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Oracle Retail Data Model – Overview
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Retail Data Model
Available
Today!
Database Technology
Retail Domain Knowledge
BI Technology
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Key Messages
Speed to
Value
Standards-based, pre-built, pre-tuned data model with
intelligent insight into detailed retailer and market data
enabling retailers to quickly gain value
Best in class Modern, topical and relevant Data Model developed
using deep retail market expertise with leading Data
Warehousing and Business Intelligence technology
Reduced Total
Cost of Ownership
Fast, easy and predictable implementation, reduced
technology & 3
rdParty costs for both immediate and on-
going operations by leveraging pre-built content
Copyright © 2009, Oracle and / or its affiliates. All rights reserved.
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Pre-built DW Schema (3NF,STAR,OLAP) with Retail best Practice embedded and Pre-tuned for Oracle data warehouses, including the HP Oracle DB Machine Automatic Data Movement from your ARTS compliant 3NF schema to OLAP, Mining & Dimensional Schema Comprehensive Retail Measures & Metadata for Business Intelligence Reporting & Ad-hoc Query Easy to Use, Easy to Adapt
Build from Scratch with
Best of Breed Approach Oracle Retail Data Model
weeks or months months or years
Speed to Value
DW Design
DW Design
Data Movement
Data Movement
Define Metrics &
Dashboards
Define Metrics
& Dashboard
Training & Roll-out
Training & Roll-out
• Delivers retailer and market insight quickly
• Rapid implementation, predictable costs lead to higher ROI
• Combines deep retail market expertise with industry-leading technology
Oracle’s Approach:
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More Value
Less Complexity
More Flexibility
Less Cost
More Choice
Less Risk
Comprehensive
Industry Portfolio
Complete
Standards-Based
Architecture
Open
Designed to
Work Together
Integrated
Reduced Total Cost of Ownership
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Best-in-class
Market Size is $6.7 Billion with 14.6% Growth YoY1
Oracle #1 for Retail Oracle #1 for Data Warehousing
Partitioning OLAP
RAC Data Mining Compression
Oracle Exadata Storage Oracle Retail Data Model
Oracle Database Enterprise Edition
• Industry Standard Compliant (ARTS)
• Embedded strong Retail expertise
• 3NF Logical Data Model
• Physical Data Model designed & pre-
tuned for Oracle
– Including Exadata Storage
• Industry-specific measures & KPIs
• Pre-built OLAP models
• Pre-built Data Mining models
• Usable within any Retail Application
Environment
• Sample reports and dashboards
– Based on Oracle BI EE Plus
Oracle Retail Data Model
An Overview
Sell-Side
Distributors Partners
Suppliers
Customer & Consumer Interaction
Customer & Consumer Interaction
In-Side
• POS (Point-of-Sale)
• Web stores & Catalog
• Order Management
• Inventory Optimization
• Advertising & Promotions
• Customer Service
• Workforce Scheduling
• Personalized Marketing
• Manufacturing/Sourcing
• Sales Forecasting
• Inventory Tracking
Buy-Side
Data
DataWarehouse
WarehouseSales Knowledge
Consumer Knowledge
Sourcing Knowledge Demand
Knowledge
Inventory Knowledge
Forecasting Knowledge
Product Knowledge
• Advanced Planning &
Scheduling (Demand Driven)
• Inventory Tracking
• Pricing
• Cost Forecasting
• Purchase Order Mgmt.
• Retail Partnerships
• Warehouse Mgmt.
Retailer Knowledge
Marketing Knowledge
Mfg Perf.
Knowledge
Oracle Retail Data Model
Foundation for Business Information Flow
Copyright © 2009, Oracle and / or its affiliates. All rights reserved.
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Oracle Retail Data Model
Foundation for Business Information Flow
Store-side
In-side
Buy-side
Data Warehouse
Sales Knowledge Consumer Knowledge
Sourcing Knowledge Demand Knowledge Inventory Knowledge Forecasting Knowledge
Product Knowledge Retailer Knowledge Marketing Knowledge
Mfg Perf. Knowledge
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Grocery
Department Stores
Discounters
Hard Goods
Apparel & Footwear
Soft Goods
Convenience Stores
Gas Stations
Oracle Retail Data Model
Industry Coverage
Oracle Retail Data Model
Key Statistics
• Data Model Contents
– 650+ Tables and 10,500+ Attributes (“ARTS++”)
– Industry-specific 1200+ Measures & KPIs with Business and
Technical Definitions
– 4 Pre-built Analytical Workspaces
– 12 Pre-built Data Mining Models
– Automatic Data Movement from 3NF to STAR schema, OLAP
Cubes and Data Mining Models
– Sample Reports & Dashboards using OBIEE
• Designed and optimized for Oracle data warehouses, including
the HP Oracle Database Machine
• Central repository for atomic level data
• Rapid implementation
Business Area Coverage
Pre-Built Measures & KPIs
Store
Operations Store performance, Shopper Conversion, Comparative Store Analysis
Point of Sale Multi Channel, POS Flow
Loss
Prevention Unusual Transactions, Hidden Patterns, Attribute Analysis
Merchandising Merchandise Performance, Item-Basket, Fast & Slow Movers
Inventory Inventory State Analysis, Forecast out-of-stock and zero selling.
Category
Management Product Mix, Shelf Analysis, Customer Purchase vs. Syndicated Data
Workforce
Management Employee Utilization, SPIFF & Split Commission Analysis
Customer Clustering & Segment - Creation, Migration, Analysis
Promotion Causal Factor, Halo Impact & Promotional Lift
Order
Management Integrated Analytic between e-commerce and Retail
Merchandising Store Operations
Business Area Coverage
Pre-Built Measures & KPIs
• Role: Commonly a merchant or planner
• Product ‘stars’ and ‘dogs’
• Inventory levels vs. planned inventory levels
• Suppliers that help / hinder performance
• Identifying locations that over/under perform
Merchandising
• Role: Commonly a store manager
• Store traffic patterns to determine staffing
• Understand opportunities to control loss
• Relative store performance rankings
• Identify what sells in the stores vs. doesn’t
• Identifying potential risks for out of stocks
Store Operations
• Role: Commonly a Category Manager
• Controlling purchase costs
• Reviewing supplier item coverage
• Understanding consumer purchases of new / current products vs. market data
• Determining store layouts and planogams
Category Management
• Role: Commonly a marketing analyst
• Identify consumer spending habits using market data
• Analyzing a retailer’s loyalty program customers to better target campaigns
• Measuring customer promotion response rates
Marketing
Oracle Retail Data Model
Components
Base Layer (3NF) Derived & Aggregate Layer Sample Reports
Value
Generation Step
1
2
3
4
5
Transactional Reporting
Transactional Reporting
Slice/Dice, Ad
Slice/Dice, Ad- -hoc, Query , BI Tools hoc, Query , BI Tools
Performance Management (KPI, Guided Analytics)
Performance Management (KPI, Guided Analytics)
Fact Fact- -Based Actions (OLAP, Statistics) Based Actions (OLAP, Statistics)
Intelligent Interactions (Data Mining)
Intelligent Interactions (Data Mining)
•How are my catalog and internet sales performing?
•What is my gross margin return on space?
•How is the business doing compared to last year?
Compared to plan?
•What are my potential out-of-stock situations?
•Is the product assortment optimal for all my regions?
Reporting
Analysis
Forecasting
Predictive
Oracle Retail Data Model
Why multiple layers
Oracle Retail Data Model
Source ETL (Data Quality,
Staging, Interface) OLTP Systems
3NF Base Reference
Lookup
Intra-ETL (Derived)
Derived Intra-ETL (Aggregate)
Aggregate
Oracle Retail Data Model
Automatic Data Movement
Leveraging Data Warehouse Features
Embedded as part of VLDB design, not an afterthought
•Partitioned Outer Join •Frequent Item Set •Ranking
•Lag / Lead
•3-5x Storage Savings •Time Series
•Forecasting
•Classification
(ABN/Decision Tree)
•Association Rules (Apriori)
•User Choice
•‘SQL’ Rewritten
Partitioning Reference Architecture Advanced Statistics
Compression OLAP Data Mining
Materialized Views
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OLAP
forecasting of
sales &
inventory to
predict
potential
stock
shortage
See which
forecasting
method fits
best
Differentiator: Smart Inventory Reports
Out of Stock Forecast (using built-in Forecasting & OLAP cubes –
numerous methods supported)
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Analyzes Sales Transactions using the Association Rules (Apriori) Model to understand the
Product Category Mix [If a Customer buys A and C, what is the likelihood the Customer would
buy D?]
Differentiator: Smart Category Report
Product Category Mix Analysis: Suggest Items/Categories to
Merchandise Together using a Pre-built Mining Model
Retail expertise with best-in-class technology ARTS based normalized data model
Modern and topical with retail depth and breadth Intelligent retail insight using OLAP & Mining Extensive business intelligence metadata
1 Easily extendable & customizable model
Usable within any retail environment Designed and optimized for VLDB
Automated data flow between components Reduced implementation risk
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