When Platform Structures Hinder New Requirements
Analytics and data platforms often grow over many years. Applications, data flows, and reporting processes become tightly intertwined, while documentation and responsibilities do not keep pace with this growth.
New requirements for data provision, AI applications, or self-service analytics then come up against platform structures that can only be expanded with significant effort. Data platform modernization provides a structured basis for assessing these dependencies and evolving the platform toward a defined target architecture.
Typical challenges
- Tightly coupled applications and data logic
Changes to data sources, interfaces, or calculation logic affect multiple applications and processes. - Fragmented data flows
Point-to-point integrations, ad hoc loading processes, and parallel data sets complicate operations and quality assurance. - Limited integration of new workloads
Existing platforms often support modern analytics, AI, or open-source workloads only with additional custom solutions. - High operational and knowledge overhead
Components that have evolved over time lock in specialized knowledge and increase the effort required for releases, monitoring, and error analysis.
As part of their SAS migration, many companies specifically choose R, for example, for statistical analyses, regulatory models, or the long-term refinement of existing analytical processes.
Migrating from SAS to R places special demands on code quality, domain-specific validation, and reproducibility. HMS supports this use case with a specialized SAS-to-R migration path that combines automated translation with domain-specific review and experienced implementation support.
Preparing Analytics Platforms for AI Applications
AI applications require more than just access to data. Data quality, interfaces, computing resources, permissions, logging, and deployment must all be factored into the platform architecture.
A modernized analytics platform establishes defined access paths and operational processes for this purpose. The specific enhancements required depend on the planned AI workloads and existing governance requirements.
Learn more about AI-ready legacy systems
Why HMS
Platform Modernization with an Architectural and Operational Perspective
HMS combines platform architecture, data engineering, and software engineering. Modernization decisions are guided not only by the target technology but also by integration, validation, and subsequent operation.
- Combining architecture and implementation
Architectural decisions are documented in a way that allows them to be implemented in data pipelines, platform services, and applications. - Technology-neutral decision-making
The selection of platforms and tools is based on customer value, integration capabilities, and long-term maintainability. - Taking complex and regulated environments into account
Requirements for data integrity, auditability, and governance are incorporated early toward a defined target architecture and implementation planning.
Anwendungsmodernisierung mit GenAI: von SAS zu Python auf AWS
Ein führender Versicherungskonzern modernisierte eine zentrale SAS-Anwendung auf AWS. HMS entwickelte Architektur und Migrationsvorgehen, setzte die Modernisierung um und prüfte GenAI für die SAS-zu-Python-Migration.
SAS Viya Migration bei einer Automobilbank
HMS migrierte die bestehende SAS-Plattform von On-Premises zu SAS Viya auf Azure und implementierte eine skalierbare Cloud-Infrastruktur mit Kubernetes.
SAS Viya auf Azure: Migration und langfristiger Plattformbetrieb
HMS migrierte die SAS-9.4-Umgebung eines großen europäischen Energie- und Versorgungsunternehmens auf SAS Viya in Microsoft Azure. Seit der Migration übernimmt HMS Betrieb, Wartung und Weiterentwicklung der Plattform.
No. In many cases, existing analytics, reporting, and data platforms can be modernized and expanded in a targeted manner, step by step, without abruptly replacing existing processes.
We help companies modernize a wide variety of analytics and data platforms - from SAS environments to modern open-source and cloud technologies using R, Python, and other platform components.
No. Depending on the architecture, governance, and operating model, we support cloud, on-premises, and hybrid target architectures.
Yes. Many companies are modernizing their existing SAS environments in phases - for example, through SAS Viya migrations, hybrid platform strategies, or the targeted integration of modern analytics technologies.
Modernization projects typically begin with a structured analysis of existing platforms, dependencies, and target requirements. Based on this analysis, appropriate modernization steps are prioritized and implemented in stages.
