Many decisions in professional and private life are taken on the basis of data that come from all sorts of information systems. Business Intelligence (BI) is about the developments in the way we can use data stored in those information systems to generate new and useful information that can support executive managers in taking business decisions. BI is an umbrella term that combines the processes, technologies, and tools needed to transform data into information, information into knowledge, and knowledge into plans that drive profitable business action. BI encompasses: data warehousing, OnLine Analytical Processing (OLAP), business analytical tools, data mining, business performance and knowledge management.
The commercial interest in BI is growing due to the increasing awareness of companies that the vast amounts of data collected on customers and their behaviour contain valuable business knowledge. Different types of knowledge can be derived from data warehouses, like rules characterizing potential customer classes, knowledge classifying groups with larger risks, and so on. Quite often useful causal relations are hidden in company databases and the goal of the BI/data mining process is to induce these from the data and to represent them in meaningful ways to improve business processes, typical business cases are: cross-selling, churn in mobile communications, and risk analysis in financial services. The emphasis in this course will be on the methodological and practical aspects of BI.
In this course the student is given an introduction in decision support systems and intelligent systems within the framework of BI. After this course the student has basic knowledge of – why computer support is needed for certain business decisions; – the principles of knowledge management en knowledge-based systems; – the construction of a data warehouse; – the business implications of a data warehouse; – OLAP database technology and reporting; – the fundamental issues of knowledge discovery in databases, i.e. data mining, such as learning algorithms for classification, prediction and risk analysis; – the data mining process; – a key data mining model: decision trees or neural networks; – performance issues, interpretation, and the business relevance of data mining models.
In addition, the skill objective for the course is to give the student some hands on experience with business intelligence software. By working with the software (at home) the student – has to develop and analyse a data cube (software Cognos); – has to analyse a simple data set with a data mining tool (software WEKA or R).