re work

If the main reason you are considering a data warehousing is to get around
the difficulties caused by a dysfunctional transaction processing system, do
the work of costing how much it will fix the transaction processing system
before you make the data warehouse decisionIt may not be surprising that the
primary motivation for the construction of many data warehouses is to get
around the difficulties caused by a problematic transaction processing
system. Immediately deciding upon a data warehouse as a "fix" can be an
expensive mistake. If you don't do the work of costing how much it will cost
to fix the transaction processing systems, you may never understand what is
really causing the problems. And then you're setting yourself up for a
situation where the same problems recur in the data warehouse and you end up
supporting both a dysfunctional transaction processing system and a
dysfunctional data warehouse.
If most of your business needs are to report on data in one transaction
processing system and/or all the historical data you need are in that system
and/or the data in the system are clean and/or your hardware can support
reporting against the live system data and/or the structure of the system
data is relatively simple and/or your firm does not have much interest in
end user ad hoc query/report tools, you may not NEED a data warehouse
Sometimes a good report generator will do just fine.

Question whether you really will benefit from certain categories of tools
For some data warehouse implementations, certain types of tools just do not
make good business sense. For example, if you have no need for the
slice-and-dice or modeling capabilities of OLAP tools, a report and query
tool may meet your reporting needs more than adequately. If you have to
perform fairly complex data transformations and/or you have relatively few
data sources and targets, you may be better off coding by hand than using a
so called "data mart" tool. The database you use for transaction processing
may do just fine based on the number of users, amount of data, and time you
have to load the database. Before buying data mining tools do your best to
assess whether they will yield "actionable" insights worth the effort in
making the data mining tool work.

Accept that data warehousing is going to be technically messy
If someone were ever to write "The Zen Of Data Warehousing" (perish the
thought - please), one of the concepts would probably be that at some point,
the more technically elegant you try to make these systems, the messier (and
more costly and less beneficial) they end up being. There are no rules for
determining where this point is. Use your judgment and intuition to make the
determination.



quadstone





The mortgage and savings arm of a large European mutual life assurance
company put a retention strategy in place with Quadstone that identified
customers at risk months ahead of their existing processes. Prior to the
initiative early redemption was not well understood. Business and analysis
groups worked together to explore the factors affecting this problem and
calculated the lost revenue impact. Being able to move more quickly using
Quadstone delivered significant incremental profit as well as providing
insight into customer behavior leading to redemption