Harnessing Data in the Internet of Reliability: Strategies for Effective Management

Welcome to Noria’s latest series, The Internet of Reliability. I’m Jeremy Drury of IoT Diagnostics, here to show you how to transform the torrent of data from connected assets into decisive, actionable insight.

Understanding the Data Deluge

For almost a decade, sensor data and quantitative metrics have guided our reliability decisions. The shift to Industry 4.0 magnifies that volume—every machine, every sensor, every piece of equipment now streams continuous information. The challenge isn’t the lack of data; it’s the lack of bandwidth to analyze it all when staffing is limited.

Our goal is simple: spend less time chasing machines and more time interpreting data that tells you precisely when and where intervention is needed. The key is balancing data ingestion with proactive maintenance.

Two Lenses for Viewing Data

Data can either empower or overwhelm. A single, well‑chosen metric can unlock immediate action, while a scatter of unfiltered figures may only highlight problems without offering solutions. Think of it as a shovel—data can dig you up if you know where to dig.

We differentiate between center‑stage data—critical, actionable metrics that drive instant decisions—and behind‑the‑scenes data—the operational background that supports deeper analysis but may not spark immediate change.

Case Study: Hydraulic Pump Efficiency

Manufacturers often stipulate an 85 % volumetric efficiency for hydraulic pumps. This metric accounts for expected leakage and indicates overall health. By monitoring real‑time efficiency, a 78 % reading instantly flags inefficiency, allowing maintenance teams to investigate potential causes such as varnish build‑up, material degradation, or filtration failure.

Beyond the shop floor, this single metric informs operations by comparing pumps across sites, identifies trends tied to age or application, and guides corporate decisions on regional performance disparities. OEMs and suppliers can also use the data to refine product design and service schedules.

Rippling Impact of a Single Data Point

Let’s walk through the five layers where a single 78 % efficiency reading cascades value:

1. Asset Level – Immediate maintenance action to restore efficiency.
2. Operations Level – Adjust production parameters to accommodate or compensate for sub‑optimal pumps.
3. Corporate Level – Compare regional performance, allocate resources, and benchmark against industry best practices.
4. Supplier & OEM Level – Inform supply chain decisions, lubrication schedules, and design improvements.
5. Strategic Insight – Feed into ROI models and long‑term reliability strategy.

Maintaining Data Control

Control starts with intention: decide how data should be displayed—PLC dashboards, SCADA screens, mobile apps, or desktop analytics—and at what frequency. Some teams need real‑time alerts every minute, while others prefer weekly summaries. Aligning your IoT platform to these preferences empowers you to build a compelling ROI case and negotiate the right data flow with vendors.

Choose the metrics that matter most, set the visual and notification parameters that fit your workflow, and keep your data strategy in the driver’s seat.

Thank you for joining me today. For more guidance on turning data into reliability, stay tuned to The Internet of Reliability series.