As we said, we'll keep this first project example quite simple by focusing more on concepts and fundamentals, rather than spending time defining and exploring every detail (that will come in subsequent chapters!).

Let's start with some background. Suppose there is a group providing state-wide transportation services. This group originally began with a single driver, a car, and a van. Over time, the owner sold out to a group of investors who have evolved the company, now employing a large team of professional drivers and offering a full range of (driving) services for individuals and specific events, with vehicles chosen from an inventory of specialized vehicles. The owners are enjoying a steady business but not, unfortunately, steady profits. What can be done?

From the beginning, drivers had to log all information about each trip and submit that...

Recently, Stephen Archut, who has held the position of Product Marketing Manager, IBM Watson Analytics, indicated that the key capabilities of Watson Analytics include:

• Automated data discovery
• Exploration
• Guided predictive analytics
• Recommended visualizations
• Dashboard creation
• Visual storytelling

Further, he has said that these are all activities that business users perform on a daily basis and that Watson Analytics can accelerate. He also explained that Watson solutions aim to enhance, scale, and accelerate human expertise, targeting a wide range of complex challenges in the world today. Watson Analytics itself will understand, perceive, and relate information at a human level for your business problems, combining analytical insights with your expertise and experience to improve everyday decisions.

With these remarks in mind, we can get started with our project...

In this section we will go through a first project covering the basics of a simple Watson Analytics project, preparing you for upcoming, more complex projects presented in later chapters.

To actually start using Watson Analytics, go through the following steps:

1. You'll need to log in and, from the main or Welcome page, click on the +Add icon as shown in the following screenshot:

2. From there, you will see Create something new and add your data. Of course, we want to add our trip data, so we can choose between two methods: Drop file or browse or Upload data. Typically, for more realistically sized data files, you'll want to select Upload data and then browse to the file...

We should take note here that we jumped right into refining our data. Sometimes, especially if you are dealing with data completely new to you, you may spend time first exploring your data using the Watson Analytics Explore feature. We will spend time creating explorations later in this book, but for now it is good to mention, that if you refine a dataset, the changes that you make to it are available throughout Watson in Predict, Explore, and Assemble, but if you modify the data in an exploration, the changed data is available only in that exploration.

The objective of Watson Analytics is to give you the ability to create highly interpretable insights from your data based on complex modeling—without having to possess a deep understanding of each and every modeling algorithm.

Using Watson Predict, Watson Analytics uses these sophisticated algorithms to quickly and efficiently deliver predictions based on your data.

The predictions include visualizations and text descriptions of the analyses that Watson Analytics runs. You can use the visualizations to see analyses at a glance and read the text for supporting explanations and statistical details.

Because Watson Analytics automatically picks the right statistical tests for the data, you don't have to be know which test is best or which one to run.

1. To begin, go to the Watson Analytics Welcome page, and click on Predict:

2. Watson will then display...

In this chapter, we used a simple use case to cover the fundamental steps that are part of almost any Watson Analytics project. We started by stating an objective for the project by defining a realistic use case problem to solve, then gathered the data, loaded it into Watson Analytics (as an Excel Worksheet), and used Watson to refine the data before finally creating a prediction and reviewing the results.

At this point, the reader should have a clear understanding of how to create a Watson Analytics project.

In the next chapter, we will focus on analyzing how effective a supply chain is for a retail department store, using Watson Analytics in an attempt to isolate the causes of delivery performance.