Bigdata is increasingly becoming a challenge for large corporations. The term "Big Data" is a metaphor for a worthless mountain of data in which knowledge is to be searched for. Bigdata Mining describes statistical methods which are used to search for trends, cross connections and new data in mass data. A manual processing of such huge amounts of data is not possible, which is why computer-aided methods have to be used. These methods can also be used for smaller data sets. Data Mining usually refers only 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
Big Data data mining involves the analysis of selections and data collections. Incomplete data sets are removed and important sources or comparison values are added. The data is then searched for specific patterns of behaviour and the results are displayed. These are then examined and evaluated by experts so that it can be decided whether the desired goal can be achieved. The knowledge gained is then used as a comparison parameter for further investigations, so that the results of the next search are even more accurate. While data mining at Bigdata was primarily used in IT in the past, more and more companies are interested in the methods used and the considerable potential of Bigdata. In the financial sector, data mining is used for fraud detection and auditing. In credit scoring, Bigdata is used to calculate the probability of payment default. In marketing, data mining is used to calculate the purchasing behaviour of customers or which advertising measures potential customers are interested in. In online shops, shopping baskets are analysed and then prices and the placement of products are changed. In addition, 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 analyse social networks. Other areas of application include medicine, bibliometrics and care.
Things to know about Bigdata and Data Mining
Bigdata or data mining can be assumed to be a discipline that is neutral on a scientific level. In data mining, data from all conceivable sources can be analysed. However, as soon as the data relates to a person, moral and legal conflicts can quickly arise. These usually do not relate to the analysis of the data, but only to the process of extraction. Data that has not been made sufficiently anonymous can, under certain circumstances, be attributed to specific persons. When Bigdata carries out data mining, it is therefore always important to ensure that the data is made anonymous so that no conclusions can be drawn about persons or groups of persons. In addition to 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 presented 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 actually confronted with the data, for example by the German credit bureau (Schufa), this can cause alienated, offended or surprised reactions. With the search engine giant Google, Google Analytics provides data about the target groups of the website operators.
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.