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ineffective use. Mehra (2005) indicated that few organizations really understand metadata,
and fewer understand how to design and implement a metadata strategy. Metadata are
generally defined in terms of usage as technical or business metadata. Pattern is another
way to view metadata. According to the pattern view, we can differentiate between syn-
tactic metadata (i.e., data describing the syntax of data), structural metadata (i.e., data
describing the structure of the data), and semantic metadata (i.e., data describing the
meaning of the data in a specific domain).
We next explain traditional metadata patterns and insights into how to implement
an effective metadata strategy via a holistic approach to enterprise metadata integration.
The approach includes ontology and metadata registries; enterprise information integration
(EII); extraction, transformation, and load (ETL); and service-oriented architectures (SOA).
Effectiveness, extensibility, reusability, interoperability, efficiency and performance, evolution,
entitlement, flexibility, segregation, user interface, versioning, versatility, and low maintenance
cost are some of the key requirements for building a successful metadata-driven enterprise.
According to Kassam (2002), business metadata comprise information that increases
our understanding of traditional (i.e., structured) data. The primary purpose of metadata
should be to provide context to the reported data; that is, it provides enriching informa-
tion that leads to the creation of knowledge. Business metadata, though difficult to pro-
vide efficiently, release more of the potential of structured data. The context need not
be the same for all users. In many ways, metadata assist in the conversion of data and
information into knowledge. Metadata form a foundation for a metabusiness architecture
(see Bell, 2001). Tannenbaum (2002) described how to identify metadata requirements.
Vaduva and Vetterli (2001) provided an overview of metadata management for data ware-
housing. Zhao (2005) described five levels of metadata management maturity: (1) ad
hoc, (2) discovered, (3) managed, (4) optimized, and (5) automated. These levels help in
understanding where an organization is in terms of how and how well it uses its metadata.
The design, creation, and use of metadata—descriptive or summary data about
data—and its accompanying standards may involve ethical issues. There are ethical
considerations involved in the collection and ownership of the information contained
in metadata, including privacy and intellectual property issues that arise in the design,
collection, and dissemination stages (for more, see Brody, 2003).

sectiOn 3.2 revieW QuestiOns
1. What is a data warehouse?
2. How does a data warehouse differ from a database?
3. What is an ODS?
4. Differentiate among a data mart, an ODS, and an EDW.
5. Explain the importance of metadata.

3.3 Data Warehousing proCess overvieW
Organizations, private and public, continuously collect data, information, and knowledge
at an increasingly accelerated rate and store them in computerized systems. Maintaining
and using these data and information becomes extremely complex, especially as
scalability issues arise. In addition, the number of users needing to access the informa-
tion continues to increase as a result of improved reliability and availability of network
access, especially the Internet. Working with multiple databases, either integrated in a
data warehouse or not, has become an extremely difficult task requiring considerable
expertise, but it can provide immense benefits far exceeding its cost. As an illustrative
example, Figure 3.2 shows business benefits of the enterprise data warehouse built by
Teradata for a major automobile manufacturer.

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