Essays on Business Intelligence - Project: Date Warehouses & OLAP & Data Mining Assignment

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An Introduction to Data WarehousingLiu (2011) defines data warehouse as a subject oriented, time-varying, integrated, and non-volatile collection of data that supports management’s process of decision making. According to Berendt and Spiliopoulou (2000), a data warehouse is a centralized depository that amasses data from multiple informational sources and changes this data into a general, multi-dimensional data model for resourceful querying and analysis. Berendt and Spiliopoulou (2000), indicates that a data warehouse has the ability to tackle a wide variety of occurrences. In keeping with Liu (2011), a data warehouse is a stockroom for an organization’s historical data.

Information got from operational systems is retrieved and imported into the data warehouse regularly. Resultantly, complex enquiries and queries are conducted through the data warehouse with very little intermission to the operational systems. Berendt and Spiliopoulou (2000), indicates that the imported data can only be read only type and can only and the existing data in the warehouse. The value of data warehouse increase with the increase of data in the warehouse, since analyses dating over a long duration of time is possible. When a user’s query crops up to the warehouse, it is possible to retrieve all historical data addressing the query, this supports in decision making. There are two forms way by which an organization manages its information, the first is through operation systems and the second is through data warehouses.

For online transaction processing (OLTP), organizations use operational systems. Data warehousing on the other hand are designed to maintain (OLAP). In reference to Liu (2011), operational systems concentrate on large volumes transactions processing on daily basis and they use real time data.

Berendt and Spiliopoulou (2000), further indicates that these systems are generally process oriented and their focus is usually on specific tasks like student registration, management of employees’ timesheet and updating financial transactions. These systems are optimized for simplicity and speed of modification, thus allowing effective, efficient and trouble-free data entry retrieval. Berendt and Spiliopoulou (2000), argues that such systems also follow historical and transactional data. In reference to Liu (2011), operational systems mainly focus on current data management. The chief function of data warehouse is to manage and store historical data.

Berendt and Spiliopoulou (2000), argues that data warehouses are generally subject specific and carry data from multiple operation systems for them to support organizational decision making. In academic institutions, data warehouses are used to address issues regarding pupil’s satisfaction, the attrition rate, and the effectiveness of new instructional techniques. In response to a concern, relevant data can be mined and utilized for data analysis and generation of reports. Data warehousing applicationsThere are three types of data warehousing applications; Personal productivity applications; these are applications like statistical packages, spreadsheets and graphic tools.

These applications are useful in presenting and manipulating data on individual PCs and they are developed for standalone environment. In reference to Maurizio et al. (2003), the tools address applications that require small volume o f warehouse data. Data query and data reporting applications; they deliver wide data access via simple, list-oriented queries. They also generate simple reports which provide view of historical data. However they do not address the enterprise requirement for in-depth analysis and planning (Maurizio et al. 2003). The plan analysis application; they address essential business requirements like budgeting, forecasting, customer profitability, f8inancial consolidation, sales analysis and manufacturing mix analysis; all that use historical, projected and derived data (Maurizio et al.


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