Rian Insights

Data analytics for connected devices — fast, practical, and production-ready

Telemetry → pipelines → dashboards → insights
Helping hardware and IoT teams turn device data into decisions

Services

Analytics & Dashboards — KPI design, realtime dashboards (Power BI / Tableau / Quicksight)
Data Pipelines — SQL/Python ETL, cloud storage & ingestion (S3, Redshift, etc.)
Performance Insights — anomaly detection, reliability scoring, predictive maintenance
Retainers & Fractional Data Leadership — ongoing monitoring, reporting, and roadmap

Fleet Performance & Telemetry Diagnostics for Distributed Energy Devices

Transforming messy device data into actionable operational insights.

Rian Insights conducted an end-to-end analysis of 60 distributed smart energy devices, including telemetry cleaning, validation, KPI analysis, anomaly detection, and interactive dashboards. This project highlights how complex IoT-style data can be transformed into clear insights to support operational decisions.

The Challenge

  • Telemetry data had missing values (~8%), duplicates, and mixed timestamp formats.

  • Device IDs were inconsistent (mixed case), and voltage readings were sometimes stored as strings with “V”.

  • Outliers in temperature and power were injected to simulate sensor errors.

  • Leadership needed a reliable, actionable view of fleet performance across multiple regions and device models.

Our Approach

  • Data Cleaning & Validation: Standardized timestamps and device IDs, interpolated missing data, corrected voltage formats, and flagged anomalies.

  • Exploratory Analysis: Calculated fleet KPIs, plotted power, efficiency, and temperature trends, and compared devices by location and model.

  • Dashboard Development: Interactive dashboards displaying high-risk devices, regional performance, KPI summaries, and operational trends.

Key Insights

  • Efficiency drops at higher temperatures (above ~30°C).

  • Outlier clusters revealed faulty sensors needing attention.

  • Regional patterns in device performance were identified.

  • Devices with frequent missing data or duplicate rows were flagged.

  • Voltage type errors were corrected without losing any historical readings.

Deliverables & Impact

  • Cleaned Telemetry Dataset: Fully validated, missing values addressed, outliers flagged.

  • Merged Analytics Dataset: Combined telemetry with device metadata for richer insights.

  • Interactive Dashboard: Fleet KPIs, trendlines, regional comparisons, and high-risk device tracking.

  • Technical Report: Documenting data issues, cleaning steps, analysis methods, and actionable recommendations.

Results:

  • Data reliability improved from ~92% → ~98%

  • Duplicate records (~2%) removed

  • Hundreds of anomalies flagged for review

  • Executive-ready dashboard delivered for actionable insights

Tools used: Python, Pandas, NumPy, SQL, Jupyter, Power BI / Tableau / Plotly, Scikit-learn

Interested in turning your device telemetry into actionable insights? Contact Rian Insights today to discuss how we can help your hardware or IoT projects generate real operational value.

Projects & Insights

Rian Insights transforms raw device and operational data into clear, actionable intelligence.
This portfolio highlights real-world analytics projects demonstrating data cleaning, anomaly detection, and fleet performance insights across distributed hardware systems.

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