Data mining tools are vital because they have significantly reduced the time taken in answering business questions, which were traditionally too much time consuming to analyze. Currently, most organizations have adopted and implemented the existing data mining software and hardware platforms to improve the value of their stored data. These hardware platforms can be integrated with new products and system as technology advances. Integrating data mining hardware platforms with other parallel processing computers or high performance client/server improves the analysis of massive databases (Hoptroff & Hoptroff, 2001).
Data mining techniques emerged as a result of product development and a long process of research. This idea was first developed when businesses began storing business information on computers. Significant improvements have been witnessed in data access and generated technologies, which allow users to search their data, in real time (Williams & Simoff, 2006). Data mining software is currently available for use, in the business world, because of the three technologies that support it, and they include data mining algorithms, massive data collection, and powerful multiprocessor computers (Williams & Simoff, 2006). The amount of raw data stored in business databases is currently exploding. A database is measured in gigabytes and terabytes. In the current, competitive business environment, raw data alone does not provide enough information for studying and predicting the market environment. This has called for the need to convert these terabytes of raw data into other significant insights that easily provide a guide for their investment, marketing and management strategies (Prabhu, 2004).
Significant improvements in data transmission, data capture, storage capabilities, and processing power are enabling companies to consolidate their various databases into data warehouse (Prabhu, 2004). Data warehousing is the process of centralizing