Data Mining In Tracking Customer Behavior Patterns - Essay Example

Data Mining Introduction Data mining is a key technology development in the sphere of data extraction. It is defined as the automated extraction of hidden predictive information from large and very large databases. Data helps managers to make intelligent decisions. Data mining helps in providing predictive information allowing the manager to be more proactive than being reactive to situations. Data mining is not just a collection of data; it is a combination of three technologies primarily contributed by the increase in the computing power, combined with improved data collection and management in addition to the improvements happening in statistical and learning algorithms. The computing power is increasing at the rate specified by Moore's law, doubling every eighteen months. The technology upgrade to parallel processing has vastly contributed to more powerful machines. There have been a number of statistical applications and algorithms that were waiting for larger computing power to arrive. Data mining makes use of these algorithms to enable data mining possibilities. In addition to these, data is being collected in a very large scale at all levels. More the data better the data mining exercise has been the watchword of most of the work that is carried out. All these combine to make data mining. Using these data and applying appropriate models, the results of the data mining is obtained. This would enable businesses to identify buying behaviour patterns from customers; identify customer demographic characteristics and predict customer response to mails. Data mining in Banking Most of the cases, both commercial and scientific establishments report a condition where there is a large quantity of data which is collected and stored. But there is hardly any information for the people to make use of. In its basics, the data mining efforts start with employing appropriate data models that would help in understanding the system and its behaviour (Hand D J, 2001). This would further help in augmenting the nature of work executed and the future of the object becomes more predictable. This is possible to do only if the object is understood well and the modelling is realised to the closest possible accuracy. A number of modelling tools help in data mining. Typically, Decision Trees, rule Induction, Regression Models and Neural networks. All these contribute to extracting needed data from the databases using the data mining tools. These are not simple straight forward SQL statements. Qualitative analysis is possible with the predicate data that would use this to identify and get objective visualisation of the object being modelled. Whereas in a quantitative analysis, the data is used for automatic processing based on specific input data or time. Based on the model the information and data available in the system is extracted to meet the requirements. In case of the banks, this would help them in identifying and detecting patterns of fraudulent credit card usage. The banks might like to identify loyal customers and those who might change their loyalty even with a minor issue. It also helps in identifying credit card spending by customer groups and finding any specific correlation between different financial indicators. Issues with using and administrating data mining products Most of the data mining work is done using tools that would execute the job required by the users of the system. These tools are made to build an appropriate statistical model that might be required for the user. These data mining exercises generally provide the industries in ascertaining the trends, patterns and relationships in the data present. This would help the companies, for instance, to identify the market segmentation, detecting fraud in systems, direct marketing, customer churn, etc., All this would help the companies in realising a large movement in the market helping them to realise where the market is moving and appropriately organise their own internal plans to take care of this movement. This would also let the businesses know what the current standing is and where the future is migrating to. Data Mining is fast becoming one of the services that needs to be provided to the industries to realise the benefits of the data already present with them. Issues in data mining The issues faced by the users in data mining are as follows: 1. The companies have IT skills that would gather the data and have them saved. However, most of the companies do not have skills to model the available data and thereby produce information out of it even if it is not very accurate. The end user usability is the major concern of most of the companies which makes it difficult for the companies to make use of data mining tools. 2. Apart from this, the tools that are employed as of date is still not user friendly and requires expert help to take care of the requirements. 3. A major social issue normally discussed when ever the issues of data mining is taken up, is the issue of privacy. The privacy in the community could get affected if large scale data mining is taken up and the data is collected from all sources. This could take up the form of collecting the data about the telephone calls of all users of a telephone company. If the data is analysed then specific telephone numbers and the nature of calls that a particular person makes will all be identified. This will also enable the company to identify where a specific person is calling and the nature of business that one is getting into. This will impinge the privacy of a person. 4. The accuracy of the algorithms is under a question always. It has been upheld a number of times that the accuracy of the information obtained through statistical algorithms is not firm and true always. They are only pointers to the algorithms and they do not conclusively suggest any specific conclusions. The accuracy of the data mining exercise has to be taken with a pinch of salt. All these issues are faced during the process of data mining. Countering the issues Data mining is still a nascent industry and needs to develop in order to bring about the needed user friendliness. The models are created by experts but the models thus created can be used by any body and the people who make use of the models need not be experts themselves. The user friendliness of the model is dependent on the way the model has been designed and understood. However, the specifications of the model generally stems from the users themselves. More and more tools are coming in regularly for data mining and they are expected to develop further and become user friendly and comfortable to work with. In addition to this, the changes are happening on the usability front as well which should make the human interface more comfortable to operate on. Infringement of privacy would continue to haunt the data mining software simply because there will be personal data which the companies and businesses would like to query about. This is true of every bank and every credit card use that is done by the people. Every transaction leaves a track that would give out the user's preferences and personal information. It is the banker who should not be divulging the personal information. The same is valid in the case of data mining software too. Divulging the personal data will be the prerogative of a few who will hold the data under their custody. The science of statistics has been growing since the days of Euclid. But still the study has never been giving firm answers to any statistical question. The whole issue lies on the probability of occurrence. Therefore, the algorithms adopted for this purpose would also be based on the question of chance. This lacuna in statistics is not an issue of the data mining exercise but that of the algorithm and this should be getting better over a period of time. Conclusion Data mining as a tool would help companies and businesses to know more about their clients; their behaviour patterns and their preferences. This would enable the company to work out strategies that would make them to be more customer-friendly. The company would have to foresee the possible options that the customer might have and the kind of choices they would prefer. This would help from deciding on the colour of the product to position of the product, size of the product and very many other physical and operational attributes that would enable the people to take to the product more. Data mining would enable the businesses to find a trend where there was nothing to find earlier. These are solutions for fuzzy behaviours. However, Data mining being a nascent industry does not possess all that is required to give the right answer to every question. And it is also not for laymen as it stands today. But with the creation of more and more models, it is only a matter of time before the models evolve for most of the common requirements and the companies are able to derive what they require out of these models using semi-skilled or unskilled people in the required technology area. References 1. Hand D J, 2001, Principles of Data Mining, Bradford Books, Camb. Mass. 2. Read More