Data has become a competitive advantage for information-driven enterprises that have the ability to effectively operationalize it across their business. This includes discovering and embedding past-, present-, and future-looking analytics into their end users’ workflow in order to move the metrics that matter. However, if this data doesn’t reach the end consumer in a timely manner, then data is left out of analyses, latency occurs in applications, and end users don’t get the information they need. This, in turn, creates a negative return on data investments. As large volumes of data continue to be operationalized across the enterprise, pressure is put on existing data infrastructure, causing challenges to arise. These challenges have compelled enterprises to rethink how they approach data management by revamping their architecture to support this new way of business. By implementing an Operational Data Store (ODS), designed for a big data world, these forward looking enterprises have complemented their existing data infrastructure to support the current and future needs of their business.