About Advanced Energy
Advanced Energy is a global provider of power conversion, measurement, and control technologies serving industries including semiconductor, industrial manufacturing, healthcare, and data centers.
With operations spanning manufacturing facilities, field services, maintenance operations, and asset management, the company depends heavily on timely and accurate operational data to maintain efficiency and support critical business decisions.
Over the years, the organization had accumulated significant complexity across its enterprise systems and operational data landscape.
The Business Challenge
Advanced Energy's operational data environment was distributed across large SAP and Oracle systems that had evolved over many years. Both platforms were deeply customized and contained highly complex schemas with intricate relationships between operational, manufacturing, financial, and maintenance data.
The challenge was not only the scale of the data environment, but also the complexity involved in understanding and integrating it. Asset information, manufacturing data, maintenance records, and operational workflows existed across disconnected systems. Producing meaningful analytics required extensive manual effort, specialist ERP knowledge, and time-consuming schema analysis.
Even relatively simple operational reporting initiatives required months of engineering effort before business teams could begin using the data. The organization needed a faster and more scalable way to access operational intelligence without depending on large manual integration projects.
Legacy Environment
Before modernization, the environment included:
- SAP ERP systems supporting procurement, asset tracking, and finance
- Oracle operational and manufacturing platforms with highly customized schemas
- Manual ETL pipelines requiring specialist maintenance expertise
- No unified operational analytics layer connecting manufacturing, maintenance, and asset data
- Large-scale schema complexity requiring significant analysis before reporting could begin
The overall environment had become difficult to scale efficiently using traditional engineering approaches.
Why Change Was Needed
The business needed faster access to operational analytics, particularly around:
- Asset health monitoring
- Maintenance planning
- Manufacturing performance visibility
- Operational cost optimization
Existing processes required too much manual effort to maintain. The amount of time required to analyze source systems, build transformations, and create usable analytics outputs was slowing operational decision-making and increasing engineering overhead. The organization needed a simpler way to work with its existing systems without replacing the underlying infrastructure.
The OpenOntos Approach
OpenOntos used its AI-powered Data Lake Accelerator to simplify the complexity of Advanced Energy's SAP and Oracle environments. Instead of beginning with lengthy manual schema analysis and custom ETL development, OpenOntos applied AI-assisted profiling to automatically analyze source systems, identify business relationships, and generate analytics-ready models.
The implementation focused on translating highly complex ERP structures into clean and understandable operational data products. Importantly, the modernization was completed directly on the client's existing Databricks environment, allowing Advanced Energy to preserve existing infrastructure investments while dramatically accelerating delivery timelines.
Migration & Modernization Strategy
The engagement focused on simplifying operational complexity and accelerating access to trusted analytics. Key implementation activities included:
- AI-assisted profiling of SAP and Oracle schemas to identify relationships and operational domains
- Automated identification of business use cases and KPIs from existing source data
- AI-generated Gold Layer models designed for operational reporting and analytics
- Simplification of complex ERP structures into clean and accessible data products
- Deployment directly within the organization's existing Databricks environment
- Automated pipeline generation replacing traditional manual ETL development
The overall goal was to make operational data easier to understand, easier to access, and faster to use.
AI & Automation Role
AI automation played a central role in reducing project complexity and delivery timelines. The SAP and Oracle environments contained extremely large schemas and deeply interconnected relationships that would traditionally require weeks or months of manual engineering analysis.
OpenOntos used AI-assisted automation to:
- Analyze schema structures
- Identify cross-system relationships
- Detect analytical patterns and operational KPIs
- Generate transformation logic automatically
- Produce analytics-ready Gold Layer models
This dramatically reduced the amount of manual engineering work required while improving accessibility for operational users. Instead of relying on deep ERP expertise to query raw systems, business teams could now work from simplified and governed operational models.
Technical Transformation Highlights
The implementation delivered several major technical improvements:
- Large SAP and Oracle schemas transformed into simplified analytics-ready Gold Layer models
- Automated resolution of complex inter-system relationships
- Unified operational, manufacturing, maintenance, and asset data into a centralized Databricks analytics environment
- Automated pipeline generation replacing manual ETL construction
- Delivery completed ahead of schedule despite the scale and complexity of the source systems
The final platform provided a much cleaner and more maintainable operational analytics foundation.
Business Outcomes
The modernization delivered measurable operational and engineering improvements:
- Delivery timelines accelerated by approximately 3× compared to traditional methods
- Significant reduction in engineering resources required for implementation
- Simplified operational data products replacing highly complex ERP structures
- Operational analytics delivered on schedule despite large-scale schema complexity
- Faster access to maintenance and cost optimization insights without requiring specialist SQL expertise
Operational Impact
Operations and field teams gained access to structured and analytics-ready operational data instead of relying on raw ERP extracts. Maintenance planning and asset management decisions could now be supported by faster and more accessible reporting.
The simplified data models also reduced the dependency on deep SAP and Oracle expertise, making operational analytics more accessible across teams. As a result, business decisions related to maintenance, operational efficiency, and manufacturing performance moved closer to real time.
Key Benefits Achieved
- Complex ERP environments simplified into accessible operational analytics models
- Significant reduction in manual ETL development and maintenance effort
- Faster delivery of analytics-ready operational data
- Reusable and governed data products supporting future operational use cases
- Reduced dependency on specialist ERP expertise
- Unified analytics foundation supporting maintenance, manufacturing, and asset intelligence
Conclusion
Advanced Energy's modernization project highlights a common challenge across industrial and operational enterprises: the systems already contain valuable data, but the complexity involved in accessing and integrating that data slows business outcomes. OpenOntos helped simplify that complexity.
By combining AI-assisted schema analysis, automated pipeline generation, and governed analytics modeling, the organization was able to move from months of manual integration work to weeks of accelerated delivery. Most importantly, Advanced Energy gained a modern operational analytics foundation without replacing the systems already running the business.
The result was faster access to trusted operational intelligence, reduced engineering overhead, and a more scalable way to support future operational analytics initiatives.