Imagine that you have a small, but rapidly expanding, business that finds itself with multiple ways of storing data. You start with the best of intentions to have one central database for all of your resources, but you've increasingly found yourself with more software suites requiring very different database management systems; and that's a problem.
It's a problem because while this information is loosely related, there is nothing you can do to link the data together. After all, SQL 2000 does not talk with MySQL 3.23; so how do we analyze the the information that's contained in so many different types of databases? We first enable data relationships through a process known as ETL or Extract, Transform, Load.
This brief video explains how a company might find itself in this situation and how ETL can assist it in combining data into a central repository known as a data warehouse. This data warehouse is a snapshot of several databases (like those listed in the video) in one central repository so that analysts can turn the data into actionable information. This process of turning data into actionable information is known as Business Intelligence.
Business Intelligence involves any action required to take a business process (like "enroll a student") to analysis (such as "how many lower income students enrolled in 2014?") to action ("Improve enrollment rates of lower income students"). These steps vary depending on the size and scope of operations, but can typically be reduced into a simple data process which has been succinctly defined by Google:
While not every company will require an ETL process, a data warehouse, or an OLAP cube, every company must prepare their data before it can be analyzed. Similarly, analysis must take place before knowledge can be accurately applied; and every company will have different method of analysis. The single commonality is that Business Intelligence requires preperation, analysis, and application in order to turn data into profit.
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