The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process. Model management
The real reason companies cleanse the historical demand is that traditional forecasting solutions were unable to predict sales promotions or correct the data automatically for shortages, or outliers. To address the short comings of traditional technology, companies embedded a cleansing process of adjusting the demand history for shortages, outliers, and
A proof of concept (POC) is smartest way for customers to evaluate if a product meets the required objectives, and the best way for vendors to demonstrate why they feel they are best placed to resolve the current outstanding problems. However, not all POCs are successful. Let’s explore why. What is
Recently I had the privilege of hearing Nathan Falkenborg, Head of Consulting & Analytics, North Asia at Visa speak at the SAS Executive Forum in Singapore. Nathan has also spoken at SAS Premier Business Leadership Series where he talked about how the analytics guy won over the marketers at Visa.
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
