Systematically overcoming challenges in data integration projects
In today's data-driven world, businesses are constantly seeking ways to streamline processes and make data integration more efficient. This is where the Bosch Semantic Stack comes into play, offering solutions to common challenges faced in data-driven projects. In the following videos you will meet two fictional characters, Frederick and Justine, and learn how they have overcome data integration challenges by using digital twins.
Challenges
Common challenges in data integration projects:
1. Missing overview:
no knowledge of the data and integration of the first solution
2. Lack of semantic context:
the data is not understood
3. Heterogeneity:
the data sources can only be used for each individual solution
4. Non-scalable IT integration:
the data pipeline is not reusable, which usually leads to silo/black box thinking
Watch the three videos and learn how to tackle these challenges!
The first video will show a quality assurance case. Meet Frederick, a quality engineer who faced the challenge of managing customer warranty claims.
Quality assurance
In the second video you will meet Justine, who is working on a predictive warning use case and faced similar challenges as Frederick.
Data integration challenge
The last video shows how sustainable data integration is done by using the power of digital twins. See successful data-driven projects and how to make them scalable for further data-driven projects in the future.
Sustainable data integration
Conclusion
Bosch Semantic Stack offers solutions to common challenges in data integration projects. It enables easy access, reuse, and integration of data sources, ultimately leading to more efficient and scalable data-driven projects.
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