How can we make SAS blogs better? Take the survey

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Remember when the SAS blogs looked like this?

How long have you been reading SAS blogs? And do you have thoughts on how we can improve them?

In 2007, we launched the SAS blog program with just one blog and a handful of bloggers. Today, we have more than 30 blogs and hundreds of active bloggers. As we continue to grow, we want to make sure it's still easy for you to find the content you need to become better SAS users and analytical leaders.

That's why we're asking for your input in this survey about the SAS blog program. It should take about 10 minutes to complete, and it gives you an opportunity to tell us what you want to see and do on the SAS blogs.

Whether you read every post via RSS or just check in occasionally to read a few updates from your favorite blogger, we want to hear your thoughts. Think about what you've seen on other blog sites, and what you want to see more of on our blogs - and let us know.

Take the SAS blog survey now, and share it with your colleagues. Read More »

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Behind the scenes with SAS R&D

While much of what they say is over my non-technical head, one thing is clear: SAS research and development team members are clearly excited by the new capabilities they have added to the newest release of SAS Analytics.

Their pride of accomplishment is plain to see -- even by me -- in this video montage that has 14 developers and testers describing what they see as the most important features in their product portfolio. Their commitment and enthusiasm shines.

Among the products are: the SAS Forecasting client, SAS Enterprise Miner and the brand new SAS Factory Miner. New open source analytics integration with SAS has also been added to meet evolving customer requirements.

Be sure and watch to the end of the short video to see another example of how the leader in analytics exceeds expectations.

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Why should you use visual analytics?

Are you eager to explore and understand your data? Do you want to improve processes and drive more revenue? Do you want to work with data using an intuitive, easy-to-use interface? This is now possible with visual analytics, technology which combines analytics with interactive visualizations.

As published in a recent TDWI Best Practices report, Visual Analytics is one of the hottest trends in BI because it empowers your employees to analyze more data faster and more frequently -- resulting in the discovery and sharing of new insights, and who doesn’t want that!?

Are you still a bit sceptical about incorporating visual analytics into your standard reports? And how it will be governed and maintained? Let me explain how visual analytics perfectly complements your other BI initiatives when applied correctly.

Let’s start from this quadrant where we have Ad-Hoc Discovery/Prototyping (read visual analytics) and industrialized reports on one axis and governance on the other. There are two areas where you as an organization want to be active -- and two areas you want to stay away from: The upper left and lower right. You DO NOT want high governance when you want to discover data and prototype -- and you don’t want low governance on your company’s industrialized reports.

selfservice-governance-v1

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¡Viva el tango! (and the adoption of analytics!)

Business adoption of analytics and technology is like a tango.No matter where I go in the world, I’m constantly amazed by the similarities in business challenges facing organizations around the globe.

One of the most common is getting business users to actually adopt and use the insights gained from the great work performed by analytics teams and IT organizations.

I recently had the privilege of visiting the vibrant and cosmopolitan Buenos Aries. This city embodies a wonderful mix of cultures, perhaps best seen it its famed tango.

As someone keenly interested in helping organizations transform themselves and gain maximum value from their data, my conversations with South American business leaders got me thinking about how to solve this challenge. And about the tango.

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Communications service providers: Are you ready for digital transformation?

TP0001-090The term “disruptive technologies” frightens many organizations, but to companies like Alibaba, Uber, Airbnb and Facebook, it’s the secret to their success. These four companies from unrelated industries have one major commonality: They don’t “own” anything, at least not in the traditional sense. What they provide is a digital service, and these service offerings have transformed their respective industries.

These companies exemplify disruptive technologies because they each identified a way to meet a consumer need that was outside of their industry’s traditional activities (i.e. value chain), or delivered services in a new way. For example, with Uber, instead of hailing a cab you simply punch a few keys on your mobile device and a car is waiting for you by the time you exit your building. Read More »

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Education meets big data: key steps in preparing for your SLDS

In my first blog post in the Education meets Big Data blog series, I explained the need for Statewide Longitudinal Data Systems(SLDS). In the blog post following, I shared an interview with Armistead Sapp, one of the authors of the book, "Implement, Improve and Expand Your Statewide Longitudinal Data System."

In this post, we discuss the key steps in preparing for your SLDS. If you are in a state that is just getting started, or doing major renovations to your SLDS, these steps can help you address the key concerns to set up your system for future growth, use, and success. Read More »

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What’s new with machine learning?

Finger touching network gridsMachine learning is all about automating the development process for analytical models. One way to extend the use of machine learning is to broaden your library of machine learning algorithms. Another way is to scale your machine learning process by reducing the time required to process machine learning algorithms on large and complex data.

Every day, I hear organizations asking:

  • How do we get people with the skills to apply machine learning successfully to solve business problems?
  • How do we integrate analytics into our business processes, helping us get data-driven answers from these models to decision makers at the right time?
  • How do we create an analytics culture where all decisions are based on the analysis of data?
  • How can our analytical talent collaborate more efficiently?

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Road to Rio: the countdown begins!

The 2016 Rio Olympics are less than a year away, and British sports fans are hoping for a performance that beats Team GB’s record medal tally at London 2012. That’s never been achieved by a country directly after hosting the Games. And the pressure’s on us in the GB Rowing Team given that our sport is one of the nation’s strongest Olympic teams and kicked off the incredible “Super Saturday” at the 2012 Games.

Fans will be pleased to know we’re working hard to improve our performance on the road to Rio. In fact, I wrote this recently from high up in the Swiss Austrian Alps, where we’re in high altitude training for the World Rowing Championships. The altitude puts our bodies through different physiological effects and makes training even more strenuous than usual! But coupled with more and better use of data and analytics, we’re confident of making those crucial marginal gains that could be the difference between gold and silver, or even winning a medal at all.

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Identifying sustainable organizations: How does it work?

Sustainability is an idea whose time has come. Individuals, organizations and governments are increasingly recognizing that it doesn’t make sense to compromise the future to meet the needs of the present. To that end, the UN recently replaced the Millennium Development Goals (MDGs) with the Sustainable Development Goals (SDGs). It’s a good start.

In my last post, I discussed the idea that organizations aren’t keeping pace with technology, instead remaining mired in authoritative and competitive mindsets that breed inequality and do little to improve our future. I introduced our work towards a new business model that can instill sustainability and further general happiness, an idea that’s covered in my book, The Sustainable Organisation: a paradigm for a fairer society. Now I want to present the reasoning behind the sustainability metric put forth in that book, and how to use it.

As people, we can make the changes that the world needs to become sustainable, but we cannot make much of a dent on an individual basis. Change can only happen if people organize and work together, meaning that organizations are the pillars of sustainability. How can we measure progress?

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An introduction to machine learning

Machine learning is moving into the mainstream. Once the sole purview of academic researchers and advanced technology firms, machine learning is now being is used by many companies in more traditional industry verticals.

Machine learning uses mathematical (not necessarily statistical) models to learn about data. In this context, learning about data basically means curve-fitting or hyper-plane fitting, without the traditional statistical concerns about underlying distributions of the data or possible differences between the current sample of data and other samples of data. Machine learning techniques handle these concerns in different ways.

Perhaps surprisingly, such machine learning models can be quite adept at making accurate predictions about new samples of data, even when the phenomenon they are trying to predict is very rare or when it exhibits nonlinear behavior.  Read More »

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