Things You Should Know About Data Mining
If you think data mining is just one of those emerging and fleeting innovations of the 2ist century, then you can stop and read this whole article. Like a magical deep well, this development can open ways to a lot of possibilities and surprises never ever been imagined before.
Data analysis and trends
Data analysis is a unique feature of data mining along with a host of other seemingly impossible things it can contribute to the betterment of human life. It must be understood that data mining is not limited to online data collection alone; it also includes, but is not limited to, the statistical analysis and techniques that are used to search over huge amounts of data in order to determine trends or patterns. This method of inquiry and system of acquiring knowledge is beneficial to all fields of human endeavor particularly in business and research.
Data mining as by-product of internet
Data mining is a fast and easy way of employing an exclusively and potent tool in the data analysis and evaluation of vast databases. Since the internet has been in full bloom, so much data has become available online. So much data can be procured from one scientific research, for instance, such that visual inspection can no longer be enough to come up with a valid and reliable explanation of the information. This gives rise to computer-generated solutions like the data mining. It was in the 1990s when natural science and computer science have become interrelated in order to yield objective and intelligent interpretations.
Giving solutions to timeless problems
Numerous problems have already been solved and helped by data mining. Its ability in predicting trends is one of its best influences in urging nations and institutions to utilize it for their own benefits. For example, government and organizational activities and profile have been benefited by data mining, such as in storing, collecting and monitoring of information in the said fields. With data mining, undesirable and irrelevant information can be detected and eradicated. This is specially benefiting criminal investigations and identifications of fraud. From tracing the patterns of activities of suspects as well as their locations and contacts, catching the culprit is made quicker and accurate.
Formulas called algorithms
Data mining uses formulas called algorithms. The two most common data mining algorithms are called classification analysis and regression analysis. Classification analysis is used to analyze data that is not numerical or qualitative data such as colors, names or opinions. This is also called the descriptive model. On the other hand, numerical or quantitative data makes use of regression analysis. A mathematical formula is constructed to describe the pattern of the data which will then be able to predict the future performance of the data, thus it is also called the predictive model.
Steps in data mining process
The process of data mining has at least seven steps, namely: definition of the problem; building of the database; examination of the data; preparation of the model to be used to examine the data; testing of the model; use of the model; and putting off the results to action. These may sound complicated but with a proper understanding of the data mining mechanics and techniques, you can get the best benefits you have never experienced before.
Linking of data from different branches
With data mining, it is now easier to connect with other units, agencies, and branches to get the information needed. For example, the FBI and the CIA may have different databases but these two agencies can link together in order to acquire the needed results such as in pursuing a criminal or in identifying fraud. The only problem with linkages is the differing structure and formats of their databases. It is therefore necessary to coordinate with each other and make use of similar of the same templates.
This dynamic concept of data mining has indeed become so broad and yet so specific. Understanding all these may appear overwhelming at first especially that it makes use of unfamiliar terms processes. However, time spent in learning its mechanisms and applying its processes cannot be compared to the enormous benefits it will bring you. Whatever your field of expertise is, whether it is business, research, social causes, nonprofit civic organizations, and others, you can greatly benefit from the blessings of data mining.
If there is any negative issue about data mining, it can be about the usual problems of data collection and storage such as plagiarism, intellectual property rights, and the like. Such problems have already been dealt with by the government, so the same solutions can be applied with the use of data mining. It is therefore necessary for every data miner to be cautious and responsible while enjoying its benefits. There should be a balance in everything; thus, the risks must be faced with ready weapons of accountability and objectivity.