Imagine that the Fed did not exist and that today you are asked to develop ways to measure economic output for the United States. How would you do that? What would you measure? At what frequency? And, from whom would you get the data?

The world is a very different place than it was in 1922 when the monthly statistical forecasts of Industrial Production (IP) started being produced by the Fed. In these past decades, the Fed has generated timely measures of activity. But, the economy of the U.S. in 2015 looks very different than it did almost 100 years ago. The modernization of economic forecasting is enabled by rapid advancements in technology, business innovation, and, more real-time data than has ever previously existed. The business drives of the financial regulatory ecosystem have also changed since the financial crises. There has been a massive torrent of new complex data that is both required by new regulatory regime and available via third parties and open source mechanisms. And, the computing power which exists today is unprecedented. The scale, complexity, cross-functional needs, and general ecosystem trends demand new ways of thinking, and best practices that are being applied in many other sectors.

The management of the newly emerging data types, in conjunction with changing business needs, requires a strategic thinking. a cultural change and an organizational shift towards a central data management structure, including a Chief Data Officer.