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DATA WAREHOUSE-LEGACY SYSTEMS-DATA MARTS-MARKETING DATABASEBy Davin Abraham
1701310002
M.tech/DB/SRM
12 rules of a Datawarehouse Data Warehouse and Operational
Environments are Separated Data is integrated Contains historical data over a long
period of time Data is a snapshot data captured at a
given point in time Data is subject-oriented
12 rules of a Datawarehouse Mainly read-only with periodic batch
updates Development Life Cycle has a data
driven approach versus the traditional process-driven approach
Data contains several levels of detail Current, Old, Lightly Summarized, Highly
Summarized
12 rules of a Datawarehouse Environment is characterized by Read-only
transactions to very large data sets System that traces data sources,
transformations, and storage Metadata is a critical component
Source, transformation, integration, storage, relationships, history, etc
Contains a chargeback mechanism for resource usage that enforces optimal use of data by end users
Life cycle of the DW
Warehouse Database
First time load
Refresh
Refresh
Refresh
Purge or Archive
1001
1007
1010
1020
Relational Database Model
31
42
22
32
F
M
M
F
Anderson
Green
Lee
Ramos
Attribute 1Name
Attribute 2Age
Attribute 3Gender
Row 1
Row 2
Row 3
Row 4
The table above illustrates the employee relation.
Attribute 4Emp No.
Multidimensional Database Model
The data is found at the intersection of dimensions.
Store
GL_Line
Time
FINANCE
Store
Product
Time
SALES
Customer
Two dimensions
Three dimensions
Data marts
Small Data Stores More manageable data sets Targeted to meet the needs of small
groups within the organization
Small, Single-Subject data warehouse subset that provides decision support to a small group of people
Data Mart
A subset of a data warehouse that supports the requirements of a particular department or business function.
Characteristics include: Do not normally contain detailed
operational data unlike data warehouses. May contain certain levels of aggregation
Independent Data Mart
Sales or Marketing
External Data
Flat FilesOperational Systems
Reasons For Creating a Data Mart To give users more flexible access to the
data they need to analyse most often. To provide data in a form that matches
the collective view of a group of users To improve end-user response time. Potential users of a data mart are clearly
defined and can be targeted for support
To provide appropriately structured data as dictated by the requirements of the end-user access tools.
Building a data mart is simpler compared with establishing a corporate data warehouse.
The cost of implementing data marts is far less than that required to establish a data warehouse.
Legacy Systems
Older software systems that remain vital to an organisation
The legacy Dilemma it is expensive and risky to replace the
legacy system It is expensive to maintain the legacy system Businesses must weigh up the costs and
risks and may choose to extend the system lifetime using techniques such as re-engineering.
The system may be file-based with incompatible files. The change required may be to move to a database-management system
In legacy systems that use a DBMS the database management system may be obsolete and incompatible with other DBMSs used by the business
Legacy System Design
Most legacy systems were designed before object-oriented development was used
Rather than being organised as a set of interacting objects, these systems have been designed using a function-oriented design strategy
Several methods and CASE tools are available to support function-oriented design and the approach is still used for many business applications
Legacy system categories
Low quality, low business value These systems should be scrapped
Low-quality, high-business value These make an important business contribution but
are expensive to maintain. Should be re-engineered or replaced if a suitable system is available
High-quality, low-business value Replace with COTS, scrap completely or maintain
High-quality, high business value Continue in operation using normal system
maintenance
Legacy System Evolution
The structure of legacy business systems normally follows an input-process-output model
The business value of a system and its quality should be used to choose an evolution strategy
The business value reflects the system’s effectiveness in supporting business goals
System quality depends on business processes, the system’s environment and the application software
Marketing Database
is a systematic approach to the gathering, consolidation, and processing of consumer data (both for customers and potential customers) that is maintained in a company's databases.
Although databases have been used for customer data in traditional marketing for a long time, the database marketing approach is differentiated by the fact that much more consumer data is maintained, and that the data is processed and used in new and more sophisticated ways.
Among other things, marketers use the data to learn more about customers, select target markets for specific campaigns (through customer segmentation), compare customers' value to the company and provide more specialized offerings for customers.
Need for a Marketing Database Emails sent based on email response
alone, not on overall purchases Gold customers are seldom recognized Long time customers treated as
strangers Customers feel unappreciated You may lose your best supporters
What You Can Do with a Marketing DB?
Store behavior and append demographic data
Create customer segments, and develop a marketing plan for each segment.
Personalize all your email communications to customers – to build loyalty and sales
Append demographic data Determine customer lifetime value.
Thank You