Taming the data beast is the first step toward smart operations that cannot be skipped

Editor’s note: This is the first part of a four-part series, the Smart Operations Playbook. The next article will explore moving from data to action by deploying practical AI that augments and empowers your workforce.

Welcome to our new series. As an analyst, I’ve spent my career in conversation with industrial leaders, and I’ve noticed a disturbing pattern. It’s a story I’m sure you’ll recognize.

It starts with a compelling promise: a pilot project for industrial AI, a new digital twin, or an advanced analytics platform. A high-performing team is assembled, a vendor is selected, and after six months of heroic effort, they present a beautiful dashboard that proves the technology works. The pilot is a "success."

Then ... nothing.

The project never scales. It dies a quiet death in what’s called "pilot purgatory." The dashboard goes dark, the team moves on, and the plant floor operates exactly as it did before, leaving a lingering sense of frustration and a management team skeptical of the next "digital transformation" pitch.

See also: Data quality issues costing manufacturers billions

Why does this happen over and over? Is the technology not ready? Is the team not skilled enough? No. It’s almost always because you’re trying to build a smart superstructure on a foundation of data chaos. You’re trying to run before you can walk.

Recent ARC Advisory Group research highlights this very challenge: ensuring data quality is the second biggest implementation hurdle organizations face when deploying industrial AI, cited by nearly 50% of respondents. This isn't a minor technicality; it's the primary barrier to progress.

Diagnosing the sickness: Silos of our own making

Before we can prescribe a solution, we have to be honest about the problem. For decades, we’ve invested in technology in a piecemeal fashion, creating digital silos that, by design, do not talk to each other.

On the plant floor, we have our operational technology systems. Our PLCs, SCADA, and historians speak a fast, real-time language, but they are focused only on the machine. They can tell you a pump's vibration, but not the work order it’s processing or the product it’s helping to make.

See also: Talking to your data: Agentic AI’s utility in process manufacturing

In the back office, we have our information technology systems. Our ERP, SCM, and MES systems speak the language of business—orders, materials, and schedules. They know the "what" and "why" but have almost no visibility into the real-time "how" of the physical process.

In the engineering department, we have our engineering technology. Our PLM, CAD, and simulation tools hold the "as-designed" digital twin—the perfect-world version of the product that is almost immediately disconnected from the "as-built" and "as-maintained" reality on the floor.

The result? This audience knows this pain intimately:

• Your operations manager spends the first week of every month arguing about whose OEE spreadsheet is correct.
• Your best engineer spends 80% of her time just finding and cleansing data instead of actually engineering solutions.
• And your new data scientist builds a brilliant predictive maintenance model that fails in production because the real-time data it’s fed from the historian lacks the context from the MES to make an accurate prediction.

Accessing data from proprietary software applications remains a top challenge for over 30% of industrial organizations trying to implement AI.

This is the "data wrangling" problem, and it’s the single biggest barrier to smart operations.

The prescription: The industrial data fabric

When I say "industrial data fabric," I’m not talking about one giant, monolithic database—we’ve all seen that movie, and it doesn’t have a happy ending.

See also: From Bridgeports to cobots: How manufacturing tech changes challenge the depleted workforce

Industrial data fabric is an architectural approach. It’s a "data unifier" that sits on top of your existing systems and creates a single, trusted source of truth without the pain of ripping and replacing. Think of it as a universal translator and GPS for all your factory's data.

It’s an operations strategy, not just an IT project. Its job is to:

• Connect to all your systems (OT, IT, and ET).
• Model the data, giving it context. It learns that "Sensor 123" is actually the "Vibration Sensor" on "Motor 4" which is part of "Production Line 2" currently running "Work Order 789."
• Deliver that single, unified, contextualized data stream to any application that needs it—a dashboard, an AI model, or a digital twin.

The data fabric doesn’t store all your data; it makes all your data available and understandable. Recognizing this, industrial leaders are making significant strategic shifts.

ARC Advisory Group data shows that investing in enterprise data fabrics is now a top-three approach for ensuring data quality for AI solutions, with nearly 40% of organizations prioritizing this strategy.

The human impact: From data wrangler to problem-solver

This is where the true impact on people and processes becomes clear. By solving the data access and context problem, you fundamentally change the nature of work for your most valuable employees.

See also: The hardware problem that is stalling half of all digital transformation projects

For the process engineer: Instead of playing "data detective" and hunting through spreadsheets, she can now go to one place to see a unified view of the process. She can finally correlate the raw material batch from the ERP with the machine settings from the SCADA and the quality results from the LIMS system to solve a complex, long-standing quality problem in an afternoon.

For the data scientist: You’ve given them the two things they crave most: trusted, high-quality data and speed. They can now access all the data they need in a matter of hours, not months, to build, test, and deploy AI models that actually work in the real world.

For the operator: You can finally deliver on the promise of a "single pane of glass" dashboard that is actually correct, showing them not just machine status but also how their performance is tracking against the production schedule and quality targets.

Achieving this requires more than just technology; it demands organizational alignment. ARC research indicates that industrial AI leaders dramatically outperform their peers in fostering collaboration between IT, OT, ET, and data science teams.

Over 75% of leaders report their teams are "fully aligned," compared to less than 10% of laggards. Breaking down these internal silos is just as critical as breaking down the data silos.

ROI: The foundation for everything that comes next

Taming the data beast isn't just a "nice to have" IT cleanup project. It is the critical enabler for every smart operations initiative you want to launch.

See also: How digital transformation and AI can redefine supply chains

The ROI is tangible and immediate. It’s measured in reduced downtime because your AI models are finally predicting failures. It’s measured in improved quality because your engineers can finally see the entire digital thread. And it’s measured in agility, the ability to reconfigure a line or troubleshoot a problem in hours, not weeks.

You cannot have a smart factory without smart data. You cannot have industrial AI, scalable digital twins, or the kind of collaborative robotics we’ll discuss later in this series until you have solved the data foundation. This is the first step you cannot skip.