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Bigdata is increasingly becoming a challenge for large corporations. The term "Big Data" stands as a metaphor for a worthless mountain of data in which knowledge is to be searched for. Bigdata mining describes statistical methods used to search for trends, cross-connections and new data. Data is searched for in mass data. Manual processing of such huge data sets is not possible, which is why computer-aided methods have to be used. These methods can also be used for smaller amounts of data. Data mining usually only refers to the analysis step within the process.
Data Mining and Big Data
With data mining, considerable amounts of data can be examined by computer-aided programs. The term data mining is somewhat misleading, since it is not about generating data, but about extracting knowledge from data. The term has become popular mainly because it is short and precise. In general, data mining can be described as a process in which knowledge is extracted that was previously unknown and is considered potentially useful. Bigdata is used to describe quantities of data that are too complex or large or simply change too quickly. Manual entry or processing with classical methods is therefore impossible. The collected bigdata to be used for data mining can come from all possible sources. These range from electronic communication from companies and authorities to records from monitoring systems. The desire to analyse bigdata in order to use the gained knowledge often comes into conflict with the personal rights of other persons, which is why it is advisable to protect yourself in advance.
Data Mining and Big Data: Conventional methods
Data mining of Big Data involves analyzing selections and data collections. Incomplete data sets are removed and important sources or comparison values are added. The data is then searched for specific behavioral patterns and the results obtained are presented. These are examined and evaluated by experts so that a decision can be made as to whether the intended goal can be achieved. The knowledge gained is fed into renewed investigations or used as comparison parameters so that the results of the next search are even more accurate. While data mining in Bigdata was primarily used in IT in earlier times, more and more companies are becoming interested in the methods used and the considerable potential of Bigdata. In the financial sector, data mining is used for fraud detection and invoice verification. In credit scoring, Bigdata is used to calculate how high the probability of default is. In Marketing Data mining is used to calculate the buying behavior of customers and which advertising measures potential customers are interested in. In online shops, shopping carts are analyzed and then prices and the placement of products are changed. Furthermore, target groups for advertising campaigns can be searched for and customer profiles can be examined. On the Internet, Bigdata Mining is used to detect attacks, recommend services and analyze social networks. Other areas of application are, for example, medicine, bibliometrics and nursing.
Things to know about Bigdata and Data Mining
Bigdata or data mining can be considered a discipline that is neutral on a scientific level. In data mining, data from all conceivable sources can be analyzed. However, as soon as the data refers to a person, moral and legal conflicts can quickly arise. These mostly do not refer to the analysis of the data, but only to the process of extraction. Data that has not been sufficiently anonymized can, under certain circumstances, be assigned to specific individuals. When carrying out data mining of Bigdata, therefore, care must always be taken to ensure that the data is anonymised in such a way that no conclusions can be drawn about individuals or groups of individuals. In addition to the legal conflicts, it should be noted that moral questions are raised. It is questionable whether computers should be authorized to divide people into "categories" or "classes". In data mining, for example, people are portrayed as creditworthy or uncreditworthy. In general, it should be noted that the process itself is extremely value-neutral and anonymous. The procedure 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 Schufa, this can cause alienated, offended or surprised reactions. At the search engine giant Google, at Google Analytics Data on the target groups of the website operators provided.
Opportunities and future prospects
In the globalised world, data mining from Big Data is becoming increasingly relevant. In the past, American companies were able to determine whether their customers were pregnant or not based on their purchasing behaviour. On the basis of these findings, shopping vouchers and shopping tips were sent out in a targeted manner, which increased sales. Due to the nature of the purchases it was even possible to predict the date of birth, although not to the day. Data Mining from Big Data is of great importance for companies today. Through targeted data mining from Big Data, significant insights can be gained about users and potential customers. Data mining ultimately leads to higher sales and profits and will therefore become even more important in the future. No wonder: In the globalised and technically savvy world, data collection is now normal and will become even more important in the near future.
