Motivation
Many work steps in engineering offices are increasingly standardized and can soon be covered by algorithms. Often only two percent of the entire order processing still offers room for new solutions and processes. Potential exists above all in the large amounts of data that arise in the course of various projects and must remain traceable, usable and reusable. Seecon sees this as an opportunity to expand its range of services with industry-specific, data-based services.
Objective
- Converting large and unstructured data volumes into structured data containers
- Deriving use cases for possible extensions of seecon's service portfolio with regard to different interests/customer groups
Results
- Analyzing suitable methods of machine learning in order to structure unstructured data sets for targeted further processing and integration into new services
- Developing a prototype on the basis of a data section including a suitable user interface with intelligent search/filter options
- Deriving suitable data-based offers for seecon for portfolio expansion
- Identifying expansion opportunities for the seecon business model with a focus on the corresponding value-added potential
