In this paper we present a system and case study for business data validation in large organizations. The validated and consistent data provides the capability to handle outages and incidents in a more principled fashion and helps in business continuity. Typically, different business units employ separate systems to produce and store their data. The data owners choose their own technology for data base storage. It is a non-trivial task to keep the data consistent across business units in the organization. This non-availability of consistent data can lead to sub optimal planning during outages and organizations can incur huge financial costs. Traditional custom data validation system fetches the data from various data sources and flow it through the central validation system resulting in huge data transfer cost. Moreover, accommodating change in business rules is laborious process. Accommodating such changes in the system can lead to re-design and re-development of the system. This is a very costly and time consuming activity. In this paper, we employ a Metadata driven rule-based data validation system, which is domain independent, distributed, scalable and can easily accommodate changes in business requirements. We have deployed our system in real life settings. We present some of the results in this paper. © 2012 IEEE.