From data silo to data platform: how companies make their data usable

We help companies move beyond this complexity, transforming fragmented data collections into integrated, scalable data platforms that create transparency and enable innovation.

The Status Quo

In established system landscapes, parallel data worlds often coexist. ERP, MES, SCADA, CRM, or isolated analytics tools all deliver valuable information, but operate independently. This leads to additional effort from manual data imports, missing shared KPIs, redundant datasets, and significant time loss as specialists have to clean and prepare data before analysis can begin.

An example from industry: A machinery manufacturer operates several plants in Europe and Asia. Each plant uses its own MES system, production data in the ERP is collected only daily, and quality reports are created in Excel. Management wants to compare Overall Equipment Effectiveness (OEE) worldwide to identify bottlenecks and guide investment decisions, but soon discovers that each plant defines OEE differently. Plant A uses “productive time divided by shift time,” Plant B includes setup times, and Plant C manually adjusts scrap values.

Company-wide reporting becomes nearly impossible. Investment and modernization decisions are based on inconsistent key figures. This leads to misplaced priorities, suboptimal investments, and a heavy analysis workload because data from various systems must be manually aligned each time. The invisible yet costly obstacle is clear: the data exists, but it does not work together.

Why a Central Data Platform Is the Turning Point

The key step is to bring all data sources together within one consistent architecture. A modern data platform breaks down silos, builds trust in data quality, and makes information accessible companywide. It ensures that all departments work with the same validated data, interfaces are efficiently reused, and unified standards guarantee stability and quality.

New sources and use cases can be integrated more easily, and consistent data provides the foundation for modern applications from predictive quality to energy optimization. Initial use cases can be implemented even during the platform’s setup, delivering tangible results and feedback that accelerate progress.

The Technical Foundation: Databricks, Microsoft Fabric, and Open Source

Technology is not an end in itself; it is the toolbox for putting data strategy into practice. We rely on proven platforms that can be flexibly combined to address diverse requirements.

• Databricks serves as the engine for Data Engineering and AI. It unites the strengths of a Data Lake and a Data Warehouse within a single environment for data-driven innovation. Its Lakehouse architecture efficiently handles large data volumes, both batch and streaming. Popular languages such as Python, SQL, and Scala are supported, and Delta Lake ensures data quality and versioning, allowing companies to reliably scale AI projects.
• Microsoft Fabric combines governance and self-service. As an integral part of the Microsoft ecosystem, Fabric with OneLake, Power BI, and Azure AD provides a unified environment for data analytics and management. This offers a clear advantage for organizations that work in the Microsoft Cloud and manage governance centrally.
• For projects with specific or cloud-neutral requirements, we use Open Source technologies. They offer maximum flexibility, open architectures, freedom from vendor lock-in, and full compatibility with hybrid or on-premises environments.

Choosing the right technology depends on clearly defined criteria: the existing IT ecosystem, data volumes, use case goals, and budget. This approach ensures that the result is not a tangled web of tools but a coherent, strategically aligned platform architecture.