Page 232 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes Wells and Thomann believe that information quality also encompasses data quality and
performance. This can be a problem area, because everyone is happy if they can get their data
overnight. Then they want it in half a day. Then immediately. So these expectations must be
managed.
12.1.3 technical Quality

Technical quality is the ability of the data warehouse to satisfy users’ dynamic information needs.
Wells describes four important technical quality factors. The first is “reach,” or whether the data
warehouse can be used by those who are best served by its existence. In today’s information-
dependent business climate, organizations need to reach beyond the narrow and typical customer
base of suppliers, customers, and a few managers.
“Range” is also important. As its name implies, this defines a range of services provided by the
data warehouse. In general, these include “What data do I have, and, can I get the data?” For
example, Web enablement, such as Hotmail, are services which allow users to get information
from wherever they are.
“Manuverability” is the ability of the data warehouse to respond to changes in the business
environment. The data warehouse doesn’t remain stable, so manuverability becomes particularly
important. It is also the single most important factor not given attention today in data warehousing,
according to Wells. Manuverability sub-factors include managing:
1. Users and their expectations,
2. Upper management,
3. The overall business,

4. Technology,
5. Data sources, and
6. Technical platform.
Finally, “capability” is an organization’s technical capability to build, operate, maintain, and use
a data warehouse.
12.2 Matadata Management in Data Warehousing

Although organizations are now successfully deploying data warehousing and decision
processing products for providing business users with integrated, accurate and consistent
business information, most companies have failed to provide a similar level of integration of
the meta data associated with this business information. This result is caused not only by a lack
of understanding of the importance of meta data, but also because meta data integration is a
complex task. The trend toward the use of packaged analytic applications in data warehousing
will make meta data integration even more difficult. Data warehouse customers urgently need
to address this problem if the full benefits of data warehousing are to be achieved. In this article
we explain why meta data management and standardization is important to the success of a data
warehouse and explore industry efforts in this area.

226 LoveLy professionaL university