Accelerating Industrial DataOps to optimize Data Management and IoT Scalability – a catalog of case studies | SPONSORED
IIoT practices are having an impact, but Developers struggle to scale successful AI/ML projects. WHY? Data management is many times the culprit. Industrial OT and Business IT are not the same. For example:
Business data is delivered in batches or transaction records complete with metadata descriptors but lacking synchronous time stamps for correlation.
Industrial OT data is high velocity time-series and event information that lacks the detailed descriptors and features needed to use it outside of operations.
That means the usual IT or OT data management techniques are insufficient for IIoT requirements.
Integrating data from business systems, control systems, historians, maintenance management, and business systems are dramatically increasing situational awareness.
DataOps techniques are improving data management for analytics delivery to increase its operationalization, better support workforce, and overcome supply chain issues.
Real time IIoT is enriching analytics applications. Increased visibility, predictability, and prescriptive powers are taking the guesswork out of decision-making.
Read this short e-book to see how Hitachi customers leverage Industrial DataOps technology to provide greater visibility, better predict operations and optimize work processes.
Download the catalog get access to the following case studies:
- Smart Building and Security for Covid-19
- Power Grid Cabinets
- Smart Poles
- Energy Performance Monitoring on Industrial Plants
- Smart Metering
- Smart Organic Waste Compost Machine
- EV Charging Solution
- Remote Condition Monitoring
- Molding Line Operations
- Ingest / Predictive Maintenance for Smart Space
- Anomaly Detection for Amusement Rides
Sponsored by Hitachi Vantara
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