Category: collaboration

“Smart” starts with context

Imagine two marketing people sitting across a table from a data analyst. Marketing wants “simple numbers,” they say, and the analyst understands. The three of them know all the past conversations, the questions that arose and were answered, the points that got made, the conclusions or unresolved questions that remained.

Easy enough. But now with the rise of so-called “smart cities,” where the data spigots open and everyone with any point of view at all takes part, things can get into a mess. There, the starkly disparate points of view, which in business get talked through in relative calm, really show teeth.

Imagine the scene: From the city might come staff analysts, department heads, even elected officials. From the public come experts from interest-groups, grassroots politicians eager to make a name for themselves, owners of mom’n’pop stores, or even the even the grandmother who’s mad as hell. Each side — there may be several sides, and even divisions within each one — may have never set eyes on the other before, though some may know each other from decades of work together. Everyone’s got a different idea of past conversations, or no idea at all.

If only they had a nice, simple tool — as simple as a book, notepad, or whiteboard but also available for fast searches before meetings or quick refreshers on a tablet held just out of view from everyone else. Something as simple as — to use a metaphor by Alation vice president of marketing Stephanie McReynolds — a bicycle. You could walk across town, but it’s a lot easier on a bike. It has no hidden parts. Like any good machine, it makes more of whatever effort you exert.

Past conversations — rich with context, observations, and ideas — can be preserved and studied. Then those who like “simple numbers” might really talk.

“Smart” starts with context. Real decisions, no matter what data anyone has, still takes place among people — every one of whom has to know the context of any decision they’re part of.

Stupid analytics gets them talking

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.

Everybody talks about collaboration

Everyone’s talking about collaboration — but what culture and tools does it take to succeed? Six principles emerged from a discussion among experts at the Pacific Northwest BI Summit in July.

Even among this moderate crowd, an old tension showed itself. BI has always had what I’ve thought of as “data police,” those who stress data quality, security, and process at the expense of natural workflows. On the other end, “data libertarians” have used self-service tools to wrest control of their data analysis and data itself — an approach that has made more sense to me. If BI is about business, why leave it up to a cabal of technicians?

Read all about it in my BI This Week column. It appears tonight. Here.