To focus more on Predictive, I wanted to first call out why I feel this is the top most level out of the data needs levels. As mentioned in earlier posts, Data Pulls are the base layer because literally getting to the data should be a base need, it is like drinking water. Products are the next level, you monitor usage and build products to make life easier, so instead of drinking water from the river 5 miles down, you build a well to have it available in town. Alerts are the next level, you want to be notified when something goes wrong (or good), so this would be having a bell go off before a flood is about to hit and the well will get too full. Lastly, Predictive is where we are at, this would be identifying when a flood could hit and making adjustments before it does hit by releasing water past the well. Of course having a predictive flood tool for your well without having a well, wouldn't do much good.
To dive deeper into what I feel Predictive data needs are, I want to highlight that there is a ton of work by data scientists today to build models, forecasts, and other tools all off of data. The way I generalize this is that Predictive data needs are built to identify alerts before it happen allowing your client to take action before something happens. This could be forecasts of sales for the next 3 months. This could provide suggestions on tests being performed and recommend action based on the test results. Finally, this could predict when more traffic hits your website if you making the recommended adjustments.
Needless to say, predictive data needs have certainly transformed how we as a society behave on a daily basis. Whether it comes from weather prediction models or it is how we interact on the internet, predictive data analytics is the pinnacle of data needs. But in order to get to the pinnacle of data needs, you to have a good solid delivery / understanding of the previous layers to get there.