What Leading Manufacturers Are Doing Differently with Their Data in 2025

In 2025, data has become the manufacturing sector’s most strategic asset—separating those who lead from those who lag.

AI investment has grown from around $2.2 billion in 2022 to nearly $9.5 billion in 2025. That kind of 5x jump shows how serious manufacturers are about using data to drive automation, performance, and profitability.

Over 92% of manufacturers say they’re planning to increase AI investments this year. But here’s the catch: only 1% have reached true data maturity.

Source: Deloitte 2025 Outlook, NAM Report

The real gap isn’t about who has data. It’s about who can trust it, use it in real-time, and link it to outcomes that matter—like OEE, scrap rate, or downtime.

1. Building Industrial-Grade Data Infrastructures

Modern manufacturers are designing scalable, interoperable architectures that unify shop-floor sensors, ERP systems, MES, and AI models.

Cloud alone isn’t enough – leaders are moving toward Unified Namespace (UNS) architectures to eliminate silos and ensure real-time data access plant-wide.

Example: Siemens
Siemens implemented a decentralized data mesh using UNS principles enabling over 70,000 users to access validated asset data in minutes instead of weeks.

Their Azure-native platform processes over 1,000 data streams globally, improving responsiveness and standardization.

2. Treating Data Governance as a Strategic Enabler

Forward-thinking teams now see data governance not as a control function but as an operational accelerant. They implement:

Deloitte notes that 78% of AI-driven firms now have Chief Data Officers reporting directly to the CEO – a clear sign that governance is being tied to strategic execution.

3. Solving the Last-Mile Data Quality Problem

The biggest challenge isn’t capturing data—it’s trusting it when decisions are on the line. Top plants are now:

Using edge AI to flag sensor anomalies (e.g., temperature spikes, pressure drift)
Validating data in real-time before it hits the data lake

With more sophisticated tech, it’s becoming easier to surface the right information, at the right moment, with confidence.¹⁰ ¹¹

4. Aligning Data Strategy with Business KPIs

Having data is one thing. Making it speak the language of performance is what drives ROI. Leading manufacturers build role-specific dashboards for:

Operators: OEE, MTTR, FPY, real-time downtime alerts
Finance teams: Cost per unit, labor-to-output ratio
Supply chain leads: Inventory turnover, lead time variability

This ensures that from floor managers to C-suite, everyone sees metrics that matter:

5. Preparing for AI’s Next Phase: Generative + Predictive Intelligence

As GenAI becomes operationally viable, data readiness becomes mandatory. Leaders are:

Scoring data sets for structure, metadata context, and completeness
Integrating RAG (retrieval-augmented generation) to ground AI in real operational data

Example: Tier-1 Auto Supplier
Used RAG-trained GenAI to reduce errors in technical manuals by 83% and enable instant frontline Q&A.

The Road Ahead: Data Mastery as a Shared Advantage

By 2026, manufacturers with mature data operations are projected to outperform peers by 34% in profitability. But the journey isn’t about doing everything at once—it’s about unblocking your next constraint.

Whether you’re working to reduce downtime, improve forecast accuracy, or enable operator-driven analytics, data maturity is about alignment, not overwhelm.