Post on 05-Sep-2015
description
Data warehouse Concepts
What is BIDW?
Introduction to BI
Introduction to DW
Need of DW
What is Data mart?
What is ODS?
Advantages ODS and Differences with DW
Trends in BI
Data Warehousing: Consolidate data from many sources in one large repository.
Loading, periodic synchronization of replicas.
Semantic integration.
OLAP: Complex SQL queries, business-oriented queries based on spreadsheet-style operations and multidimensional view of data.
Data Mining: Exploratory analysis; essentially, fishing
for interesting trends and anomalies.
Data Warehouse
A data warehouse is a
subject-oriented
integrated
time-varying
non-volatile
collection of data that is used primarily in
organizational decision making.
-- Bill Inmon, Building the Data Warehouse 1996
Subject Oriented
Data is Integrated and Loaded by Subject
D/W Data
1996
1997
1996
1998
A/R
O/P
Cust
Prod
Time Variant
Designated Time Frame (3 - 10 Years)
One Snapshot Per Cycle
Key Includes Date
Data Warehouse
View of The Business Today
Operational Time
Frame Key Need Not Have
Date
Operational System
Operational Systems
Order Processing Order ID = 10
D/W
Accounts Receivable Order ID = 12
Order ID = 16
Product Management Order ID = 8
HR System Sex = M/F
D/W
Payroll Sex = 1/2
Sex = M/F
Product Management Sex = 0/1
Integrated
Non-Volatile
CRUD Actions
Operational System
Read
Insert
Update Replace
Create
Delete
No Data Update
Data Warehouse
Load Read
Read
Read
Read
Subject Oriented
Integrated
Time Variant
Non-volatile
Summary
A Data Warehouse Is A Structured Repository of Historic Data. It Is:
It Contains: Business Specified Data,
To Answer Business Questions
Evolution of Data mining
QUERY OLAP DATA MINING
Extraction of
detailed and
summary data
Summaries, trends
and forecasts
Knowledge discovery
of hidden patterns and
insights
Information Analysis Insight and
Prediction
Who
purchased
mutual funds in
the last 3
years?
Who will buy a
mutual fund in the
next 6 months
and why?
What is the
income
distribution
of mutual
fund buyers?
Popular uses of DataWarehousing
To build customer centric views by consolidating islands of information
To enhance financial consolidation and business consolidation
Identifying and monitoring Key Performance Indicators and Business Metrics
Leverage on centralized information by coupling DW with the Internet
Why is it Important?
Predict New Trends - Beat The Competition To Market
Understand and Better Service the Customer
Helps to understand the business
Operational Systems are focused on running the business, not understanding It!
Profitability - Increase Productivity Per Employee
Approaches..
Data Warehouse
A Multi-Subject Information Store Designed For DSS Apps
Typically 100s of Gigabytes to Terabytes
Data Mart
A Single Subject Data Warehouse
Often Departmental or Line of Business Oriented
Typically Less Than a 100 Gigabytes
Source from many to feed many...
Good Not so good
Single view Difficult of truth to build Quick to No single build version of truth, Management issues
Centralized Data Warehouses vs. Departmental Data Warehouses
The Data Mart Dogma...
Source once feed many
The Data Warehouse Dogma...
Data Marts vs. Data Warehouse
Data Mart Data Warehouse
Payback 1.7 Year ~3 Years
ROI 532 % 321%
Avg Cost $1.3 Million $2.2 Million
Risk Low High (IDC - ROI of Data Warehousing, 62 companies)
70% of DSS/Data Warehouse Projects are Departmental or Data Marts. Sentry Market Research study of 700 Major I/T Purchasers