Chapter 12. Decision support

In the previous chapter, we looked at a fairly simple decision scenario. It took a while, because we needed to learn a lot about decisions, about good and bad decisions, cognitive biases, weasality, and other things.

With that under our lobes, let's kick things up a notch.

We met Sara way back at the start of the book. Her job is to figure out what products to add to and drop from the catalog.

Sara is critical to the long-term success of the Dog House. The catalog is their main sales tool. If Sara misses a trend, the company misses an opportunity. If she fills the warehouse with products that don't sell, the Dog House can waste a lot of money. Nobody wants to end up with 10,000 singing fish they can't sell.



Sara looks at the profitability of things already in the catalog, new products manufacturers tell her about, trends in dog ownership, competition from traditional retail stores as well as other mail order houses, and feedback from customers. She thinks about what types of products will appeal to different types of customers, what types of customers the Dog House is interested in, and how the economy might affect purchases of luxury items. And much more.

Sara is a serious data cruncher. She needs a lot more data than MaryAnn, and needs it for years into the past. She needs to analyze the data to make sense of it. She needs statistical software and graphing tools.

Fundamentally, though, her decisions have the same form as MaryAnn's. The three things still apply. Options, measures, and decision rules.

In this chapter, we'll look at decision support toolkits. They can help people like Sara with their data and analysis needs.

Here's the chapter's contents:

• Modeling
• Statistical analysis
• Data sources
• Middleware
• OLAP
• Data mining
• Summary
• Exercises