Hello readers!

This week we’re looking at the three things holding digital twins back and a C-level roadmap to implementation, international IoT day, and more!

A paper published last month in Scientific Reports does something the digital twin hype cycle rarely bothers with: it identifies the ceiling. Written by a researcher at the German Cancer Research Center, the editorial traces the technology from its NASA origins through its current spread across manufacturing, healthcare, smart cities, and planetary-scale climate modeling — then names the three specific problems keeping it stuck in pilot purgatory.

The first is data integration. A digital twin is only as good as what feeds it, and most industrial environments are a tangle of incompatible sensors, legacy systems, and proprietary protocols that were never designed to talk to each other. The paper flags this directly: fragmented data sources and the absence of interoperable standards prevent any coherent twin from forming. IoT practitioners in energy, water, and manufacturing have been running into this for years — Gartner data shows 62% of IoT-enabled organizations are developing digital twins, but only 13% are actually using them. That gap is largely a data plumbing problem. You can have a sophisticated model sitting idle because the sensor data feeding it is incomplete, delayed, or formatted three different ways depending on which vendor installed what equipment, five years apart.

The second bottleneck is model validation — whether you can trust what the twin tells you. In manufacturing, a bad prediction costs a maintenance window. In healthcare or aerospace, it costs more. The paper is direct: for decisions that depend on twin outputs, users need rigorous uncertainty quantification and continuous calibration against real-world measurements. The approach it points toward is physics-informed machine learning — hybrid models combining the physical consistency of traditional simulation with the speed of AI, building twins that are fast enough to update in real time and grounded enough that their outputs can be trusted. It is promising, but it remains largely research-stage technology being asked to underpin production-critical systems.

The third problem gets the least attention in vendor pitches: governance. As digital twins scale from individual machines to entire cities — integrating data on buildings, transit, utilities, and people's movement in real time — the ethical and privacy questions compound fast. Who controls the twin? Who has access? The paper argues that humans must be accounted for both within and outside digital twins: inside, because human behavior needs accurate modeling; outside, because someone needs to regulate access and use. Most deployments currently have a blank space where that second part should be.

Digital twins are delivering real value today, particularly in well-scoped industrial applications where data is clean and failure modes are understood. But the next phase — interconnected city-scale twins, personal health twins, climate models — requires solving all three problems together. For IoT teams building or evaluating implementations, the practical read from this paper is simple: the bottleneck is rarely the modeling software. It is the data architecture upstream of it, the validation rigor around its outputs, and the governance layer that decides who acts on what the twin says. Getting those three things right is what separates a twin that generates dashboards from one that actually changes decisions.

There's a quiet shift happening in boardrooms across industries. CEOs, CTOs, and COOs are no longer asking "What is a digital twin?" They're asking something more pressing: "How do we build one that actually moves the needle?"

Read more

4G is sunsetting, but full 5G is overkill for most IoT deployments. Where does that leave smart grid, SCADA, oil & gas, and other long-life industrial systems?

5G RedCap — the mid-tier 5G standard purpose-built for industrial and commercial IoT — is the answer. Omdia projects 963.5M RedCap connections by 2030.

Semtech's new whitepaper unpacks the technical architecture, real-world use cases, and 10 critical questions to plan your migration.

Most IIoT teams don't realize Postgres is at its limit until queries start failing in production.

Our new white paper, The IIoT PostgreSQL Performance Envelope, maps exactly where Postgres hits its limits with industrial sensor data and what you can do before you're forced into a split architecture. No hand-waving. Real benchmarks, real query patterns, real thresholds.

If you're building on IIoT telemetry and still deciding whether Postgres can scale with you, this is the data you need.

Access the data

In this episode of the IoT For All Podcast, Matthias Wagner, Founder and CEO of Flux, joins Ryan Chacon to discuss AI-assisted hardware design for IoT. The conversation covers the historical challenges of hardware design, the current capabilities of AI tools, compressing the hardware iteration cycle, integration challenges, the limitations of AI, and enabling IoT innovation.

YouTube • Spotify • Apple • YT Music • Amazon

Interested in becoming an IoT For All Partner? Reach out here!