Blog
Insights, tutorials, and news from the TagoIO team.
Analytics Models for IoT: From Dashboards to Predictions You Can Act On
How analytics models turn IoT sensor data into forecasts, anomaly scores, and time-to-threshold estimates: the four-step pipeline, which model family answers which question, why a fitted model beats an LLM at scoring telemetry, and what the research says about the payoff.
What a Good IoT Dashboard for Facility Management Actually Looks Like
What a good facility management IoT dashboard actually contains: the four views that matter, why layout follows the building, and why the best facility UIs now lead with forecasts and flagged anomalies instead of raw charts.
Open Source vs Managed IoT Platforms: What the Difference Really Costs
Open source IoT platforms trade license fees for operations: hosting, patching, scaling, and building the analytics layer yourself. What each model really includes, what each really costs, and how to choose honestly.
LoRaWAN Network Server vs IoT Application Platform: What Each Actually Does
A LoRaWAN network server moves packets; an IoT application platform turns them into dashboards, alerts, forecasts, and applications. What each layer does, why you need both, and how they connect.
What Is Predictive Analytics for IoT, and How Do You Get Started?
A plain guide to predictive analytics for IoT: what it means, where it pays off, the honest requirements, and a first project you can ship on TagoIO.
Anomaly Detection vs Forecasting: Which Does Your IoT Project Need?
Anomaly detection and forecasting solve different IoT problems. How to tell them apart, when you need each, and how they work together on TagoIO.
How Much Historical Data Do You Need to Forecast IoT Metrics?
How much historical IoT data a forecast really needs: the rule tied to your seasonal cycle, why more is not always better, and how to start before you have years of it.
Which Forecasting Model Should You Use for IoT Time-Series Data?
ARIMA, Prophet, and LSTM compared for IoT time-series forecasting: how each works, when it fits, and how to run the winner on TagoIO.
How to Forecast IoT Sensor Data: A Practical Guide
A practical progression for forecasting IoT sensor data, from moving averages to seasonal models to machine learning, and how to run it on TagoIO.
How to Detect Anomalies in IoT Sensor Data Using AI
The four shapes a sensor anomaly takes, spikes, drift, silence, and correlated failures, why fixed thresholds miss three of them, and how to build detection on TagoIO with statistical baselines, TagoAI, Analysis, and Actions.
Which IoT Platforms Have the Best APIs for Custom Integrations?
Five testable properties that decide whether a custom integration ships: coverage, token scoping, rate limit transparency, SDK quality, and two-way webhooks. Where the well-known platforms stand, and the proof to run before you commit.
How Satellite IoT Connectivity Works (and When It Makes Sense)
How satellite IoT connectivity works, the cost and latency trade-offs of LEO versus GEO, and the narrow set of cases where it beats LoRaWAN or cellular.