You have to be "in it to win it" as they say. This is becoming the case for many organisations that need to start using data to make better, evidence-based business decisions. Today, using analytics is not so much a data lottery as a data necessity. Some businesses may not
Good news...an analytics survey last year found that 72% of insurance executive agreed that analytics is the biggest game-changer in the next 2 years. Bad news...compared to other industries the adoption rates of analytics in the insurance has lagged other industries. To reverse this trend and help insurers travel down the
During a lighthearted moment in a serious conversation, Howard Schmidt, cyber security advisor to multiple presidents, told a Wall Street Journal interviewer that relying on a government agency as your primary backstop during a major cyber security breach is akin to calling Ghostbusters: you might not get the help you
Luckily, or perhaps better said, hopefully, we only need to make the big life decisions every now and then. What school to go to? Who to marry? What job to take? Where to live? There’s no penultimate answer to these decisions, but we all take them to the best of
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
