In part 1, we explored how predictive maintenance helps reduce downtime and extend equipment life. But turning early insights into action requires the right mix of connectivity, analytics, and scalability.
Whether you’re maintaining medical equipment, smart infrastructure, or industrial systems, the ability to anticipate issues depends on a tech foundation that can ingest, analyze, and act on high volumes of real-time data.
Every organization has different data maturity, systems, and priorities. That’s why flexibility matters just as much as functionality. A strong predictive maintenance approach calls for a partner who can meet you where you are—with tools that scale and adapt as your needs evolve.
This blog breaks down the essential capabilities behind predictive maintenance—and how OpenText’s analytics and IoT technologies deliver on them.
The technology behind predictive maintenance: sensors, AI, and IoT
Predictive maintenance is only as good as the tech behind it. To move from reactive to predictive, you need a data warehouse or data lakehouse platform that can connect to your assets, capture real-time data, and turn that data into insights—fast. Here’s how it works:
Start with the right data: Smart sensors
The first step to predictive maintenance is getting clean, reliable data from your equipment. That means:
- Equipping critical assets with smart sensors
- Monitoring real-time metrics like temperature, vibration, pressure, oil quality, and more
- Streaming that data continuously to a central system
Sensors provide the raw telemetry. Without them, there’s nothing to analyze—and no way to detect early warning signs.
Make it intelligent: AI and machine learning
Here’s where the “predictive” part comes in. With enough sensor data, machine learning models can learn what “normal” looks like for each asset—and flag anomalies that suggest potential failure.
But effective predictive maintenance models require:
- Historical and real-time data—so they can compare what’s happening now to what’s happened before
- Context—like asset type, workload, maintenance history, and operating conditions
- Continuous learning—so models evolve with your environment
Pro tip: Look for platforms with built-in time series and ML functionality. You don’t want to bolt on AI later—you want it built in from the start.
Keep the data moving: IoT and connectivity
Sensor data is useless if it’s locked in a silo. Predictive maintenance needs fast, secure, and flexible data integration between:
- OT systems (e.g., SCADA, PLCs, historians like OSIsoft PI)
- IT systems (e.g., ERP, EAM platforms like Maximo and SAP)
- Real-time analytics engines
- Dashboards for decision-makers
Industrial IoT (IIoT) connectivity enables you to gather and analyze data without moving it through disconnected tools, saving time and reducing risk.
From insight to action: Visualization and alerts
The goal of predictive maintenance is action, not just awareness. Teams need tools that translate AI insights into visual dashboards, alerts, and reports that are easy to understand and act on.
Look for solutions that support:
- Drag-and-drop dashboard creation
- Role-based access and filtering
- Trigger-based alerts tied to anomaly scores or thresholds
- Clear model explainability
What’s needed to make predictive maintenance work
Here’s what we’ve learned from teams that have successfully implemented PdM:
| Technology | Why it matters |
| High-quality sensor data | Garbage in = garbage out. Start with solid inputs. |
| Unified data architecture | Avoid data silos that slow down insight generation. |
| Built-in machine learning | Reduces complexity and dependency on data scientists. |
| Visualization and alerting | Ensures insights are actionable—not just theoretical. |
| Security and compliance | Sensitive machine data must stay protected. |
| Modular, scalable architecture | Start small. Scale fast. No rip-and-replace. |
How OpenText enables scalable predictive maintenance
Once the right data is flowing, you need a platform built to handle it—at scale, in real time, and across environments. OpenText provides the tools to make predictive maintenance work across diverse industries and use cases.
OpenText™ Analytics Database (Vertica)
Built for high-speed, high-volume analytics, OpenText Analytics Database ingests data from IoT devices at a petabyte scale—up to 10 million records per minute. With built-in time-series functions and machine learning, it analyzes data where it lives, eliminating delays and enabling accurate predictions without the need to move data between systems.
OpenText™ Aviator IoT
OpenText Aviator IoT connects your physical assets to the analytics layer, allowing organizations to monitor equipment condition, capture operational metrics, and act on insights in real time—across smart factories, field systems, or infrastructure networks.
OpenText™ Intelligence (Magellan)
This business intelligence and reporting platform transforms complex datasets into intuitive dashboards and visualizations. With OpenText Intelligence, teams get fast access to critical performance and risk insights—so they can act before minor anomalies turn into operational issues.
Together, these technologies support a flexible, future-ready foundation for predictive maintenance—giving your team real-time visibility, accurate forecasts, and the tools to keep operations running efficiently.
Keep operations running like clockwork
Turning equipment data into action takes more than just monitoring—it takes the right data infrastructure, AI, and real-time intelligence. With the right foundation, predictive maintenance becomes a strategic advantage.
Download the asset performance optimization playbookComing up in part 3: Real-world ROI from predictive maintenance: See how teams are measuring uptime gains, reduced costs, and smarter resource planning.
