Page 233 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 12: Metadata and Warehouse Quality

12.2.1 Benefits of Integrated Meta Data notes

To date in data warehousing most organizations have avoided the issue of meta data management
and integration. Many companies, however, are now beginning to realize the importance of meta
data in decision processing and to understand that the meta data integration problem cannot be
ignored. There are two reasons for this:
1. The use of data warehousing and decision processing often involves a wide range of
different products, and creating and maintaining the meta data for these products is time-
consuming and error prone. The same piece of meta data (a relational table definition, for
example) may have to be defined to several products. This is not only cumbersome, but
also makes the job of keeping this meta data consistent and up to date difficult. Also, it is
often important in a data warehousing system to track how this meta data changes over
time. Automating the meta data management process and enabling the sharing of this so-
called technical meta data between products can reduce both costs and errors.
2. Business users need to have a good understanding of what information exists in a data
warehouse. They need to understand what the information means from a business
viewpoint, how it was derived, from what source systems it comes, when it was created,
what pre-built reports and analyses exist for manipulating the information, and so forth.
They also may want to subscribe to reports and analyses and have them run, and the
results delivered to them, on a regular basis. Easy access to this business meta data enables
business users to exploit the value of the information in a data warehouse. Certain types of
business meta data can also aid the technical staff examples include the use of a common
business model for discussing information requirements with business users and access to
existing business intelligence tool business views for analyzing the impact of warehouse
design changes.

12.2.2 improved productivity

The benefit of managing data warehouse technical meta data is similar to those obtained by
managing meta data in a transaction processing environment improved developer productivity.
Integrated and consistent technical meta data creates a more efficient development environment
for the technical staff who are responsible for building and maintaining decision processing
systems. One additional benefit in the data warehousing environment is the ability to track how
meta data changes over time. The benefits obtained by managing business meta data, on the
other hand, are unique to a decision processing environment and are key to exploiting the value
of a data warehouse once it has been put into production.
Figure 12.1 shows the flow of meta data through a decision processing system as it moves from
source systems, through extract and transformation (ETL) tools to the data warehouse and is
used by business intelligence (BI) tools and analytic applications. This flow can be thought of as
a meta data value chain. The further along the chain you go, the more business value there is in
the meta data. However, business value depends on the integrity of the meta data in the value
chain. As meta data is distributed across multiple sources in the value chain, integrity can only
be maintained if this distributed meta data is based on a common set of source meta data that
is current, complete and accurate. This common set of source meta data is often called the meta
data system of record. Another important aspect of the value chain is that business users need to
be able to follow the chain backward from the results of decision processing to the initial source
of the data on which the results are based.

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