Flexible Data Extraction for Analysis using Multidimensional Databases and OLAP Cubes

Abstract: Bright is a company that provides customer and employee satisfaction surveys, and uses this information to provide feedback to their customers. Data from the surveys are stored in a relational database and information is generated both by directly querying the database as well as doing analysis on extracted data. As the amount of data grows, generating this information takes increasingly more time. Extracting the data requires significant manual work and is in practice avoided. As this is not an uncommon issue, there is a substantial theoretical framework around the area. The aim of this degree project is to explore the different methods for achieving flexible and efficient data analysis on large amounts of data. This was implemented using a multidimensional database designed for analysis as well as an OnLine Analytical Processing (OLAP) cube built using Microsoft's SQL Server Analysis Services (SSAS). The cube was designed with the possibility to extract data on an individual level through PivotTables in Excel. The implemented prototype was analyzed, showing that the prototype consistently delivers correct results severalfold as efficient as the current solution as well as making new types of analysis possible and convenient. It is concluded that the use of an OLAP cube was a good choice for the issue at hand, and that the use of SSAS provided the necessary features for a functional prototype. Finally, recommendations on possible further developments were discussed.