From research to applications

Having discussed how research is done in AI, it’s now time to focus on applications. Assuming you already have a data science team in place and preliminary research on a problem you want to solve done, the next step is to gather and clean data. This process can be short if most of your business is digital with easy access to data, or long and painful if you have many sources to look at and data is far from clean (say, surveys of customers done in various formats). If that’s the case, preprocessing is a task that would need a separate team to complete. It’s especially essential for all the later work, so don’t ignore cleaning data.

Applying research to business applications means using machine learning models on data coming from your business and measuring how well they behave compared to how you usually solve the problem at hand (e.g. time spent on a business process, marketing/sales, number of relevant leads). After receiving...