Introduction According to Mihai & Crisan (2010), in the world today, almost every transaction that takes place is recorded and kept in files for later references. This has resulted to an increase in the data produced and stored in various fields of activity. In fact, every enterprise has accumulated data on operations, activities and performance. All these data hold valuable information, e.g., trends and patterns, which can be utilized to improve business decisions and optimize success. Mihai & Crisan (2010), point out that that traditional methods of data analysis that are based mostly on humans dealing directly with the data, basically do not scale to handle these large amounts of data sets. For this reason, various advanced technologies called data mining techniques have been developed to process the huge volumes of data. According to Han & Kamber (2000), data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases and data warehouses. The data mining process is employed by several sectors to manage and deal with troubles that are normally related to clients and which hider efficient operations of various entities. Important features of data mining tools Data mining tools integrate many operations and provide an easy-to-use way to perform the data mining process. There are many different types of data mining tools. The tools are diverse in design and implementation. However, there are several important features for data mining tools that enable them to be accommodated by users in the most efficient and effective manner. The first important feature is the capability to access various data sources. Usually, data is obtained from a variety of sources in diverse designs. A good tool should enable the user to access different data sources without difficulties. It should also posses the ability to run on huge data sets. This is very important in circumstances where an entity stores large amount of data (Mihai & Crisan, 2010). Han & Kamber (2000) argue that good data mining tools should be user friendly. According to them, this is significant since most of the times the persons using the tools are not specialists. The data-mining tools should possess the ability to process data in a most efficient manner since this is often considered crucial in solving problem. Connolly, Begg & Holowczak (2008), point out that data mining tools should also enable good data and model visualization. This is will enable users to properly analyze the data and make sound decisions. They further state that the data mining tools should also enable users to easily integrate different techniques during the mining process. This is because there is no one tool that can effectively handle all prevailing dilemmas. Therefore, superior data-mining tools should allow users to incorporate a variety of procedures in order to deal with various dilemmas. Han & Kamber (2000), assert that since the rate of innovation is very rapid, new techniques and algorithms are always present in the market, it is also important that data mining tools provide good extensibility means. This design allows users