Education meets big data: implement, improve and expand your SLDS

In my previous blog post, I discussed the benefits of a Statewide Longitudinal Data System (SLDS) and shared a SAS book on the subject: Implement, Improve and Expand Your Statewide Longitudinal Data System by Armistead W Sapp III and Jamie McQuiggan.

Implement, Improve and Expand your Statewide Longitudinal Data System

Today, I'm sharing a conversation I had with one of the book’s authors, Armistead Sapp. In it, we discuss state funding, big data and overcoming challenges with patience and persistence.

What was the initial impetus for writing the book and how has it been received?

Armistead Sapp: We wrote the book to address the questions that states might have with implementing, improving and expanding their SLDSs. When I first starting write this, I envisioned we would be talking to the state education leaders and the folks in charge of the SLDS in each state.

As the federal grant program expanded, we realized there was an abundance of funding but there was also a lack of direction. Even after several rounds of funding, states were still struggling. Some states were doing things well and some not so well.

When I started working with the State of North Carolina, on their SLDS at SAS, I realized that there were questions that every state needed to answer. So, I thought it would be important to write a book to share the best practices we were seeing by working with the states on these projects.

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Analytics and Hadoop partnering for success

Alan Saldich, VP of Marketing at Cloudera

Alan Saldich, VP of Marketing at Cloudera, discusses how SAS and Cloudera are tackling Cybersecurity

In a complicated, fast-paced and connected world, you don’t succeed alone.  SAS and Cloudera have a successful  partnership that dates back several years. Our products are complementary and provide significant quantifiable value to customers who run them on the same cluster.  Add Intel to the mix and you have a trio of success as evidenced at the recent Analytics + Hadoop Event in New York featuring SAS, Cloudera and Intel.

Cloudera was the first commercial distributor of Hadoop; It enables SAS analysts to access a unified (and essentially unlimited) set of data - structured, unstructured, new, legacy - using familiar tools and frameworks.

Companies are looking to modernize their analytics with Hadoop at the core and to capitalize on the myriad ways to extract value from their data.  The process itself is relatively straightforward: Read More »

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If you don’t buy a ticket to the data lottery, your competitors will

LotteryYou have to be "in it to win it" as they say. This is becoming the case for many organisations that need to start using data to make better, evidence-based business decisions. Today, using analytics is not so much a data lottery as a data necessity.

Some businesses may not have embraced analytics at all, while others may not be applying it across all aspects of the business, or may be in need of a modernisation programme to bring them up to speed with competitors.

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Who you gonna call (for a cybersecurity issue)?

Man holding up an old fashioned phone During a lighthearted moment in a serious conversation, Howard Schmidt, cyber security advisor to multiple presidents, told a Wall Street Journal interviewer that relying on a government agency as your primary backstop during a major cyber security breach is akin to calling Ghostbusters: you might not get the help you need when you need it.

Joking aside, the question of whom to call was a real one, posed to a group of CEOs during a cyber-attack simulation exercise. Unfortunately none of the group could answer with certainty. In fairness to the CEOs, “who you gonna call” is a loaded question because the agencies themselves lack clarity on this issue.

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From strategic to operational decision making: Decisions at scale

Man in suit overlooking city landscapeLuckily, or perhaps better said, hopefully, we only need to make the big life decisions every now and then. What school to go to? Who to marry? What job to take? Where to live? There’s no penultimate answer to these decisions, but we all take them to the best of our knowledge, feeling and ability.

Likewise in organizations, we don’t make big strategic decisions every day. Which customers to focus on? Which products? Which regions? Strategy revolves around crunching the numbers, evaluating the situation, incorporating past experience, and choosing a direction. Since emerging on the organizational agenda, analytics have always played a role in informing strategic decisions.

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Communications from the final frontier

Pluto as seen by New Horizons

Pluto photographed by New Horizons

When I was a kid learning about the solar system and building those models out of hastily-painted styrofoam balls of varying sizes, Pluto was a planet. A full-fledged, legitimate planet just like the other eight. But In August of 2006, just 7 months after NASA launched the New Horizons Mission to explore Pluto and beyond, the International Astronomical Union designated Pluto a “dwarf planet”—putting a highly controversial end to its 76-year-old claim as the furthest planet from the sun.

Prior to the New Horizons Mission, the most powerful telescopes we had were only able to show Pluto as a blurry disk. Decades of scientific speculation had determined it to be an icy rock. Our knowledge about this astronomical anomaly was very limited—after all, it’s 3 billion miles away from Earth. Nine years later, New Horizons has made that long journey and is finally reporting back, giving us a glimpse of what this mysterious icy mass and its surroundings really look like.

The probe made its closest Pluto flyby on Tuesday of this week. And on Wednesday, we already had photos. That may not seem impressive until you realize just how far that data traveled to get back to us.

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Machine learning in action today

What's that productivity related quote by Charles Dickens?

"My advice is never do tomorrow what you can do today."

For years, machine learning has been written about and discussed widely with a focus on the benefits it will bring in the near future. But guess what? The future for machine learning is now.

The ability run a model tournament across a wide variety of analytical models, to provide hundreds or even thousands of the best predictive models for each segment of your choice and then take action is available to you today. The fusion of machine learning with data mining has made this possible today.

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Why the Internet of Things will change how you interact with customers

Blurry people walking on city streetThere's a lot of talk right now about the Internet of Things and how it's likely the prime catalyst for the digital transformation of organizations over the next few years. Billions of sensors, and devices with sensors, all generating data in a hyper-connected world where it can be easily shared or transmitted – what could possibly go wrong?

While the Internet of Things holds out promises to revolutionize healthcare, improve energy consumption and more, the area where it will touch most of our lives first is in how we, as consumers, engage with brands.

This is why you need to understand the importance of the Internet of Things beyond the hype. It will change the way you interact with customers in a market that will be differentiated by digital services and unique user experiences.

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Three ways to save with a big data lab

333380_Big_Data_Lab_Labor_640_x_640In my previous post, "Big data use cases and the big data wake up call," I focused on the discrepancy between big data investment fears on the one hand and successful use cases on the other.

In the last couple of weeks, I’ve had a lot of discussions with business leaders around that topic. Most of them agreed that "the quest for the golden use case" takes too much time and is usually not successful in the end. Ultimately, this quest can lead to paralysis and unpleasant questions from executives.

Everyone I talk to agrees that allowing experimentation is key to changing a culture and enabling digital transformation. During these discussions, the concept of the big data lab drew interest as a pragmatic way to drive innovation forward. Read More »

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What do an elite rower and a leading data scientist have in common?

Rowing; GBR Media WRC Day

Members of the GB rowing team prepare for practice. (Image credit: Intersport images)

We recently met up with Paul Bennett, a member of the GB Rowing Team and current World Champion, and Laurie Miles, Head of Analytics for SAS UK & Ireland, who has been analyzing the team's data. They chatted about data, the life and mind of an elite sportsman, and uncovered some surprising things they have in common! Follow the discussion below.

You both have something in common – would you like to tell me what it is?

Paul Bennett: I wouldn’t quite say we are both data scientists, but Laurie is certainly a data scientist and I’m very interested in data science.

How did this interest come about?

Bennett: I have a previous educational background in maths and computer science, and this leads quite fluidly into a fascination with numbers and an interest in knowing what they can do professionally, commercially and privately in a sporting context. I’m an Olympian – well hopefully soon to be an Olympian – I currently row for the British national squad. This partnership between SAS and GB Rowing is a great opportunity to try and find ways of how numbers can help the dream of making more gold medals more possible.

Laurie, you are a data scientist? Tell us all about it.

Laurie Miles: I am. Right from an early age, I was always interested in maths and numbers. I have always been passionate about using maths to solve problems; be it from a business perspective or – as we are doing with the GB Rowing Team – from a sporting perspective.

Numbers have a beauty because they are consistent, and unlike people they are very predictable. It’s this predictability that allows us to solve business problems. For example, we can use transaction behaviour to spot fraud patterns for HSBC, and we can use customer behaviour to help Waitrose decide what stock to put on which shelves in which stores. So exactly the same techniques are used for two totally different things. And it’s the same techniques that we are using to look for patterns within rowing data that help us establish what makes a good rower good, what makes the boat go faster, how we can optimise that performance and so on. Read More »

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