The document discusses several key concepts related to data warehousing and business intelligence. It describes the entity relational data model, which divides data into discrete entities with relationships. It also describes extract, transform, and load (ETL) processes, data cleansing, and operational data stores. Dimensional data models are discussed as well, including how they are designed to easily retrieve information and integrate heterogeneous source systems. The levels of business intelligence from strategic to operational are outlined. Finally, frameworks for business value and scope from opportunistic to pervasive are presented.

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2. Proprietary
ETLAdapters
ERP-HR
Planning & Budgeting
CRM
Scorecards& Dashboards
DataCleansing
& Slice Modeler
WMS
Supply Chain
ECM-KPIs
LEGASY
Finance - ECM
ΚΒΣ
Disciplines
Analysis& Discovery
Forecasting
Operating Data
Storage Schema
Sales CRM
ManagerialReporting
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3. Entity Relational Data Model

Created in 1960’s to address Performance issues with
Relational Database Implementations

Normalized to most Efficiently Get Data in

Divides the data into many Discrete Entities

Many relationships between these entities

This approach was documented by
C.J. Date in An Introduction to Database Systems
ERP-HR
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CRM
WMS
LEGASY

4. 
All Tables look the Same

Prone to Entity Violated Result

Users can’t Visualize/Remember Diagrams

BI Software can’t navigate as Schema

Business Processes Mixed together

Proprietary Knowledge Required

Many Artificial keys Created

Rudimentary Data Quality Assurance

Limited Decision Support
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5. ETL: Extract, Transform, and Load

Extract data from the Source Systems

Predetermined Stored Procedures for each Source

Transform Data into Standard Form

Scheduled Data Loader into the (ODS) Data Storage
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6. Data Cleansing

Is the process of Validating and Enriching the Data as it is published to the ODS

Implementation of Data acquisition & Cleansing processes represents
from 50 to 80% of effort on typical DW projects

Inaccurate Data Content can be ‘The kiss of death’ for User Acceptance
Operational Data Store (ODS)

Is a Single point of Integration for Disparate Operational Systems

Contains Integrated data at the most detailed level (transactional).

It may be Loaded in ‘Near Real time’ or Periodically

Can be used for Centralized Operational Reporting and can also
be used as a Staging area or for Master/Reference Data Management
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7. Dimensional Data Model

Also called Star or Snowflake schemas Grouped in
subject-specific stores called Data Marts

Designed to easily Get Information Out enhanced by Calculations

Fewer Relationships & Constrains than Information Sources

Integrates Heterogeneous Source Systems

Consistent Historical Data Store

Developed in 1960’s by data service providers, formalized
by Ralph Kimball in The Data Warehouse Toolkit
Supply Chain
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Finance - ECM
Sales CRM

8. Feature
OLTP
OLAP
Flexible schema


Real time data access


Single data store


Simple management


Detail reporting


High performance


End-user oriented


Rich analytics


Ease of navigation and exploration


Rich semantics


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10. What Happened?
Reports
Why did it happen?
What is Happening?
Scorecards
Dashboards
Business
Strategy
Reporting
Root-CauseAnalysis
KPIsComparison
What will Happen?
Budgeting
Forecasting
What do I want to happen?
Consolidation
Performance Modeling
The Process requires Past Information, Present Conditions and Future Outcomes
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11. Strategic
Strategic:
Tactical
Focused:
Operational:
&Cost Reduction
Business Effectiveness
Scope:
Scope:
Opportunistic
Excel
Department
Executionand
Improve
Process Efficiency
Dashboards
Interactivequery
Intra-department
Measure
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IntegratedBusiness
Optimize
Management.
Scope:
Scorecards,Analytics
Corporate Portal
Pervasive:
Agilitywith
Changeand Innovation.
Scope:
IntegratedCorporate
Performance Experience.
CommunicateActionsabout
‘One version of theTruth’.
Inter-departments
Decide and Manage
Increasing Business Value
Lead and Innovate

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