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    Online Analytical Processing (OLAP) is a category of software for performing multidimensional analysis at high speeds on large volumes of business data from a data warehouse or centralized data store. It provides capabilities for complex calculations, trend analysis, and sophisticated data modeling. OLAP is part of a broader category of business intelligence, which includes relational databases, report writing, and data mining.

    What is OLAP used for?

    OLAP serves as the foundation for many business applications such as business process management, planning, budgeting, forecasting, financial reporting, knowledge discovery, and simulation models. Users of OLAP can perform ad hoc analyses of data in multiple dimensions, consequently providing the insight and understanding needed for decision making.

    OLAP cube

    The term OLAP cube is used to refer to a multidimensional dataset, in which the data can have two or three dimensions—or more in the case of a hypercube. For example, a company can organize its sales data in an OLAP cube by product, city, salesperson, and time frame. In this cube, product, city, salesperson, and time frame are the four dimensions.

    The analytical operations of an OLAP cube consist of four basic processes: drill-down, roll-up, slicing and dicing, and pivot (rotate).


    Data is fragmented into smaller parts. It converts less-detailed data into more-detailed data by either moving down in the concept hierarchy or adding a new dimension to the cube.


    Roll-up, or consolidation, involves the process of aggregating the huge amount of data that helps the analyst to count and find out the maximum, minimum, and sum of the data. After finding the necessary details, it can be rolled up to get the summary of data, and one or two dimensions need to be eliminated while initiating the roll-up process.

    In the example of sales data, the location data (cities) needs to move up from the city to the country while rolling up the data; therefore, the dimension of the city wants to be eliminated.

    Slicing and Dicing

    In slicing, the analysts can choose one dimension from the parent cube and create a new subcube. For example, the analyst can choose a product and can create a subcube by adding other dimensions to that cube.

    Dicing is similar to slicing, with the only difference being that the analyst can choose more than one dimension to create a new cube.

    Pivot (Rotate)

    The cube is rotated to display a new representation of data, which enables the dynamic, multidimensional views of data.

    What are the advantages of using OLAP?

    As a BI tool, OLAP is a suitable data analytics platform for businesses of all sizes. The following are some of the significant advantages of OLAP:

    • A well-designed OLAP cube can execute complex queries with a faster response time.
    • It’s easy for the analyst to navigate through large datasets to get aggregated as well as detailed data stored in an OLAP cube with multidimensions.
    • The hierarchical structure of cubes allows users to extract the information in desired combinations. 
    • It enables users to apply “what if” scenarios that help them to analyze the outcome that may happen if any changes are introduced to a business strategy.
    • OLAP data can be understandable for the end-users without any technical background.

    OLTP vs. OLAP

    Although the functions of OLTP and OLAP look similar, there are some significant differences between both systems. Here are some of them:

    • OLAP handles complex queries, whereas OLTP handles simple and small transactions.
    • The response time to queries in OLTP is milliseconds, while OLAP may take more time since it depends on the volume of data to process.
    • OLAP generally handles large datasets instead of small datasets, which OLTP processes.
    • OLAP is based on denormalized databases, whereas OLTP is based on normalized databases.
    • OLAP helps businesses plan and make decisions to gain insights from datasets and more, but OLTP facilitates businesses to run essential operations.

    Types of OLAP

    There are multiple types of OLAP, but three main ones exist:

    Relational OLAP (ROLAP)

    An extension of relational database management systems. It is multidimensional data analysis that operates directly on data on relational tables without reorganizing the data into a cube first.

    Multidimensional OLAP (MOLAP)

    The fastest and most practical type of multidimensional data analysis. It works directly with a multidimensional OLAP cube.

    Hybrid OLAP (HOLAP)

    Aggregated totals are stored in a multidimensional database while the detailed data is stored in the relational database. Offers both the efficiency of the ROLAP model with the performance of the MOLAP model.

    Other types of OLAP include Desktop OLAP (DOLAP), Web OLAP (WOLAP), Mobile OLAP, and Spatial OLAP.

    Best OLAP tools

    Since OLAP is considered a part of BI, it’s essential to implement such tools to make business functions smooth and result-driven. Here is the list of the top five OLAP tools:


    BOARD, a full-featured BI software, helps medium and enterprise-level businesses to perform business analytics, data mining, and reporting functions. It enables companies to view data in a manner that supports making effective business decisions.


    This data analytical tool can perform BI functions such as multidimensional expressions (MDX), sophisticated cube modeling, and server monitoring. 

    IBM Cognos

    IBM Cognos is an AI-driven BI solution that enables businesses to generate reports, design cubes, analyze data, and more. The in-built features of IBM Cognos include IBM Cognos Framework Manager, Report Studio, IBM Cognos Transformer, IBM Cognos Analysis Studio, etc.


    Mondrian, a comprehensive data visualization software, has the ability to process large datasets. It also possesses statistical features such as data scaling and density estimation. 


    Tableau helps businesses to analyze a huge amount of data and gain meaningful insights and make decisions accordingly. The visual data analytics features and the interactive maps also facilitate users to analyze data across various sources.


    This article was reviewed and updated in February 2022 by Siji Roy.