The need to share data is just as important as the need to share metadata. Often, several departments in the same organization need the same information. For example, each department may need to read the same product data from operational sources, perform the same profitability calculations, and format this information to make it easy to review.

If each department reads, transforms, and writes this product data separately, the throughput for the entire organization is lower than it could be. A more efficient approach is to read, transform, and write the data to one central data store shared by all users.

A central data store improves throughput at the level of the entire enterprise. To improve performance further, you might want to capture incremental changes to sources. For example, rather than reading all the product data each time you update the central data store, you can improve performance by capturing the inserts, deletes, and updates that have occurred in the PRODUCTS table since the last time you updated the central data store.

You can format data in a standard fashion with the central data store. For example, you can filter employee data that should remain confidential. You can also display date and time values in a standard format. You can perform these and other data cleansing tasks when you move data into the central data store instead of performing them repeatedly.