Metadata is any data about data and, as such, is an important aspect of the data warehouse environment. Metadata allows the end user and the business analyst to navigate through the possibilities at a higher business object level
Metadata management is a comprehensive, ongoing process of overseeing and actively managing metadata in a central environment which helps an enterprise to identify how data is constructed, what data exists, and what the data means. It is particularly helpful to have good metadata management when customizing Oracle Communications Data Model so that you can do impact analysis to ensure that changes do not adversely impact data integrity anywhere in your data warehouse.
Metadata is organized into the following major categories:
Business metadata describes the meaning of data in a business sense. The business interpretation of data elements in the data warehouse is based on the actual table and column names in the database. Business metadata gathers this mapping information, business definitions, and rules information.
Technical metadata represents the technical aspects of data, including attributes such as data types, lengths, lineage, results from data profiling, and so on.
Process execution metadata presents statistics on the results of running the ETL process itself, including measures such as rows loaded successfully, rows rejected, amount of time to load, and so on.
Since metadata is so important in information management, many organizations attempt to standardize metadata at various levels, such as:
Metadata Encoding and Transmission Standard (METS). A standard for encoding descriptive, administrative, and structural metadata regarding objects within a digital library.
American National Standards Institute (ANSI). The organization that coordinates the U.S. voluntary standardization and conformity-assessment systems.
International Organization for Standardization (ISO). The body that establishes, develops, and promotes standards for international exchange.
Common Warehouse Metamodel (CWM). A specification, released and owned by the Object Management Group, for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment.
When you implement your metadata management solution, reference your data warehouse infrastructure environment and make the decision which standard to follow.
You manage metadata using a Metadata Repository. At the highest level, a Metadata Repository includes three layers of information. The layers are defined in the following order:
Physical layer: this metadata layer identifies the source data.
Business Model and Mapping layer: this metadata layer organizes the physical layer into logical categories and records the appropriate metadata for access to the source data.
Presentation layer: this metadata layer exposes the business model entities for end-user access.
The first step in creating a Metadata Repository is to scope your metadata management needs by:
Identifying the metadata consumers. Typically, there are business consumers and technical consumers.
Determine the business and technical metadata requirements.
Aligning metadata requirements to specific data elements and logical data flows.
Then:
Decide how important each part is.
Assign responsibility to someone for each piece.
Decide what constitutes a consistent and working set of metadata
Where to store, backup, and recover the metadata.
Ensure that each piece of metadata is available only to those people who need it.
Quality-assure the metadata and ensure that it is complete and up to date.
Identify the Metadata Repository to use and how to control that repository from one place
After creating the metadata definitions, review your data architecture to ensure you can acquire, integrate, and maintain the metadata.
As the data keeps on changing in your data warehouse day by day, update the Metadata Repository. When you want to change business rules, definitions, formulas or process (especially when customizing the Oracle Communications Data Model), your first step is to survey the metadata and do an impact analysis to list all of the attributes in the data warehouse environment that would be affected by a proposed change.