Most companies, especially production facilities, are often in need to efficiently handle thousands to millions of documents including operating instructions, technical drawings, licensing documentation and more. Assuming that data warehouse concepts are already in place to centrally store this huge amount of data, it can still be quite cumbersome to find specific information of interest.
For such big data problems, a full-text search cannot be performed any more. Thus, clustering documents in predefined groups is of particular interest to be able to access the required information. However, executing this classification manually is a very expensive and time-consuming task, so the benefit of replacing it with an AI tool appears obvious.
Natalie is a Data Science Consultant at Applied Statistics GmbH located in Vienna. She is responsible for supporting companies in their digitization processes by developing custom data-driven solutions based on statistical methods. Her work is focused on gaining maximum value from data, starting from data engineering and feature extraction as a basis for machine learning algorithms, up to providing BI tools and software solutions to clients.
(Image / Video (c) Thomas van Emmerik)