OLAP - Definition

What is OLAP?

Online Analytical Processing (OLAP) is the foundation for business intelligence tools – it is software for multidimensional analysis database queries to permit high speed processing on large volumes of data. They work with cloud data warehouses, data marts, and other centralized data stores and can be used for report views, predictive analysis, and other analytical calculations. 

The multidimensional nature of OLAP is due to the fact that most businesses have multiple sections into which their data is broken down for different purposes. OLAP extracts data from multiple relational data sets to reogranize and recategorize it into different formats for better processing and analysis. This technology is used by solutions like Oracle’s Essbase, Microsoft SWL Server Analysis Services, and Cognos. 

OLAPs work based on cubes which are array-based multidimensional databases. They are more efficient than traditional relational databases which makes them a good option for cloud computing, too. Cubes extend the traditional relational database’s single table concept and add additional dimensions to it to expand the hierarchy of data. The top layer of the cube would have one form of organization while additional layers would filter and sort in different ways. What makes OLAP cubes different is that they can potentially and theoretically contain infinite layers so you could have multiple permutations of tables and organizations of data. Simply: cubes allow you to slice and acquire the data you really need to understand your business analytics.

In addition to multidimensional OLAP cubes, there are also relational OLAP (ROLAP) and hybrid OLAP (HOLAP). ROLAP is multidimensional data analysis that takes place on data in unorganized relational tables – this is best when there’s more emphases on working with massive amounts of data than performance or efficiency. HOLAP functions through the combination of multidimensional OLAP and ROLAP to divide processing load. It is ideal for data processing efficiency and high scalability for larger companies but won’t be as fast as ROLAP. It’s more expensive as well because of its complex architecture and frequent up-keep.

Nowadays, OLAP is used commonly with cloud computing as it’s less expensive and easier to set up with so much cloud-based data. This is thanks to MPP – massively parallel processing – wherein OLAP can tap into high-level analytics on cloud-based data warehouses.  For companies, this is extremely beneficial as it has the potential to maximise the usability of corporate data, leading to more thorough analysis on business insights. This can be especially useful for companies that would otherwise have physical data silos in different regions: the cloud optimizes access while OLAP makes it all more efficient.

All in all, OLAP is great for situations where you need to conduct complex analytics on large datasets to generate simply and readable reports quickly and consistently. OLAP systems, especially on the cloud (rather than legacy systems) are optimized for read-heavy situations and datasets and is ideal for trend-spotting and data exploration.

OLAP Advantages

Many organizations are switching to building Business Intelligence (BI) solutions using OLAP technology due to factors like speed, efficiency, and structural integrity.

  • Speed: OLAP solutions are fast and help users perform data analysis and reporting on their own. They are best for data mining or discovering data items’ relationships. With these tools, you do not have to perform calculations manually and compose complex reports anymore. OLAP tools help run queries in seconds. 
  • Centralization: They store all the transactional data, customer information, supplier information, data related to the company employees and their performance, etc., in a centralized location. 
  • Data Organization: OLAP tools follow a multidimensional approach where all the data is organized into various dimensions (sharing common characteristics) and later used for analysis. These dimensions include simple business categories that are easy to understand even for non-technical users. 
  • Efficiency: OLAP tools avoid the manipulation of database table fields. It is one of the best computing methods that help users extract and analyze information from a different point of view.
  • Ease of Use: One can collect information through a data warehouse and perform business analysis without any technical background. OLAP systems provide complete documentation, tutorials, and prompt technical support for users. 
  • Response: One can also practice “what-if” scenarios with the help of OLAP systems. If the OLAP cubes support write-back functions, you can replace values and look for other outcomes, foresee losses, and make strategies to prevent them. As a result, the tool helps organizations make better decisions to improve profit, sales, brand image, marketing strategies, and more.
  • All-Encompassing: Furthermore, the system allows users to create reports like sales forecasting, management reporting, financial reporting, trends analysis, and more on their own. As a result, it helps reduce the demand for IT resources.

The OLAP system is highly beneficial as it helps aggregate and sum up data in cubes that are further rolled up, sliced, and diced to form the best solution. Once the cubes are made, teams can use existing business intelligence tools to instantly connect with the OLAP model and draw interactive real-time insights from their cloud data. The high-speed data processing features present in these tools enables users to run queries and prepare reports in just a few minutes.

OLAP Challenges

OLAP systems are a great investment for big companies as they can bring in more profit in the future. But, implementing OLAP systems in most cases can present some challenges.

  • Set-up: Organizations find difficulties when structuring a database and creating a decision support architecture on their own. Thus, various organizations opt out and make no effort. The database must be structured properly to perform analysis on large amounts of datasets. 
  • Structuring: To use an OLAP system, one needs to define its structure in advance, i.e., pre-calculate each column and data type before creating a table. Similarly, having a good OLAP engine is essential. Without it, one cannot convert data into a pattern, and they will find it hard to operate directly. Overall, it may result in causing difficulties for quick results.
  • High-Level: You will require IT pros at some stage, even if the OLAP system is meant for users to operate and run queries. Traditional OLAP tools follow a complex modeling procedure. Thus, to write codes/scripts/SQL, use ETL tools for integration, or use the map dimensions of a model, you will require an IT expert and human resources. A non-technical user cannot handle this part by themself.
  • Power Requirement: Some systems may lack computational power resulting in less flexibility of the OLAP tool. In the majority of cases, analyzers have to depend on a third party to perform calculations as they are restricted to a small area.

No doubt, OLAP tools are highly advantageous, but various challenges work as a setback. All the above-listed challenges without fixation can result in inappropriate or incomplete information to the users. This may further cause problems in decision-making. 

The only method to combat these challenges is to strongly integrate the analysis tools and databases. Both the primary components of the OLAP system should not impose any restrictions on one other. It is essential to ensure that the ability to manage or analyze data will not be compromised in any way while structuring the components.