Data That Delivers: Using the Right Data at the Right Time

In another case, a frozen food plant used simple vibration trending (collected weekly with handheld tools) to spot a failing bearing on a critical spiral freezer motor, just days before peak production. They avoided a catastrophic breakdown and saved more than $150,000 in lost product and unplanned downtime.

These examples share a common theme: small, specific data sets tied to high-impact decisions. Not all data is equal. The key is choosing data that leads directly to action.

Defining “useful” data: context, actionability, and consistency

So, what makes data useful?

First, it must be relevant to the operation—not just interesting, but immediately actionable. Useful data is specific, timely, and clearly connected to a decision. It supports root cause analysis, validates a suspicion, or highlights an emerging risk.

Second, it must be consistently defined. Far too often, different machines (even from the same vendor) use different tags, formats, or definitions for the same concept. One reports “uptime,” another “availability,” a third just “run status.” That inconsistency adds friction, confusion, and rework.

To address this, the OpX Leadership Network recently released a Data Management Standard that defines a common set of baseline data points across packaging and processing equipment. Developed by a cross-industry team of OEMs, integrators, and CPG manufacturers, the standard includes:

• A core data model with required data fields (e.g., state transitions, performance metrics, loss tracking)
• Standard naming conventions and data types
• Guidance on system architecture and data handoffs

The goal is simple: help CPGs get useful, comparable, plug-and-play data from every machine, regardless of vendor.

It’s not a software platform. It’s not expensive. It’s a shared understanding, so that whether you're a plant manager or a line engineer, you know what “OEE” means and where it comes from.

Layering data access: operators, maintainers, managers

It’s also critical to recognize that the same data point has different value depending on the role. That’s why layering and access control matter.

• Operators care about real-time visibility and alerts. “Am I running? Am I starved? Is something jammed downstream?” They need data that supports quick action and immediate feedback.
• Maintainers look for trending data and anomalies. “Is this motor pulling more current than usual?” “Has the number of minor stops spiked this week?” Their decisions are predictive, not reactive.
• Managers and engineers want aggregated, loss-categorized data. “Where are we losing the most time?” “What’s the ROI of a new CIP skid?” Their focus is strategy and investment.

The takeaway: Don’t blast the same dashboard to everyone. Instead, deliver targeted, role-specific data that empowers each team to do their job better. That’s how you build a culture of data-informed action—not just data overload.

Getting started: simple wins without big systems

Here’s the good news: You don’t need a full-blown MES or ERP integration to use data effectively.

Most plants can start making better decisions with what they already have—or by adding one or two low-cost elements.

Start by asking:

• Where are we losing the most time or product?
• What questions do we argue about every week?
• What’s a recurring issue that’s still unresolved?

Then, pick one metric that could help clarify or challenge the narrative. That might mean:

• Tracking idle time during sanitation
• Recording first-pass quality yields by shift
• Measuring unplanned stops by fault code

Don’t try to build a full dashboard on Day 1. Instead, run a two-week experiment:

1. Pick the metric
2. Define how to measure it (manually or automatically)
3. Collect the data and use it to inform one improvement decision

You’ll learn more from that one focused cycle than from a dozen abstract meetings about “data strategy.”

Conclusion: practical, actionable, and scalable

At its core, data isn’t about dashboards, sensors, or systems. It’s about better decisions.

The plants that win aren’t necessarily the ones with the biggest tech stack. They’re the ones that use data intentionally — choosing just enough, presented clearly, to the right person at the right moment.

Tools like the OpX Data Management Standard provide a clear foundation for this kind of smart data use. They reduce confusion, improve integration, and give teams a shared language to build on.

So if you’re trying to do more with less, don’t start by buying more data tools. Start by asking: What decision am I trying to make? Then find the data that helps make it.