StudyDaddy Article Writing IntroductionAssume your organization sells cars and there are 5 models of cars. The company sells all over the United States and so the organization is broken down into regions of the country which in

IntroductionAssume your organization sells cars and there are 5 models of cars. The company sells all over the United States and so the organization is broken down into regions of the country which in

Brooke:

Online Analytical Processing, or OLAP, is a computing method that allows users to easily and selectively extract and query data to analyze it from different points of view (Rouse, 2018). The use of OLAP aides in trends analysis, financial reporting, sales forecasting, and budgeting among other planning purposes (Rouse, 2018). Data can be collected from multiple sources and then cleansed an organized into cubes of related data, this data can further be connected to other cubes, or tables creating a multidimensional database to analyze together (Rouse, 2018). Analysts can perform five types of OLAP analytical operations for multidimensional databases: roll-up, drill-down, slice, dice, and pivot. (Rouse, 2018) Roll-up, also known as drill-up or consolidation, summarizes the data along the dimension. Drill-down allows analysts to navigate further into the dimensions of data, such as taking “time period” to “years” to “months” while analyzing sales growth (Rouse, 2018). Slicing enables analysts to take one level of information for display (Rouse, 2018). Dicing is similar to slicing but allows a selection of data from multiple dimensions to be analyzed (Rouse, 2018). Finally, pivot allows for the analyst to gain a new perspective by turning the data cube, or table on its axis (Rouse, 2018).  

A cosmetic company could use OLAP to chart and analyze cosmetic sales. For instance, MAC cosmetics, has stores in various countries, across various regions, even doubling down in some malls. Likewise, they employ multiple salespersons and makeup artist per store. Their sales can be broken down into type of product such as face product, lip product, eye product and so on, and then further broken down from there such as dividing lip products to lipstick, gloss, lip pencil, and so on, creating a large database to follow.  

Traditional query database data-comparison is limited. It can manage smaller databases, but larger databases can become overwhelming and it makes it harder for the analyst to translate data into information that can be used in decision making ("Online Analytical," 2013). OLAP searches large databases for relationships and aides in analyzing results to produce patterns and trends that were not visible otherwise ("Online Analytical," 2013).  

In my example of a cosmetics company, OLAP can be immensely helpful. The company can utilize OLAP to find correlations to find “best sellers” countries or regions of countries, or see what items have a hard time leaving the shelves in other areas. OLAP can also aide in discovering certain times of the year certain products are more likely to sell and the age range of the customers. Using this information, the company can make decisions for marketing in certain areas at certain times and even decide to discontinue products that are no longer doing well in terms of sales. 

 

References 

Online Analytical Processing (OLAP). (2013, January 4). Retrieved from https://www.techopedia.com/ definition/1225/online-analytical-processing-olap 

Rouse, M. (2018, October). OLAP (Online Analytical Processing). Retrieved from https://searchdatamanagement.techtarget.com/definition/OLAP 

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Bob:

Online Analytical Processing (OLAP) is a high-level concept that describes a category of tools that aid in the analysis ofmultidimensional queries. OLAP enables users to easily and selectively extract and query data to analyze it. OLAP business intelligence queries often aid in trends analysis, financial reporting, sales forecasting, budgeting and other planning purposes. Predictive "What if" scenarios are some of the most popular uses of OLAP software and are made extremely more possible by multidimensional processing. (OLAP.com, 2020).

An example of Online Analytical Processing (OLAP) is a soft drink manufacturer that wants to analyze financial data to better understand current performance and identify areas of opportunity. The basis for analysis is sales and expense data for each product sold, during each month of the year, in each state in the U.S. The data in this scenario is made up of all the different kinds of soft drinks sold, various container sizes, different regions in the U.S., different months and years, profits, and inventory, etc. All these different aspects of data would need to be compared which is what Online Analytical Processing (OLAP) is intended for.

Database querying and Online Analytical Processing (OLAP) are the same in that they both databases to analyze information. The difference between the two is as follows:

Database Querying – A query allows you to utilize a complex database to filter the data from many tables, storing a large amount of data, into a single table so that you can analyze it more easily. Queries also can perform calculations on your data or automate data management tasks. You can also review updates to your data before committing them to the database (Chapple, 2019).

Online Analytical Processing – Online Analytical Processing is a category of software that allows users to analyze information from multiple database systems at the same time. It is a technology that enables analysts to extract and view business data from different points of view (OLAP.com, 2020).

One of the benefits of using Online Analytical Processing (OLAP) is consistency of information and calculations. No matter how much or how fast the data is processed through OLAP software or servers, the reporting that results is presented in a consistent presentation, so analysts and executives always know what to look for where. This is especially helpful when comparing information from previous reports to information contained in new ones and projected future ones. It avoids the lengthy discussions about who has the correct information. Also, multidimensional presentation can create an understanding of relationships not previously realized. Another benefit of multidimensional data presentation is that it allows a manager to pull down data from an OLAP database in broad or specific terms instantaneously. Basically, reporting can be as simple as comparing a few lines of data in one column of a spreadsheet or as complex as viewing all aspects of a ton of data (Bogue, 2005).

Pivot tables and charts fit the mode of being an Online Analytical Processing (OLAP) tool because they allow you to summarize, analyze, explore, and present summary data. Pivot Charts complement PivotTables by adding visualizations to the summary data in a PivotTable, and allow you to easily see comparisons, patterns, and trends. Both PivotTables and Pivot Charts enable you to make informed decisions about critical data in your organization.

References

Bogue, r. (2005). An introduction to the benefits of online analytical processing (OLAP). TechRepublic. Retrieved from https://www.techrepublic.com/article/an-introduction-to-the-benefits-of-online-analytical-processing-olap/

Chapple, M. (2019). What is the definition of database query? Lifewire. Retrieved from https://www.lifewire.com/query-definition-1019180

OLAP.com. (2020). What is the definition of OLAP? Retrieved from https://olap.com/olap-definition

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