S2 Data Systems + Monnit — analytics on top of your sensor investment
Monnit ships excellent wireless IoT sensors. Once your fleet is deployed and reporting to iMonnit, the next question is how to turn that telemetry into operational decisions, business KPIs, and ML predictions. That’s where S2 Data Systems comes in. We’re hardware-agnostic by design — we connect to Monnit data via API and webhooks, ingest it into your own cloud account, and build the analytics, ML, and dashboard layer your business actually needs.
What we build for Monnit-instrumented operations
- Streaming pipelines from iMonnit webhooks into AWS, Azure, or GCP — Kafka, Kinesis, Event Hubs, Lambda, EventBridge.
- Data lakehouse on Snowflake, Databricks, BigQuery, or Synapse — joined with ERP, MES, CRM, and weather/utility feeds.
- Predictive maintenance models trained on Monnit temperature, vibration, current, and humidity signals.
- Cross-sensor anomaly detection — multivariate ML that catches correlated drift across a sensor fleet.
- Executive dashboards in Domo, Power BI, Looker, or Grafana — beyond iMonnit’s built-in UI.
- Automated workflows into PagerDuty, ServiceNow, Slack, Teams, ServiceTitan, or your ticketing system.
- Historical archives and replay — never lose telemetry; build cohort analyses across years of sensor history.
Typical engagement
- 30-minute discovery — your sensor types, iMonnit account, target use case, cloud preference.
- 4-6 week proof of value — live iMonnit-to-cloud pipeline, one analytics use case, one production dashboard.
- Scale to platform — multi-source ingestion, ML model deployment, full operational-intelligence layer.
- Managed services — telemetry SLAs, model retraining, ongoing analytics improvement.
Frequently asked questions
Does S2 Data Systems replace Monnit?
No. Monnit and S2 are complementary, not competing. Monnit manufactures wireless IoT sensors and ships them with the iMonnit cloud-monitoring portal. S2 Data Systems builds the custom analytics, machine-learning, data-warehouse, and operational-dashboard layer that consumes sensor telemetry from Monnit (or any vendor). If your sensors are already in the field, S2 adds the analytics layer on top.
Why would I need anything beyond iMonnit?
iMonnit covers basic monitoring and alerting well. Enterprises typically outgrow it when they need: (1) joining Monnit data with ERP, MES, or CRM systems, (2) running custom ML models on the telemetry (predictive maintenance, anomaly detection), (3) cross-sensor analytics that combine Monnit with non-Monnit data, (4) executive dashboards in Power BI, Domo, or Looker, or (5) a unified data lakehouse for historical analysis.
How does S2 get data out of Monnit?
Monnit supports webhooks and API access from iMonnit. S2 builds streaming ingestion pipelines that pull sensor telemetry into the customer's cloud (AWS, Azure, or GCP), land it in a data lake or warehouse (S3, ADLS, Snowflake, BigQuery), and process it with Kafka, Kinesis, or Event Hubs for real-time analytics.
Can S2 build alerts that go beyond iMonnit's built-in alerting?
Yes. Common requests include: cross-sensor correlations (alert when temperature drops AND humidity rises), predictive thresholds (alert when sensor trend predicts failure in N hours), business-rule alerts that combine sensor data with non-IoT signals (e.g., production schedules, weather forecasts, energy prices), and automated workflow triggers into PagerDuty, ServiceNow, Slack, or ticketing systems.
Are you a Monnit partner?
S2 Data Systems is hardware-agnostic and integrates with Monnit sensors as a customer's existing infrastructure. We are not contractually a Monnit reseller. We work alongside Monnit's hardware in the customer's deployment, just as we work alongside Wovyn, AWS IoT, Azure IoT Hub, and other ecosystems.
Where is S2 Data Systems located?
S2 Data Systems is US-headquartered at 131 Continental Dr, Suite 305, Newark, DE 19713, and serves Utah enterprises across Salt Lake City, Provo, Lehi, Draper, Ogden, and Park City — including Monnit customers across the Wasatch Front and beyond.
Related reading
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