Finally, at this stage in the project, we should have already created and reviewed a reasonable number of visualizations, and hopefully gained insights not previously known that will help us prepare a more accurate product revenue forecast.
In this project, we might not be so interested in sharing assets with colleagues as much as wanting to just use the information and insights gained in creating the product forecast we are responsible for. To that point, we most likely would not use the Watson Analytics Assemble features (as we have done in previous chapter projects).
Although we might not use Assemble, the Exploration can be saved so that we can keep those views and visualizations that we feel are valuable enough to us and that we can use regularly to prepare future forecasts. The following is a simple example.
If we have asked the following question:
Watson Analytics will generate the following visualization:
If we've saved our Exploration, the view is saved as part of that Exploration. Notice in the view that we can see the Revenue by Product for each quarter in our data (Q1 2012 through Q3 2014).
Now, suppose that at some time in the future we have a new extract file from our Planning Analytics cube that includes more data, for more periods. We can update or enhance the data that is in that dataset by simply replacing the data that it contains.
When you replace the data in a dataset, the predictions, explorations, and views that are based on that dataset are automatically updated. You can add or delete columns and rows or change the existing data. To refresh the data, you can do the following:
- On the Welcome page, find the dataset whose data you want to replace and click on it.
- Click Replace all data.
- Select your dataset.
- Close any open message boxes when your data upload is complete:
Now, if we reopen our saved Exploration and look at that same visualization, we can see that it includes new periods of data in the visualization:
