One good example of when data could help make an informed decision is deciding how much merchandise to purchase and sell. In retail, using the previous year's sales of products and forecasts of how much is expected to be sold is a great way to ensure there is enough on the shelf but not overstock which won't sell and takes up valuable space. Scheduling is another example of where data could be helpful to see how many hours are needed and how many people to schedule on a given day. For example, during Christmas, more hours will be needed compared to a random weekday in February when there is low traffic. For these examples, previous sales of a product, and the forecast of sales for a product are some good data points to consider for the first example and many factors can go into staffing like the amount of freight coming into the store, or how many transactions are happening in a given day. They all can be personalized for each store which can more effectively schedule and keep the store in smooth operations