Retail Data Model

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Oracle Retail Data Model – Overview

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Retail Data Model

AvailableToday!

Database Technology

Retail Domain Knowledge

BI Technology


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 classModern, 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 & 3rd Party costs for both immediate and on-going operations by leveraging pre-built content


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 withBest of Breed Approach Oracle Retail Data Model

weeks or monthsmonths 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:


More Value Less Complexity

More Flexibility Less Cost

More Choice Less Risk

Comprehensive Industry Portfolio

Complete

Standards-Based Architecture

Open

Designed toWork Together

Integrated

Reduced Total Cost of Ownership


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 ModelAn Overview

Sell-Side

Distributors

Partners

Suppliers

Customer & Consumer InteractionCustomer & 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

DataDataWarehouseWarehouse

SalesKnowledge

ConsumerKnowledge

SourcingKnowledge

DemandKnowledge

InventoryKnowledge

ForecastingKnowledge

ProductKnowledge

• Advanced Planning & Scheduling (Demand Driven)

• Inventory Tracking

• Pricing

• Cost Forecasting

• Purchase Order Mgmt.

• Retail Partnerships

• Warehouse Mgmt.

RetailerKnowledge

MarketingKnowledge

Mfg Perf.Knowledge

Oracle Retail Data ModelFoundation for Business Information Flow


Oracle Retail Data ModelFoundation 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


Grocery

Department Stores

Discounters

Hard Goods

Apparel & Footwear

Soft Goods

Convenience Stores

Gas Stations

Oracle Retail Data ModelIndustry Coverage

Oracle Retail Data ModelKey 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

OperationsStore 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

ManagementProduct Mix, Shelf Analysis, Customer Purchase vs. Syndicated Data

Workforce

ManagementEmployee Utilization, SPIFF & Split Commission Analysis

Customer Clustering & Segment - Creation, Migration, Analysis

Promotion Causal Factor, Halo Impact & Promotional Lift

Order

ManagementIntegrated 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 ModelComponents

Base Layer (3NF) Derived & Aggregate Layer Sample Reports

Value

Generation Step

1

2

3

4

5

Transactional ReportingTransactional Reporting

Slice/Dice, AdSlice/Dice, Ad--hoc, Query , BI Tools hoc, Query , BI Tools

Performance Management (KPI, Guided Analytics)Performance Management (KPI, Guided Analytics)

FactFact--Based Actions (OLAP, Statistics) Based Actions (OLAP, Statistics)

Intelligent Interactions (Data Mining) Intelligent Interactions (Data Mining)

•How are my catalog and internetsales 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 ModelWhy multiple layers


Source ETL (Data Quality, Staging, Interface)

OLTP Systems

3NF Base

Reference

Lookup

Intra-ETL (Derived)

Derived Intra-ETL (Aggregate)

Aggregate

Oracle Retail Data ModelAutomatic Data Movement

Leveraging Data Warehouse FeaturesEmbedded 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


OLAP forecasting of sales & inventory to predict potential stock shortage

See which forecasting method fits best

Differentiator: Smart Inventory ReportsOut of Stock Forecast (using built-in Forecasting & OLAP cubes –

numerous methods supported)


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 ReportProduct 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

2

3

4

5

6

7

8

9

10

Why Oracle Retail Data Model?Top 10 Reasons

HP Oracle Database Machine / Storage

Database

Middleware


Business Intelligence Foundation

EPM Applications

EPM Workspace

BI Applications

The Oracle Solution SetComplete, Integrated, Open

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Retail Data Model