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“BI for the other 80 percent” at
Information Management

How can business survive without data? Well, 80 percent of eligible users, according to most surveys, do seem to go without. The industry salivates in anticipation of someday colonizing that territory, and it shudders in frustration because they haven’t done it yet.

That topic came up last summer at the annual Pacific Northwest BI Summit. I’ve written here before about the session led by industry icon Claudia Imhoff and IBM vice president Harriet Fryman. Now I’ve published a column about it to a bigger audience at Information Management.

The column offers a strategy: storytelling. Humans are wired for it. The industry might as well take advantage.

The trick is to learn how to do it. Or even before that, the trick might be to accept storytelling as legitimate business practice. Though it’s widely practiced in the C-suites, those below them, the middle managers — so prone to insecurity — seem queasy about it.

See the Information Management column here.

Got analytics? Who will promote the industry?

Business people have everything. They’ve got data, and often it’s clean. They’ve got tools, and many are easy to use. They’ve got visualizations, many of which help. They’ve got domain knowledge, at least most do. What some front line observers find they lack is analytical thinking.

Given descriptive data, few business users that BI icon Claudia Imhoff sees ask even the simplest followup questions, like “why?” Is what the data shows good for the business or not? If not, what can be done?

Claudia, along with IBM marketing director Harriet Fryman, raised the question this summer at the Pacific Northwest BI Summit. This week I caught up with Claudia on the phone.

“The big elephant in the room is that they [business people] don’t know what analytics is,” she said.

What will it take to solve that? Education, of course. But by whom?, I asked. Some education takes place within some organizations, but the quality and reach varies. Isn’t a broad, industry-wide program necessary? Doesn’t the industry need the equivalent of a “Got milk?” campaign?

You may remember the ads. To a variety of problems, milk was always the answer. The “Got data?” campaign would promote analytical thinking.

“I like that idea!” she joked. But really, perhaps analytics itself could use a boost.

The big question is who can do it? As Claudia pointed out, we should count on no help from tool makers. The only answer they know is that you’re not trained on their interface.

“This isn’t a technical problem,” Claudia said, “It’s a business problem.”

Assuming her premise of analytical disinclination is valid — I can think of one BI anti-icon who would disagree — the ideal organization to lead such education and advocacy would have several characteristics: First, it would be well known already within the industry for training. Second, it would have relationships with vendors eager to support research. Third, it would have relationships with industry experts, both technical and business.

Will anyone step up?

Execs vs. data analysts gives us the Analytics Gap

One insurance company’s chief data scientist is “terrific at what he does,” says his boss, even “brilliant.” The company is lucky to have him. But he never sees the inside of the executive suite.

“He is a quirky, quirky guy,” says the boss, “and he is a super powerful dude in what he’s doing for us.” But upper executives would judge him harshly. “There would be a terrible outcome.”

Let’s call it the Analytics Gap. At least in this company and probably in many others, some of those who interpret the data don’t stick to rigid, unspoken rules of behavior. And those who with the greatest executive authority can’t quite appreciate what these people can tell them.

“These [analysts], including me,” says the analyst’s boss, “we’re different.” The brilliant data scientist — who as a hobby builds computers from video cards because “‘there’s a lot of power in video cards'” — makes a bad impression in person. He wanders off topic and, for example, has been known to digress into world politics and weird analogies.

“When I hear something come out his mouth that’s a little off color,” the boss says, “I chalk it up to his brilliance.” The company’s top executives, on the other hand, are “a harsh crowd.”

This crowd judges the off-color analyst harshly and fails, for example, to appreciate the real meaning of his risk models and other aspects of advanced analytics. They isolate themselves from the abstract evidence in data analysis that could correct or at least be reminded of their own biases.

The executives tell the analyst’s boss they want that. “I say that if you want that, you have to realize you’re dealing with people whose skill sets are completely different from anything you’ve dealt with. In a perfect world, my chief executive officer would say, ‘I understand how his analysis is applicable.’ No. Nothing.” They just don’t respect what he has to offer.

We’ve seen the work of harsh crowds before. Didn’t we just witness something like this on Wall Street? Those who knew the data saw the mortgage meltdown coming. But those who could have prevented it either dismissed the warnings or misunderstood them. What idiots, we say now. They should be jailed or made to work at Panda Express the rest of their working lives.

Potential consequences of the Analytics Gap are mostly minor by comparison but more widespread and chronic.

“I don’t think it’s a stretch to suggest that micro-versions of [the meltdown] could happen in product development, pricing, marketing, risk, and other domains” — unless data analysts and executive leadership come to understand each other. 

Why not train more quants? It won’t work, he says. Generally, those interested in data analysis tend to be too technically minded for management.

Big Data, big hype, big danger

A remarkable thing happened in Big Data last week. One of Big Data’s best friends poked fun at one of its icons: the Three V’s.

The well-networked and alert observer Shawn Rogers, vice president of research at Enterprise Management Associates, tweeted his eight V’s: “…Vast, Volumes of Vigorously, Verified, Vexingly Variable Verbose yet Valuable Visualized high Velocity Data.”

He was quick to explain to me that this is no comment on Gartner analyst Doug Laney’s three-V definition. Shawn’s just tired of people getting stuck on V’s.

How strange to be stuck on a definition, but we get stuck all the time trying to define Big Data. Other terms are easier. We’ve always known what visualization is. We seem to agree on “self service BI.” We also know what relational databases are, what ETL is, and all kinds of other established technology. We don’t agree on “business intelligence” or “decision support,” but somehow we don’t dwell on it. We don’t even quibble too heartily with “easy to use,” even though I could argue that we should.

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BI’s “promised land”: bigger than tech

At first glance, this pair of tweets last week sounds like a version of BI’s traditional campfire song:

I’ve seen the promised (BI) land, and we are there: databases that fly and process any data; BI tools that are easy to use and fast. Wow! I’d retire but mainstream firms will take 10 years to capitalize on all the new technology & overcome dirty data & politics.

The refrain might go, “When tools fly, they will fly by themselves!” Other lines would caution us to update often, eliminate “politics,” and eat our carrots.

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