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Stupid analytics gets them talking

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If data analysis somehow ends up supporting an unwise choice, should we blame the data? Of course not, no more than we should blame a stethoscope for a bad medical diagnosis or fingerprints for a bad courtroom verdict. Common sense says the only ones responsible are the humans involved.

AllAnalytics contributing editor James M. Connolly wrote last week in defense of common sense in a good blog post, “Analytics won’t cure stupidity.”

Let’s admit it, we know the limitations of data analysis. The obvious truth whispers to us in the quiet of our own little minds, and that truth can also find a voice in private business conversations over a beer or lunch.

And that truth of the limitations of data analysis also finds voice in online comments, such as in those responding to Connolly’s post. Such conversations go around and around, often failing to convince anyone. They may have no effect at all on, say, the foolish executive who insists that data must has a predetermined result. But at least some people test arguments, let others know when they’re really off, refute facts, and even make a few people think again. I find what might be thought of as “campfire behavior” — known these days as collaboration and storytelling — exhausting, often annoying, but also crucial. This is where supposedly hard facts, including data analysis, assume the rightful position: subordinate to review by smart people.

Such dangerous ideas! The first comment in the string following the post sounded like a slap at Connolly himself: “This article shows you have the diplomacy and tact in handling issues from the viewpoint of a toastmaster.” The same person commented again a few minutes later with a myopia that’s useful for close examination of data but only obstructs decision making: “You’ve given me a choice: cursing the data or cursing the stupidity. Neither. I look at the facts and draw a conclusion very carefully. I am mathematically-oriented.”

“Toastmaster” Connolly replied, “No, Judith, you don’t have to choose between data and stupidity. Just be aware that even the best data project could place the results in the hands of the very fallible human…” She replied, seeming to have missed his point again.

Others joined in. One told a story about presenting analysis to executives only to discover that they didn’t care about the analysis, they only wanted a certain conclusion. He wrote, “The endpoint was already decided.”

Another asked what if the analysts are the “stupid ones”? Still another told about the eroded trust that results from predetermined results. And another commenter wrote, “I’ve heard that technology will never truly change the world because the problem isn’t technology, it’s humans.  I guess the same could be said of analytics.”

That’s right, the problem — and the promise — is with humans.

The best remedy for stupidity is a traditional one: conversation. It’s not perfect, and often it takes a long succession of campfires ignited and exhausted before sense emerges. And sometimes the result is still stupid. But it’s always smarter than leaving everything to the data, no matter how “careful” any data analysis may have been.

People who analyze data, collaborate!

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In business, we’ve got data scientists, we’ve got accidental analysts, and we’ve got a million variations in between. Dave Wells thinks they can learn from each other. To help that start up, he’s forming an organization he’s calling Business Analytics Collaborative.

The diversity and fragmentation that challenge those trying to reach this market are the reasons that community building is difficult. Our conclusion is that we need to build the community from the ground up – taking a sort of grass roots approach that begins with local meetups. By stepping away from digital overload and starting locally and face-to-face we can move away from fragmentation while at the same time finding value in the diversity of the analytics space. Once we achieve a workable level of local communities, we can then start to bind the local groups together as a virtual analytics community where collaboration becomes a powerful tool for innovation.

You may recall Dave as the education director at TDWI. He still teaches at TDWI conferences. Now at his new organization, he’s modestly named himself director of community development.

He’s holding a series of meetups. The first occurred on August 8 at Tableau headquarters in Seattle, where 82 people showed up. The next was Wednesday night at TDWI conference in San Diego.

To keep up, go to bacollaborative.com. And show up at a meetup when there’s one near you.

“Good enough” data analysis will lead to good data analysis

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Someone on Quora asks, “What are the most promising disruptive innovations for the next decade (2011-2020)?”

The always incisive Venkatesh Rao offered a long list. At least one of his predictions brings to mind the decision support industry’s many do-it-yourself tools.

Deprofessionalization/democratization: if you thought blogging taking down professional writing, and amateur photography threatening professional photography were big, you ain’t seen nothing yet. Tons of problems that previously required professional expertise to solve can now be re-engineered so that amateurs can solve them. Movies and education are next.

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Look around. If the tools don’t exist, but the high-margin professionals and eager amateurs do, try and invent them.  A big opportunity here is for amateur animated video shows. There are some primitive things that get animated characters speak out scripts, but we’ve barely scratched the surface.

Note the absence of data analysis. He could have listed it along with movies and education. But he didn’t, most likely because he knows too much about it to accept “good enough.”

People tend to go along with the good-enough except in their own field. There they see exactly why “good enough” just isn’t. Professional writers and photographers still scoff today at amateurs’ work even 20 years into that takedown.

When you need something done well, you hire a pro. But for many jobs, you can get by on “good enough” — until you’re skilled enough to know what you’re missing or curious enough to want something better.

That path played a big part in the California wine industry’s recent history. Back in the early ’80s, I talked to the finance officer of one of the many new craft brewers in California. He had seen how mass tastes work a decade before with the rise of a bad jug wine, Italian Swiss Colony. (“Made by that little old winemaker, me.”)

Sure, the stuff was awful by today’s standards. But the marketing got Americans drinking wine. Before long, many of them wanted to know how the good stuff tasted. So throughout the ’70s and even today, small wineries thrive thanks to that new thirst.

So it will be with data analysis, not to mention movies and education.