Even though our focus is on data federation, we should know that a graph is not the only way to display data. Two other commonly used vehicles are text and tables. In this video, we discuss the three common vehicles and contrast them. By the end of this video, you should be able to determine when it is appropriate use each of them. The first one we would like to discuss is text. Text should be use primarily in two cases. First, if you only have a few numbers, displaying the numbers using text may be the most effective method. You can actually display precise values in text. Your audience really does not need graphs to help them digest just a few numbers. Second, if you have a really complex dataset and relationships in the data, tables and graphs may not be able to express patterns and relationships well. Tables and graphs can be limited in that they really do not go beyond two or three dimensions. If you choose to use text, you are not restrict to two or three dimensions. Sometimes, text is used together with tables and graphs express important or complex ideas. Infographic is a popular way to present information quickly and clearly. Text is often used as a primary way to present data and infographics as, typically, we only want to draw audience attention to a few key statistics. An example is an infographic on global warming. Note that only one data graph is used where text is the primary way to display data. This is also true for many other examples we can find. Table is another popular vehicle to display data. In a table, data is arranged in columns and rows. A table has several distinctive advantages. First, it makes it easy to look up individual values and compare multiple values. Second, it can also represent numbers precisely, which should be contrasted with most graphs, which cannot display data precisely unless data labels are added. Third, it is very easy to include several different variables with different units of measurement. Finally, you can combine summary and the detailed information in a table, which is not always possible to do in graphs. This table shows several different but related variables nicely in a table. It includes units sold, sales a month, and a market share. Notice that it is three variables are in different units. If we were to display this information using graphs, we may have to use multiple graphs. Our focus in this module are data graphs. Graphs have several advantages of their own, which explains why they are very popular. First, graphs can present patterns of the data, something that's really difficult to do with tables. Because of that, graphs can also make it easy to see the similarities and differences between multiple sets of values. Finally, graphs can display large data sets in a way that can be readily perceived and understood. A table with values becomes very difficult to display and understand. The same is not true for graphs. In fact, in the era of big data, graphs sometimes are generated from millions or even billions of data points. Many expert actually believes that data visualization can be the last step of analytics project if it is sufficiently displays the interesting aspect of the data. To further contrast tables and graphs, it is helpful to think about several scenarios for the sales data example we discussed before. What if there are sales data for multiple products that we need to display? This typical is not a problem for tables. We can simply add a few columns to the table. We may also do the same with graphs. However, the task is trickier. To start with, even one product will potentially need multiple graphs, which means that information can be a little more scattered if we choose to use graphs. Well, we can certainly display multiple products on each graph, and it may not work well if the scale of the numbers is substantially different for the different products. What if sales for one product is in billions, whereas the other one is only in millions? That is not to say that tables are not always their own limitations. If we have ten years of data, then we're going to have a very long table and it is probably not feasible anymore. Displaying the information graphically is only feasible method in that case. A graph can also reveal patterns that are hard to discover in tables, such as sales trend and seasonality. Text, graphs, and tables are not mutually exclusive. We can often combine them. For example, for really large datasets we can use graphs to show patterns in the data, and use tables for summary information, possibly on multiple measurements. We can then use text to emphasize individual values or explain complex patterns and ideas.