Upon completion of this course, participants will be able to:

• Design, configure, and manage a high-availability **Time-Series Data Historian** architecture.
• Implement effective data compression and storage strategies to balance fidelity and performance.
• Master querying and reporting tools to extract meaningful operational and compliance data.
• Develop basic **Asset Models** to contextualize historian data for advanced analytics.
• Apply statistical process control (SPC) techniques to identify process deviations and anomalies.
• Integrate historian data with business intelligence (BI) and enterprise systems (ERP/CMMS).
• Understand and apply data cleansing and validation routines to ensure data integrity.
• Utilize historian data to support predictive maintenance (PdM) and root cause analysis (RCA).

• SCADA/Historian Administrators and Database Managers
• Process Engineers and Optimization Specialists
• Reliability and Predictive Maintenance Engineers
• Industrial Data Scientists and Analysts
• IT/OT Integration and Analytics Leads
• Compliance and Performance Reporting Officers

• Hands-on lab exercises using historian software to configure compression settings and optimize query performance.
• Group activity: developing a contextual **Asset Model** for a complex production line.
• Individual exercises focused on using query tools to generate an SPC chart and identifying an out-of-control condition.
• Case studies demonstrating ROI from predictive maintenance programs based on historian analytics.
• Discussions on the challenges of balancing data fidelity and long-term storage costs.

Unit 1: Historian Architecture and Design

• Defining time-series data and its unique storage requirements (high volume, high velocity)
• Overview of major historian platforms (e.g., OSIsoft PI, Aveva Historian)
• The role of data collectors, buffers, and the historian server

• Implementing data compression algorithms (e.g., exception and compression deviation)
• Strategies for managing archive files, data backup, and disaster recovery

Unit 2: Data Quality and Contextualization

• Techniques for validating data, handling bad quality flags, and filling data gaps
• Implementing effective tag naming conventions and metadata management

• Building logical **Asset Models** (hierarchies) to organize tags by equipment and location
• Linking time-series data with static asset attributes and transactional data (e.g., work orders)

Unit 3: Querying and Reporting Tools

• Mastering historian data access tools (e.g., add-ins, client applications)
• Writing efficient queries to retrieve high-volume historical data for specific time windows

• Developing historical reports for regulatory compliance (e.g., EPA, NERC) and auditing
• Designing executive dashboards that visualize key historical performance metrics

Unit 4: Advanced Analytics and Optimization

• Applying **Statistical Process Control (SPC)** charts (e.g., control limits) to monitor process stability
• Using historian data for in-depth **Root Cause Analysis (RCA)** of past failures

• Fundamentals of using historian data (e.g., vibration, temperature) to train simple predictive maintenance models
• Calculating **Equipment Health Indicators** and Remaining Useful Life (RUL) metrics

Unit 5: Integration and Governance

• Securing data sharing channels between the Historian and the IT network (BI/ERP systems)
• Handling data security, user access, and compliance across the OT/IT boundary