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Arianna Huffington speaks at the SAS Global Forum Executive Conference
Our society lives under a collective delusion that burning out is a requirement to success.
We’ve been conditioned to believe that sacrificing family, relationships and what’s personally important opens the door to achievement. But how can you be an effective leader, run a successful company or properly manage employees when you aren’t functioning at your optimal potential?
Arianna Huffington came to that conclusion the hard way, when she found herself with a broken cheek bone after collapsing from complete exhaustion. With a bloodied face, Huffington had to ask herself one tough but honest question: “Is this what success really has to look like?”
If you look at science, the answer is no. Modern science proves that if individuals take care of themselves they are more effective, yet we rarely act with that proven research in mind.
“Everyone knows the exact battery life remaining on their cellphones,” said Huffington, “But how many of us are self-aware enough to know when our own battery life is getting low?”
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When something comes between you and your customers – like not having product in-stock or providing offers that aren't relevant to the customer – it causes delays and makes it harder to complete transactions in a satisfying way. But, an inexpensive sensor, beacon or Radio Frequency Identification (RFID) tag placed between you and your customers can help you get closer.
Those simple little devices are part of the trendy Internet of Things (IoT) conversation happening now -- and they can help you sense who your customer is and what she wants. They’ll also help you better understand behavior and preferences and allow you to act on those insights to create a more engaging customer experience.
When retailing was a simpler business, store owners knew their customers. As society advanced, the stores got bigger, and entire chains of retail stores emerged around the country and the world. As the businesses grew, merchants became separated from customers and relied more on spreadsheets and reports to run the business than a handshake and a smile. With the advent of the Internet, the modern merchant was able to track and understand their customers better, but only while they were online shopping. This was impersonal and, in many cases, the retailer was not able to tie the online customer to the in-store customer, creating customer dissatisfaction and operational failures. This is where embracing the IoT can be a genius move for retailers.
By bringing sensor technology into a bricks and mortar store, the website and store can be on equal footing to recognize customers and meet their expectations. There are three main ways the IoT can help the retailer: Read More
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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.