Datadoodle

You are here: Home / Archives for SAS

SAS

Alpine Data and Goliath

by

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.

Answering the real questions in data analysis

by

A guy walks into your cube and asks you to whip up an econometric model. You’re a statistician, after all, and you’ve got a Ph.D. in something or other. You do this for lunch, he figures.

He “over-thought,” says the one whose cube such a guy walked into. Theresa Doyon has been routinely navigating datasets in the 50 to 300 million-unit range for 10 years. She’s good at all-terrain tools like SAS and KXEN. She could have produced the report he wanted. Instead, she asked him, “Why?”

They talked for an hour. What he actually needed, she discovered, was more of a descriptive report — something that gives a picture of a current situation. All he wanted to do was figure out how to allocate resources.

This is where things usually go wrong. “We’ve got all these great tools,” she says, “but I don’t see us using them as well as we could.” We could write this off as a communication problem, but she believes it’s much more than that.

It’s something like a man who walks into a kitchen with a carton of eggs and tells the cook to fry them all — when actually he’s just hungry and happened to have found eggs. Something else might suit him better. Bacon?

Business people complain that they get reports, not insights. But they’re not sure how to ask for the data. Meanwhile, analysts complain that they’re asked for a fire hose of analysis and that it’s always due yesterday. But they deliver. Asked for a report, they produce a report. Asked for a model, they produce a model.

“But that’s not what the business people really want,” says Theresa. ‘What they really want is to answer some sort of business question, like how’s my marketing doing? What can I do differently?”

“If you had a strategy and you did bite-size tests and learned as you go,” she says, “you could start to use the analytics that would really drive the insight.” We can really improve the way things are done. That’s the missing link.

At least some organizations have found that link. A home furnishings retailer she worked with recently had her work closely with marketing people as their questions and her analysis evolved.

The retailer had been suffering as ever more of its customers feared they’d soon lose their homes. The living room sets that looked so good just months before seemed to lose their appeal. Theresa’s assignment was to, in effect, come up with a silver lining.

“It was a very big and ambiguous question,” she recalls. She worked with the client on a series of projects over nearly two years as the sour economy evolved. She estimated opportunities, customer targets, and gave the marketing people she worked with critical guidance on the launch of new programs. She recalls, “It came out quite well.”

See her LinkedIn page here.

Migrating mindsets is the real challenge in ETL

by

I was intrigued by Donald Farmer’s recent tweet about ETL: “Migrating technologies is just work. Migrating people and mindsets is the real challenge.” I asked him to elaborate.

Donald is principal program manager of SQL Server Analysis Services at Microsoft. He sees “numerous” examples of migrating users who reject perfectly good methods in favor of “the Informatica way of doing it,” or the Datastage, or whatever they’re used to. Many IT workers see their career paths as not “DBA” but “Oracle DBA” or other brand, causing much extra expense in licensing and support. He writes in email, “CTOs have to battle that very hard.”

He sees this within Microsoft’s marketing analytics program. There, most recruits come from SAS — and they’re used to a much different approach.

SAS users, especially advanced users, are used to deploying a battery of statistical analysis and building a few carefully crafted models to deliver theoretically compelling results. In SQL Server, our users tend to iterate quickly and efficiently through many models, validating and testing as they go. These approaches and mindsets are very different.

We can generally answer the business questions that SAS answers, just as effectively for practical use — but SAS users will prefer the SAS methodology, and will often insist on it.

What’s a CTO to do? “The most effective technique I heard of for changing that mindset,” he writes, “was from an excellent CTO who simply made the SAS team responsible for their own budget. Two years later … well, SPSS not SQL Server, but the moral is sound.”