5 KEY SHIFTS
in Industrial Data Management
Discover the transformative changes reshaping how industrial organizations collect, process, and leverage data for competitive advantage in the digital era.
The Key Shifts
Five fundamental transformations driving the future of industrial data management
From Monitoring to Control
Data is now used for optimization and automation—not just awareness.
Manufacturing systems are shifting from reactive monitoring to proactive control, enabling real-time optimization and predictive maintenance.
Context at the Edge
Tag data at the source (machine, process, location) to avoid data swamps and enable AI.
Edge computing enables contextual data processing at the source, reducing latency and improving data quality for AI applications.
Time Series with Purpose
Integrate with systems like CMMS and EMS to drive real ROI.
Strategic integration of time-series data with operational systems transforms historical data into actionable business insights.
Use-Case Driven Architecture
Not all data goes everywhere—filter at the edge and model in the cloud.
Smart data architecture prioritizes purpose-driven flows, optimizing storage and processing based on specific business requirements.
Digital-First Design
Build physical systems after defining data flows and operational needs.
Modern manufacturing prioritizes digital architecture design before physical implementation, ensuring optimal data connectivity and operational efficiency.
Expert Sources
Industry leaders shaping the future of industrial data management
Evan Kaplan
CEO, InfluxData
John Harrington
Co-Founder & Chief Product Officer, HighByte
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Learn more about these insights and discover how they can revolutionize your industrial data management approach.
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