Data Warehouses, OLAP and Data Mining Assignment

Data Warehouses, OLAP and Data Mining Assignment Data Warehouses, OLAP and Data Mining â€" Assignment Example > The paper “ Data Warehouses, OLAP and Data Mining”   is an informative version of an   assignment on the information technology. Data management can be performed using different types of data analysis and reporting options. One such option is a data warehouse. Data warehouses are used by organizations to collect data from multiple sources on a large scale in order to perform analysis of the accumulated data for trends and predictive forecasts. The nature of the data warehouse is such that it is a repository of data that lies at the heart of a data management system. They are made up of several components, some of which include the means of data sources, data transformation, reporting, metadata, operations as well as optioning components that might be required by specific organizations (Berson and Smith, 1997). The data warehouse is explored in detail in the following sections. Fundamental CharacteristicThe fundamental characteristics of a data warehouse pertain to the inheren t nature of the data warehouse which depicts the data warehouse to be subject-oriented, including time variance, while being nonvolatile a providing for the integration of data. Aside from this, the data warehouse is basically a repository and it stores mainly historical data. The data warehouse should be implemented and employed when large amounts of historical data need to be managed, which are sourced from many disparate sources. Guidelines for a Success Data WarehouseIn order to develop and implement a data warehouse successfully, some guidelines need to be followed. The business should start off by having a clear and stated goal and set objectives for why the data warehouse is to be implemented. This helps in clarifying the reason for the data warehouse. The decision of building the data warehouse in house and buying an off the shelf product needs to be addressed as per the requirements of the company. The gap between IT and the business needs to be addressed for effective im plementation. The implementation of the data warehouse should be done in a gradually incremental manner while allowing for growth and scalability. The architecture and the structure if the data warehouse needs to be set up to provide for efficiency. Aside from this, it is important for the data to be stored, to be cleaned in the extraction, transformation and loading process. In addition, the querying capability should be enabled and allowed for at the time of implementation. Dimensional ModelingDimensional modeling is a particular type of data modeling that is associated with data warehouses. It is significantly different from the entity-relationship modeling that is employed for databases. The dimensional modeling instead involves denormalization of data that are grouped as per the star schema (Kimball and Ross, 2002). The star schema corresponds to a fact being associated with several dimensions. A fact table stores facts and foreign keys to the dimensions, while the dimensions are referred to by the fact and can be hierarchical in their different levels. The dimension table and the fact table association are depicted through the following diagram which depicts the star schema for a sample retail store. The fact table is titled store sales, while the store, payment type, time, product and customers are dimension tables.