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As we look at the last 40 years of innovation using analytics, it can be both humbling and inspiring.
I mean, who would have anticipated 40 years ago that SAS® would be used to analyze genomic data and help develop specialized medications as a result? Who would have guessed that a car manufacturer could analyze streaming data from sensors and onboard devices to improve safety? Who would have imagined in 1976 that someday a global retailer would develop a customer loyalty app that runs SAS Analytics in the background to provide real-time mobile phone offers?
Of course, nobody knows what the next 40 years will bring, but we do anticipate that SAS will be used more and more in the cloud, with big data, with streaming data, with automated applications, and with cognitive computing tools. To name a few.
Overall, we know that our customers want to run SAS anywhere, anytime and by virtually anyone. It sounds like a big set of requirements, but we’re making it possible with SAS® Viya™, which opens SAS to run inside almost any environment you can imagine. Built for the cloud and deployable anywhere from a common code base, SAS Viya can be in-memory, in-database and in-Hadoop. With the ability to flow seamlessly from the device, to “the fog” and right back to the cloud, we’re helping to put advanced analytics inside your biggest ideas.
Maybe we can’t imagine exactly how you’ll use SAS in the next 10 to 40 years, but we can imagine that you’ll need to be working in one of these environments, and we want to make sure you can take analytics, predictive capabilities and machine learning along with you.
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If I were to show you a picture of a house, you would know it’s a house without even stopping to think about it. Because you have seen hundreds of different types of houses, your brain has come to recognize the features – a roof, a door, windows, a front stoop – that make up a house. So, even if the picture only shows part of the house, you still know instantly what you’re looking at. You have learned to recognize houses.
Deep learning is a specialization of artificial intelligence that can train a computer to perform human-like tasks, like recognizing, classifying, and describing images of houses. But how are deep learning methods and applications used in business, and what benefits does deep learning promise for the future of analytics? We turned to Oliver Schabenberger, SAS VP of Analytic Server R&D, to learn more about deep learning and how it works.
How do you define deep learning?
Oliver Schabenberger: Deep learning methods are part of machine learning, which is considered a form of weak artificial intelligence (AI). We say weak AI, because we do not claim to create thinking machines that operate like a human brain. But we do claim that these learning methods can perform specific, human-like tasks in an intelligent way. And we are finding out that these systems of intelligence augmentation can often perform these tasks with greater accuracy, reliability or repeatability than a human. Read More
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Many companies are sitting on a goldmine: their data. But they may have no idea of its value.Companies that are not already thinking about analytics as the next logical step to harvest value and insights from their data need to rethink their strategy. They are in a way, very similar to my friend’s five-year-old son.
When I was visiting my friend, his son came in and asked for change to buy candy. My friend rummaged around and produced one large denomination coin. Further rummaging produced three smaller-denomination coins, adding up to slightly less. To our amusement, the child immediately demanded an exchange: He wanted to give back the first coin, and get the three smaller ones. He was adamant that three coins were better than one. His father clearly has some work to do to teach him about the value of money!
Why am I telling this story?
I see a parallel between my friend’s child, who didn’t realize the value of what he already possessed and companies who don’t realize the value in their piles of data.
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Across the globe, governments are losing billions in revenues to organised VAT fraud. The most recent VAT Gap study published by the European Commission estimates that EU countries lost an estimated €168 billion in VAT revenues in 2013. That's equivalent to 15.2% of the total expected VAT revenue from the 26 member states. South American countries face an even higher VAT fraud rate, due to large underground economies.
How can you stay ahead of VAT fraudsters and reduce the tax gap?
VAT fraud drains vast sums of money from public coffers. It also makes fair competition difficult and leads to restrictions on legitimate businesses.
If you're a public official, fighting VAT fraud should be high on your list of priorities.
Skeptics may say, “Tax agencies have actively fought VAT fraud for decades. What’s so different about it now?”
The answer? A lot. VAT evasion is changing. It's organized. It involves well-planned strategies. In many ways, the latest version of VAT fraud is like an elaborate chess game.
While most tax agencies understand the evolving VAT fraud schemes, until recently, they haven’t taken full advantage of the tools available to address it. Thus, they’ve been playing catch-up with fraudsters. Unlike traditional tax returns where agencies may have years to recover lost funds, most VAT fraud schemes are high velocity. They’re planned and executed quickly -- often, fraudsters can steal money and shutter the businesses before a tax agency even knows a crime has been committed. Read More
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Lisa Moore, Institutional Research Analyst at University of Oklahoma.
How do universities predict which students will enroll? And how do they determine what actions recruitment officers should take to entice students to pick their university?
These were two of the key questions tackled by Lisa Moore, Institutional Research Analyst at University of Oklahoma, during her presentation at The Texas Association for Institutional Research (TAIR) conference.
Universities are competing to entice the best and the brightest students. But because of increasingly restrictive budgets, recruitment officers must focus their limited resources on the students who are most likely to enroll.
While intuition and instinct have been sufficient in previous years, Moore explained that predictive modeling offers a better way. It's a robust analytical approach that identifies the students with a high likelihood of enrollment. By narrowing the focus to this smaller list of students, recruitment officers can pursue better prepared students -- and use fewer resources to do it. Read More
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For thousands of years, the human experience has been recorded by storytellers. Stories tell the tale of our lives: beginning, middle and end. Stories document the triumphs and tragedies of heroes and villains and everything in between. Human beings are storytellers -- it's a trait as uniquely human as an opposable thumb.
But the ways in which we tell stories have changed. Storytellers began with pictures carved onto cave walls. Written language later allowed the poets, playwrights and philosophers of Greece to document the human experience in much greater detail. Gutenberg’s advances in printing made it possible to mass produce books, ushering in a new era of storytelling. Photography in the 19th century, moving pictures in the 20th century – first silent, then with sound, and finally today's Internet have all radically changed the way we tell our stories.
But the newest tool in the storyteller’s toolbox is one that might, at first, seem out of place: Data.
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With the recent changes to federal education policy, I wanted to learn more so I interviewed Emily Baranello, Vice President SAS Education Practice and Susan Gates, SAS Special Advisor on Education. In part 1 of the interview, they were helpful in explaining the new policies, impact, opportunities and challenges for P-12 education across the U.S. Below is the remainder of the interview.
How are states directed to address low-performing schools?
Susan Gates, SAS Special Advisor on Education
Gates: States must identify the lowest-performing five percent of their schools and high schools that graduate less than 67 percent of their students. In addition, states must identify any school where one or more subgroups of students are under-performing. Each state can design its own interventions in these schools because ESSA eliminated prescribed turnaround models.
How can data and analytics help states determine the effectiveness of their interventions for low-performing schools?
Baranello: Data and analytics are critical tools for helping states ascertain if interventions are, in fact, working. Real-time analytics will give teachers, principals, and superintendents immediate insight into what's happening on the ground. Predictive analytics can help them make smart decisions about next steps based upon real-time data. For example, they can determine which courses to place students in and ensure students are challenged with rigorous coursework when they're ready for it. In addition, teachers can use dashboards to drill down to their class and see predicated analytics for each student so interventions can be taken. Data and analytics can also help educators better understand the impact of suspensions and chronic absenteeism and help in the design of evidence-based interventions to get students back on track. Read More
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I've got scale on my mind! While speeding down the rails from Brussels to Paris on the TGV (the sleek, high-speed train), the scale of speed is breathtaking. In previous generations, going from Brussels to Paris for a single-day meeting would have inevitably involved a plane, with check-ins, security, travel to the airport and inflexible schedules. Travel would have easily eaten half the day. Now, with the TGV, I’m door-to-door in two hours.
There’s a metaphor here for scalable, enterprise analytics. Typically in analytics, there can be a great emphasis on speed. In the value case for the TGV, the speed of the train naturally plays a huge role. But so do other factors:
- Automation of check-in.
- Seamless handoffs from one train to another.
- Convenience and flexibility with trains every half hour.
- Direct travel from city-center to city-center.
At these levels, airplanes cannot scale, where high-speed trains can.
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I've worked at SAS for quite awhile, and people always want to know more about SAS, especially it's great work environment. In response to those questions, I've written this story full of SAS product and programming puns to describe how SAS has evolved over time and remains LASR focused on helping our customers solve their business problems.
Decisions at Scale
Employees are empowered to make many decisions, and some of us like to walk around the beautiful SAS campus to help clear our minds. There's one particularly large CLUSTER of trees on our on campus nature trails where many decisions get made; one might call these particular trees SAS' DECISION TREES. The rest of the trails on campus simply pass through RANDOM FORESTS, and don’t get me started on the beautiful DATA LAKE that sits upstream from one of our data centers.
Our company, like any well run company, is concerned with minimizing costs OR maximizing profits not only for us, but also for our customers. SAS has done so well for 40 years that some believe our leaders must have ESP, which, ironically enough, we do now offer as a solution.
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How comfortable are you with hard decisions? If it affected you, how comfortable would you be with losing your agency and having someone else make the decision for you? What if that decision isn’t made by a person but a machine?
More than abstract questions, these are going to become as divisive a social issue as genetic manipulation. In many ways, they strike at the heart of what we believe makes us human.
Let’s play things forward a bit; sometime in the near future, a car’s travelling from Chicago to New York. It’s mid-afternoon, and somewhere among the highways and byways of the interstate, the driver sets the vehicle to assisted control. While she sleeps, the car shadows a semi-trailer, keeping a safe distance.
Cutting through Toledo, the truck blows a tire. As it tips and starts to roll, the assisted safety systems in the car activate. Continuing forward will almost certainly paralyse or kill the driver in a collision. Swerving left will save the driver but send the car into a crowd of people. Swerving right will take the car into a playground full of children.What’s the right thing to do? Read More