Telemetry for Plants | Cannventure Journal

3.25%

Sensor Big Data

EU27 enterprises (GE10), 2020

28.67%

IoT Use

EU27 enterprises (GE10), 2021

37.73%

Cloud Storage

EU27 enterprises (GE10), 2025

The point of telemetry is not to admire charts. It is to make decisions repeatable: the room behaves, the team aligns, and surprises become explainable.

In cultivation, the best operators have a feel for the room. Telemetry doesn’t replace that feel — it protects it. It lets you separate “this batch is different” from “this sensor is different,” and it gives your team a shared language when things drift. Start with a loop you can run without heroics: measure, timestamp, store, review, adjust. The loop is the product. The sensors are just inputs.

What rises first

A rank view of selected adoption indicators (derived from Eurostat time series).

Source: Eurostat ISOC_CICCE_USE, ISOC_CIWEB, ISOC_EB_AI, ISOC_EB_IIP (EU27_2020); ranks derived.

Section

A loop has four parts

1) Sense: capture the variables that actually change decisions. 2) Store: preserve timestamps and context. 3) Review: look for drift, gaps, and outliers. 4) Act: change one thing and watch what follows. Most teams stall at “store.” They build a lake and never fish. A loop is smaller. It forces a weekly rhythm: you don’t need perfect coverage; you need consistent review.

Telemetry loop (sense → store → review → act)

A telemetry system is a workflow with ownership, not a dashboard.

Source: Eurostat context metrics used to scale the nodes (ISOC_EB_IOT, ISOC_CICCE_USE, ISOC_EB_AI).

Section

Choose IoT by purpose (not by catalogue)

Across Europe, enterprises report IoT usage for pragmatic purposes: energy, security, production, logistics, condition-based maintenance. That is a helpful checklist for cultivation too. If a sensor does not change a decision, it’s noise. If it changes a decision, it needs care: calibration, redundancy, and clear “what happens when it’s missing.” Telemetry is operational only when it is maintained.

IoT by purpose

EU27 enterprises (GE10), 2021. Selected purposes (production, maintenance, logistics, energy, security).

Source: Eurostat ISOC_EB_IOT (EU27_2020, size_emp=GE10, time=2021).

Section

The “big data” leap is rarer than the IoT leap

Many teams install sensors. Fewer teams build analysis that reliably uses sensor streams at scale. That isn’t a criticism; it’s a reminder that analysis is an operating practice, not a dashboard. If you want crop prediction, start by earning the right to predict: clean timestamps, stable schemas, and a history that survives staff turnover. The model comes after the memory.

Big data from sensors is still a niche

EU27 enterprises (GE10). Big data analysis from smart devices/sensors (selected years).

Source: Eurostat ISOC_EB_BD (E_BDASDS, EU27_2020, size_emp=GE10).

Section

Storage is common. Compute is not.

Cloud adoption is not uniform across services. File storage is far more common than hosted compute or development platforms. That matches reality: most teams start by keeping records. Fewer teams build systems that change how work is done. For cultivation operations, this is a feature, not a bug. You can start with storage and still get value — as long as you run the review loop.

Cloud adoption (paid services) over time

EU27 enterprises (GE10), selected years.

Source: Eurostat ISOC_CICCE_USE (E_CC, EU27_2020, size_emp=GE10).

Section

Adoption is a curve, not a switch

When you look at adoption over time, capability grows in layers. First comes basic storage. Then databases. Then security tooling. Then environments for testing and deployment. Telemetry for plants follows the same path. Start with what makes your logs reliable. Expand only when the team is ready to maintain the next layer without it becoming shelfware.

Cloud usage is layered

EU27 enterprises (GE10), 2025. Share of cloud users by sophistication tier (derived).

Source: Eurostat ISOC_CICCE_USE (EU27_2020, size_emp=GE10, time=2025); derived from E_CC and E_CC1_* indicators.

Section

What climbs fastest is not always what impresses

In enterprise surveys, the widely adopted tools are often the unglamorous ones. What climbs fastest over time tends to be what reduces friction: storage, standardized software, repeatable reporting. The cultivation version is simple: the best “automation” is a calendar invite. If you review exceptions weekly, you will outperform a fancier stack that no one maintains.

IoT adoption does not automatically become analysis

Gap between IoT usage and sensor big-data analysis (derived).

Source: Eurostat ISOC_EB_IOT (E_IOT1, 2021) and ISOC_EB_BD (E_BDASDS, 2020), EU27_2020.

Section

A pipeline is a promise to your future self

A pipeline is where many telemetry projects fail. Not because the technology is hard, but because ownership is vague. Who fixes a broken integration? Who validates time stamps? Who closes the loop when a sensor disagrees with the room? Write down ownership. Make it boring. Then build the smallest pipeline that can survive a bad week.

Cloud compute is the rare step-change

Selected paid cloud services (storage vs compute), over time.

Source: Eurostat ISOC_CICCE_USE (EU27_2020, size_emp=GE10).

"Telemetry scales the craft by making decisions repeatable."

Section

Where “work” concentrates

In most organizations, work concentrates in a few categories: exceptions, handoffs, and unclear interfaces. Telemetry projects are the same. A handful of failure modes create most of the pain. Use a distribution view to find those hotspots. Then fix one. You do not need more sensors; you need fewer recurring failures.

Service mix: storage, databases, compute

EU27 enterprises (GE10), 2025. Selected services as reported usage rates.

Source: Eurostat ISOC_CICCE_USE (E_CC_PFIL, E_CC_PDB, E_CC_PCPU, EU27_2020, size_emp=GE10, time=2025).

Section

Throughput is decision throughput

Cultivation is full of decisions: irrigation timing, environmental adjustments, harvest planning. Telemetry improves throughput when those decisions become less emotional and more repeatable. If you can explain a decision with one chart and one sentence, the operation scales. If you need a meeting to explain the meeting, the queue grows.

Where the signal comes from

Big data sources reported by enterprises (sensors, social media, geolocation, other).

Source: Eurostat ISOC_EB_BD (EU27_2020, size_emp=GE10, time=2020).

Key Milestones

Week 1

Define the loop

Pick one decision loop the team will run weekly — and write it down.

Week 2

Instrument by purpose

Add sensors only where they change a decision; calibrate those first.

Week 3

Own the pipeline

Assign ownership for timestamps, missing data, and broken integrations.

Week 4

Add prediction last

Only model what you can already measure consistently for months.

Sources & References

EurostatICT usage in enterprises datasets (ISOC_*), via Eurostat Dissemination API↗

Telemetryfor Plants

What rises first

A loop has four parts

Telemetry loop (sense → store → review → act)

Choose IoT by purpose (not by catalogue)

IoT by purpose

The “big data” leap is rarer than the IoT leap

Big data from sensors is still a niche

Storage is common. Compute is not.

Cloud adoption (paid services) over time

Adoption is a curve, not a switch

Cloud usage is layered

What climbs fastest is not always what impresses

IoT adoption does not automatically become analysis

A pipeline is a promise to your future self

Cloud compute is the rare step-change

Where “work” concentrates

Service mix: storage, databases, compute

Throughput is decision throughput

Where the signal comes from

Key Milestones

Define the loop

Instrument by purpose

Own the pipeline

Add prediction last

Sources & References

Start with clarity.Scale with discipline.