There is a reason Data is literally becoming the talk of every town in America. It has proven itself to be one of the most powerful tools in any arsenal. And while the massive increases in data center projects are controversial for many reasons including the nefarious uses of that data, when data is put to the correct use, its impact on your manufacturing operation can’t be ignored. Manufacturers are sitting on a gold mine of information from their machines, operators, ERP systems, supply chains, and production processes. The challenge is figuring out what machine data actually matters, how to collect it, and what to do with it once you have it.

In this episode of Manufacturing Insiders, Nate Wheeler sits down with Tom Ferrucci, a Chief Information Officer at NATCO HOME with decades of experience connecting IT (Information Technology) and OT (Operational Technology) in manufacturing. Tom explains how manufacturers can start turning their operations into a source of actionable data — even when they’re working with older, legacy equipment.

We cover how to identify what data you actually need to collect, how to bring digital machine data collection to legacy manufacturing equipment, how to use ERP systems and data warehouses to centralize manufacturing machine data and build dashboards for operators, managers, and executives, why data quality and data governance matter more than simply having more data, and the increasing use of AI systems to quickly detect patterns, anomalies and insights hidden in data. One of the biggest takeaways from the conversation is simple: If your data is bad, better technology won’t fix the problem.

Nate Wheeler is the owner of weCreate Web Design & Marketing, a nationally recognized marketing agency that helps manufacturers grow, save money, and become more efficient. Reach out to us below.

Watch the Interview on YouTube!

Interview Chapters

• 0:00 Introduction to Tom Ferrucci
• 3:00 Introduction to Natco
• 4:30 The Role of IT: Way Beyond Making Sure The Lights are On
• 8:10 Connecting Data Streams of Legacy and New Machines
• 12:50 How Small Shops Can Learn from Big IT Data Collection Operations
• 15:50 How to use Data Collection Systems
• 22:30 Why Microsoft Power BI is Natco’s Choice
• 24:30 The Growing Role of AI in these Systems
• 30:25 Addressing Common Pushback on the Value of Data
• 34:25 The Intersection of IT and OT Security Systems
• 39:50 CMNC and ISO Frameworks Can Help Your Data Implementation

Manufacturing Insights

1. Start with the manufacturing problem, not the technology

One of the most important points in the podcast episode is that technology shouldn’t dictate what a manufacturer measures. The operations team should identify its biggest pain points and inefficiencies first. Technology can then be used to address those specific problems. This is a great overarching principle: “What problem are we trying to solve?” not “What can this technology do?”

2. Legacy machines don’t have to stay disconnected

Manufacturers don’t need to replace every older machine to begin collecting useful manufacturing data. Tom describes using additional sensors—including vibration, heat, and vision systems—to collect information from older equipment and send it back to a central system. That’s a particularly strong point for so many manufacturers who are operating a mixture of new equipment and legacy machinery.

3. Sensors can turn reactive maintenance into proactive maintenance

A particularly good example from the episode is machine downtime. Instead of relying on an operator to notice a problem, sensors can identify conditions such as overheating or excessive vibration and automatically trigger a maintenance ticket. That creates a clear chain: Detect → Alert → Respond → Reduce downtime This is much easier for a manufacturer to justify investment than the vague idea “we need more data.”

4. Data collection should have a measurable business purpose

The transcript provides a really good argument for connecting manufacturing data to financial impact. Tom describes tying equipment information to costs so manufacturers can prioritize problems based on their actual financial consequences. A machine producing a high-value product can warrant a different response than one producing a low-cost product. This could be a major section of the article: Data becomes valuable when it is connected to dollars.

5. Don’t overlook the machine data you’re already collecting

Manufacturers may already have useful information sitting inside their ERP, warehouse management system, quality systems, or other software. Tom’s advice at the end of the episode is specific advice for certain manufacturers: before purchasing another system, manufacturers should investigate what their existing software is already capable of doing. That’s a great cost-conscious recommendation. The next manufacturing technology investment might already be sitting in the software you’re paying for.

6. Good data matters more than sophisticated analytics

This is probably one of the strongest quotes/concepts from the podcast episode. Tom essentially argues that an advanced system with bad data will produce a wrong answer quickly, but we want a correct answer. Therefore Manufacturers need to think about:

• Data quality
• Data completeness
• Data governance
• Standardized inputs
• Consistent measurement
• Who is responsible for maintaining the data

AI cannot compensate for fundamentally bad manufacturing data. That’s an excellent  for an SEO article because it lets you move beyond “collect more data.”

7. Visualization is what makes data useful to different levels of the organization

Tom describes using different levels of reporting for operators, managers, and executives. Operators need granular information about the equipment or SKU they’re working with, while executives need a higher-level view with the ability to drill down into the underlying data. That’s an important distinction. The same manufacturing data doesn’t necessarily need to be presented the same way to everyone.

8. AI can make manufacturing machine data easier to access

The AI discussion is strongest when it’s framed as reducing the friction between people and data. Instead of requiring someone to know exactly how to build a report or filter a database, AI can potentially allow employees to ask questions in plain language and get answers from the underlying data. That’s a much more compelling manufacturing AI use case than simply saying “AI will analyze your factory.” But making existing information accessible to more people.

9. Manufacturing machine data doesn’t have to stay inside the factory

Another interesting point is that Tom’s data infrastructure combines manufacturing information with outside data sources. For example, they use information about port congestion and weather to improve supply-chain and transportation decisions. Manufacturing decisions can improve when companies combine: machine data, production data, inventory data, ERP data, supply-chain data, weather data, transportation data, and customer data to optimize processes. The ideal goal is connecting information that previously lived in separate systems.

10. IT and OT need to work together

This is another major theme running throughout the episode. Operations understands the physical equipment, its failure modes, and its production requirements. IT understands connectivity, networks, applications, data, and integration. The best solutions combine both areas of expertise. That’s especially relevant as manufacturers add sensors, connected equipment, AI, and cloud systems to older production environments.

11. Machine data can help manufacturers attack the biggest problems first

The transcript makes a good case for prioritization rather than trying to digitize everything at once. Tom specifically talks about identifying the biggest issues and tackling those first, and identifies this practical framework:

1. Identify the biggest operational problem.
2. Determine what information would help explain it.
3. Automate data collection where practical.
4. Connect that data to the relevant business systems.
5. Build a useful visualization.
6. Take action.
7. Measure the result.

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