As per Gartner, poor data quality costs Organizations $12.9 million annually. Good data allow organizations to measure the effectiveness of its business strategy and KPIs to make informed decisions and to take right actions. Measuring business KPIs and taking swift actions improve the Organization’s capabilities, products, services and thus driving customer satisfaction. However as Organizations grow, the IT systems and its data sources keep increasing thereby rendering the data estate multi layered and complex. Voluminous data calls for quality control mechanisms that are critical for effective business decisions. Manually measuring data quality against several dimensions like Completeness, Accuracy, Uniqueness, Timeliness, Consitency, Validity in a big data landscape could be time consuming and resource intensive. Manually detecting data quality issues are costly and have serious repercussions to business operations. This mandates the need for a scalable and an automated data quality framework. Following the Fail fast design principle, Tiger built a solution that can quickly deliver business impact by detecting the data quality issues early in the Analytics value chain. The platform has helped us build a configurable metadata driven framework with the follwoing capabilities: ● Self-service UI to quickly profile and automate rule discovery ● Configuration-based backend processing ● AWS cloud native and open-source technologies ● Monitoring and Alerting