How and why education customers are using SAS

Recently, I interviewed four higher education customers to hear firsthand how each is using analytics in real life (IRL). In this blog series, we will learn how educators are using analytics, why they chose SAS, and the impact it has had on their users and their institutions. In addition, they will share best practices for analytics and tips for gaining buy-in for reporting and analytics projects.

I want to thank each of the following wonderful customers for taking time to chat with me and sharing their insights on these topics.

  • Gina Huff, Senior Applications Programmer Analyst at Western Kentucky University
  • Karl Konsdorf, Acting Director, Research, Analytics and Reporting at Sinclair Community College
  • Dan Miller, Director for Business Intelligence for the North Carolina Community College System
  • Sivakumar Jaganathan, Executive Director, Data Warehouse and Business Analytics for the University of Connecticut

In this first post of the blog series, let’s start with learning how each is using SAS and why they chose SAS. Read More »

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Hey, hey mama, it ain’t easy being a black dog

Black Dog Syndrome may sound like the clinical term for a Led Zeppelin earworm but, as any animal shelter worker will tell you, it actually describes the challenge black dogs face in getting adopted. This phenomenon is in the spotlight today because Oct. 1 is National Black Dog Day!

This is Rocky, the adorable dog of SAS Voices editor, Alison Bolen. How could you not adopt that??

This is Rocky, the adorable dog of SAS Voices editor, Alison Bolen. How could you not adopt that??

Black Dog Syndrome was explored by 10 year-old Carah Gilmore, who used data visualization software to analyze and present data that would help a charity dear to her heart: a local pet adoption agency. Carah uncovered factors that determine how fast a dog gets adopted. Her analysis verified Black Dog Syndrome, the idea that black dogs spend more time waiting for a new home than lighter-colored dogs.

Carah and two other budding data scientists used analytics in their fifth grade science projects to learn more about their passions. Ryan Chase analyzed World Cup data. Jane Phillips created her own “Big Data” experiment. This caught the eye of people at analytics software provider SAS, who invited the kids and their parents to the company’s world headquarters for a special day.

Jane Phillips designed an experiment to engage visitors to help her build out a data set. She set up a mini basketball court and challenged visitors to sink two baskets from a distance of about 8 feet. She recorded the number of attempts each visitor needed to complete the task, and recorded the results visually. Over two days, results from over 100 visitors were recorded. She said, “By the end, I could pretty much predict in my head how many shots it would take someone based on what I had already seen. Pretty cool.”

Ryan Chase combined two of his favorite things – soccer and data. Ryan analyzed World Cup attendance, as well as data on players’ home countries, including countries with the most high-scorers and countries with the most goals scored.

These kids show that analytics is not just the domain of PhD statisticians. It can be cool and relatable to anyone. Experience with data analysis can put kids on a path to rewarding careers in analytics or other STEM (science, technology, engineering, math) disciplines. These three kids represent the next generation of data scientists and will help fill a persistent analytics skills gap that is forecast to grow.

The kids presented their research to a sports analytics expert and a person who analyzes data on service dogs to breed out undesirable traits. The day was capped off with a presentation from the co-founders of WildTrack. WildTrack converts photographs of animal footprints to data, then analyzes that data to track individual endangered species. They met with SAS CEO Jim Goodnight, received a coding tutorial, and heard about opportunities and organizations that help kids learn to code.

If you think your kids are ready to embark on a path in data science, you should check out the educational resources at, MIT’s Scratch project and Khan Academy’s computer programming courses. Google for Education and ITunes U both offer computer science resources. SAS offers a high school programming course, as well. Older kids, college students and adult learners can jump start analytics careers with free software and online programming and statistics tutorials.

What is your child passionate about? What kind of data would help them explore their passions?

Lastly, if you’re looking to add a furry family member, please adopt and don’t forget, black dogs need love, too! Follow the conversation on Twitter at #blackdogday.

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Three tips for building a data scientist team

Maths GeniusIf I were to believe the feedback I get, statisticians are among the most difficult people to work with. What’s more, they’re the only group that should be allowed to work in data analytics. It sounds harsh, but this may explain why big data projects continually fail.

Businesses need statisticians who are both easy to work with and can take the conversation beyond math and statistics to actual business solutions. Because, not surprisingly, conversations based primarily on maths and statistics do not solve business problems -- far from it.

Businesses need to overcome the perception that data science is about feeding data into an engine and analyzing statistics to get answers. Answers require a logical mind as well as a creative one. And the starting point should be: What are the business challenges we need to solve? The statistics bit comes later and is just part of the process in getting to your business solution. Read More »

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Technologies for the future: yes or no

When I started college 25 years ago, we didn’t use email. I moved into the dorms my freshman year with a Brother Word Processor, convinced I would never have a single computing need beyond the necessity to type, save and print text. It’s incredible to consider how wrong I was. I never saw the Internet coming, let alone smart phones or the Interet of Things.

Matthew Horn looks at three monitors plus an iPad

Matthew Horn in the SAS Emerging Technologies UI Lab

Unlike me, Matthew Horn, who manages the SAS Emerging Technologies UI Lab, was dismantling PCs and writing computer programs around that same time period. Still, he never predicted his work with computers would go from punch cards to optical discs.

Which brings us to the next 25 years. In 2040, will you be viewing analytics dashboards on a transparent plasma screen, visualizing data overlays through augmented reality glasses or even perceiving data outputs right inside your brain? Maybe you’ll receive alerts from an implanted ear piece. Or maybe all analytic based decisions will become operational, transmitting triggers from machine to machine without the need for human intervention.

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Your brain versus analytics

cyber_brain_1MBWhen I was discussing decision making and analytics with a colleague, he recommended I read the book Your Brain at Work by David Rock. I took his advice because I wanted to find out how the brain processes information and how it might relate to analytics.

Rock (you gotta love that name) explains the importance of the prefrontal cortex and how it sorts information during the decision making process. He says the prefrontal cortext is like a stage and there are a limited number of actors (information/thought processes) allowed on the stage at one time.

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Transporting analytics to the Internet of Things

IoT in TransportationWhy are so many companies across a diverse set of industries investing in and around the Internet of Things? Everywhere I go, every blog I read … I sound like my favorite band from the 80s: the Internet of Things is watching me.

In reality, it’s the reverse: I'm seeing the Internet of Things (Iot) everywhere: companies investing in sensors, networking and applications with the expectation that this investment will increase revenues, lower costs and improve profitability over the short and long term.

While the term the Internet of Things was coined in 1999 by Kevin Ashton at Procter & Gamble, the mainstream application of IoT is just getting started. As the trend has heightened, I've been evaluating the potential for IoT to support better decision making in travel and transportation.

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Meet your SAS #StrataHadoop Team: Keith Renison

We're wrapping up our “Meet the Team” blog series with SAS Solutions Architect Keith Renison. I was introduced to Keith earlier this year and was immediately impressed with his Keith_Ducati knowledge of advanced analytics and his enthusiasm for technology.

He describes himself best: “I’m a combination of a data visualization snob and an architecture geek.” And while that’s true (and he’ll explain more in his Q&A below), what he is NOT telling you, is that he’s AWESOME in so many other ways. Here’s what I know about Keith… He drives a Ducati Monster 796 (awesome), he likes good tequila (make-that-a-double awesome), and he’s an awesome dad. The only other guy with this level of awesome-ness is Chuck Norris -- thus, I hereby name Keith the Chuck Norris of Data:

  • When Chuck Norris throws exceptions, it’s across the room.
  • Chuck Norris writes code that optimizes itself.
  • To Chuck Norris, everything contains a vulnerability.

(Find more Chuck Norris-isms here).  See what I mean by reading my Q&A with Keith: Read More »

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Meet your SAS #StrataHadoop Team: Derek Hardison

A week from today, we'll be in New York City for Strata + Hadoop World, where we’ll kick things off at the Opening Reception. Be sure to stop by booth 543 to meet the team IRL (in real life)! They are excited about the event and eager to talk with attendees.

Next up in our “Meet the Teaderektwitterm” series is Derek Hardison, systems engineer for SAS Data Management. I met Derek back in April when he demoed SAS Data Loader for Hadoop to our team. And I have a twitter post to prove it! :-)

He frequently refers to SAS Data Loader for Hadoop as “The Easy Button for Hadoop.” My response is always “It’s so easy, Brooke Fortson can do it!” It's true - this product's intuitive user interface empowers business users to prepare, integrate and cleanse big data without writing code. Stop by our booth to take a look. Read More »

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Meet your SAS #StrataHadoop Team: Dan Zaratsian


Dan Zaratsian, SAS Solutions Architect, will be at Strata+Hadoop World Sept. 29 - Oct. 1.

It’s me again!! We're at the halfway point of meeting our Strata + Hadoop World dream team. So far, you’ve met machine learning guru Patrick Hall; data management expert Clark Bradley; and  advanced analytics specialist Rachel Hawley. Next up … Dan Zaratsian!

I met Dan a few years back while preparing for Analytics 2013 in London. He showed me how to analyze Super Bowl tweets with SAS Text Analytics and SAS Visual Analytics (now, if only he could show me how to win in my Fantasy Football league ...).

Dan specializes in text analytics and event stream processing, and will have a lot of cool things to demo in SAS booth #543 at Strata + Hadoop World, including live, streaming sensor data and real-time text analysis of conference social media feeds.

What’s your background and education?
I have a Master's in Analytics from NC State's Institute for Advanced Analytics and a B.S. in Electrical Engineering from the University of Akron. Prior to SAS, I worked as an engineer designing and implementing sensor networks and automated control systems primarily for aerospace, biotech and pharma. I'm currently a Solutions Architect at SAS, where I specialize in text analytics and real-time event stream processing. Read More »

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Focus on outcomes, not inputs

42-22194437Last weekend I realized I wanted something very specific: a great-looking lawn by mid-October that would require minimal effort from me. This meant that the output I needed to produce this desired outcome was the target date on which I needed to have aeration and over-seeding done in my yard. At that point, the analytical side of me took over, and I started thinking through the input variables that would affect this outcome such as: current lawn quality, soil composition, current ground moisture level, seed variety, and weather forecasts.

I could have created a model (or multiple models), collected relevant measurements and historical data, and simulated the next month a few thousand times to arrive at a best-odds forecast. My main barrier in doing that – in addition to competing Saturday afternoon interests like trail running or watching college football – was a lack of specialized knowledge and access to the right kind of algorithms and data. Whoever says a nice-looking lawn is left to chance is wrong – you have to be analytical when it comes to having a lawn that neighbors envy.

Organizations of all sizes and in all industries face this same dilemma every day. Do they invest in acquiring the assets and developing the competencies required to address specific business problems? Or do they have someone else do it for them, understanding that the economic terms for that arrangement may be quite unique?

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