Management of modern companies comes with the necessity to handle more and more complicated and processed data. Usual administrative, finance or accounting systems are often no longer able to fulfill those needs. Businesses looking ambitiously into the future use the most professional technologies for working with data. Such a technology is OLAP (Online Analytical Processing).
OLAP is a technology, created for mass processing and organization of huge commercial databases and handling of business analysis. OLAP allows for simultaneous information analysis from multiple data sources.
Where is OLAP being used?
OLAP databases are divided into modules. Every module is being designed in a way, so it would fit to the method of data downloading and analysis. Thanks to that, it’s possible to create reports in the form of pivot tables or charts. In times when effective data processing and analysis can define a competitive edge for your company, the technology and OLAP systems are must haves when it comes to making aware decisions based on real-time information.
OLAP helps to answer questions like:
- How do you compare total sales of services from last year to sales from a previous year?
- What is the distribution of income for each product for different age groups of customers?
- How to compare the company’s current profitability with last year’s profitability?
These are just examples of analyzes you can perform using OLAP. OLAP tools are used by organizations from various industries for sales, business and marketing analyses. OLAP is also widely used in supporting data processing for the purposes of controlling and management accounting.
OLAP vs. data warehouse systems
Data analysis using OLAP technology is often compared with another category of IT systems – data warehouse systems. Data warehouses also enable comprehensive analysis of data from various sources. The system collects data from multiple databases, cleans them and unifies them, enabling insight into the data from a broader perspective and faster finding of relationships between them, on the basis of which it is possible to observe trends. Data warehouses also allow you to analyze data slices and create very detailed reports.
OLAP vs. OLTP – what differs between those systems?
So let’s explain the differences between OLAP and OLTP data processing. Although both systems work with data, they play completely different roles. OLAP, as we explained earlier, supports extensive queries on large amounts of data. The system collects data from various sources, processes it during the ELT (Extract, Load, and Transform) process, and then stores it for extensive analysis in the reporting process. Thanks to OLAP, we can examine the effectiveness of activities among individual customer groups, predict churn based on sales data and analyze trends or forecast demand. On the other hand, we have OLTP (online transactional processing), i.e. data processing involving the simultaneous execution of many transactions. Databases store information on a specific section of the business (e.g. transactions in an online store) and are updated on an ongoing basis. OLTP technology perfectly processes a large number of simple queries, but it is not designed for advanced analytics. It works perfectly where there is a large number of transactions or registrations – however, the system’s performance is not sufficient to store more than just the latest information.
OLAP tools
The OLAP structure is currently considered as one of the most effective for processing various types of data in different fields. It allows you to quickly and easily access the data you need for immediate use in subsequent queries and analyses. Thanks to this, it is possible to create reports necessary to make intelligent business decisions. OLAP tools change transactions stored in relational databases into multidimensional logical structures. These structures are arranged in the so-called cubes, thanks to which you can view and analyze data from different points of view. You can use data sources built on OLAP cubes, e.g. creating reports in the NAVIGATOR system.
What is a OLAPs cube?
An OLAP cube is a multidimensional database built using data from transactional systems. These may be Workflow, ERP or other systems. The base has the shape of a cube, which includes several dimensions. OLAP cubes are a kind of vectors in which accurate and reliable information that appears at the right time is placed in a hierarchical manner. OLAP cubes, thanks to their multidimensionality, enable data analysis in many sections, such as time, category or organizational structure. Due to the lack of transactionality of database operations, data analysis takes place instantly. The extension of the possibilities of OLAP tools can be additional cubes, thanks to which it is possible to use more than three dimensions in data processing.
Types of OLAP cubes
OLAP cubes as a system for data analysis work well in every company, regardless of size and industry in which it operates. However, in order to best adjust the tool to the needs of various organizations, several types of OLAP cubes have been created.
MOLAP – multidimentional OLAP
MOLAP cubes use specialized data and database structures to organize and analyze the collected data. This type of tool uses array technology and efficient data storage techniques to reduce volume requirements. The use of MOLAP cubes helps, for example, to estimate system performance gains. If MOLAP is used for data supporting specific decision-making processes, it provides very high performance and additionally uses less disk space than other systems. The only disadvantage of the MOLAP cube-based system is that the system may have difficulty loading large amounts of information (among others, due to showing repetitive data), which may slow down the data processing
ROLAP – relational OLAP
Of all the types of OLAP systems, relational is the most used. The popularity of this type of solution is the result of the ability to analyze increasingly large databases. ROLAP is most often compared with the MOLAP system. A ROLAP cube can be used in any type of relational data table. It allows you to make corrections in the ETL code in accordance with the needs of a given organization. Each user can easily use the information in the database, e.g. using tools designed in SQL reports. SQL engines, which are part of some ROLAP tools, additionally support complex multidimensional analyses. As for the disadvantages of this solution, experts most often emphasize the relatively poor performance of the system when creating queries.
HOLAP – hybrid OLAP
HOLAP cubes are carried out using RDBMS systems or use intermediate MOLAP systems. The system was called hybrid because it uses two technologies: MOLAP and ROLAP, using their best parameters. The data is stored in the relational engine, and another part of it is placed in a multidimensional database. HOLAP uses the data depending on the needs of the processed data. Thanks to this, the system achieves high processing speed and properly determines the scale of data and adjusts the support for querying the database.
DOLAP – desktop OLAP
The last of the four most popular OLAP systems – desktop OLAP – stores data in the user’s environment. The advantage of DOLAP is the ability to handle multidimensional processing, thanks to the use of local engines that support this process. In order for multidimensional processing to be unhindered, small pieces of data are required that are distributed in advance or on demand. To manage the DOLAP system, a central server is necessary, as well as special procedures responsible for the preparation of data cubes.
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Summary
OLAP is particularly useful in companies that use aggregation calculations on large amounts of data. It is worth considering implementing this system if:
It is necessary to perform complex analytical queries.
You need a simple data-driven reporting tool to make business decisions based on it. OLAP is perfect, for example, when working with documentation management platforms.
You want to provide a series of aggregations to provide users with fast and consistent results.
Thanks to the OLAP system, you can almost completely eliminate the SQL language when applying complex analyses. This is a huge advantage for people at managerial levels who make business-critical decisions every day, but do not necessarily support technical databases. OLAP systems are perfectly optimized for scenarios with a large number of read operations. This is of great importance in the implementation of processes such as business analysis.
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