Asset predictive maintenance

Reduced unplanned downtime, improved predictive maintenance, and accelerated data-driven decision-making

The challenge

The client struggled with fragmented data across drilling, production, and maintenance systems. Additionally, SCADA, IoT sensors, and legacy databases operated in silos, restricting visibility into asset performance, production forecasting, and operational efficiency.

The solution

Our team leveraged Snowflake and Azure to integrate structured and unstructured data. Using ADF, we ingested sensor data, maintenance logs, and financial reports. Additionally, Azure Machine Learning enabled predictive maintenance and real-time anomaly detection.

Measured impact

30%

Reduction in Mean Time to Repair (MTTR),

40%

Improvement in Mean Time Between Failures (MTBF),

25%

Reduction in data retrieval time