Most large businesses and organizations collect a huge amount of rich data. That data can, theoretically, be used to make business decisions and personalize customer experiences. But most large organizations cannot actually use that data, because they suffer from what we call a Babel Problem. In a nutshell, this is the problem of having large amounts of data that is not at all integrated or updated in a timely manner, rendering the data almost useless. This especially happens when different departments or locations are using their own distinct databases.
A great way around the Babel Problem is to implement a data warehouse. A data warehouse is a central database that acts as the “main” database for an organization’s data applications. The data warehouse represents data in a standard, ready-to-use format. It is the job of an ETL (Extract-Transform-Load) system to take data from all of the other databases in an organization (“Extract” the data), translate that data into the format used by the data warehouse (“Transform” the data), and then load batches of transformed data into the data warehouse (“Load” the data).
With an ETL system loading into a central data warehouse, querying data becomes quicker, more efficient, and closer to “real time”, making business insights more frequent and more timely.