The cottage industry was based on workers buying raw materials, bringing them home and producing hand-crafted items to sell. The system worked, but was slow, tedious and expensive, producing goods that were affordable only by the rich. The Industrial Revolution changed all that. The factory system brought machines and workers
After acquiring personal IoT data in part 1 and cleaning it up in part 2 of this series, we are now ready to explore the data with SAS Visual Analytics. Let's see which answers we can find with the help of data visualization and analytics! I followed the general exploratory workflow
We have spent a good deal of time at the Analytic Hospitality Executive advocating for the value of big data for hospitality. Just a few months ago, for example, I wrote a two part series on how Big Data was a “big opportunity” for hotels and casinos. Our goal at
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
We often hear questions like: Are the shared service chargebacks to my business units’ cost centers accurate and transparent? Will I save any money by using a centralized shared service? Why should I consider a centralized shared service? These are all good questions. To answer them, you need to understand
You are going to be spending proportionately more of your IT budget on security than you have previously spent or ever wanted to spend. Why? Because you and everyone else on this planet is engaged in the still early stages of an escalating information arms race, that, while you didn’t
It seems like everyone is searching for ‘best practice’ these days. We are constantly looking to learn from what is being held up as good, leading and perhaps even the best itself. While this is a valid exercise, I believe we are missing an opportunity to take a closer look
The gaming business moves fast. Casinos serve a multitude of entertainment options to thousands of patrons 24 hours a day, a pace that results in a myriad of interaction points with their patrons. Competition in this service industry is fierce. If patrons at a casino do not feel that
Do you “buy and build as you go” with your analytics architecture? Most companies do, and have for decades. The result is a heterogeneous environment for analytics with a variety of hardware, software, databases and analytical applications used in silos. There’s tremendous duplication of data and inconsistency in the analytical
In part 1 of this series we looked at how to acquire personal data from the Internet of Things for our own exploration. But we found that the data was not yet ready for analysis, as is usually the case. In this part, we will look at how we can use SAS
