Uncategorized

Analytics | Students & Educators
Lisa Morton 0
College-bound students with visual impairments learn to independently analyze data

This summer the Accessibility and Applied Assistive Technology team at SAS launched a new course that teaches students with visual impairments how to independently analyze data, which is a critical skill that all students need for success in college and their careers. However, many students with visual impairments don’t have

Advanced Analytics | Machine Learning
Tim Clark 0
Can the artificial intelligence of things make the supply chain intelligent?

As you will have read in my last blog, businesses are demanding better outcomes, and through IoT initiatives big data is only getting bigger. This presents a clear opportunity for organisations to start thinking seriously about how to leverage analytics with their other investments. Demands on supply chains have also

Analytics
Leonid Batkhan 10
Selling sand at the beach

Have you ever thought of selling sand on the beach? Neither have I. To most people the mere idea is preposterous. But isn’t it how all great discoveries, inventions and breakthroughs are made? Someone comes up with an outwardly crazy, outlandish idea, and despite all the skepticism, criticism, ostracism, ridicule

Analytics
David Annis 0
Five considerations for your cloud-first strategy

Across all industries, organizations are adopting a cloud-first strategy. What does it mean to be cloud-first? Broadly speaking, cloud first means using shared infrastructure instead of building and hosting your own private storage facility, systems, etc. Benefits of adopting a cloud-first strategy include cost savings and productivity improvements. However, what

Advanced Analytics | Analytics | Artificial Intelligence | Machine Learning
Charlie Chase 2
How do I explain a flat-line forecast to senior management?

How do you explain flat-line forecasts to senior management? Or, do you just make manual overrides to adjust the forecast?    When there is no detectable trend or seasonality associated with your demand history, or something has disrupted the trend and/or seasonality, simple time series methods (i.e. naïve and simple

1 2 3 230