Question

Now that we are discussing OLAP and dimensions, the question is: Why do we want our data broken down? What are the benefits?

Answer 1

In previous days the companies were using online transaction processing systems or OLTP for capturing data mostly related to transactions. Now the focus has changed from mere data capturing to data analysis that are used while taking important business decisions. The analysis of collected data in the data warehouses is termed as Online Analytical Processing or OLAP. The information within OLAP systems are broken down and stored as cubes and the cubes are multidimensional structures that contain the subsets of data from a data warehouse. Each cube consists of dimensions which are categorized into members that share common characteristics. Member data is organized into hierarchies within cube dimensions. The data consumers can move up or down through the hierarchy depending upon the level of detail they need because the highest level contains the most summarized data and the lowest contains the most detailed data. We want our data to be broken down to achieve greater scalability, flexibility and capability. When the data is organized into cubes it becomes easy for the users to navigate, understand and use the data for business purposes according to the requirement. As the data is divided into dimensions it is easy to analyze the data by dragging the dimensions into appropriate locations. The preparation of business reports become so easy and efficient reducing the time delays in data conversion. You can start analyzing the data by reading the summary and later dive into more detailed analysis using OLAP’s feature of dimensions.   Another benefit of breaking down the data into cubes and dimensions is data security as the cubes can control the levels of access given to the users through assigning the role for each cube with the user’s name as a member and by setting dimension security to restrict the user access to the dimensions.