There are hundreds of breakout sessions happening at SAS Global Forum in both the Users and Executive programs. Since we couldn’t just pick one to highlight from opening day, we decided to put together a SAS Global Forum day 1 session roundup, highlighting some of our very favorites!
The big data behind fantasy football
With millions of users, peak traffic seasons and thousands of requests a second for complex user-specific data, fantasy football offers many challenges for even the most talented analytical minds. Clint Carpenter, one of the principal architects of the NFL fantasy football program, shared strategies and lessons learned behind football fanatics’ favorite application.
Fantasy football combines a high volume of users with detailed personalized data; multiple data sources; various data consumers; and high peak volumes of request. The challenge is to process the data from the stadium playing field and user devices, and make it easily accessible to a variety of different services. If there’s something to learn from developing and analyzing fantasy football over the years, Carpenter said it’s these three things: don’t blindly trust data or specifications; spend time planning upfront to avoid problems in the end; and test for data integrity, performance and for the whole system. “If you test well, you will have happy stakeholders,” said Carpenter. “If you don’t, you are asking for unhappy users and sleepless nights.”
One university’s solution to the data science talent gap
Is it time for a Ph.D. in data science? If you ask Jennifer Lewis Priestly, who happens to be the director of Kennesaw State University’s new Ph.D. in data science, the answer is yes, but there are areas we have to consider and address in order to make it work.
“Closing the talent gap is a problem and a challenge for our global economy,” said Priestly. The demand for deep analytical talent in the United States could potentially be 50 to 60 percent greater than its projected supply by 2018. And that demand is creating a first for academia, forcing companies across industry sectors to chase the same talent pool of students.
But it’s not just the skills gap that has to be addressed, Priestly said we also have to consider the rising master’s degree explosion. Today, analytically-aligned master’s programs are popping up across the country, and most can be completed between 10 to 18 months. But can institutions transform a student into a data scientist that fast? Offering a data science Ph.D. allows students to dive into the complexity of data science, rather than skim the surface.
So, if we find the talent and design the program, who will teach all of these students? “We have to put these students out into the market to fill these jobs, but we also have to put them back into colleges and universities to train up our future talent,” Priestly said.
Turning data into stories
Your data matters, but unless people emotionally connect with the data presented, it’s going to fall short. By not offering context, you risk having an audience miss your vision, draw their own conclusions or misunderstand the root of the problem you are trying to solve.
The question then becomes how? How do you actually get someone to engage and connect with the numbers? You’ve got to tell a story. Bree Baich, Principal Learning and Development Specialist with SAS Best Practices, gave her session attendees tips and tricks to turning data into stories that make sense.
“Data storytelling is a real thing, connecting the logical and emotional parts of our brain to not just make sense of the data, but to connect it in a way that causes a response,” Baich said. With an easy, four-step plan, Baich helped attendees see how getting data-driven stories is easier than we think.
- The story setup allows your audience to become curious and garner interest from the start. It’s a way to spark curiosity upfront by using a hook.
- The context paints a picture of the current realities, providing real understanding of the information at hand.
- The options show your audience where you want them to go. Think of it as an opportunity to demonstrate why your option is the better choice that will make a real difference.
- The action leaves a call to action and is key to pushing stakeholders to make a decision or getting customers to purchase.
Remember, data shouldn’t stand alone. Next time, shape it with a story!