Data management provides the basis for the success of modern companies by transforming raw data into valuable information. Data architectures and data modeling are essential disciplines that ensure the flexibility, efficiency and longevity of analytical systems.
Data management provides the basis for the success of modern companies by transforming raw data into valuable information. Data architectures and data modeling are essential disciplines that ensure the flexibility, efficiency and longevity of analytical systems.
Modern data architecture and data modeling form the foundation for flexible, high-performance and future-proof analytical systems. By combining agile architectures such as data mesh or data fabric and tailored modeling approaches (e.g. data vault, multidimensional models), data can be made available in an efficient and user-oriented manner. The focus is on features such as real-time capability, self-service, automation and scalability.
synvert supports companies in developing the right architecture for their use cases, selecting suitable modeling methods and integrating them seamlessly into existing environments. The aim is to enable data-driven decisions through clear structuring and high data quality – today and in the future.
synvert seamlessly integrates data architecture and modeling. The focus is on an architecture that meets both current and future requirements. From data extraction, modeling and analytics to governance, all aspects are considered. Standardized process models and innovative concepts such as data stewards or data mesh guarantee a sustainable and flexible data strategy that supports data-driven decisions.
Architecture design – development of modern architectures (e.g. data warehouse, data lake, data mesh, data fabric) tailored to specific requirements such as structuring, real-time capability or exploration of unstructured data.
Adaptable Data Modeling – selection of optimal modeling approaches such as multidimensional models, data vault or 3NF to ensure flexibility, performance and scalability.
Self-service and automation – provision of self-service data platforms and implementation of automated pipelines for efficient data integration and preparation.
Cross-domain governance – introduction of central governance mechanisms to ensure uniform standards, data quality and the use of a ubiquitous language.
Integration of modern technologies – use of AI and machine learning to improve data preparation, data quality and governance processes.
End-to-end support – support from use case definition, architecture development and modeling through to system integration and analytical evaluation.
synvert develops architectures that can react flexibly and scalably to future requirements.
Tailor-made data modeling for different use cases such as BI, explorative analyses or operational data management.
Definition and visualization of data quality KPIs and implementation of automated data pipelines to ensure data standards.
Use of tried-and-tested approaches such as data mesh or data stewards for the efficient use and governance of data.
Design and implementation of systems that can be easily integrated into existing environments – on-premises, hybrid or in the cloud.
Development of systems with streaming functions for real-time analysis and data integration.
Consideration of all aspects – from data extraction and modeling to final analysis – for an end-to-end solution.
synvert can contribute 30 years of experience and proven methods to projects in order to implement individual requirements efficiently.
Provision of platforms that allow users to access and analyze data independently and efficiently.
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