10 Kellogg Insight Analytics vs. Artificial Intelligence Start with the Problem Too often, Zettelmeyer says, managers collect data without knowing how they will use it. “You have to think about the generation of data as a strategic impera - tive,” he says. In other words, analytics is not a separate business practice; it has to be integrated into the business plan itself. Whatever a company chooses to measure, the results will only be useful if the data collection is done with purpose. Like all scientific inquiries, analytics needs to start with a question or problem in mind. Whether it is a software com - pany that wants to improve its adver - tising campaign or a fast-food company that wants to streamline its global opera - tions, the data collection has to match the specific business problem at hand. “You can’t just hope that the data that get inci - dentally created in the course of business are the kind of data that are going to lead to breakthroughs,” Zettelmeyer says. “While it is obvious that some kinds of data should be collected, customer inter - actions have to be designed with analyt - ics in mind to ensure that you have the measures you need.” Based on insights from Florian Zettelmeyer It turns out there’s not a single, agreed-upon way in which these terms are used. Ask a dozen data scientists, and you’ll get a dozen different answers. (Ditto when it comes to defi- nitions for “big data” or even “data scientist.”) But here’s how Zettelmeyer sees it. Analytics: The science or process of transforming data into knowledge. This is how we derive useful con- clusions from data. Analytics can include processes such as machine learning, but it can also include conducting experiments or running sta- tistical analyses that allow you to make causal inferences. Artificial Intelligence: When automation allows systems to perform “intel- ligent” actions such as learning, creating knowl- edge, making inferences and decisions, and solving problems. In Zettelmeyer’s view, AI is so exciting because it scales analytics.