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Unable to attend SAS Global Forum 2017 happening now in Orlando? We’ve got you covered! You can view live stream video from the conference, and check back here for important news from the conference, starting with the highlights from last night’s Opening Session.
While the location and record attendance made for a full house this year, CEO Jim Goodnight explained that there couldn’t be a more perfect setting to celebrate innovation than the world of Walt Disney. “Walt was a master innovator, combining art and science to create an entirely new way to make intelligent connections,” said Goodnight. “SAS is busy making another kind of intelligent connection – the kind made possible by data and analytics.”
It’s SAS’ mission to bring analytics everywhere and to make it ambient. That was exactly the motivation that drove SAS nearly four years ago when embarking on a massive undertaking known as SAS® Viya™. But SAS Viya – announced last year in Las Vegas – is more than just a fast, powerful, modernized analytics platform. Goodnight said it’s really the perfect marriage of science and art.
“Consider what would be possible if analytics could be brought into every moment and every place that data exists,” said Goodnight. “The opportunities are enormous, and like Walt Disney, it’s kind of fun to do the impossible.”
Driving an analytics economy
Executive Vice President and Chief Marketing Officer Randy Guard took the stage to update attendees on new releases available on SAS Viya and why SAS is so excited about it. And he explained the reason for SAS Viya comes from the changes being driven in the analytics marketplace. It’s what Guard referred to as an analytics economy – where the maturity of algorithms and techniques progress rapidly. “This is a place where disruption is normal, a place where you want to be the disruptor; you want to be the innovator,” said Guard. That’s exactly what you can achieve with SAS Viya.
As if SAS Viya didn’t leave enough of an impression, Guard took it one step further by inviting Goodnight back on stage to give users a preview into the newest innovation SAS has been cooking up. Using the Amazon Echo Dot – better known as Alexa – Goodnight put cognitive computing into action as he called up annual sales, forecasts and customer satisfaction reports in SAS® Visual Analytics.
Though still in its infant stages of development, the demo was just another reminder that when it comes to analytics, SAS never stops thinking of the next great thing.
AI: The illusion of intelligence
With his Segway Mini, Executive Vice President and Chief Technology Officer Oliver Schabenberger rolled on stage, fully trusting that his “smart legs” wouldn’t drive him off and into the audience. “I’ve accepted that algorithms and software have intelligence; I’ve accepted that they make decisions for us, but we still have choices,” said Schabenberger.
Diving into artificial intelligence, he explained that today’s algorithms operate with super-human abilities – they are reliable, repeatable and work around the clock without fatigue – yet they don’t behave like humans. And while the “AI” label is becoming trendy, true systems deserving of the AI title have two distinct things in common: they belong to the class of weak AI systems and they tend to be based on deep learning.
So, why are those distinctions important? Schabenberger explained that a weak AI system is trained to do one task only – the system driving an autonomous vehicle cannot operate the lighting in your home.
“SAS is very much engaged in weak AI, building cognitive systems into our software,” he said. “We are embedding learning and gamification into solutions and you can apply deep learning to text, images and time series.” Those cognitive systems are built into SAS Viya. And while they are powerful and great when they work, Schabenberger begged the question of whether or not they are truly intelligent.
Think about it. True intelligence requires some form of creativity, innovation and independent problem solving. The reality is, that today’s algorithms and software, no matter how smart, are being used as decision support systems to augment our own capabilities and make us better.
But it’s uncomfortable to think about fully trusting technology to make decisions on our behalf. “We make decisions based on reason, we use gut feeling and make split-second judgment calls based on incomplete information,” said Schabenberger. “How well do we expect machines to perform [in our place]when we let them loose and how quickly do we expect them to learn on the job?”
It’s those kinds of questions that prove that all we can handle today is the illusion of intelligence. “We want to get tricked by the machine in a clever way,” said Schabenberger. “The rest is just hype.”
Creating tomorrow‘s analytics leaders
With a room full of analytics leaders, Vice President of Sales Emily Baranello asked attendees to consider where the future leaders of analytics will come from. If you ask SAS, talent will be pulled from universities globally that have partnered with SAS to create 200 types of programs that teach today’s students how to work in SAS software. The commitment level to train up future leaders is evident and can be seen in SAS certifications, joint certificate programs and SAS’ track toward nearly 1 million downloads of SAS® Analytics U.
“SAS talent is continuing to building in the marketplace,” said Baranello. “Our goal is to bring analytics everywhere and we will continue to partner with universities to ready those students to be your successful employees.”
Using data for good
More than just analytics and technology, SAS’ brand is a representation of people who make the world a better place. Knowing that, SAS announced the development of GatherIQ – a customized crowdsourcing app that will begin with two International Organization for Migration (IMO) projects. One project will specifically focus on global migration, using data to keep migrants safe as they search for a better life. With GatherIQ, changing the world might be as easy as opening an app.
There's much more to come, so stay tuned to SAS blogs this week for the latest updates from SAS Global Forum!
SAS Viya, AI star at SAS Global Forum Opening Session was published on SAS Users.
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Editor's Note: 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, we decided to put together a SAS Global Forum day 2 session roundup, highlighting some of our very favorites!
Don’t overlook data management when it comes to cybersecurity analytics
There’s a constant buzz in the market around analytics and its role in the cybersecurity space, but often the conversation overlooks the important role data management plays. Data management is a fundamental component SAS cyber experts want to be sure organizations understand – just because the investment is being made in cyber analytics doesn’t mean companies can ignore data quality and data management.
“There are countless solutions and dollars spent to protect organizations,” said SAS’ Director of Cybersecurity Christopher Smith. “All of those pieces – firewalls, endpoints and email gateways – play a vital role, but those systems don’t communicate with each other.” Even with all the investment organizations are making to protect themselves, there is still no greater insight being gained into what’s actually happening inside company walls.
What’s needed is business context, and that’s something isolated solutions cannot provide. While those systems are valuable in identifying what’s good, what’s bad and what can be defined, they offer limited business intelligence.
But the challenge isn’t just about obtaining data, it’s about the speed, type, structure and volume of data being generated per second.
“We are working in a society where everyone is looking for a silver bullet,” said Vice President of Business Consulting and Data Management Evan Levy. “People are buying products to solve problems, but it’s more complicated than that. The volume, need and diversity of content and sources isn’t something we could have ever predicted.”
Levy said that’s where data management becomes critical. Companies have to enlist the proper data management techniques to avoid lagging in security and exposing themselves to added risk with every attack. By looking at what’s actually happening, companies can see what the data is saying and then develop an effective response.
The fear today is not what happened, it’s the unknown of what else has happened that we haven’t yet identified. “Once data is created it will always be an asset to the business,” said Smith, which means it must be catalogued to offer value. Effective cyber protection requires sophisticated analytic prowess with rich data history in order to protect organizations from the clever and skilled hackers.
Learning from past mistakes
In his April 20 Executive Conference breakout session, Sterling Price, Director of Customer Analytics at WalMart Stores, Inc., cautioned against relying too heavily on completed analytical projects, assuming that new technologies and massive data sets produce an accurate and relevant result. He used several historical examples, from the Google Flu prediction mishap to the faulty prediction outcome of the 1936 US presidential race, to help prove the point.
Big data, it turns out, is simply the newest phenomenon tempting leaders to believe their outcomes are statistically sound. "We owe our organizations objective analysis based on science, not wishful thinking," said Price.
Here are five points gleaned from his personal experience at Walmart as well as the historical examples he shared:
- Don't fall prey to the belief that results will be accurate and useful because of how much data was used.
- We still need to sample things, but a badly chosen large sample - even a really big one - is much worse than a well-chosen small sample.
- Methodology still matters. Big data by itself does nothing. How we use it defines its value.
- Scalability should be considered up front.
- Don't mistake statistical significance for practical significance. They are not the same.
Arrest Prediction and Analysis in New York City
Analyzing "stop and frisk" data captured by the New York City Police Department can lead to insights that help cops make better decisions about whether to arrest a person or not, say two Oklahoma State University graduate students.
Karan Rudra and Maitreya Kadiyala looked at open source data from the NYPD to understand the propensity of arrest and optimize frisk activities. This type of analysis can potentially reduce the number of stops and impact the arrest rate.
The pair examined 56 variables, including in which precinct a stop occurred, whether a stop led to an arrest, whether the officer produced an ID and shield, and whether a person was stopped inside or outside of a building.
Using SAS® Enterprise Miner™, they built and compared four models, determining that a polynomial regression model was the best. Some findings from their research include:
- In the Bronx and Manhattan, females have the highest percentage of arrests after a stop and frisk.
- In Staten Island, though there are a high number of stops per area, the number of resulting arrests is comparatively low.
- Blacks and Hispanics have a higher percentage of arrests after a stop.
- The overall arrest rate of the data sample was 6 percent.
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!
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Say hello to a new way of analyzing data in Hadoop was published on SAS Voices.