Competing with the help of data mining

Ever tried making decisions and wonder if they were the right ones? Ever tried going to business knowing exactly what will happen after a month or so? Obviously not. The instinct of a businessman can only help him so much, and from there, another question arises, "What now?". That has been the dilemma of most businessmen for quite some time. Business itself relies on current data and clientele to maintain a number of employees and equipment to handle, but that's just the surface of it all. Businessmen have to translate that data and be able to think at least two steps ahead to maintain and expand clientele being that there are certain variables which cannot be foreseen and trends that cannot be spotted right away. Corporations, schools, and other income generating businesses operating under strict competition environments must have that edge in order to stay competitive as well as flourish in the process. This is where data mining comes in.

Data mining, is defined by the article as the extraction of hidden predictive information from large databases. It is helpful in so many aspects especially business decisions. Data mining uses data stored from data warehouses and databases and with the use of data mining algorithms businesses are given special capabilities namely automated prediction of trends and behaviors, a handy tool in business especially when the scope of the business is service oriented, and automated discovery of previously unforeseen patterns which are very important as it will help boost product purchase( in sales) or usage (service). Although, without a powerful computer to analyze data takes so long to handle even by the best statisticians, that is why a prerequisite of data mining aside from mass collected data and data mining algorithms is a powerful computer. But even with visible patterns, there will also be the fact that not most of them are to be used as well as most of them has a low probability of coming into play. This, is resolved by the statistics involved in data mining. Statistics or statistical techniques as explained by the article are not data mining although it is very helpful in solving problems concerning data mining. With data mining, businesses can make informed decisions and take calculated risks.

Models, a very helpful tool in analyzing mass amounts of stored data in your data warehouse, are generated and sampled onto the computers. Models generally generate answers for your questions. And from broad questions, you can get to answer more in-depth questions answered and your once unknown territory, you will be able to do selective targeting. After making a model, data mining tools are used. Commonly used tools in data mining are the following: artificial neutral networks, decision trees, genetic algorithms, nearest neighborhood method and rule induction. As stated by the article, the classical techniques are statistics, neighborhoods and clustering while the next generation techniques are the trees, networks and rules.

Relation of data mining and the data stored in the data warehouse require a relational database system. This will help in faster acquisition of desired data. On-line analytical processing or OLAP as defined in the article refers to array-oriented database applications that allow users to view, navigate through, manipulate, and analyze multidimensional databases. OLAP is more effective method of analysis compared to the usual decision support systems.

Data mining is a really effective technology in which gives long and short term benefits, it is actually a trend in which all large scale companies should follow, especially now that dependency to technology is becoming more and more a factor to society. Still, even if it is a tool for success, there are still factors to consider as data mining is still dependent factors in which it derives its success from: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied. When these factors are resolved, customer relations are improved and thus resulting to success for the business.

Techniques also fall in the same arena, data mining techniques such as histograms, rules, linear regression, clustering and so on cannot be used over and over again to solve a problem. Each problem can and most probably will be different from each other that is why, as stated in the article, techniques to be used are mostly based on trial and error. A constantly changing world means a company can only desire but not be able to formulate the perfect model for them. Some of these techniques are being taught to us right now in school. Methods like linear regression, linear relationship between the dependent and independent variable, which have applications (ex: QM for windows) that help understand the basic insight and know-how to manipulate and have a general knowledge on how linear regression works. Clustering which helps analyze competition properly and knowing the niche and competition in that niche. Although we may not know the total concept, the important thing is grasping the basic idea of it especially because we are future businessmen ourselves.

Concluding it all, data mining does have a lot of positive feedback, but there is also the element of surprise, an unpredictable turn of events (example given in the article: internet) as the world is, and will always be, continuously changing. Relying on technology does have a lot of benefits, but ingenuity of the human mind and entrepreneurial "gut feeling" should not be ignored.

submitted to Mr. Ramon Duremdes for our DATAMIN class.
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