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“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.

People who analyze data, collaborate!

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.

The future of BI in two words

What’s the future of BI? Last fall, one sharp source of mine answered, “Two words: Tableau and QlikView. You didn’t hear it here.”

Those are startling words coming from that source, a well-regarded BI consultant known for big-name clients and their big deployments.

At about the same time, a column of mine appeared in Information Management titled “Don’t call it BI” — in which I mentioned Tableau and a few smaller tools. A reader emailed, “You should also become familiar with QlikView.”

My many Tableau-using friends say QlikView is hardly worth a look. Poor visualization! Control panels! Scripting! “It’s so — yesterday,” one emails.

It’s “yesterday” to some yet it’s the future to others. It’s time for a look.

Both Tableau and QlikView promise the same magic: Listen to one pitch and you might think that you’re listening to the other. Each sets itself up against traditional, big-iron BI. Each claims to empower business users by giving them all the data and control they need for free discovery. Each is easy to use. Go inside each tent, though, and you see how different they are.

Metaphorically speaking, Tableau is West Coast. It’s built for discovery by the individual. Just show up and ride on the breeze, the demos seem to say, free as a seed fairy on a meadow. The inevitable mistakes of discovery are quickly undone and forgotten. Create the most dazzling visualizations — “vizzes” — thanks to built-in best practices that nudge you toward beauty and punch.

One of the most attractive aspects is users’ effervescence. They seem to be riding on the wind and solving business problems all at once. Their rapture sweeps me away every time I’m near it.

If Tableau is West Coast, QlikView is East Coast. Its community is bigger, the third-party add-ons are more plentiful, support seems more available, and overall workflow feels more structured. It too is built for discovery, but it’s discovery rooted in community. The “associative experience” reveals relevant data, and you can create your own views and in quick succession ask any questions, anticipated or not. But unless you’re working alone, someone else probably defined the data and its structure for you. This is QlikView’s counterpart to Tableau’s meadow, though it’s more like a manicured garden than Tableau’s unfenced field of daisies.

QlikView’s boundaries may be more apparent than Tableau’s, but I suspect that there’s at least as much power there. I just haven’t yet been able to judge it for myself well enough.

The trouble for me is that I’ve used it alone, as if stuck in a remote cabin. Though even Thoreau might have liked the “associative experience,” QlikView really comes alive only when you link to others.

As in Tableau, any QlikView user can create or modify a workspace, a document linked to one or more sets of data with any number of displays. Unlike Tableau, QlikView isn’t so finicky about data; for one thing, linking to Excel spreadsheets is easier.

I can’t speak with assurance just yet on the differences between QlikView and Tableau Server — more on that later — though I think I see a QlikView edge there.

One other advantage for QlikView is clear: built-in collaboration. True, Tableau workbooks can be passed around in a variety of ways forever. But as with our atomized life on the West Coast, such a community would be for me, the hypothetical manager of a group, too loose for comfort.

Tableau users will shudder, as if about to be extradited back to Maine. “Great, central authority all over again,” they would say. Yet when I imagine myself managing a group, I would feel disabled without a tight, integrated social structure.

“It’s the soft stuff that matters,” TechTarget research director Wayne Eckerson likes to say. Such stuff is what interests me more than anything: Who are these people and how did they choose what they did?

Have most Qlik or Tableau users chosen their tool the way most of us choose spouses, religion, and politics — guided by our relationships? How many software shoppers qualified their candidates with lists of requirements and features and followed through based on evidence? Did they do what a veteran sales person at a large BI vendor sees?: “They gather requirements, they issue RFPs, they visit trade shows, they talk to vendors, and ultimately they pick one because they like its color.”

I think it’s usually about “color,” color being the cover story for something most people can’t quite describe. For now, though, I’m happy to say that at least my first question has been answered: Yes, QlikView belonged on that list in “Don’t call it BI.”

Simple tool, same old resistance

I’ve rarely heard resistance to BI expressed quite so well.

A woman at an oil company said to Metrics Insights founder Marius Moscovici, “If someone wants to know something, they pick up a phone.”

The hell with easy information delivery. Make them ask for it.

I might understand what Marius witnessed if the woman were faced with any of the heavy hitters in business analytics. These things come encrusted with big promises, but big promises come with big upheaval. Data gets disturbed that some people wish would stay buried. They want to say, “Everything’s fine here. Go away.”

You can imagine why. They might fear that data quality isn’t up to snuff, or that someone’s got to govern all that stuff whether it’s “big data” or little data, or they could simply fear anything not invented back when they had nothing to lose.

It’s harder to understand when the information has already been refined and put in a box — and simply needs delivery to a doorstep.

The simple tool from Metrics Insights — the company and the tool go by the same name for now — seems to follow in big tools’ wake and fill in where they won’t. Users drag and drop tiles to assemble other tools’ output onto one screen. It’s simple self-service BI for easy focus, collaboration, and mobility.

It’s making headway with four customers that include Barnes and Noble. Looks to me like a good tool that’s just trying to bridge that last yard.