Generally speaking, the overall goal of data science is to help you improve business outcomes. This often translates into being more efficient, more productive, or testing various approaches (such as flexible working versus the usual 9-to-5 setup). The point is that you get these answers from solid data, often in large quantities, that you would never be able to handle without data science or the latest technologies associated with it - namely automation and machine learning.
What can data science be used for? In a marketing sense, it mostly boils down to dealing with datasets that are otherwise too large for you to handle. If you're industry mailing list small business, analyzing five years of data to discover how weather patterns are affecting your sales isn't something you can do manually -- something that's difficult for even the largest businesses to do.
However, with proper data processing, you can sit back while an algorithm compares the past five years of weather data and cross-references it with public holidays, sporting events, geographic locations, and any other datasets you deem important. Is five years not enough for you? Well, if you think this will improve the quality of your insights, add a zero to the end and analyze the last 50 years of data. With today's off-the-shelf automation and machine learning technologies, data science can be applied at almost any scale, no matter how big your business or marketing team is.