In this chapter, a Watson Analytics project was used to test product forecasting performance; it was created by analysts using IBM Planning Analytics. We created an extract file from the Planning Analytics cube and loaded it into Watson Analytics. From there, we used Watson's Explore functionality to examine the data and gain insights that can help us better understand our data and hopefully create a more accurate product revenue forecast. We also saved the Exploration, refreshed our data, and reused the Explorations saved visualizations with the updated data.
In the next chapter, we will use Artificial Intelligence (AI) from a Watson perspective in order to walk through an example use-case project that relates to the banking industry, in which transactions are evaluated to identify fraud.
