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Qlik finally set to leapfrog Tableau?

Who’s your rival? I carelessly asked a Qlik person at the company’s annual analyst reception Monday night in Miami if she hadn’t once worked for Tableau. Her revulsion was immediate. “No! Never!,” she said.

We smiled. There was so much more to talk about. For one thing, how will private equity change things? Qlik wasn’t doing so well at the public-equity thing, you may recall, and over the last few months they went private.

Knowledgeable Qlikkers assure me with apparent sincerity that “good things” will ensue. I can think of no reason to doubt them. It must be nice to have the riffraff off your back, which one experienced business person described to me as “having ants in your pants.”

Tableau’s still public, though not quite as shiny as it was. It has that well worn feel of a recently plush restaurant. No one notices in the mood lighting and boozy good vibes, but the cleaning crew sees it plainly enough when the bright lights go on after closing.

To be rid of ants might just set Qlik on the way to leapfrogging Tableau. Old-timers will recall that Tableau was once the upshot that caught Qlik by surprise. Now Qlik might show off what it’s learned.

We’ll see on Tuesday and Wednesday.


Five Tips for Better Data Stories

Originally published on September 22, 2015 in BI This Week, a TDWI publication.

A “data story” sounds like such a great idea. You just mix data with storytelling and you’re done — except that most data storytellers get one thing wrong: they drown out the story with data.

Such storytellers, I believe, assume that audiences enjoy data as much as they themselves do. For them, data ranks with love, food, and air. They throw visualization upon visualization at audiences because in the belief that data is the new language of business.

The trouble is that many of us in business — roughly 80 percent of business people, according to business intelligence industry studies — don’t embrace data with such warmth. We just want its meaning. We’re perfectly happy with business’s first language: stories.

In almost a year of talking to data storytellers one-to-one and co-teaching a data storytelling class at TDWI conferences, I find that most nascent data storytellers need help finding the right data-to-story balance, and they miss a few other critical points, too.

A good data storyteller’s first priority is context and meaning, supported by data. The most effective stories use the native human grammar: a beginning, middle, and end that presents and then resolves a problem or question.

Here’s my best advice for would-be data storytellers.

Organize facts into a narrative, and include a protagonist if you have one. Classic story structure organizes facts into a narrative. It starts with a problem that upsets life as people have known it, follows with attempts to resolve the problem, and ends with the resolution. Another useful element is the protagonist: a person whose goals drive the story for whom readers or listeners have empathy, which is easier felt for an individual than for a committee.

Judge your audience’s appetite for depth of data. Max Galka, founder of a New York City-based apartment-rating site Revaluate and a data analyst by trade, had dozens of factors available for renters to judge prospective housing. He quickly realized it was overwhelming. Revaluate’s score used many factors, including complaints about restaurants on the ground floor, neighbor complaints about the building, and statistics on the neighborhood overall. It was all verified with subjective observations. Meanwhile, a competing site offered just one score for each apartment building — and it won more traffic.

Galka said, “If that’s how I like to see data. I had a difficult time imagining how other people wanted to see it.” He learned to focus on the high level. What to a data analyst looks like uncrossed T’s is just fine, he said. “If I were to look at a building’s overall score but saw that there wasn’t any detail behind it, I wouldn’t put much credence in it, but consumers do like it.”

The more tech-savvy the audience, though, the more likely they’ll want details. He found that one group from a large tech company known for its “big data” liked detailed data.

Stories can elicit conversation better than naked data can. “BI is a social problem,” says Juice Analytics founder and CEO Zach Gemignani. “Ultimately it is about people trying to understand their environment and do something about it.” Most people, even sophisticated business people, talk about the tangible and meaningful more readily than the abstract.

Storytelling with data has skeptics. Get used to it. The most common objection seems to be from a weariness of trends; the data industry always has some new shiny thing up its sleeve, they say, and storytelling must be one of them. These skeptics should separate the hype from the fact. Storytelling is a basic method for conveying information, revealing insight, and inspiring action.

Other, more thoughtful skeptics worry that stories tend to warp data. They forget that humans warp everything we touch.

New methods almost never escape ridicule. When visualized data first gained popularity, skeptics called it “pretty pictures” and scoffed that spreadsheets were good enough. When data became widely available for decision support, some insisted their “gut” told them everything they needed to know. Ignore the critics.

Take off your analyst’s hat and put on a journalist’s hat. To the data-driven, this may be heresy, but let’s be rational. Data stories and data journalism stitch facts into a coherent, memorable whole. They help people organize data in their minds so that they can rake over each story’s assumptions and facts. Data storytelling can learn a lot from journalism. Look for inspiration in journalism’s variety: the news story written in pyramid style, the sensual feature story, or the passionate and articulate online comment. Study the use of text, video, and images. In story after story, people see a tapestry of insights coalesce that goes higher, wider, and deeper than data could ever go by itself.

If you want to show data, show data and don’t bother calling it a data story. Otherwise, put the story out in front. Don’t leave your audience asking, “Where’s the story?”


Six surprises on my way from data to analysis

Originally published on November 29, 2011 in BI This Week, a TDWI publication.

What do you do when you have a bunch of data on your hands and your skills fail to make sense of it? That’s where I was the other day.

As much as I’ve written about data analysts, I’m not one myself. When, in late September, I finally closed my survey of analysts, I needed a pro to help interpret my new data. I was like the amateur cook who could flip eggs and found a side of beef in his kitchen. That was one meal I wasn’t going to cook alone.

It’s not easy to find the right guide. What skilled analyst has the time? Who cares about this kind of data? Who has the patience to answer my beginner’s questions? It’s a problem I suspect that many business people face.

I was lucky. I knew someone: He’s the expert’s expert in the Tableau Software community, the one I’m most familiar with. He’s renowned among fellow analysts for his expertise —and his generosity. He’s on the Tableau forum every day answering questions from users of all levels.

His name is Joe Mako and he is mostly self taught. Soon after two Army tours in Iraq, he went to work at a large Internet service provider managing documents. His first taste of data analysis came when his boss asked him to figure out if certain customers were getting a free ride —and with that Joe found a career and a passion.

My transition to data analyst was serendipitous.

Surprise #1. Even as bare numbers, data’s thrilling if it’s yours. It must be like coming across cave paintings. Someone had left them there, full of meaning that’s left behind to be revealed.

Surprise #2. Data analysis is harder than marketing makes it look. It looks so easy when the experts fling dimensions and measures around, dragging them on and off of shelves to make bar charts, scatterplots, and heat maps appear and reshape and altogether dazzle. (TV cooks make their acts look easy, too.)

Surprise #3. Data analysis doesn’t begin with analysis. It begins with data. I told Joe that I wanted to see the whole process from the start, so he first walked me through “the boring part”: data preparation. My export of the data from the service provider, Survey Monkey, was far from Tableau-ready. Multiple-choice questions posed special problems with their double row of headers.

Surprise #4. Data analysts have their own style. Other analysts, Joe acknowledged, would not go to the same lengths of data preparation. In fact, he suggested, some are careless.

Joe, for example, takes measures I think other analysts would skip. For example, instead of going from Excel straight to Tableau, he took the data through a third tool, Lyza. There he made the data more easily show who did not respond to all questions.

Once data was imported into Tableau, Joe checked the data once more. “I’m confirming that I have every possible combination of response ID and question ID available,” he explained deliberately and calmly. “That’s how I get my high degree of flexibility with Tableau.”

Surprise #5. The analyst’s personal interest matters. Within the first few minutes, I came to appreciate Joe’s passionate interest in this data.

One question in my survey asked about the relative importance of statistics. I wanted to know whether beginners would rate its importance lower than experienced analysts. Some marketing seems to suggest that it’s optional. In the results, however, only a few rated it unimportant.

Still, Joe didn’t like to see even those few. He didn’t say anything for a few seconds after the stacked bar chart appeared. His cursor ceased the usual circular movement that preceded leaps into new views. “That throws me for a loop,” he said. “It just seems strange to me that someone would say that statistics is unimportant.”

“Why don’t we compare these guys with someone else?” I said, “or see what skills they do value.”

“Let’s see,” he said, enunciating with delicious anticipation. These respondents would now either save themselves or prove themselves idiots. His cursor circled busily again, and he flipped through a new succession of views and dialog boxes.

Surprise #6. Some data’s a dud. We found little about respondents who judged statistics of low importance. We couldn’t explain it except to assume that they had understood the question differently from others. I had written a faulty question.

The next questionnaire will be better. What may surprise me, though, is to find an expert in questionnaire design as generous with his time as Joe is with his data analysis.


How to find a story in data: What a news reporter would do

Originally published on December 15, 2015 in BI This Week, a TDWI publication.

A data analyst raised her hand in a class I taught on data storytelling and asked the question I hadn’t even thought about since journalism school: How do you “see” a story in a jumble of facts?

It’s a novel problem for data analysts, but it’s an old one for journalists. In fact, as confusing as the task seems to analysts, the confusion is a mystery to journalists. Don’t analysts know a story when they see one?

Now in the grand new confluence, journalists use data and analysts tell stories — and each side shudders with the other’s ham-handed work. Yet, as other once-irreconcilable factions have done and others may do yet, we all might as well get used to it and learn from each other.

What advice do journalists give analysts about seeing a story? I thought I’d find an easy answer with Google, and searches came up with page upon page of advice — just about all of which stayed on the data analysis side of the chasm. Not one looked across to journalism.

I gave an impromptu answer to the data analyst: Take off your analyst’s hat and put on your journalist’s hat. Here are a few approaches to do that.

Focus on the audience. Stop thinking about the data and think of what the audience wants to know of what it means, what’s new, or what’s different.

Think of what story you would tell a member of the audience over coffee. Forget your grand entrance, forget the brass band, forget about your boss staring at you. Just tell the story simply and plainly between sips of coffee. What aspects would you emphasize and what would you leave out? How would you structure it? You might find your story’s germ there.

Is anything significant in your data? Events become newsworthy with timeliness, proximity, novelty, or impact. The reporter covering a house fire, for example, may ponder various angles: a house that burned down within the news medium’s area is more significant than one outside of it. Yesterday’s fire is more significant than last year’s fire. The mayor’s house is more significant than those of most former mayors. On the other hand, George Washington having slept in the house even one night trumps everything.

Look for anomalies. Everyone’s heard of “man bites dog,” the anomaly that explains what becomes news. “When everything goes as you expect — the sun comes up, spring follows winter, the airplane works flawlessly — there’s no story,” writes Stephen Denning in The Leader’s Guide to Storytelling (2011; Jossey-Bass). “Paying attention to apparent anomalies is one of the reasons that we have survived as a species.”

Remember that the data is not necessarily the story. This is the most common discovery I’ve heard from data analysts. A vice president at AT&T once told Fern Halper, now director of TDWI Research for advanced analytics, to just tell him something that is 80 percent correct. Don’t get too get down in the weeds. “For him,” she said, “good enough was good enough.”

Max Galka, a cofounder of Revaluate, an apartment-rating service for renters in Manhattan, found that his customers wanted simple data. “You have to focus on the high level,” Max said. At first, he displayed data the way he likes it. “I wouldn’t put much credence in a building’s overall score [if] there wasn’t any detail behind it,” he said. In fact, he could offer deep, rich data on scores of apartments, “but consumers like [simplicity].”

He tried to lure people into logging into the site to get rich data in elaborate tables and hierarchies, the way he likes it. Few did. “One guy checked 10 or so buildings every week without logging in.”

Deciding what data to show, lose, or summarize has to be guided by audience and medium. What does the audience really want to know? What does it know already? What incomplete stories can you support or question?

Is the anecdote about the guy checking so many buildings without logging in meaningful as data? No, it’s about just one person — but it’s the story that’s remembered and retold. Galka, a data analyst, had taken off his data analyst’s hat and put on his journalist’s hat.


Bohemian Grove a la BI

The Bohemian Grove of the BI industry convenes for the fifteenth time in just three weeks. Naturally, you ask the obvious question: Are you serious? The Grove? A summit?

The answer begins with a fond recollection of the Grove. If you’ve never attended the Bohemian Grove yourself — I haven’t, though I live in the same metropolitan area — you may know of it as that century-old, mid-July pow-wow of leaders from big-iron industry, national politics, and old-time movie-making. Ronald Reagan slept there.

They ate, and they told stories, and they all went back to work a little more satisfied with the world as it was. Or not. Something in the stories coming through that fine Cuban cigar smoke might have stirred their hearts.

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