Transcript Database_Part2b.ppt

Database – Part 2b
Dr. V.T. Raja
Oregon State University
External References/Sources:
Data Warehousing – Sakthi Angappamudali at Standard Insurance;
BI – Business Week; Lee Martin at Hitachi Consulting
Outline
 Some database trends (past and recent)
 Why learn about databases?
Some Database Trends
 Centralized and Distributed databases
 Online Transaction/Analytical Processing (OLTP/OLAP)
 Data Warehouse and Data Marts
 Data Mining, Business Intelligence (BI) and Analytics
Centralized and Distributed databases
 Centralized Databases
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Advantages/Disadvantages?
 Distributed Databases
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Partitioned Databases
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Data spread across two or more smaller databases
Connected via communication devices
Replicated Databases
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Partially replicated databases
Fully replicated databases
 Advantages/Disadvantages?
The Decision Making Roadmap
Business Planning
Actions
Vision
Knowledge
Transaction
Systems
Decision
Support
Systems
Data
RUN
•
•
•
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Operational
Functional
Current
Detailed
Users
Information
MANAGE
•
Analyze What If
Scenarios
• History
• Detailed
Knowledge Brokers
Executive
Information
Systems
GROW
•
MultiDimensional
• History
• Summary
Management
On-line Transaction Processing (OLTP)
and On-line Analytical Processing (OLAP)
 OLTP: Immediate processing/analysis and handling
of multiple concurrent transactions from
customers/users
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Example:
 OLAP: Capability for manipulating and analyzing
large volumes of data from multiple perspectives
(multidimensional analysis)
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Example:
Data Warehouse
 Large repository of detailed and summary data used
to support the strategic decision making process for
the enterprise
 Stores current and historical data (internal and
external)
 Integrates data from organization’s disparate
information systems used by functional units
 Involve hundreds of gigabytes, and terabytes of data
 Run on very powerful computers
 Expensive
Data Warehousing Process
OLTP, DW and DM - Data Characteristics
• OLTP - Raw Detail
No/Minimal History
•DW-Integrated •History
• Targeted
•Scrubbed
•Summaries • Specialized (OLAP)
Data Mart
Data
Warehouse
OLTP
Systems
Functional
IS
External
Data
End User
Workstations
Central
Repository
•Extract
•Design
•Scrub
•Mapping
•Transform
•Load
•Index
•Aggregation
•Replication
•Data Set Distribution
Data Mart
 Data Mart
 A small data warehouse containing only a portion
of the organization’s data for a specified function
or population of users. It is a subset of a data
warehouse (e.g., marketing/sales data mart)
An Incremental Approach
Sales
Distribution
Product
Glossary
Marketing
Customer
Common Business
MetricsAccounts
Common Business Rules
Common Business Dimensions
Operations
and Inventory
Common Logical Subject Area ERD
Finance
Vendors
Individual Architected Data Marts
The Eventual Result
Sales
Distribution
Product
Architected
Enterprise
Foundation
Marketing
Finance
Customer
Operations
and Inventory
Accounts
Vendors
Enterprise Data Warehouse
Data Mining
 Provides a means of extracting previously unknown,
predictive information from the data warehouse
 Uses sophisticated, automated algorithms to discover
hidden patterns, relationship among data
 Some Benefits:
 Market Segmentation
 Fraud Detection
 Market Basket Analysis
 Trend Analysis
Business Intelligence
 BI/Analytics software (suite):
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Used to collect, store, analyze and present
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sufficient and accurate information in a timely
manner and in a usable form
Includes OLAP, data mining, statistical
analysis
Has a positive impact on business strategy,
and operations
Addresses analysis paralysis?
Business Intelligence
Enterprise BI Suites
and Platforms
Other Trends
 Linking Web Applications to Organizational
Databases
 Highly specialized database team - DBA,
Data Administration Staff and Data Analysts
 Information Resource Management
Why learn about databases?
 Minimize disadvantages of traditional file environment
 Improve productivity on personal/professional fronts
 Without support and understanding of management
at different levels, database efforts fail
 Budget vs. Cost
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Could be expensive in the long run
 Maintaining qualified DBA staff
 Creating Data Warehouse
Why learn about databases?
 Communicate effectively with DBA and his/her staff
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Data model should reflect key business processes
and decision-making requirements
Information Policy
Which current trends in database are important for
your unit/firm?
Smooth transition for newly hired DBA staff
 Information Resource Management