SAS Voices

Analytics

Find out how analytics, from data mining to cognitive computing, is changing the way we do business

Analytics

Paul GittinsMarch 3, 2017 1

How to apply design thinking to your analytics architecture

There aren’t many things that keep me awake at night but let me share a recent example with you. I’ve been grappling with how to help a local SAS team respond to a customer’s request for a “generic enterprise analytics architecture.” As background, this customer organization had recently embarked on

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Analytics

Greg WillisMarch 1, 2017 0

Should data scientists buy into the DevOps philosophy?

DevOps is enjoying a lot of attention at the moment, mostly because of its promise to increase software quality at lower cost. DevOps is a hybrid term linking development and operations. It's a philosophy that focuses on improving efficiencies in how we build and deliver software, solutions and the supporting environments.

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Analytics

Mike F. SmithFebruary 28, 2017 0

2020 vision for utilities: What’s the prescription?

Do you remember the 90s? It seemed like every company and organization had some sort of strategic plan that had “2000” in its title. And they were all going to achieve and exceed these Year 2000 goals … if their systems didn’t crash at 12:00:01 on January 1, 2000! So

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Energy & Utilities

Analytics

Tim ClarkFebruary 27, 2017 0

Why quality is the answer to manufacturing challenges

Manufacturers are used to operating in challenging circumstances – whether financial, economic, political or competitive. And it's a good thing since the coming years seem likely to be as uncertain as those following the global economic crash. But manufacturing will have an excellent opportunity to thrive because, like many sectors,

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Manufacturing

Analytics | Data for Good | Fraud & Security Intelligence

Rory MacKenzieFebruary 17, 2017 2

Modern investigations: It’s not like the movies

I’ve always loved watching crime series or films – from Homeland and True Detective to Sherlock Holmes. But after years of building software products and solutions designed to help security and law enforcement agencies solve crimes, I've noticed there are several clichés that need to be conquered by technology. No.

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Government

Analytics | Data Visualization

Natan MeekersFebruary 13, 2017 0

Boost your analytics: The rise of the citizen data scientist

Are you struggling to hire talented data scientists to glean insights from your corporate data? There’s currently a lack of big data talent hampering corporate analytics and causing nightmares for CIO’s, but I have good news for you: You may already have all the data scientists you need! There are

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Analytics | Data for Good

Georgia MarianiFebruary 9, 2017 0

Third-grade reading proficiency is key to reversing the “skills gap”

How important is reading to the skills gap? It's crucial. Through third grade, children are learning to read. After that, they read to learn. That is why reading proficiently by the end of third grade is one of the most reliable predictors of future success for children. Students who develop

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Education

Analytics

David PopeFebruary 3, 2017 0

3 attributes of an open and unified analytics environment

Having addressed the adaptability and power of an analytics environment in my last two posts, I thought I'd close out this mini-series of blogs by providing the business and technology implications of three attributes that need to define any truly open and unified analytics environment: Cohesion Business: The platform enables

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Analytics

David PopeJanuary 26, 2017 0

3 attributes that define a powerful analytics environment

In my last post I described "4 adaptability attributes for analytical success," and in the past I've discussed the strategic role analytics play in helping organizations succeed now and into the future. Now I'd like to discuss three attributes that define a powerful analytics environment: Speed Accuracy Scalability [NOTE: Any

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Analytics

Alyssa FarrellJanuary 25, 2017 0

4 tips for the utility data scientist

Tell me if you’ve heard this before: Your company hired (or re-titled) a talented data scientist and they have great skills and no data. Or they're marginalized by IT because they're misunderstood. They're offered “cleansed” data that will fit into the hardware provisioned. What they want is “all” relevant data

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Energy & Utilities

Blogs

Blogs

Analytics