The term OLAP was introduced in the 1990s by Ted Codd, who also defined the famous twelve rules for OLAP technology, including transparency, client/server architecture, multidimensional view, multi-user support, etc.
OLAP stands for On-Line Analytical Processing and is a technology that enables users to extract, view, and analyze business data from different points of view (dimensions), i.e., OLAP analytics is an integral part of multidimensional data analysis.
How did OLAP Analytics Start?
The objects that contain the analyzed data are called OLAP cubes. The technology of OLAP cubes was developed as early as the 1970s for storing data in a format that would be similar to the way humans see data and at the same time, could be easily queried by business intelligence (BI) software. The main advantage of OLAP cubes was how easily and quickly developers and data analysts could pull data together from the data warehouse into one repository. They helped to overcome the limitations of relational databases and ensured rapid multidimensional data analysis.
The rising popularity of OLAP data analytics and OLAP cubes led to the introduction of the first OLAP server, Microsoft Analysis Services, in 1998, which created momentum for further OLAP technology development.
Pros and Cons of OLAP Processing
As any other technology, OLAP processing has its pros and cons. Among the pros, one can mention:
- Consistency of information and calculations
- Absence of complex sql query statements
- Possibility to apply “what if” scenarios in data analysis
- Using broad business terms instead of narrow business analysis ones
- The wide popularity of spreadsheet applications using OLAP processing
- flat learning curve
Of course, there are also some cons:
- There might be issues with the data integration
- Management and maintenance of complex OLAP data cubes can be pretty resourceful
- Processing the cube data can take a long time in case the amount of data is huge
It is fair to say that these are important factors that lead to heated discussions of OLAP becoming too expensive and cumbersome as compared to other approaches.
Of course, depending on your business model, the structure of your business data, and infrastructure, some pros and cons can outweigh the others. While OLAP processing technology can be the perfect data analysis choice for one business, it might mismatch another one.
Is OLAP Really Dead?
Articles on whether OLAP analytics is becoming obsolete have been resurfacing on the Internet for the last fifteen years, opinions varying from OLAP being long dead to it being more alive and better than ever.
Neither of these polarized opinions seems to be completely correct or completely wrong.
Undoubtedly, the business intelligence market has undergone great changes during recent years, and numerous new technologies have appeared, many of them outperforming the old ones in certain aspects. However, when we look at the array of popular BI tools, such as Ranet OLAP or Tableau, or OLAP vendors, which include such tech giants as Microsoft, IBM, and SAP, it becomes obvious that OLAP analytics is definitely not dead, but rather undergoes massive changes. The model of dimensions and measures is still topical, but the underlying technology and the way we use it are changing.
As a result, OLAP-based BI tools are undergoing drastic changes, too, trying to provide their users with more and more advanced features. For example, one of the most popular tools nowadays, Ranet OLAP, has been introducing more data sources that can be used for data analysis, developing UI for complicated forms of business analysis and forecasting, making the tool as easy to integrate into other solutions as possible, etc. All these features are becoming more and more sought after in a BI tool, and BI vendors are increasingly developing them.
All in all, OLAP analytics and BI tools that use it have been facing heavy competition from other technologies but continue to occupy an important place in the BI world. OLAP will keep changing and improving to encompass more complex budgeting, forecasting, and business planning capabilities.