Automatically creating data models

When data models are to be created in companies, this is usually done after discussions with domain experts and a review of existing data. The complexity of the task grows with the demands. Above all, a data overview of all data sources and exchanged data throughout the company can be difficult and time-consuming. As a result, such an overview may either not be created at all or the result may already be virtually obsolete by the time it is completed. Artificial Intelligence can help create such a data model in an automated way.

Already during the analysis of the data, Artificial Intelligence can generate an abstract data model in a very short time. In the process, data types such as the product ID or design drawings and the relationships between the data types are determined. As part of the data governance or data management concept, data mining can be used to gather the necessary information for a data overview. With the help of data mining, data can be analyzed and even huge amounts of data (such as Big Data) can be evaluated. Data mining uses algorithms from statistics and Artificial Intelligence methods to automatically find patterns, trends or correlations in data sets.

Through processes such as dependency parsing and prediction, the relationships between data types can be identified, described, and assigned a probability. Dependency parsing is a process that analyzes the grammatical structure of a sentence and identifies related words and the type of relationship between them.

Sort data like emails

Methods such as clustering or statistical classification are often used to form the data overview in the form of a model and to divide it into areas. Statistical Classification is about putting unmarked data into marked classes or categories. This works like with spam filters in an e-mail program. In this case, untagged emails are scanned and classified as either ‘spam’ or ‘non-spam’.

The greatest benefit of an automated data model creation is the increase of data quality and the reduction of media breaks. A data model can be used as a starting point for AI application development. Furthermore, an end-to-end data model is the key to a fully comprehensive Digital Product and Process Twin.

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