Page 167 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 8: Data Warehouse Refreshment

The data cleansing process is also helping reduce marketing costs. Banco Popular will use notes
the Trillium Software System® to enforce standardization and cleanse data. This will be
the key to an accurate “householding” process, which is a way of identifying how many
account holders live at the same address.
By doing this, the bank can eliminate duplicate mailings to the same household, which
makes the bank look much more efficient in its customers’ eyes, and saves at least $70,000
in mailing expenses every month. Banco Popular’s home-grown address standardization
system will soon be replaced by Trillium Software’s geocoding solution. This will save the
cost of changing and recertifying the system each time the US Postal Service changes its
standardization requirements.
DB2 can easily handle customer information systems containing millions of records in
multiple languages that are initially cleansed with the Trillium Software System. Not only
is Banco Popular expanding, its customers may be represented within complex financial
records on the database in either English or Spanish. Trillium Software scales in step with
the growing DB2 database and works in numerous languages to provide a global solution
for this multinational bank.

8.4 summary

l z DWH refreshment so far has been investigated in the research community mainly in
relation to techniques for maintaining materialized views.

l z In these approaches, the DWH is considered as a set of materialized views defined over
operational data. Thus, the topic of warehouse refreshment is defined as a problem of
updating a set of views (the DWH) as a result of modifications of base relations (residing in
operational systems). Several issues have been investigated in this context.

l z The extraction method you should choose is highly dependent on the source system and
also from the business needs in the target data warehouse environment.
l z Very often, there is no possibility to add additional logic to the source systems to enhance
an incremental extraction of data due to the performance or the increased workload of
these systems.
l z Sometimes even the customer is not allowed to add anything to an out-of-the-box
application system.

8.5 keywords

Corporate Data Store: The corporate data store can be complemented by an Operational Data
Store (ODS) which groups the base data collected and integrated from the sources.
Data Cleaning: Data cleaning can be applied to remove noise and correct inconsistencies in the
data.
Incremental Data Extraction: Incremental data extraction can be implemented depends on the
characteristics of the data sources and also on the desired functionality of the data warehouse
system.
The Design Phase: The design phase consists of the definition of user views, auxiliary views,
source extractors, data cleaners, data integrators.

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