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Democratic pollster: Hillary campaign’s data malpractice

Hillary Clinton’s data analysis failed her — even with the help of Barack Obama’s 2008 data cruncher. The problem, says a Democratic pollster, wasn’t in how they crunched the data. The problem was the data they ignored — with a result that’s rarely so clear in business.

Democratic pollster and strategist Stanley Greenberg explained in a blog post a few days ago.

… When campaign developments overtake the model’s assumptions, you get surprised by the voters — and this happened repeatedly. … Astonishingly, the 2016 Clinton campaign conducted no state polls in the final three weeks of the general election and relied primarily on data analytics to project turnout and the state vote. They paid little attention to qualitative focus groups or feedback from the field, and their brief daily analytics poll didn’t measure which candidate was defining the election or getting people engaged.

Some on the team were worried, such as campaign chair John Podesta. He wanted to fire the data guy, Robbie Mook. But Clinton refused, recalling his work for Obama.

The trouble was that Hillary lacked Obama’s star power, something she probably understood but dismissed. Listen to her post-election interviews and you’ll hear her miss the people point again and again. The difference created a soft but crucial margin that put Obama over the top and left Clinton losing to a candidate no one should have lost to.

Without the Obama zing, Mook was riding bareback. The data analysis itself had to be right on, but it wasn’t — having been selected on bad assumptions that went unmodified by what sounds like a smug disregard for all that fell outside of the model.

Narrative and analytics: brothers

Dave Wells, my collaborator in a TDWI course on data storytelling, tears up a popular misconception about data storytelling and data analytics.

On the surface, narrative storytelling appears to be the opposite of analytics – anecdotal instead of quantitative. But quantities aren’t the only way, or necessarily the ideal way to convey information. Not everyone in business is a quant who thinks natively in numbers. Some think in pictures, thus the popularity of data visualization: “Show me the shape of things, not the quantities. …” Visualization is powerful, but even more powerful is the ability to connect visuals, and to tell a story with data.

Read the full post.

Malcolm Gladwell: why oral data’s different

Why would you present data orally instead of in print? You might think that if all you have is data, why bother with the sweaty palms? Just post the paper online and let people read it!

Not if you want to test your conclusions. Oral and written renditions have different effects, and elicit different responses.

Malcolm Gladwell told how he realized this on an always interesting podcast, the Ezra Klein Show.

Here’s Gladwell’s explanation almost verbatim, starting at 57:25.

My father taught math and would be constantly going off to conferences. I was always very skeptical.

I thought, What possible value is there for him to go and present a paper when they could just send someone the paper? It’s equations! Isn’t it just easier to just read the equation! My father’s going to Istanbul this summer because they all want vacations in Istanbul.

But now I realize that there is a reason there’s so much emphasis in academia on person to person oral presentation of arguments, data, etc. When it’s presented in oral form it’s so easier to honor the conditionality of the work. To argue with it, to fix, to backtrack, to amend, to do all those things. The minute it’s on paper it has a kind of permanence and authority that maybe it doesn’t deserve.

…Whenever I read an academic paper, I try to imagine the author presenting it orally. And that helps me to not jump in head over heals with some of the conclusions. Just to imagine their voice when they came to the conclusions! In every conclusion in an academic paper, there’s a point where they recognize all the potential problems with the conclusion. This may not be true because of A, B, and C.

When you’re reading it, you skip over that. You know, “yeah, yeah, I’m not interested. I just want to know what the conclusion is.” But if they were presenting it, you know you’d that should be a crucial point of the presentation. Everyone in the room is waiting for you to go through all the reasons it might not be true. That’s where the discussion’s going to begin.

So the very thing that’s almost principle importance in an oral presentation is an afterthought when it’s on the page.

The six genres of data stories

This appeared originally on the TDWI site in September behind a paywall. It’s still there, but today they’ve had the 90 days of exclusive use that I agreed to.

Survey after survey reveals that about 80 percent of business users don’t use data analysis—despite all the marketing and “easy to use” tools.

As if in response to this sad showing, renowned author and academic Tom Davenport proclaimed that data scientists should know “data storytelling.” He’s right. Storytelling has transmitted knowledge and motivated action in every medium we’ve ever known. Stories around a fire, stone tablets, Gutenberg’s books, news, and e-books have all made use of stories. Data is a natural.

The data community lost no time swarming all over it. Trouble is, most of them seem to have heard “data” but not “story.” Even now, years into the data story trend, they still play mostly to each other with the only genre they seem to know, the parade of visualization—a waste of time for all but the already initiated.

It’s not so hard to reach non-data users with other genres, which are just sets of conventions that satisfy different audiences and moods. War movies, for example, deliver noise, action, and beefy male heroes. Romantic comedies deliver jokes, pastel scenery, and romance. Each genre satisfies different needs.

Here are six data story genres. The “naked data” genre seems to have become the default; search Google for “data story” and that’s what you find. Although the other five genres are barely recognized as data stories—I’ve never found any labeled “data story”—that is what they are.

Genre 1: Naked Data

The naked data genre lets data march alone. It is ideal for those who find data exciting. Search Twitter for #datastorytelling and this is the type you’ll find.

The naked data storyteller is like the host of a stone soup lunch. “Here’s the data,” guests are told. “Now make of it what you will.” The data-loving guests unpack the sack of knowledge they carry with them and apply it with their own curiosity and determination.

Genre 2: Narrated Data

Naked data transforms easily into the narrated data genre. The mother of them all is Hans Rosling’s 2006 rendition on childhood mortality around the globe. Rosling’s animated bubble chart now seems dated, but his presentation is timeless. His passionate narration explains the movement of the bubbles like a sports announcer at a football match. The data is more than interesting—it is thrilling. In a later instance, he told another story, this time not with computer visualizations but with pebbles on parking lot pavement. A parking lot space never looked so good.

Genre 3: Explainer

The explainer genre consists almost entirely of words. It uses one or two visualizations, if any.

The Upshot column in the New York Times makes frequent and effective use of this genre. A recent story on the U.S. economy, for example, runs about 800 words with a single, simple visualization. In “GDP Better Than It Looks,” the author explains that, although growth in the second quarter of 2016 was just 1.2 percent, this was almost entirely the result of a contraction in business inventories. That’s not a good predictor of future growth, according to the author. A much better rate of 2.4 percent shows up when looking at GDP excluding inventories, because final sales are a better measure of underlying growth. The bad news comes in shrinking investment and poor growth in productivity.

That offers plenty of data about GDP and is about as much as many people want to know or have time to think about.

Genre 4: Executive

This is for the executive suite. It is brief, perhaps just a minute long, and it may contain little data—sometimes none at all aside from footnotes that cite the underlying data.

A monthly report at financial services firm Charles Schwab, for example, is compressed into a 60-second story in several steps. First, a data analyst dives into the period’s data and comes up with questions and preliminary conclusions. Then a bigger group with representatives from marketing, HR, and other functions joins the discussion. Each person has a take on the period’s events and results.

From there, John F. Carter, senior vice president of analytics and business insight, distills the story further, as he described it to me. The presentation begins with the main conclusion, similar to news reports. Less is more, he explains, “but the right less.” Executives don’t have time to get into the weeds.

Another executive I spoke with—a veteran Silicon Valley CFO who requested anonymity—dismissed “the illusion of certainty that numbers provide…. Execs, at least the good ones, know they are dealing with a messy and uncertain world.”

Genre 5: Detective Story

This one starts out as an explainer but ends with a question. We have a mystery, the storyteller says, but we don’t know what it is. As in a traditional detective story, the audience gets all the facts—nothing’s hidden. Yet this is no game and the storyteller needs help.

Take the declining balances case that longtime TDWI instructor Dave Wells and I have used in our data storytelling class at TDWI conferences. Bank executives have come to recognize declining balances across multiple account types. Why? What can be done to reverse it? The keys are the customer stories behind this behavior, many of which weave into a bigger story. It all leads to the answers.

Genre 6: Scenarios

With scenarios, storytellers start with data and imagine a reality that may develop from it. The data sets the stage and imagination takes it from there. German data scientist Joerg Blumtritt approvingly described to me this kind of data story as “fiction.”

Fiction shouted to an audience of data people empties the room—even though many of them already create stories that are actually fiction. They are extrapolations of data to imagine future events. For example, credit scores are based on data of past behavior to predict default.

An even more sophisticated kind of fictional data story underlies scenario planning. When presented, scenarios may offer mere crumbs of the underlying data. In the 1970s, Royal Dutch Shell famously predicted several trends that competitors hadn’t foreseen. Scenario planning helped warn Shell’s leadership of the 1973 energy crisis, the late ’70s oil shock, the fall of the Soviet Union, and the rise of Islamic radicalism.

More Stories Ahead

There’s a genre for every business user and more than a few I haven’t thought of. The missing 80 percent are waiting.


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?”