NETZSCH Group centralizes operations and drives predictive maintenance through AI
NETZSCH Group, a global manufacturing company in Germany, transformed its historically siloed business units by consolidating data and deploying scalable AI and analytics capabilities. Previously, each business unit maintained separate data and reporting systems that limited operational insights and could not accommodate large-scale AI or IoT workloads. Facing challenges with data silos, legacy infrastructure, and an increasing demand for digital, predictive services, NETZSCH Group initiated a cloud data migration and consolidation journey. Partnering with Adastra and Microsoft FastTrack, they implemented the Microsoft intelligent data platform, integrating Azure Synapse Analytics, Power BI, and Azure Databricks. The new centralized data lake enables rapid analysis of millions of data rows and supports company-wide data quality management. Predictive maintenance and new data-as-a-service models are now possible, opening additional growth opportunities. The company plans further AI adoption, including Copilot, to expand intelligent services and data-driven business models. Each business unit previously had independent data silos and reporting tools, causing inefficiencies. Legacy, on-premises infrastructure could not support large-scale AI, IoT analytics, or rapid reporting. Growing demand from customers for digital insights and predictive maintenance created internal pressure for innovation. Diverse and unstandardized business analytics complicated company-wide decision making.
- Organization
- NETZSCH Group
- Industry
- Manufacturing
- Location
- Germany
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Predictive Maintenance for Industrial Equipment
- 2Company-Wide Data Lake for Advanced Analytics
- 3Centralized Data Governance and Quality Management
- Each business unit used isolated systems, resulting in fragmented data and insights.
- On-premises legacy hardware limited scale for AI and large data analytics.
- Customer demand for digital services and predictive maintenance increased.
- Analytics across the organization lacked consistency, slowing decision-making.
- Data quality could not be managed centrally, risking unreliable insights.
- Consolidated enterprise data into a company-wide data lake with Azure Synapse Analytics.
- Deployed Microsoft intelligent data platform (Azure Synapse Analytics, Power BI, Azure Databricks).
- Partnered with Adastra and Microsoft FastTrack for cloud migration and AI adoption.
- Leveraged Power BI for streamlined self-service analytics and advanced reporting.
- Prototyped Azure-native solutions for SAP data integration and company-wide data quality assessment.
- Reduced analytics runtime from hours to under five minutes for 82 million-row datasets.
- Enabled predictive maintenance and AI-powered business insights at scale.
- Improved data quality, accuracy, and centralized data governance.
- Launched foundation for data-as-a-service offerings and cross-unit business innovation.
- Expanded opportunities for company-wide decision making with unified data.
Architecture
Data from business units and SAP systems is collected into a centralized Azure data lake. Azure Synapse Analytics, Power BI, and Azure Databricks provide layered analytics, while notebooks enable rapid insight generation. Data flows from SAP into Synapse, where machine learning and AI workloads are run. Data quality is managed centrally, and reports are distributed with Power BI.
Implementation partners1
Sources & evidence2
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