The amount of data being produced and captured from the plant floor today is staggering. When you add it all up, a factory can easily generate over 34 Terabytes of data per day, or nearly 9 Petabytes a year - that's 9 Million Gigabytes! Yet, with all of those systems, and all
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
When I was a kid learning about the solar system and building those models out of hastily-painted styrofoam balls of varying sizes, Pluto was a planet. A full-fledged, legitimate planet just like the other eight. But In August of 2006, just 7 months after NASA launched the New Horizons Mission
What's that productivity related quote by Charles Dickens? "My advice is never do tomorrow what you can do today." For years, machine learning has been written about and discussed widely with a focus on the benefits it will bring in the near future. But guess what? The future for machine learning
There's a lot of talk right now about the Internet of Things and how it's likely the prime catalyst for the digital transformation of organizations over the next few years. Billions of sensors, and devices with sensors, all generating data in a hyper-connected world where it can be easily shared
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
We recently met up with Paul Bennett, a member of the GB Rowing Team and current World Champion, and Laurie Miles, Head of Analytics for SAS UK & Ireland, who has been analyzing the team's data. They chatted about data, the life and mind of an elite sportsman, and uncovered some
I'll admit I am sometimes given to hyperbole. But is it a stretch for me to say SAS Factory Miner is to data science what the tractor was to farming? Consider the following: I am now hearing people describe traditionally created data models as “handcrafted.” One well-known manufacturer now relies on more than
Technology has brought the world a great deal of good, but the downside is that we’re increasingly vulnerable to some seriously scary stuff: Terrorists taking control of airplanes through the in-flight entertainment system. Governments breaking into secure systems and stealing identities. Thugs messing with the steering of self-driving cars. When
It’s time. The first of the big data V’s, volume, seems to be coming under control technology wise, even if that technology has not been implemented everywhere. Prices of disks and memory are tumbling and the introduction of technologies, such as Hadoop, are making vast amounts of data cheap to
