Page 16 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes 2. A data warehouse can enhance business productivity since it is able to quickly and
efficiently gather information, which accurately describes the organization.
3. A data warehouse facilitates customer relationship marketing since it provides a consistent
view of customers and items across all lines of business, all departments, and all markets.
4. A data warehouse may bring about cost reduction by tracking trends, patterns, and
exceptions over long periods of time in a consistent and reliable manner.
5. A data warehouse provides a common data model for all data of interest, regardless of
the data’s source. This makes it easier to report and analyze information than it would be
if multiple data models from disparate sources were used to retrieve information such as
sales invoices, order receipts, general ledger charges, etc.

6. Because they are separate from operational systems, data warehouses provide retrieval of
data without slowing down operational systems.

1.6.2 process of Data Warehouse Design

A data warehouse can be built using three approaches:
1. A top-down approach

2. A bottom-up approach
3. A combination of both approaches
The top-down approach starts with the overall design and planning. It is useful in cases where
the technology is mature and well-known, and where the business problems that must be solved
are clear and well-understood.
The bottom-up approach starts with experiments and prototypes. This is useful in the early stage
of business modeling and technology development. It allows an organisation to move forward at
considerably less expense and to evaluate the benefits of the technology before making significant
commitments.
In the combined approach, an organisation can exploit the planned and strategic nature of the
top-down approach while retaining the rapid implementation and opportunistic application of
the bottom-up approach.
In general, the warehouse design process consists of the following steps:
1. Choose a business process to model, e.g., orders, invoices, shipments, inventory, account
administration, sales, and the general ledger. If the business process is organisational and
involves multiple, complex object collections, a data warehouse model should be followed.
However, if the process is departmental and focuses on the analysis of one kind of business
process, a data mart model should be chosen.
2. Choose the grain of the business process. The grain is the fundamental, atomic level of data
to be represented in the fact table for this process, e.g., individual transactions, individual
daily snapshots, etc.

3. Choose the dimensions that will apply to each fact table record. Typical dimensions are
time, item, customer, supplier, warehouse, transaction type, and status.
4. Choose the measures that will populate each fact table record. Typical measures are
numeric additive quantities like dollars-sold and units-sold.
Once a data warehouse is designed and constructed, the initial deployment of the warehouse
includes initial installation, rollout planning, training and orientation. Platform upgrades
and maintenance must also be considered. Data warehouse administration will include data

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