Introduction to Data Warehouse

1. TIF32604
Data Warehouse
“When you are willing to make sacrifices for a
great cause, you will never be alone.”
Data Warehouse
Nova Eka Diana (nova.diana@yarsi.ac.id)
Fakultas Teknologi Informasi
Universitas YARSI

2. Objectives
• Understanding the evolution of Decision Support System
• Understanding two types of data
• Understanding the architected environment
• Understanding the difference between classical SDLC
and Data Warehouse SDLC

3. • Aplikasi dibangun dengan Fortran
atau COBOL
• Disimpan pada magnetic tape
• Murah, tapi akses data bersifat
sekuensial
• Disk Storage
• Direct Access Storage Device
(DASD)
• Tidak perlu akses data 1,2,3,…,n
untuk mengakses data n+1
• Waktu akses lebih cepat daripada
magnetic tape
• DBMS (Database Management
System) mempermudah
penyimpanan dan pengaksesan
data pada DASD
• Tugas DBMS:
• Menyimpan data pada DASD
• Indexing data
• Dst
• OLTP (Online Transaction
Processing) mempercepat
pengaksesan data untuk tujuan
bisnis
• PC & Fourth-Generation Languages (4GL)
• Tidak hanya sekedar memproses transaksi
online
• MIS (Management Information System)
mampu untuk diimplementasikan

4. Master’s Files Problem
• The need to synchronize data upon update
• The complexity of maintaining programs
• The complexity of developing a new program
• The need for extensive amounts of hardware
to support all the master files

5. Extract Program

6. Spider Web

7. Problem
• Data Credibility
• Productivity
• Inability to transform data into information

8. Problem: Data Credibility
• Out of sync information among each
departments
• Difficult to reconcile different information from the
different departments
• The crisis of data credibility occurs:
• No time basis of data
• The algorithmic differential of data
• The level of extraction
• The problem of external data
• No common source of data from beginning

9. Reason

10. Problem: Productivity
• There is a need to analyze data across the
organization
• Management wants to produce a corporate
report using many files and collections of data
over the years
• The designer’s tasks:
• Locate and analyze the data for the report
• Compile the data for the report
• Get programmer/analyst resources to accomplish these
two tasks

11. Productivity Problem
• Virtual Storage Access Method
(VSAM)
• Information Management
System (IMS)
• Adabas
• Integrated Data Management
System (IDBS)

12. Time Estimation

13. Problem: Data to Information
• “How has account activity differed this year from each of
the past five years?”
• DSS Analyst
• Applications were never
constructed with integration in
mind
• There is not enough historical
data stored in the applications

14. No Historical Data

15. Another Approach: Data Warehouse

16. Differences: Primitive & Derived Data
• Primitive data is detailed data used to run the day-to-day
operations of the company. Derived data has been
summarized or otherwise calculated to meet the needs of
the management of the company.
• Primitive data can be updated. Derived data can be
recalculated but cannot be directly updated.
• Primitive data is primarily current-value data. Derived data
is often historical data.
• Primitive data is operated on by repetitive procedures.
Derived data is operated on by heuristic, non repetitive
programs and procedures.
• Operational data is primitive; DSS data is derived.
• Primitive data supports the clerical function. Derived data
supports the managerial function.

17. The Architected Environment
Redundancy Data

18. Example: Customer Data

19. The Architected Environment: Data
Integration

20. Data Warehouse User
• DSS Analyst
• Define and discover information used in
corporate decision-making
• Classical System Development Life Cycle (SDLC)
• Assume that requirements are known,
• Able to be known at the start of design, or
• Can be discovered
• DSS analyst
• New requirements usually are the last thing to be
discovered

21. SDLC Comparison

22. Organizations’ use of Data Warehouse
• Retail
• Customer Loyalty
• Market Planning
• Financial
• Risk Management
• Fraud Detection
• Airlines
• Route Profitability
• Yield Management
• Manufacturing
• Cost Reduction
• Logistics Management
• Utilities
• Asset Management
• Resource Management
• Government
• Manpower Planning
• Cost Control