3D printing: yes or no?

3D printer printing a test print

A 3D printer test print.

By now, you’ve probably seen a video of a 3D printer discharging layers of plastic to create a model of a building or a plastic figurine. You may have heard stories about 3D printed guns, 3D printed airplane parts and even 3D printed body parts.

While 3D printers are becoming more common, they are still a long way from being common household or break room devices.

How will 3D printers be relevant in the field of analytics? And what are their true strengths? We asked Matthew Horn, manager of SAS’ Emerging technologies UI lab for his thoughts – and here’s what we found out.

What it is

3D printing is a method for creating physical objects or models by “printing” multiple thin layers of materiel successively to form an object. 3D printers can easily print intricate shapes and interlocking pieces. Read More »

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How to get buy-in for data and analytics projects

Do you have a great idea for an analytics project but need approval to get started? Or maybe you've had some initial successes with analytics and you're ready to expand the program. We talked to four analytics leaders in the higher education industry to get their advice on how to gain buy-in for analytics projects.

In my first two posts in the Analytics in Real Life blog series, we learned how higher education customers are using SAS and why they chose SAS. Then they shared the positive impact of using SAS and analytics for their users and institution.

Today the following customers share tips use for gaining buy-in for data and analytics projects:

  • Gina Huff, Senior Applications Programmer Analyst at Western Kentucky University
  • Karl Konsdorf, Acting Director, Research, Analytics and Reporting at Sinclair Community College
  • Dan Miller, Director for Business Intelligence for the North Carolina Community College System
  • Sivakumar Jaganathan, Executive Director, Data Warehouse and Business Analytics for the University of Connecticut

Analytics in real life: gaining buy-in

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3 cybersecurity tips from the Lone Star State

With so many cybersecurity stories and large scale data breaches in the news, it's no wonder that cybersecurity is a top priority for leaders in many industries. But did you know analytics can help protect your customer and organizational data?


Franklin Witter, Principal CyberSecurity Consultant, recently shared these three cybersecurity tips during his presentation on security analytics at SAS Day at Texas A&M:

  1. Understand that individuals are still your weakest cyber link, and that spear phishing (email that appears as though it is from someone you know, but isn't) attacks still have an 85 percent success rate.
  2. Collect net flow data, merge it with other data sources and enrich with analytics.
  3. Data quality is essential to the success of any security analytics program.

For more detailed information about using analytics to prevent cybercrime, check out Witter's presentation slides: Security Analytics: Gaining an Information Advantage Over Cyber Adversaries.

For more cybersecurity information from experts across government, energy and financial services industries, read this white paper: Big Data and Predictive Analytics: On the Cybersecurity Front Line, or learn how analytics can provide an essential layer for cyberdefense.

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What communications companies need to play in the digital arena

Blog CEMALike retailers, Communications Service Providers (CSPs) interact with customers in multiple ways during the buy, use and share journey. These interactions take place at stores and on websites, Facebook pages, call centers, and other channels. However, unlike retailers, CSPs are having a harder time providing a seamless experience across their many channels. For example, have you ever been on a CSP's website then contacted the a call center only to get a completely different offer or message?

Customer satisfaction rankings like net promoter scores for internet, cable and satellite services companies are lower than in any other industry. Clearly, CSPs are missing the opportunity to connect with customers as they interact with their brand at various touch points.

The majority of my customer meetings with global communications service providers (CSPs) this year were spent exploring the customer journey and how to provide an omnichannel experience. In addition, most CSP leaders wanted to discuss how they could have more visibility into the customer’s experience with their network. It's become increasingly urgent for CSPs to study attribution paths and understand the customer journey in its different stages like buying, using and sharing (as documented by TM Forum in their recent "Customer Experience and Analytics in a Digital World" report).

Several of these CSPs started with research and surveys into what mattered to their customers and what motivates their customers to buy from them. Is it having the fastest network speeds and quality of service? Is it feeling like they're part of a bigger community? Is it the Facebook experience? Or the ability to connect the retail store experience to the provider’s website where the purchase was ultimately made? Some CSPs used the surveys to analyze customer motivation, but they also used predictive analytics to validate their hypothesis and mine the big volumes of textual data. Read More »

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Visualizing data from a World Series team

~ Coauthored by Varsha Chawla and Dale Rierson ~

Here at SAS, we take our sports seriously. With our corporate headquarters in a city surrounded by major universities and with offices all over the world, it’s no wonder that sports data often becomes the forefront of our demos and projects.

As the World Series took place last week, I thought it would be fun to visualize some data from the New York Mets. Luckily, much of the historical World Series baseball data can be found online. With this data source you can analyze schedules, team batting, team pitching, wins, attendance, and comparison to other teams, dating all the way back to 1871. Using SAS Visual Analytics, analyzing lots of data fast is easy.

Using SAS Visual Analytics we can see how the 1969 Mets team did against their opponents that year and even look for trends on how well they played on different days of the week.

MetsCorrected CorrectionMets

Correction (HT: Mets Fan Dan)

Thanks go out to Mets fan Dan for pointing out that our screenshot needed correcting.   Also, the Opponent in this graph represents the team franchise.  For example, WAS represents the Washington Nationals franchise which most baseball fans know did not exist in 1969.  In 1969, that team was known as the Montreal Expos.

The screenshot is meant to convey the changes in ERA that each team sees during the course of a season.  There are varying windows of time because it took the Mets longer to play 15 games with the St. Louis Cardinals (STL) than it did to play 9 games with the San Diego Padres (SDP).  In the highlighted section, the blue line shows the Mets cumulative ERA in Cardinals Stadium at 3.77.  While the Cardinals playing at home against the Mets had an ERA of around 6.    Translation:  The Mets outpitched the Cardinals on the road but were not as dominant at Shea.

The second graph shows you just how dominating the Mets were when they played on Sunday or even on Tuesdays at home, but were pretty ordinary Thursdays on the road.

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5 tips for using big data for the public good

Over the last few years I’ve seen and worked on many transformational projects with big data, especially those that tap into big data’s ability to provide new and improved services for the public good. But there’s also a danger that analytics, unchecked, can do social harm by indirectly discriminating against certain groups.

The London Fire Brigade uses data analytics to predict fire risk areas, while New York City uses analytics to improve parking and traffic control. There’s even a project involviCity_New Yorkng a food bank run by churches in Liverpool. It uses data analytics to get quick insights into how often the food bank is used, who is using it, why they’re using it and where they’re from. While these projects are noteworthy – crucially they all rely on "someone" capturing and analysing information about the general public.

Recently I attended and presented at the Data for Policy 2015 conference at the University of Cambridge in the UK. There were people from all walks of life, including government policy makers, actively thinking and coming up with new ways to improve ethics and security implications around data.

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Navigating big disasters with big data

Population movement is higher than ever. Buoyed by freedom of movement and critical events such as armed conflict and the European migrant issue, large groups of people are migrating across borders.

In fact, population movement has now reached record levels. There are currently more displaced people globally than during the whole of the Second World War.

Why is population migration coming to a head now, and how can we address some of the problems associated with it?


I recently attended the Oxford Humanitarian Innovation Conference 2015, where population migration was one of the key issues discussed. At the conference, I was invited to sit on a panel to discuss how technology innovation can help in disaster relief efforts. Disasters often precipitate large movements of people.

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On the mound with baseball and analytics

With the 2015 World Series currently underway, there couldn't be a more exciting time to discuss how analytics is transforming the game of baseball. It comes as no surprise to say that managers and front office executives, not to mention the players, are continuously looking for a competitive advantage over their opponents. Over the years, those competitive advantages have come in various forms, but as Mark McGuire once said to the House Committee of Government Reform back on March 17, 2005 , "I'm not here to talk about the past."

I'm here to talk about the future. The future of analytics in baseball is now, and for some teams analytics is playing an increasingly important role in how strategic decisions are made, both on and off the field.

baseball2_1024x512_Watermark Read More »

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4 big data analytics trends to watch now

How can you grow a culture of innovation, scale your data, modernize your legacy BI strategy - and more? With "analytics in action," says Paul Bachteal, Senior Director of the Global Technology Practice at SAS.

Bachteal recently presented to a standing room only crowd as part of the 2015 Texas A&M SAS Day. He started his talk by discussing four overarching industry trends that are impacting big data analytics:

  1. The Internet of Things (IoT) and all the data it will create.
  2. Crowdsourcing, which makes it easy to share and  use data for personal and public good.
  3. The cloud, which offers new storage and hosting capabilities for analytics.
  4. The rise of the millennials and their effect on the workplace as this generation continues to enter the workforce.

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University challenge: Getting students to enroll is a big data job

Higher education is a buyer’s market today, with the student being the buyer, according to Nick Hillman, director of the Higher Education Policy Institute. And don’t universities know it. Not only are they dealing with tuition fee caps, but they face mounting competition from other educational institutions.

University pupils

There are many UK students choosing to travel overseas to study and many choosing not to study in formal education at all. With new rules allowing unlimited expansions for university campuses, students are receiving unconditional offers, cash bursaries and scholarships to attract them. This is all while the value of tuition fees is said to be declining in real terms – so much so that some argue they need to be protected to ensure the future of UK education.

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