Bob Zurek, Senior Vice President of Products, Epsilon
Was it really just three years ago that an IT leader needed nerves of steel to commit to a business-critical application that used Hadoop? As Epsilon's VP of Products Bob Zurek can tell you: It was.
While the open source big data framework was considered a huge gamble for digital marketing agency Epsilon three years ago, the company couldn’t be more pleased with its bet-the-company technology choice. Consider Epsilon’s position today as described at SAS Global Forum 2015 by Zurek: Read More »
Since it's once again May the 4th, an unofficial Star Wars holiday, I thought I'd revisit the topic. In the paper, I was mainly focused on overall trends from the three movies. In this edition, I'll zero in on some of the characters.
For Star Wars fans, you'll already be familiar with the results because you've seen the movies often enough to have the dialogue memorized. Validation can actually be a very positive thing. It shows that your data processes are sound.
I read an interesting article recently that suggested analyst and data scientist job positions may be on the way out. The author argued that analytics are being incorporated more and more heavily into operational systems, making “analytic capabilities” more readily accessible to business users without the involvement of a data scientist.
Being a data scientist and a manager of an analytics team, this insinuation definitely gave me pause.
It is true that operational systems, in an effort grow their business and stay competitive, are continuing to focus on added built-in analytics for their solutions. Honestly, it’s been a while since I’ve come across an operational system that doesn’t offer some form of data visualization or dashboard capabilities.
It’s an incredibly exciting time for data science.
Just ask Jake Porway, former New York Times Data Scientist and now CEO of DataKind, who opened his April 28 SAS Global Forum keynote by asking busy conference goers to pause and reflect on the revolutionary times we are living in.
“Cell phones now outnumber the number of people on the globe. Almost every activity we take online is digitized and tracked…We’re starting to instrument our bodies as if they were machines,” he said. “Almost every interaction we now have with our world or between each other takes place with a digital interface in between, something that creates data…data that allows us to see things we’ve never seen before.”
As the age old idiom goes, the early bird gets the worm and the early adopter gets the break. New technologies give clear advantages to those organisations that figure out before their early-adopting competitors how to use them effectively, an advantage that recedes as others catch up, and the technology becomes mainstream.
Whether it's the invention of the spinning machine during the industrial revolution, Ford’s adoption of the moving assembly line transforming car manufacturing, or city traders feverishly gaining computing speed for millisecond long advantages – being early can be the difference between success and missing the boat altogether. Consider Blockbuster Video, HMV, BlackBerry etc. ... just a few of the many sob stories from businesses that moved too little, too late.
Analytics, while still a significant source of competitive advantage, is showing signs of having peaked as a competitive differentiator. In short, the use of analytics has become mainstream. However, what we’re now seeing is a new kind of gap emerging. Not between the adopters and the non-adopters but in enterprises’ ability to apply what they’ve adopted. The distinction is in how effectively organisations can consume analytical insights.
When it comes to library amenities, it’s tough to top those at North Carolina State University.
The Hunt Library bookBot (Photo courtesy of North Carolina State).
The historic D. H. Hill Library on North Carolina State University’s campus boasts an ice cream shop that whips up sundaes and milkshakes with NC State’s own ice cream.
But when the new James B. Hunt, Jr. Library opened in January 2013, it served up more than 31 flavors of modern collaboration spaces, electronic walls, recording studios, 3D printing and the ultimate robotic search engine: the bookBot, which stores books in 1/9 the space of traditional shelving.
In the bookBot, books are barcoded, sorted by size, and stored in over 18,000 bins. Library patrons browse and request materials through a computer interface and request for the book to be delivered via a system of robotic cranes and delivery locations.
The Hunt Library attracted plenty of visitors who practically salivated over the space in its debut year, but what effect did it have on actual book circulation – the long-standing bread and butter of college campus libraries?
Ravi Shanbhag, UnitedHealthcare, speaks at SAS Global Forum Executive Conference
We’ve all heard the old saw, “If you torture data long enough, eventually it will confess to something.” But when it comes to spurring real change, how about ditching the dungeon-master act and thinking like a venture capitalist instead? Wouldn’t that pay bigger dividends?
That was the tip from Ravi Shanbhag, Director of Data Science at UnitedHealthcare. The health benefits provider is one of SAS’ largest customers and partners, so Shanbhag joined the SAS Global Forum Executive Conference as a longtime user of SAS. In a presentation entitled “Visualizing the Customer Journey through Analytics,” he detailed two use cases for social media – one that provided a clearer picture of competitors, and another that explained how to help customers in trouble. Read More »
A colleague and I were looking for a good example of how analysts used to use graphics to report data, a data visualization before and after, so to speak. We needed a good "before" screenshot for a “before and after” comparison for our SAS Global Forum 2015 paper, Visualizing Clinical Trial Data.
Mike Drutar and I often give presentations where we talk about the old days and how people used to visualize data. Of course, when we talk about how people used to do this, we are really talking about how we used to do it. I can remember coding JCL on a mainframe to run SAS, although even under a combination of phenobarbital and hypnosis, I doubt I could recall a line of that code. And Mike was implementing BI back when OLAP was the latest new thing.
Before: a data visualization circa 2010.
So, what better to use for an example of the "before" screenshot than something we ourselves did in that paper we wrote 10 years ago? The only problem was that, when we went to retrieve the paper, we realized that it was not 10 years ago; it was only five. Five years. What seemed like ancient history to us was not even a decade old, hardly the stuff of analytic antiquity.
We found our example, a lovely static dial, circa 2010.
The code in that paper still works. But it is amazing how much things have changed in only five years.