Context of Data Mining in Business Information
ABSTRACT
Presently, Business Intelligence (BI) analysis solutions are manually operated which makes it time consuming and difficult for users to extract useful information from a multidimensional set of data. Henceforth, by applying Data Mining (DM) algorithms for Business Intelligence, it is possible to automate the analysis process, thus comes the ability to extract patterns and other important information from the data set.
Understanding the reason why Data Mining is needed in Business Intelligence and also the process, applications and different tasks that Data Mining provides for Business Intelligence purposes is the main subject area in this essay. Data mining process is also commonly referred to as the Knowledge Discovery in Databases (KDD) process.
1. INTRODUCTION
Business Intelligence (BI) solutions has been a hot topic among companies for many years, due to optimization and decision making capabilities in their business processes. The demand for more sophisticated and intelligent BI solutions is constantly growing due to the fact that storage capacity grows with twice the speed of its processor power. This unbalanced growth relationship will over time make data processing tasks more time consuming when using traditional BI solutions.
Data Mining (DM) offers a variety of advanced data processing techniques that may beneficially apply for BI purposes. This process often requires customization of the DM algorithm with respect to a given BI purpose. The comprehensive process of applying BI for a business problem is referred to as the Knowledge Discovery in Databases (KDD) process and is vital for successful DM implementations with BI in mind.
BI can be applied in many interesting ways with one important thing in common which is aiding the user in the process of analyzing extensive quantities of information. However, the BI complexity of the individual solution varies a lot and it can be used in distinguishing the solutions in terms of how
Presently, Business Intelligence (BI) analysis solutions are manually operated which makes it time consuming and difficult for users to extract useful information from a multidimensional set of data. Henceforth, by applying Data Mining (DM) algorithms for Business Intelligence, it is possible to automate the analysis process, thus comes the ability to extract patterns and other important information from the data set.
Understanding the reason why Data Mining is needed in Business Intelligence and also the process, applications and different tasks that Data Mining provides for Business Intelligence purposes is the main subject area in this essay. Data mining process is also commonly referred to as the Knowledge Discovery in Databases (KDD) process.
1. INTRODUCTION
Business Intelligence (BI) solutions has been a hot topic among companies for many years, due to optimization and decision making capabilities in their business processes. The demand for more sophisticated and intelligent BI solutions is constantly growing due to the fact that storage capacity grows with twice the speed of its processor power. This unbalanced growth relationship will over time make data processing tasks more time consuming when using traditional BI solutions.
Data Mining (DM) offers a variety of advanced data processing techniques that may beneficially apply for BI purposes. This process often requires customization of the DM algorithm with respect to a given BI purpose. The comprehensive process of applying BI for a business problem is referred to as the Knowledge Discovery in Databases (KDD) process and is vital for successful DM implementations with BI in mind.
BI can be applied in many interesting ways with one important thing in common which is aiding the user in the process of analyzing extensive quantities of information. However, the BI complexity of the individual solution varies a lot and it can be used in distinguishing the solutions in terms of how
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