Understand the importance of cognitive starting points in Watson Analytics, and learn how they can be used to reveal meaningful, unbiased insights that otherwise may have been lost in the noise.
- [Instructor] Now that we've loaded and refined our data set, it's time to shift into the next mode of analysis, discover, and this is where things get exciting. It's where we start to find patterns, reveal insights, and develop visualizations to help us craft our story. Let's go ahead and click the Watson Analytics logo to return to our home screen, where you will now see the new refined airline satisfaction data set. We will kick it off by simply clicking on the data set to reveal what Watson calls cognitive starting points.
In other words, these are trends and relationships that have been detected during the upload process, and ones that are good friend Watson is prompting us to explore. We have a number of potential paths to choose here, plus options to create our own visualizations from scratch, if we would rather go rogue. Let's start with the first starting point, what are the values of price sensitivity by origin state. Clicking that starting point will automatically create an appropriate visualization, and place it in the first tab of our new discovery set, which is basically a collection of visualizations that we can later use as elements to assemble a dashboard.
We've got some options here, all of which are specific to this particular visualization. On the top left, we can change the visualization type, or formatting options, or we can make adjustments using the data tray along the bottom of the screen. For example, if we wanted to see price sensitivity by destinations, instead of origins, all we need to do is drag our destination state city hierarchy from the data tray, and swap out the origin state city.
Now, because we're looking at hierarchy field, it means we can select a particular state, like Iowa for instance, expand the options by clicking the ellipses, and go down, or drill down into the city level. Now, we're seeing price sensitivity by city within the state of Iowa. To go back, I can either right-click the city labels, and choose go up, or I can simply use the undo button at the top of the screen.
It's important to note that we also still have access to data shaping tools, like calculations, groups, and hierarchies via the data tray. For instance, if we click one of the fields, click the ellipses, you will see we have the same options that we saw in our refine interface. The only difference is that now the modifications that we make will remain local to this particular discovery set. This visualization is almost what I'm looking for, but I would also like to get a sense of volume by state, in terms of the number of survey respondents.
To do this, I will simply scroll to find my rows column, which is here, and drag it into the size tray in the lower left. Whala, we have a custom geospatial map that now shows price sensitivity and response volume by state. Let's double-click tab to give it a custom name. I'll call it Price Sensitivity by State, and there you go, we've just created our first discovery.
- Reviewing key differentiators
- Navigating the 3 Ds of Watson Analytics: data, discovery, and display
- Importing, joining, and refining data
- Using natural language querying
- Understanding key drivers
- Interpreting decision trees
- Displaying insights
- Assembling multitabbed displays and dashboard filters
- Modifying and sharing displays