However a review ordered by management has identified that some of the data is underutilized or is too expensive to collect relative to its value while, at the same time, information required to check the working of the system of business and manufacturing processes is not always available. Data cleansing is a routine part of the initial Data Mining process and aims to ensure that the right data are collected in the right quantities and of an appropriate quality to meet business needs. Consequently information emerging form this process can be used to cut out expenditure on gathering inappropriate data. However, it also has the potential to facilitate a more proactive 'lessons learnt' approach to system management. Although this is not usually promoted as a fundamental part of Data Mining, this positive side-effect can potentially play a significant part in contributing to organisational, as well as product, development in future.
Data mining aims to make sense of data in relation to the organisation's decision needs.
Data miners are experienced in statistical modelling for decision support and use a number of fundamental tools such as data visualisation , summarisation and pattern recognition through to more sophisticated modelling tools such as cluster analysis , discriminant analysis, regression analysis , neural nets and other machine learning techniques. Data mining is NOT abstract data analysis. Rather it involves direct interaction with the client who has the domain knowledge so that the most appropriate analysis is conducted. Data mining is a collaborative partnership between OR practitioners, who can develop and apply the scientific tools, and the client organisations, which possesses the intuitive understanding of the issue. It is this partnership that will lead to analysis and modeling that will help the organisation to better understand its customers and systems so that an increased return on investment in the data collected can be realised.