As with all technologies there are certain scenarios/situations where use of the technology will result in benefits not realised or even a negative outcome. The reasons for this within data mining are as follows.
Data suitability
In some cases some or all of the data held by an organisation may be unsuitable for data mining and this is one of the reasons that some regard the simplicity of modern data mining with caution (Bramer, 1999 p.xii).
The principles of data warehousing can be applied in identifying suitability of data for data mining:-
- Subject orientated
It is important that there data is available that is related to the subject concerned, non subject related data that is mined will clearly result in false positives (Khan, 2005 p.151).
- Time variant
Any data that is considered for mining should be taken from an appropriate time period (Khan, 2005 p.152), data that is mined and that is particularly old may result in patterns being highlighted that no longer affect the business.
- Non volatile
Data that is to be mined should also be non volatile or static, with any amendments being on a periodic basis, this contrasts to databases which are subject to frequent change as transactions etc are processed (Khan, 2005 p.152).
- Integrated
Data that is to be used in data mining should be integrated which means pulling together the data from the various tables within the database and also includes where appropriate bringing data from other databases (Khan, 2005 p.151).
It is therefore not always possible to mine the data held by an organisation, this can be frustrating to staff who wish data mining to be applied in the organisation.
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