Data Services
IT architectures in transition
The constant progress in the digital world and the associated new data sources pose a challenge for today's IT architectures. In order to make the most of these new opportunities, a smart data architecture with suitable analytical services is required - so-called smart data services.
Structured and semi-structured data
A smart data architecture can handle structured and semi-structured data and process and store it in classic "night runs" or near-real-time (right-time) integration processes. The timely storage of volatile data, for example from machine sensors, is particularly important for long-term analyses or predictive maintenance.
Integration of heterogeneous data sources
Smart data architecture is at the heart of combining traditional ERP or CRM data with data from social media channels, for example. This makes it possible to obtain relevant information for business management.
Smart data architectures
A smart data architecture provides the necessary data basis at the right time, in the right place, at the right level of detail and in a secure state so that smart services can be used on this basis.
These can be use cases for data analytics, planning or artificial intelligence - or ideally everything combined. Our experts will be happy to support you with the implementation.
Our Smart Data Services
In the celver Store you will find ready-to-use solutions that will take your data processes to the next level.
To the celver storeclient testimonials
I think that our project approach with the close involvement of the different user groups and the support of celver AG, as well as the agile implementation approach was crucial for the success of the 'DataSpot'.
The manual preparation and merging of data is no longer an issue today. In contrast, fast and flexible analyses are now possible at any time. All developments are continuously transparent from the most diverse perspectives.
By integrating state-of-the-art programs such as R and Python as well as open source components into our existing solution architecture, we were quickly able to significantly improve the forecast quality.
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