Cobuilder

Wienerberger AG identified the need to provide relevant data to their customers more effectively within the software they use (BIM authoring software) and improve the customer journey across all touchpoints. The company's data stewards were faced with numerous challenges concerning the consistency and standardisation of product (systems, materials etc.) data across the whole group and its brands. In order to improve the quality of their data services, Wienerberger AG set an objective to standardise data internally, and decided to introduce an enterprise Product Information Management (PIM) system which would be the single source of data for all their clients. However, a traditional PIM did not fully meet their needs. They needed a solution that also could ‘translate’ data to the particular needs of users within different markets and across different software, without maintaining data duplicates and numerous local ‘silo’ databases.

Pipelife Norge, a subsidiary of Wienerberger AG, was one of the first to implement Wienerberger’s enterprise-level data model. The model is aimed at adopting a systematic and centralised approach to product data management at enterprise-level, as the digital readiness across the entire group varies significantly from brand to brand and company to company. Pipelife Norge also reported the increasing demand, especially in their home market, for relevant and accurate product data that can be used across the supply chain by various software and actors. One of the main challenges was that most of Pipelife’s product information was stored in siloed systems and file formats that hindered the flow of information. In addition, Pipelife’s web-catalog was maintained by the respective Product Managers responsible for the different product lines, which sometimes led to inconsistencies in the quality and richness of the product information.

NCC, a leading construction company, was facing challenges in implementing a paperless workflow related to the specification and selection of building elements used in internal processes and in the procurement process. The use of different words and definitions within the industry, as well as different data structure and language in systems, made it time-consuming to secure the quality of the data collected and exchanged. The difference in data models made it difficult to get different systems to process data in an efficient way.