Tiger Analytics

The client relied on static, reactive business intelligence dashboards that lacked business context, actionable insights, and decision support. The existing reporting environment limited users' ability to quickly interpret business performance and take timely action across different functions.

Tiger Analytics deployed its ReImagine BI accelerator to build a unified analytics platform featuring persona-based dashboards, an AI-powered Insights Engine, a Simulation Studio for scenario modeling, proactive KPI alerts, and conversational analytics using plain-English queries. The platform delivered role-specific insights directly linked to business action levers, enabling a shift from traditional reporting to intelligent, AI-assisted analytics.

The client managed more than 800 data pipeline refreshes across Azure Data Factory and Databricks and experienced approximately 250 operational incidents each month. High mean time to resolution (MTTR) and increasing service-level agreement (SLA) risks affected the reliability and operational efficiency of the enterprise data platform.

Tiger Analytics deployed its Stripes.AI agentic suite to automate enterprise data platform operations. The implementation introduced AI-powered incident auto-triaging, zero-touch Level 1 auto-healing for common pipeline failures, AI-assisted Level 2 and Level 3 incident resolution integrated with ServiceNow, and a real-time observability dashboard to improve monitoring and operational reliability.