Ensuring Reliable, Governed Access to Data
Modern data platforms must do more than just store data. They connect different data sources, support demanding analytics and AI applications, and meet security, governance, and scalability requirements.
MS combines target architecture, data engineering, integration, and production operations in the development and modernization of data platforms.
Data Platforms as the Foundation for AI Applications
AI applications in production require accessible, quality-assured, and controlled data. HMS prepares data platforms to meet these requirements:
- Providing relevant data for AI applications
- Integrating machine learning workflows
- Supporting GenAI and agentic applications
- Designing platform components for growing AI workloads
Moving AI applications from experimentation into production requires coordinated data provision, integration, governance, and operations.
Why HMS
Developing Data Platforms With a Focus on Integration and Operations
When building a data platform, architecture, technology selection, governance, and operations are all intertwined. HMS brings these decisions together within the project and aligns them with the existing system landscape:
- Connecting architecture and engineering
- Evaluating technologies independently of vendors
- Incorporating governance from the start
- Taking responsibility through to operations
A modern data platform integrates data sources, data pipelines, processing, governance, and operations. It provides data for reporting, analytics, machine learning, and generative AI in a controlled manner.
The process begins with an analysis of the existing data landscape and a clear definition of business requirements. This leads to the development of a target architecture, the selection of appropriate technologies, and a roadmap for technical implementation.
The choice depends on the data landscape, integration needs, security requirements, and operating model. HMS works with Microsoft Azure, Amazon Web Services, Snowflake, Databricks, and Kubernetes-based platforms, among others.
Data engineering connects source and target systems. Automated data pipelines handle data integration, transformation, quality assurance, and delivery.
Data governance controls responsibilities, access, data quality, and traceability. This includes role and access models, data catalogs, lineage, and platform governance.
AI applications require accessible, quality-assured, and controlled data. A suitable data platform provides this data and integrates machine learning, GenAI, and agentic workflows.
