Case Study: Large Multinational Telecommunications Company achieves 50-minute early service-failure predictions, reduced downtime, and accelerated model delivery with Dataiku

How a Multinational Telecommunications Company Developed AI-Enabled Service Failure Prediction

Large Multinational Telecommunications Company faced costly service downtime and complex, subtle degradation patterns that were hard to define and predict across diverse architectures. The company's global IT operations team needed to embed ML at scale while getting accurate input from operations SMEs, and they turned to Dataiku — using the Dataiku platform’s catalogs, reusable frameworks and deep-learning capabilities — to standardize failure definitions and accelerate model development.

Using Dataiku, the team built a catalog of failure indicators, reusable pipelines and automated modeling so IT ops could self‑serve and collaborate with data scientists; deep learning models now train in under 20 minutes on months of data and are monitored in production. As a result (via Dataiku) the company went from one model taking six months to producing 40 models within six weeks, predicts major incidents on average 50 minutes earlier, reduced MTTR and P1 failures, and increased overall service availability.