Metadata is essentially the information about the data. Metadata contains information concerning when data was collected, how it was collected and by whom. This helps enhance business intelligence and provides teams with a better understanding of what data their company possesses. Through automated metadata management, BI and analytics teams can instantly locate relevant data, identify origin points for the data, and create sound insights. By creating data about the data, teams can also set processes and policies that make sure the information can be easily accessed, shared, linked, integrated, and analyzed. This ensures that the data is relevant and accurate for all members of the company.
Metadata helps populate the data dictionary. Within the data dictionary, BI teams can upload any data elements that they already have saved from different databases or descriptions. It is a file that contains the basic definitions of the database. A data dictionary is the main tool that BI professionals utilize to organize all of their metadata. All information concerning the data that exists in your company’s data warehouse (DWH) is stored within the data dictionary. The data dictionary is used by technical teams and is the main place to reference different data attributes, including constraints, data type, default values, length, transformation regulations, and business definitions. By establishing cohesive definitions understood across the company, all teams can be on the same page. This helps maintain the validity of the data and enables consistency within an organization.
