OLAP is an acronym for On-Line Analytical Processing. OLAP is a software technology classification that allows analysts, managers, and executives to get insight into information through quick, reliable, interactive access to data that has been converted from raw data to reflect the true dimensionality of the company as perceived by the clients. OLAP allows for multidimensional examination of corporate data while also allowing for complex estimations, trend analysis, and advanced data modelling. It's rapidly improving the foundation for Intelligent Solutions, which includes Business Performance Management, Strategy, Budgeting, Predicting, Financial Documentation, Analysis, Modeling, Knowledge Discovery, and Data Warehouses Reporting. End-clients can use OLAP to perform ad hoc record analysis in several dimensions, giving them the information and understanding they need to make better choices.

Following guidelines must be followed while selecting an OLAP system:-

• Multidimensional Conceptual View: This is one of an OLAP system's most important capabilities. It is feasible to use methods like slice and dice that require a multidimensional view.
• Transparency: Make the technology, the underlying data repository, computing operations, and the disparate NATURE of source data completely accessible to consumers. Users' efficiency and productivity are improved as a result of this transparency.
• Accessibility: OLAP systems must only allow access to the data that is truly needed to do the analysis, giving clients a single, coherent, and consistent picture. The OLAP system must map its own logical schema to the disparate physical data storage, as WELL as to conduct any required transformations. 
• Consistent Reporting Performance: As the number of dimensions or the size of the database grows, users should not experience any substantial reduction in documenting performance. That is, as the number of dimensions grows, OLAP performance should not deteriorate.
• Client/Server Architecture: Make the OLAP tool's server component clever enough that the various clients can be connected with minimal effort and integration code. The server should be able to map and consolidate data from disparate databases.
• Generic Dimensionality: Each dimension in an OLAP method should be seen as equal in terms of structure and OPERATIONAL capabilities. Select dimensions may be granted additional operational capabilities, although such duties should be available to all dimensions.
• Dynamic Sparse Matrix Handling: To optimise sparse matrix handling by adapting the physical schema to the unique analytical model being built and loaded. When confronted with a sparse matrix, the system must be able to dynamically assume the information distribution and change storage and access in order to achieve and maintain a constant level of performance.
• Multiuser Support: OLAP technologies must allow several users to access data at the same time while maintaining data integrity and security.
• Unrestricted cross-dimensional Operations: It gives techniques the ability to determine dimensional order and to perform roll-up and drill-down operations within and across dimensions.
• Intuitive Data Manipulation: Reorientation (pivoting), drill-down and roll-up, and other manipulations can be done INTUITIVELY and precisely on the cells of the scientific model using point-and-click and drag-and-drop methods. It does away with the need for a menu or several VISITS to the user interface.
• Flexible Reporting: It provides efficiency to corporate clients by allowing them to organize columns, rows, and cells in a way that allows for easy data manipulation, analysis, and synthesis.
• Infinite Dimensions and Aggregation Levels: There should be no limit to the number of data dimensions. Within any given consolidation path, each of these common dimensions must allow for an almost infinite number of customer-defined aggregation levels.