Stephen Few, a data visualization guru, shares how organizations like the CIA teach new spy recruits to make observations. They're trained to first get an overview of what's going on around them. And then, when they spot something abnormal, they shift from a broad observational awareness, to a more focused perspective in which they analyze the details. In this module, you'll learn how to use Tableau to do with data, what spies do when observing their surroundings. Get an overview of the data, and then narrow in on certain aspects of the data that seem abnormal, and analyze them. Stephen Few goes on to further quote some data visualization colleagues, about why this approach is so useful. Quote, having an overview is very important. It reduces search, allows detection of overall patterns, and aids the user in choosing the next move. A general heuristic of visualization design therefore, is to start with an overview, but it is also necessary for the user to access details rapidly. One solution is overview plus detail to provide multiple views, an overview for orientation, and a detailed view for further work, end of quote. In fact, there's something called Shneiderman's Mantra which is quote, overview first, zoom and filter, then details-on-demand, end of quote. Tableau is a great tool for implementing Sheiderman's Mantra. Tableau is optimized for visualizing data, and so making charts is very quick and intuitive. Tableau opens the door to make a wide variety of charts, and then to use those charts to zoom in and filter the data, to get a more focused perspective. You can also interact with charts and share charts in many ways. In some sense, Tableau is like an adult version of Excel's pivot table and pivot charts functionality. Like Excel, Tableau is pretty intuitive to use, and you really don't need to know much about programming to use it. Also like Excel, Tableau has tools for interacting with the data. You can manipulate the shape of the data from wide to long. You can create new fields that are based on calculations of other fields. You can split or combine text fields. You can also perform joins and lookups in Tableau. Thus, many of the key data assembly processes that can be done in Excel, can also be done in Tableau. The method for doing so is different, and often takes fewer steps. One key difference in working with data in Tableau, is that you can't manually point and click on individual cells to edit them. Thus, it's ultimately less flexible in what you can do with data, at least in my opinion. However, the benefit is that Tableau can handle much larger amounts of data than Excel can handle. Oftentimes, the tools go hand in hand. Data is collected, entered, and cleansed to some extent in Excel, and then once it's pretty tidy, it's passed on to Tableau for visualization and analysis. So Tableau is a very powerful tool, yet it is only a tool. Unless you first have a well-framed question and have assembled good data, which are the first two parts of the fact framework, then you're not as likely to convert data into action. Even when you do have a well-framed question and good data, obtaining actionable insights is not a straightforward process. Quoting his data visualization colleagues, Stephen Few writes, quote, users often try to make a good choice by deciding first what they do not want. That is, they first try to reduce the data set to a smaller, more manageable size. After some iterations, it is easier to make the final selection from the reduced data set. This iterative refinement of progressive querying of datasets, is sometimes known as hierarchical decision-making, end of quote. In my experience, I found that this quote is accurate. Tableau helps with this hierarchical decision-making process, by using visualizations to help identify and communicate useful relationships in the data. This is not a comprehensive tutorial on Tableau. There are many excellent Tableau tutorials for becoming familiar with all the bells and whistles. The objectives of this module are for you to one, create basic charts in Tableau for exploring and communicating financial data. Two, build and analyze a data frame that is based on two or more data frames. Three, add depth to the basic data visualizations, by adding dimensions and applying analytic tools in Tableau. And four, create an interactive dashboard for exploring and communicating relationships in the data.