Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series)

Imagine you are a retailer. Imagine walking through the aisles with customers and recording every place they go, every item they look at, every item they pick up, every item they put in the cart and then take back out. Imagine knowing whether they read nutritional information, if they

development ADS might have hundreds or even thousands of variables or metrics within it.

doing a cross-channel analysis that accounts for activity in other channels, are a lot of sales closed in a second channel after an interest is generated on the web via an ad or search?

Third, many big data sources are not designed to be friendly. In fact, some of the sources aren’t designed at all! Take text streams from a social media site. There is no way to ask users to follow certain standards of grammar, or sentence ordering, or vocabulary. You are going to get what you get when people make a posting.

retailers often build “propensity to buy” models for important product categories. It doesn’t make sense to have a custom model built for slower-moving, less frequently promoted categories.

Where appliances are often designed for one or two specific workloads, the enterprise data warehouse is designed to support many workloads.

analytics to identify patterns that can be leveraged, and how will those analytics be acted upon? Analytics is an ever more important tool that organizations use to drive competitive advantage.

Abandoned basket statistics can be adjusted to account for the Dreamer segment. Abandoned baskets are often viewed as a failure by organizations. However, through the examination of browsing history, it can be clearly identified that 10 abandons were due to one customer who is known to repeatedly abandon a lot of products on a regular basis. As

There are benefits to a table-based enterprise analytic data set. First, you have a true “compute once, use many” scenario. The total system load from analytic professionals is going to be vastly reduced. This is because instead of having each person repetitiously running the same type of process to do big joins and aggregations, it’s going to be r