Granularity of data modeled in multidimensional data structures is an important factor for a data warehouse. Grain sizes and number of dimensions participating in the model are critical in ascertaining the quality of analytical queries that are run on such data warehouses. In this paper, exploration and production data of Australian resources industry, pertinent to oil and gas, over the past five decades have been examined for multidimensionality and grain size. This research shows how using an ER approach combined with multidimensional data modeling helps in considerable reduction in the size of the data warehouse, making it more effective and efficient.