1. Data Warehousing – An Introductory Perspective
Selling your ideas is challenging. First, you must get your listeners to agree with you in principle. Then, you must move them to action. Use the Dale Carnegie Training® Evidence – Action – Benefit formula, and you will deliver a motivational, action-oriented presentation.
DWCC BBSR

2. Agenda Why Data Warehouse Definition and Architecture Terminology
Open your presentation with an attention-getting incident. Choose an incident your audience relates to. The incidence is the evidence that supports the action and proves the benefit. Beginning with a motivational incident prepares your audience for the action step that follows.

3. The Business Need Business Decisions Are not made by Rolling Dices
I think…. errrr,
I guess so
Business Decisions
Are not made by
Rolling Dices
We Don’t know
What we don’t know
Next, state the action step. Make your action step specific, clear and brief. Be sure you can visualize your audience taking the action. If you can’t, they can’t either. Be confident when you state the action step, and you will be more likely to motivate the audience to action.

4. Current Business Environment
Competitive
Ever Changing
Chaotic
Global
Urgency to make decisions
Competitive advantages stems from well informed decisions
Based on an understanding of:
Your Products
Your Customers Preferences
The Competition
Your own company strengths
To complete the Dale Carnegie Training® Evidence – Action – Benefit formula, follow the action step with the benefits to the audience. Consider their interests, needs, and preferences. Support the benefits with evidence; i.e., statistics, demonstrations, testimonials, incidents, analogies, and exhibits and you will build credibility.

5. The Value Pyramid Increased revenue Increased productivity
Each layer provides
Value en route to a
targeted business
Outcome
Increased revenue
Increased productivity
Reduced costs
Competitive advantage
To close, restate the action step followed by the benefits. Speak with conviction and confidence, and you will sell your ideas.

6. Definitions
A collection of integrated, subject oriented databases designed to support the DSS function where each unit of data is relevant at some moment of time (Inmon 1991)
A copy of transaction data specifically structured to Query and Analysis (Kimball 1996)
Data Warehouse is NOT a specific technology
It is a series of processes, procedures and tools that help the enterprise understand more about itself, its products, its customers and the market it services.
It is NOT possible to purchase a Data Warehouse
But, it is possible to build one.

7. sachin_kambhoj:
sachin_kambhoj:
FEATURES
Non Volatile - Used mainly for reporting purpose and it is independent of transactional data.
Subject Orientation- All relevant data is stored together. Ex: Sales, Finance, Marketing, Customer data etc.
Historical data- Can contain data of several years depending on company requirements.

8. Subject Orientation. Operational Datawarehouse AUTO Customer HEALTH
Policy
LIFE
Premium
CASUALTY
Claims
Applications
Subjects

9. Goals and Applications
Goals of a Data Warehouse
Provide reliable, High performance access
Consistent view of Data: Same query, same data. All users should be warned if data load has not come in.
Slice and dice capability
Quality of data is a driver for business re-engineering.
Data Warehousing Applications:
Customer Profitability Analysis
Customer satisfaction and retention
Buyer behavior.
Pricing, Promotion Analysis
Market research
Inventory optimization

10. OLTP v/s Data Warehouse
OLTP system runs the business, Data Warehouses tell you how to run the business
Characteristic
OLTP
Data Warehouse
Orientation
Transaction
Analysis
Data Access
Record at a time
Set at a time
Updates
Frequent & Unscheduled
Periodic & Scheduled
Response time
Seconds required
Minutes acceptable
Concurrent users
Many
Few
Availability
Guaranteed
As needed
Data structures
Highly normalized
Often de-normalized
Data nature
Current
historical

11. If most of your business needs are
To report on data in a single transaction processing system
All the historical data you need are in the system
Data in the system is clean
Your hardware can support reporting against the live system data
The structure of the system data is relatively simple
Your firm does not have much interest in end user adhoc query/report tools
Data warehousing may not be for your business!!

12. Modeling Constructs Entity Relationship Diagram Star schema
Snow flake schema
Within the implementation of a warehouse, several of these constructs may be integrated to form an optimal design

13. Entity Relationship Diagram
Based on set theory and SQL
Highly normalized
Optimized for update and fast transaction turnaround
Not suited for querying in a data warehouse environment
diagrams like these are very difficult for users to visualize and memorize.

14. Star Schema Facts are numerical measurements of business with
A central fact table surrounded by a number of dimension tables.
Dimensions are business entities on which calculations are done. They can be numeric or alphanumeric.
Example: Product table comprising brand name,
category, packaging type, size.
Facts are numerical measurements of business with
respect to dimensions.They are numeric and additive
(summable across any combination)
e.g. A sales fact table could contain time, product and store
key along with dollars sold, units sold, dollars cost.

15. Snow Flake Schema
Normalized version of the star schema with the addition of normalized dimension tables.
Normalization helps to reduce redundancy in the dimension tables, but affects performance and user comprehension.

16. DW Terminology Granularity
Granularity (or grain) defines the level of detail stored in the physical warehouse
Low granularity indicates lot of detail while high granularity indicates less detail.
Example: A commercial airline is building a data warehouse. What will the granularity be?
Choice A: Each record represents a flight
Choice B: Each record represents the customer on a flight
There is no correct answer. To a large extent, the granularity depends on the business User’s exploitation needs.
However, you should be aware that the granularity of data affects
Volumes of Data, Data Maintenance, Indexing
Level of Data Exploration
Query and Reporting constraints

17. DW Terminology Metadata
At all levels of the data warehouse, information is required to support the maintenance and use of the data warehouse.
Metadata is data about data.
There are two views of Metadata
Business – are warehouse attributes and properties for use by business users
Technical – describe data flow from Operational systems into the data warehouse
OLAP
Online Analytical processing
Tool(s) for Analytical Reporting including Graphical capabilities.

18. DW Terminology
OLAP Tools available for exploring the information built in a DW :
Multi-dimensional On-line Analytical Processing (MOLAP)
The data from data warehouse is queried and dumped periodically on to a server on local network to a data storage called Multi-dimensional Database (MDDB) provided by the OLAP tool. This MDDB forms a Data Mart which is then used for querying and reporting.
Relational On-Line Analytical Processing (ROLAP)
Refers to the ability to conduct OLAP analysis directly against a relational warehouse without any constraints on the number of dimensions, database size, analytical complexity, or number and type of users.
Hybrid On-line Analytical Processing (HOLAP)
An environment with a combination of MOLAP and ROLAP data storage. Summarized information is typically stored in an MDDB and detailed data is stored in a Relational environment.

19. Terminology
Data Mart- Contains Data about a specific subject. Eg. Official data, Customer data, Campaign data etc.
Metadata- Data about data. Describes the data stored in Data warehouse.
Data Cubes- Central object of data containing information in a multidimensional structure.
Data Cleansing- Regular cleaning of data.
ETL- Extraction, Transformation and Loading of Data.
Data Mining- A mechanism which uses intelligent algorithms to discover patterns, clusters and models from data.

20. Stages Extraction, Transformation & Loading (ETL)
Business Intelligence
Heterogeneous Source Systems
Query &
Reporting
Operational
Staging
Area
Data
Warehouse
OLAP
Legacy
External
Data Mining

21. A Typical Data Warehouse
Summarized Data
Facilitates in firing queries on detailed data.
Meta Data
Detailed Data
Data
Mart
Data
Mart
Data
Mart
Data marts contain data specific to a subject.

22. MOLAP/ROLAP/HOLAP MDD Proprietary API MDD Proprietary API SQL Data
Warehouse
(RDBMS)
SQL
Custom
Loader
Query Tool
by MDD Vendor
OLAP
Engine
Rows
Rows
Cubes
MDD
Database
Storage
Periodic,
Manual
Data
Load

23. OLAP Terminology
Region
State
Region
District
Location
Month
Product
Analytical technique whereby the user navigates from the most summarized to the most detailed level.

24. OLAP Terminology Rotation Or Dicing Region Month M O N T h RegionU C t
Product
Region

25. OLAP Terminology
Slicing
Region M O N T h
Product

26. Products and Vendors Data Warehouses OLAP tools Data Mining
Oracle
Sybase
DB2
OLAP tools
Oracle Express
Hyperion Essbase
Data Mining
Oracle Darwin
IBM Intelligent Data Miner
Querying & Reporting
Oracle Discoverer
Business Objects