Bigdata is becoming increasingly challenging for large corporations. The term "big data" stands as a metaphor for a worthless data mountain in which to seek knowledge. Bigdata Mining describes statistical methods to search for trends, cross-connections and new data in mass data. It is not possible to process such huge amounts of data manually, which is why computer-based methods must be used. These methods can also be used for smaller amounts of data. As a rule, data mining refers only to the analysis step within the process.
Data Mining and Big Data
With data mining, significant amounts of data can be examined by computer-aided programs. The term data mining is a bit misleading because it's not about generating data, it's about gaining knowledge from data. The term has prevailed mainly because it is short and precise. In general, data mining can be described as a process of extracting knowledge that was previously unknown and considered potentially useful. Bigdata describes amounts of data that are too big, big, or just too fast to change. Manual collection or processing with classical methods is therefore excluded. The collected bigdata to be used for data mining can come from any source. These range from electronic communication of companies and authorities to records of surveillance systems. The desire for the analysis of Bigdata, in order to use the knowledge gained, often comes into conflict with the personal rights of other persons, which is why should be secured in advance.
Data Mining and Big Data: Conventional Procedures
Data mining of big data analyzes selections and data collections. Incomplete records are removed and important sources or comparison values are added. Subsequently, the data is searched for certain behavioral patterns and the results obtained are displayed. These are examined and evaluated by experts so that it can be decided whether the desired goal can be achieved. The knowledge gained is used in new investigations or used as a comparison parameter, so that the results of the next search are even more accurate. While data mining at Bigdata used to be primarily used in IT in the past, more and more companies are interested in the methods used and the significant potential Bigdata offers. In the financial sector, data mining is used to detect fraud and audit. Credit scoring uses bigdata to calculate the likelihood of default. In marketing, data mining calculates how the buying behavior of customers fails or which advertising measures interest potential customers. At online shops, shopping carts are analyzed and subsequently prices and the placement of products are changed. In addition, target groups for advertising campaigns can be searched for and customer profiles examined. On the Internet, Bigdata Mining serves to detect attacks, recommend services and analyze social networks. Further areas of application are, for example, the fields of medicine, bibliometrics and nursing.
Worth knowing about bigdata and data mining
In bigdata or data mining, one can assume a discipline that is scientifically neutral. With data mining, data from all kinds of sources can be analyzed. However, once the data relates to a person, moral and legal conflicts can quickly arise. These usually do not refer to the evaluation of the data, but only to the process of extraction. Data that has not been adequately anonymised may be assigned to specific individuals. When carrying out data mining by Bigdata, it is therefore always necessary to ensure anonymisation that does not allow conclusions to be drawn about persons or groups of persons. In addition to the legal conflicts, it should be noted that moral issues are raised. It is questionable whether computers should be allowed to divide people into "categories" or "classes". In data mining, for example, people are portrayed as creditworthy or unworthy. In general, it should be noted that the method itself is extremely value neutral and anonymous. The method does not know the consequences and probabilities of the calculation. However, as soon as people are confronted with the data in real terms, for example by the Schufa, this can cause strange, offended or surprised reactions. The search engine giant Google Google Analytics data about the target groups of the website owners are provided.
Opportunities and future prospects
In the globalized world, data mining becomes more relevant to big data. American corporations have been able to tell in the past about the buying behavior of their customers, whether they are pregnant or not. On the basis of these findings, shopping vouchers and shopping tips were sent specifically, which increased sales. By the nature of the purchases it was even possible to predict the date of birth, albeit not to the exact day. Data mining of big data is very important for companies today. Through targeted data mining of big data, significant insights can be gained about users and potential customers. Data mining ultimately leads to higher revenues and profits and will therefore become even more important in the future. No wonder: In the globalized and technically savvy world, the collection of data is now normal and this will be much stronger in the near future.