Alpine Data and Goliath

Vision and bravado came from the Alpine Data Labs CEO and a spokesman for the venture fund at the recent Alpine Data Labs reception. But the stack of books near the exit told the real story: Malcolm Gladwell’s latest, David and Goliath, tells how Alpine intends to take on the “giants.”

CMO “and data geek” Bruno Aziza, in his second month since SiSense, says that where the Goliaths demand coding, Alpine demands none. Where the Goliaths sit on the desktop, Alpine floats on clouds. While the Goliaths extract from Hadoop, Alpine rides on top. Go, Alpine.

Barry Devlin: People are the third element of the business “trinity”

Barry Devlin’s elegant writing style and the wisdom of his observations in Business unIntelligence convinced me at first graze that it would be worth reading. Then today, as I needed a reason to make time, I came across this.

Business exists, not to make money, but for and because of peoples’ needs to interact and trade the fruits of their labors.

I think that’s correct. It’s also a bold statement to an industry run by technology within a business culture mostly driven by other values. Even better, it promises more. We can hope it gives the BI industry a prod. Who has more credibility to do so than a co-inventor of the data warehouse?

It’s also a mere stone’s throw from a half-formed opinion of my own, until now expressed only among friends. I suspect that the greatest value of data analysis is to start conversation and to give it focus. The usual benefits touted in marketing collateral all rank lower. Someone who runs one of the biggest sources of such marketing has said as much in the privacy of lunch.

People, Barry writes, are the third element of the modern business “trinity” with information and process.

In business intelligence, I believe, too much attention goes to the first two. I suppose that’s because information technology is easier to sell and process is easier to talk about. People are much more difficult. Based on my long experience at all levels of business, I’ve found people’s behavior is not only equally important but much more interesting.

He goes on:

Over history, we see that one may have been emphasized over the others at different times or in different circumstances. … But, it’s important to understand that the biz-tech ecosystem can only function properly with a proper balance of all three elements.

The book, Business unIntelligence: Insight and Innovation Beyond Analytics and Big Data (2013; Technics), goes now to the top of the must-read stack.

Also: See his interview with Radiant Advisors editor Lindy Ryan.

Don’t call yourself a “data scientist”

Someone introduced himself recently as a “data scientist” to a data warehouse pro I know. “I thought he was a fool,” said Interworks consultant Tim Costello. He says it’s a meaningless term — and I believe it’s another one of those distractions thrown around in a roiling industry.

One of the most interesting of the others I’ve heard is Scott Davis, the founder of Lyzasoft. He emailed this week from somewhere on the Caribbean, “Data scientist is just a term someone applied to a set of skills and practices that have existed for a very, very long time. Like, since the invention of the abacus.”

What does he say to those who think it’s an attempt to distinguish between data analysts of any level and those at the top of the discipline? “The attempt is not by someone who does this stuff,” he writes. “It is by someone who is selling something…either a book or a training regimen or a system or a degree or a tool or….”

I looked for Stephen Few’s opinion, and of course he had one. In his 2,500-word blog post “Are you a data scientist?,” he cites interesting articles and poses good arguments, and essentially agrees with Scott and Tim. “There is indeed a science to data sensemaking,” he writes at the end, “but data science by any other name (and there are many) would smell as sweet.”

But why do we care? We do waste a lot of breath on terminology that means little one way or another. But sometimes it does matter — such as in Jill Dyché’s now-famous blog post “Why I wouldn’t have sex with a data scientist.”

Was he too busy, as she originally thought? Or was the poor guy just too full of himself? Whichever, it demonstrates natural selection the way it’s supposed to work.

Create value with data by ending IT’s sequestration

Just give business users the data. Don’t wait for perfection. Just hand it over, clean or not, and let it start creating value.

That’s the kernel of Blake Johnson’s advice to organizations who want to make use of data. He’s a Stanford University consulting professor and a Teradata academic. He has become a scholar of data analysts and data scientists.

“The whole idea of the unwashed masses having access to the data is totally new to some execs,” he told me last week. “A lot of people on the IT side still don’t buy it.”

I thought access to the masses had worked its way into the IT mind. But Blake says the idea hasn’t gone very far. The resistance comes from lack of awareness, he says, and fear. IT is fundamentally risk averse. They strive for perfection.

Business people have little patience for perfection. Their task is to create value, and if the process is a little sloppy, it’ll work itself out.

“IT will say it’s got a certain percent of the data modeled and clean. But who cares? Let business users in, they can help prioritize for business value. Instead of some arbitrary perfection, let it be a joint exercise from the start.” Let it be “ROI driven.”

One company he has worked with rated data for quality with colored flags. Green was the best, red was the dirty stuff. “Business guys know.” Let them grade it.

To make this happen, he says, someone in authority has to say, ‘Hey, Mr. IT guy, I know that’s possible, but let’s let business in on this,” he says.

Once the wall comes down, he says, the timeline from access to value is generally consistent. For example, when one business or one function opens its data, it usually cooks for about 12 months while the internal mechanics of access get sorted out. Then things accelerate. Success stories start to surface and spread.

The critical factor: Executives who are aware and who open the data gates. “That will trigger things.”

Next for QlikView and data discovery

QlikView has recently looked to me like a faded movie star who gazes fondly into the mirror. Though friends lavish compliments, and the phone still rings every day with offers, the buzz keeps slipping away to younger rivals. The proud old star won’t hear it: Never mind them, says the star, I’m still the biggest and the best.

Lucky for Qlik, there is rebirth in the software business. From what I heard at a recent two-day briefing, QlikView just might have rediscovered the magic. (more…)