Alteryx and Tableau, yin and yang

Many of those who watch the amazing acrobatics in Tableau or other visualization tool find there’s one more spectacle to come: setting up the data — cleaning it, fixing incomplete sets, combining sources, and so on. Only when that’s over can real analysis be done.

Many advanced users still prefer to use a specialized tool for prep. Strong contenders have appeared for graduates of Excel, including Paxata and Trifacta. But it’s Alteryx I finally got a look at not so long ago.

I was curious about Alteryx. It seems to have become the go-to among advanced users of Tableau, which at first seemed odd. To my novice eye, Paxata and Trifacta seem more like Tableau, so why wouldn’t users flock there instead? Based on what I’ve heard from one Tableau super-user I know, Joe Mako, the answer must be in the contrast. Alteryx and Tableau just approach work differently.

Alteryx chief scientist Dan Putler walked through a data analysis challenge. The question: Which of many alumni who have never donated any money are most likely to give some if asked in a new donation drive? A certain public university’s development office would solicit the most likely ones by phone. As if that weren’t enough work, the development staff could make no more than about 10,000 calls per year. Who should they call first? Data on each alumnus included age, area of study, social or familial associations with the university, and a handful of other facts. The answer: the alumnus’s area of study. Putler showed it in a line chart, the results of three of his competing models compared with past experience.

It was an interesting problem, but getting to that answer was most of the fun.

Putler describes Alteryx as a “visual programming framework,” he says, that’s often compared to Visio, the Microsoft diagramming application. Just like in Tableau, its drag and drop interface is friendly to beginners. Unlike Tableau, coding is there for the advanced. A wide range of ready-made possibilities line an upper row of the workspace. Handy documentation has explanations and samples, effective even for those whose ghost-like memory of a statistics class years ago lingers on the edge of awareness.

Also unlike in Tableau, all the details are right out in the open. You see exactly what happens to the data as you do what you will with it.

Putler’s models grow into a diagram that shows exactly what will happen to the data at every step. This is the part of data analysis that resembles detective story, where you sift facts to find the most cogent narrative.

That transparency and control is why Joe Mako prefers Alteryx. Joe is the almost legendary Tableau super-user and volunteer guide. For more than five years, he’s spent hours every week helping even some of the most advanced users figure out how to make Tableau do what they want it to do.

“It’s like having a kitchen,” he says, “and Tableau and Alteryx are your appliances. You can cook without them, but your life in the kitchen is so much better if you have them.” Even better if you use them the way they work best.

“Tableau and Alteryx are wonderful together,” he says. “To me, they’re like yin and yang. They’re both wonderful, but they’re both challenging.”

Tableau’s goal is to make its complexity invisible, which is both good and bad. It powerfully enables the cycle of visual analysis, the uninterrupted flow of questions, insights, and more questions. He says, “No one else comes close.”

But the magic has a flip side. Though the unseen magician almost always guesses correctly what you want to do, it can be fooled by unusually complex work — such as the kind Joe often does. That’s when the magic fails. The computation logic could change without the user’s knowledge or control. A feature known as data densification seems to be one of Joe’s main culprits. (See his video on data densification.)

If Tableau employs an unseen magician, you might say that Alteryx employs a fully visible carpenter. What it does, it does in full view, consistently. Joe says, “It’s the tool you turn to once you need to work with a millions of records and do hard computing and make it a repeatable, self-documenting process instead of a manual, brute-force, hand jammed thing. It lets me codify a thought process.”

Though complex computations can be done without writing code, you can write code if you want to. The user with a sketchy knowledge of some complex predictive logic can open a module with a use case and a description of what it does at input and what comes out on the other end.

Even the icons signify with consistent colors and shapes what each one does. In a series, they reveal at a glance a chain of functions. Joe says, referring to a process he’s constructed, “I see here that I’m pulling the data in, joining it, and I split it out into some groups, and then I put it all back together one piece at a time, precisely how I want it to be pulled back together, and then output it. I get exact control.”

The main downside, he says, is weak interactive visualization. He believes Alteryx simply wasn’t designed for it.

You pick the tool for the job: Tableau to do the visual analysis, and Alteryx to do the tech-side stuff. Joe says, “Together, they’re nirvana.”

Tableau’s storytelling, conversation, and journalism

I imagine the Tableau marketers sitting down over the coming year’s menu of trends. “What, storytelling again?” one says as if dreading the taste of dim sum for the hundredth time.

Storytelling was a staple there at the trendy headquarters. The research department had not too long ago lured Robert Kosara away from an academic career. First thing, he and Jock Mackinlay published a whitepaper on the future of storytelling.

But no marketing stays fresh forever. Even before business people finish chewing on the last trend, new ones come along with the alacrity of T-shirt slogans. Fortunately, there were choices on the menu that would give new zing to “storytelling” — not with one new trend but with two.

“Ooh,” says another marketing exec pointing to the menu, “this looks good.”

Conversation is to trendy BI marketers what crickets and plant waters are to foodies. It’s also one of the tastiest components of storytelling. Throw a chart on the screen, and you want people to say something, ask you something, object, stand up and threaten to walk out if you don’t explain your data selection. This makes all the difference in meaning and effect.

Then comes the second trend. It’s salty, unpredictable, and challenging. It’s an old recipe made new: It’s journalism. Forget storytelling and its funny aftertaste. Journalism is the word for what data has always needed to reach the masses — yes, they exist — who in their hearts don’t give a god damn about data. All they want to know is what it means.

Of course, when Tableau says journalism they mean work by those who call themselves journalists. What business needs, though, are people who practice journalism no matter what they call it. I wish more data analysts would just take off the data hat and put on the journalist’s hat. Give the story — in journalism, every piece is called a “story,” with or without classic story structure — and present data only as they would other facts.

“Mmm,” that’s tasty,” the talented data analysts will say. “Let’s try that.” And the whole business world will thank them.

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Data storytelling: promoted but barely researched

Data storytelling struck a chord with the data industry when Tom Davenport, of Competing on Analytics fame, gave it his blessing two years ago. Storytelling was a data scientist’s most important function, he said. Enthusiasts responded with columns, papers, and blog posts — but most of it did little more than echo Tom.

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