SAS Voices

Data Management

Blend, cleanse and prepare data for analytics, reporting, or data modernization efforts.

Data Management

David PopeJuly 31, 2015 1

In the oil and gas industry, analytics are used to improve both upstream and downstream operations, from optimizing exploration and forecasting production to reducing commodity trading risk and understanding customer's energy needs. If you plan to derive value from the digital oil field, big data, and analytics, one of the first things

Data Management

Kelly McGuireJuly 23, 2015 2

Just get me my data! (Please): How advances in data management are making it easier for business users to access data

We’ve been talking about data recently at the Analytic Hospitality Executive. I’ve advocated to use whatever data you have, big or small, to get started today on analytic initiatives that will help you avoid big data paralysis. In this blog, I’m going to get a bit more technical than usual

English

Hospitality

Advanced Analytics | Data Management

Andreas GöddeJuly 20, 2015 1

Three ways to save with a big data lab

I’ve had a lot of discussions with business leaders around the discrepancy between big data investment fears and successful use cases. Most of them say 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

Analytics | Data Management

Mark TorrJuly 7, 2015 2

What’s the future of analytics within the enterprise architecture?

What does the future of analytics look like in your organizations enterprise architecture? Does it include thinking about a two speed approach to analytics which includes both: An agile rapidly changing analytics platform for innovation (a lab) seperated from operations and broad enterprise audience usage A slowly moving systematic enterprise analytics platform (a factory)

English

Analytics | Data Management | Data Visualization

Greg WoodJune 25, 2015 1

Are you missing out when it comes to data monetization?

After doing some recent research with IDC®, I got to thinking again about the reasons that organizations of all sizes in all industries are so slow at adopting analytics as part of their ‘business as usual’ operations. While I have no hard statistics on who is and who isn’t adopting

English

Analytics | Data Management | Data Visualization

Daniel TeacheyJune 22, 2015 0

Build a culture that embraces big data challenges

Big data. Streaming data. Complex data. We’ve all heard the reasons why organizations feel like they’re facing an insurmountable data challenge. Now, it’s time to do something about it. For the past few years, SAS has helped some of the world’s leading companies make sense of an avalanche of data.

English

Data Management | Data Visualization

Leo SadovyJune 16, 2015 1

Is Data an Asset? The importance of the metaphors we use for data

The metaphors we choose to describe our data are important, for they can either open up the potential for understanding and insight, or they can limit our ability to effectively extract all the value our data may hold. Consisting as it does of nothing but electric potentials, or variations in

Data Management | Data Visualization

Daniel AunvigJune 9, 2015 0

All customer intelligence must be woven into CRM programs – online and offline

You've probably heard many times about the fantastic untapped potential of combining online and offline customer data. But relax, I’m going to cut out the fluff and address this matter in a way that makes the idea plausible and its objectives achievable. The reality is that while much has been

Data Management

Hugo D'UlisseJune 1, 2015 0

Use Hadoop to visualize what wasn’t visible before

You might have lots of data on lots of customers, but imagine if you could suddenly add in a huge dollop of new, highly informative data that you weren’t able to access before. You could then use analytics to extract some really important insights about these customers, allowing you to

Data Management

Dr. Laurie MilesMay 20, 2015 0

There’s no ‘i’ in data science team

Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all

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Data Management