis. The Tableau white paper “Visual Analysis for Everyone: Understanding Data Exploration and Visualization” describes it — visually, of course — on page five. I suspect the subject deserves a book.

Let’s see if Few’s got that one up his sleeve for later.

The popular New York Times columnist and Nobel laureate Paul Krugman often disagrees with his fellow economists. Let’s all raise our eyebrows in shock.

Not! My shock is at the idea among some of his readers that there’s a right and wrong answer. Today, for example, Krugman again refers to his opinion that the stimulus passed early this year should have been bigger. To that, “Ray” comments:

I might suggest that Professor Krugman undercuts the supposed expertise of the economics profession every time he accuses a fellow Nobel laureate of not understanding elementary economics or being unable to read a graph. That sort of thing does not inspire confidence from the rest of us.

Guess what, Ray. Though there’s often a single version of the data, it’s much harder to agree on a single version of the truth.

If you haven’t already, ask around: Exactly what is “business intelligence”? Some say it’s all about business decision making, and others seem to think it’s all about tools.

We struggle with definitions, but usually not in public. Perhaps that’s why the recent uproar on the weblog of eminent visualization critic Stephen Few felt like a refreshing breeze.

It all began with Few’s damning review of a product whose promoters tripped and gave it the now-sexy “visualization” label. Oops.

Usually, Few’s readers sit back and enjoy the show. He’s one of the few Bi writers with the courage to call out a stinker. But this time, several people sat up in protest. Comments erupted into a weeks-long discussion-turned-scholarly-fistfight over definitions.

After a few swipes at his “mean-spirited” tone—which I don’t see—and other complaints, they found the deeper issue. Colin White, president of BI Research and a keynote speaker at this year’s TDWI World Conference in Las Vegas, arrived late to the discussion but soon led the charge.

One term they fought over was data visualization. To Few, it’s a business function. He wrote that it’s “the use of visual representations to explore, make sense of and communicate data…”

White disagreed. He prefers a more “pragmatic” definition to accommodate the term’s variety of uses. He wrote, “If data or information is presented to a user in a format that aids decision making, then that contitutes data visualization.”

Though White writes that experts must “use clear definitions and terminology,” he wrote in the next sentence, “However, it is important that we accept that other people may have different definitions, and we need to find common ground.” He went on, “We also have to accept that business users may employ technology and use some terms in a completely different way, and it is important to adjust our positions and explanations accordingly.”

Did he mean that terms mean what the person who uses them says they mean? White leaves that and other things unclear in his careful yet still foggy pronouncements. He doesn’t even state his definitions of business intelligence and data warehousing, even when he condascends to Few that his definition is “outmoded.”

Few politely called White’s definition of data visualization “not useful,” and I agree. No term can be useful that has lost its meaning. As Alice said to Humpty Dumpty in Alice in Wonderland, “The question is whether you can make words mean so many different things.”

Label inflation makes it tougher to find a toehold in the market, to write about techniques and tools, and even to have a conversation. When marketing collateral shouts “data visualization” to the general BI market, who will look up if it could mean bad Powerpoint slides? It hurts the whole industry if worthy products can’t find words that make would-be buyers listen.

Few’s review of Lyza looks to me like a case of mistaken identity. Perhaps the company should never have entered the visualization arena. Also, according to at least one BI expert I respect, it is actually a valuable tool. A bloody nose for nothing.

To Alice’s question about making words mean many things, Humpty Dumpty replied, “The question is which is master. That’s all.”

If everyone’s a master, we have label chaos. Instead, industry leaders, journalists and smart marketers should use words as they’re most widely understood. As a rule, the master should be business, the data train’s final stop.

Also see sascom editor Alison Bolen’s “What we call what we do: a lesson in evolving industry key words.”