Multi-dimension analysis of data refers to viewing data that is aggregated or summarized across various dimensions. Let’s say your data consists of individual orders for the last year. You might want to see how many orders were received in each month – time is a dimension. You might want to see how many orders were received for each product – product name (or ID) is a dimension. Dimensions can also have hierarchies: time for example can be grouped by week, month, quarter, year. Product might be individual product, product family, product category. The hierarchical nature of the data also allows for something often referred to as “drill down” – where you start with a high level summary and then move down the hierarchy of a particular dimension to view more detail.