Online Analytical Processing:
OLAP is an acronym for Online Analytical Processing and it is considered as an extension of decision support systems. OLAP designates a category of applications and technologies that allow the collection, storage, and reproduction of multidimensional data. Multidimensional analysis is the analysis of data based on more than one factor. The two basic components of OLAP are dimensions and measures. The dimensions that are included in the analysis are time, location, product, and customers. Measures are the quantitative representation of dimensions. Examples are revenues, costs, and units sold. The main task of an OLAP is to transform relational or non-relational data into a highly explorable structure, which means that data can be broken down into small units to derive meaningful information. These explorable structures are commonly called cubes or Power Cubes.
OLAP is useful to managers, analysts, and executives. It supports multidimensional data analysis and makes data access easier and faster. Moreover, the ability to view data in different formats makes the system flexible. Apart from answering questions like who and what, an OLAP also provides answers to what-if and why. Refer to Exhibit 6.3 on OLAP technology used by Toyota Financial Services.
Characteristics of OLAP
Apart from facilitating a multidimensional view of data, there are two more features that OLAP provides: complex modeling and time intelligence. These characteristics are discussed in detail here.
Multidimensional views
All business models have a minimum of three dimensions: time, location, and product. These dimensions may vary according to the analysis. Managers should have the flexibility to use the data for analytical processing irrespective of the database design. In other words, database design should not be a constraint for the access or use of information. Managers should be able to analyze data at any level with ease. The OLAP software should hide complex queries from the user. Managers with no knowledge of database concepts or programming should also be able to access data with ease. Moreover, the time taken by the OLAP system to process user requests should be consistent.
Complex modeling
The most important use of OLAP is its ability to perform complex calculations. The key performance indicators of a business are derived using complex calculations. For example, sales analysis is done using the trend algorithm. Competitor analysis also requires modeling complex relationships. The ability to model complex relationships is a cardinal feature of OLAP. The OLAP software should provide powerful but simple tools for complex calculations. The methods used for computation should be clearly understood; otherwise using the system would be difficult and time consuming. OLAP systems are rated on the basis of their ability to create information from data.
Time intelligence
Time is also an important factor in analytical processing. It is unique because it is the only dimension that follows a sequence. Generally, business analysis is done over a period of time, i.e. monthly, quarterly, etc. An example for timeliness is calculation of a moving average of the last five years of sales. The use of the time hierarchy differs from other hierarchies. For example, managers may seek a break-up of sales in a week, a month, or during weekends. However, it is unlikely that they will ask for details regarding the first five shirts sold.
Benefits of OLAP
OLAP software can be very useful to an organization, especially with respect to data management. But many organizations are not familiar with the benefits of OLAP. Such organizations fail to capitalize on the opportunity to improve analytical processing capabilities. Some of the benefits provided by OLAP are listed here:
- A well-designed OLAP increases productivity.
- It makes complex modeling easy.
- Software specifically designed for OLAP may be useful in developing applications quickly and providing better service.
- Faster application development reduces application backlog.
- The query drag option helps in easing network traffic in transaction processing and Data Warehousing.
- It helps the organization respond quickly to market demand by modeling real business problems and using human resources efficiently.
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