Online Analytical Processing

Online Analytical Processing (OLAP) is a technology that helps organizations organize and study large business databases from a multidimensional perspective. Hence, it aims to facilitate the analysis of historical data acquired from data marts, warehouses, or other data stores to resolve complex queries and interpret future trends or patterns.

OLAP is a widely applicable software technology that aids in analyzing and reporting business data in the company's finance, accounting, sales, marketing, and production departments. It provides a base for business intelligence, data mining, and decision-making processes. Moreover, OLAP systems are easily accessible on various interfaces, including web-based dashboards, desktop applications, and mobile devices.

• Online Analytical Processing (OLAP) is a technological system that facilitates the evaluation of a large database from multiple perspectives to derive meaningful conclusions and interpret the future trend in the business.
• The different OLAP systems organizations employ are multidimensional, relational and hybrid.
• Such software aids the firms in data mining, business intelligence and decision-making across the different departments of an organization, including finance, accounting, sales, marketing and production.
• It traces data from the data warehouse, such as online transaction processing, to perform complex queries in seconds or minutes, ensuring prompt results.

Online Analytical Processing (OLAP) definition refers to a system that stores data in a multidimensional format. Hence, this allows users to analyze data in multiple dimensions, such as time, geography, product, and customer. Furthermore, online analytical processing has various applications across different industries and domains. Moreover, OLAP cubes are the most common representation of multidimensional data, where each cube cell represents a specific combination of dimensions and contains a measure or value.

Therefore, this technology allows users to perform complex analyses such as trends, forecasting, and what-if analyses. Thus, online analytical processing systems are designed to support complex data analysis and provide users with interactive and intuitive ways to explore and understand data from different perspectives.

There are different types of OLAP systems:

1. Multidimensional OLAP (MOLAP) - stores and summarizes the data in a multidimensional format while providing swift query processing and performance.
2. Relational OLAP (ROLAP) - maintains data in relational databases, I.e., rows and columns format, while facilitating scalability and flexibility.
3. Hybrid OLAP (HOLAP) - a mid-way approach that combines multidimensional and relational OLAP features.

Hence, the type of OLAP depends upon the various requirements and guidelines as prescribed by Dr. E.F. Codd, who is the father of the relational model; these include:

1. Multidimensional Conceptual View
2. Accessibility
3. Transparency
4. Consistent Reporting Performance
5. General Dimensionality
6. Flexible Reporting
7. Dynamic Sparse Matrix Handling
8. Intuitive Data Manipulation
9. Multiuser Support
10. Client/Server Architecture
11. Unlimited Dimensions and Aggregation Levels
12. Unrestricted Cross-dimensional Operations

Overall, online analytical processing in accounting enables accountants and financial professionals to perform in-depth analysis, generate meaningful reports, and gain insights into financial data.

Let us understand the concept better with the help of an example.

Example #1

Let's assume The Kroger Co., A retail company in the US, operates in multiple cities a tracks sales data. Therefore, they use online analytical processing systems to analyze their sales performance. Therefore, the OLAP system has a sales cube that includes dimensions such as time, product, and location.

Thus, by using the OLAP system, the company can perform various analysis operations:

• Drill-Down: They can drill down from yearly to quarterly or monthly sales and further drill down to specific cities or stores to analyze sales performance at different levels of detail.
• Slice and Dice: They can slice the data to analyze sales for a specific product category or brand or dice the data to view sales for a specific period and city simultaneously.
• Aggregations: They can quickly retrieve aggregated sales figures, such as total sales revenue for a specific quarter or total sales quantity for a particular product category.
• Comparisons: They can compare sales performance across different cities, stores, or products to identify top-performing areas or areas that require improvement.

Therefore, by utilizing this technology, the company can gain valuable insights into its sales data, identify trends, make informed decisions, and take action to optimize its sales performance and profitability.

Example #2

Mr Accuracy Reports released a detailed business research on the "Online Analytical Processing (OLAP) Tools Market." This research includes historical data, current market developments, future year's product environments, advertising approaches, technological advances, upcoming technologies, emerging trends or opportunities, and technical advancement in the related industry.

It aids in the comprehension of how to interact with clients, how to compare with the opposition, and how to formulate the future plans. It is crucial to create goods and services, put them on the market, and promote them to customers.

According to Mr Accuracy Reports, the global market for OLAP tools is anticipated to grow at a CAGR of 7.6% from 2017 to 2032, reaching a value of USD 3.60 billion. In-depth coverage of pricing analysis, patent analysis, and technological advancements is also provided in the study on the worldwide OLAP Tools market.

Online Analytical Processing (OLAP) is a tool that helps study real-time business data for decision-making. The various other benefits of OLAP are as follows:

• Processes large volumes of business data from multiple sources in a fraction of seconds or minutes.
• Helps in prompt and better decision-making based on the acquired OLAP reports.
• Interprets data from various dimensions such as time, geography, product, and customer.
• Thus, it aids in better understanding the data by establishing relationships between various data sets.
• Provides data accuracy due to real-time data analysis.
• Facilitates comprehension of complex data through interactive and visual representation of the OLTP data.
• Improves overall business efficiency by enabling users to perform complex data analysis tasks quickly and effectively while reducing cost and time.
• Moreover, it provides more flexibility through customization of data analysis and reporting based on the specific needs and requirements of the users.

OLAP has numerous pros, but some of its drawbacks must be addressed. These are:

• It can be complex, so its implementation requires specialized skills, which becomes challenging for organizations with limited resources or expertise.
• Moreover, this technology is expensive since the hardware, software, and specialized personnel are expensive.
• Difficult to handle a sizeable unorganized pool of data.
• They provide unreliable results if the data is incomplete, inconsistent, or inaccurate.
• Hence, by dealing with sensitive data, there are chances of data theft, loss, or breach without proper data governance.

Online analytical processing is a widely used technology for multidimensional study of business data, but data mining is an emerging field of data science. Following are the various other distinctions between the two:

OLAP and Online Transaction Processing (OLTP) are the two dimensions of data management using an IT system. However, OLTP aids in business data warehousing, while OLAP helps in its analysis. Therefore, the other crucial differences between the two technologies are discussed below:

This article has been a guide to what is Online Analytical Processing (OLAP). We explain its examples, advantages, & differences with online transaction processing. You may also find some useful articles here -

• Transaction Processing System
• Data Warehouse
• Data-Mining Bias