<Insert Picture Here> Oracle Retail Data Model Overview

Transcription

1 <Insert Picture Here> Oracle Retail Data Model Overview

2 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle.

3 Retail Data Model Database Technology Available Today! Retail Domain Knowledge BI Technology Copyright 2009, Oracle and / or its affiliates. All rights reserved. 3

4 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 Reduced Total Cost of Ownership Fast, easy and predictable implementation, reduced technology & 3 rd Party costs for both immediate and ongoing operations by leveraging pre-built content Best in class Modern, topical and relevant Data Model developed using deep retail market expertise with leading Data Warehousing and Business Intelligence technology Copyright 2009, Oracle and / or its affiliates. All rights reserved. 4

5 Speed to Value Build from Scratch with Best of Breed Approach Training & Roll-out Define Metrics & Dashboards Data Movement DW Design months or years Oracle Retail Data Model Training & Roll-out Define Metrics & Dashboard Data Movement DW Design weeks or months Oracle s Approach: Delivers retailer and market insight quickly Rapid implementation, predictable costs lead to higher ROI Combines deep retail market expertise with industry-leading technology Easy to Use, Easy to Adapt Comprehensive Retail Measures & Metadata for Business Intelligence Reporting & Ad-hoc Query Automatic Data Movement from your ARTS compliant 3NF schema to OLAP, Mining & Dimensional Schema 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 Copyright 2009, Oracle and / or its affiliates. All rights reserved. 5

6 Reduced Total Cost of Ownership Complete Open Integrated Comprehensive Industry Portfolio Standards-Based Architecture Designed to Work Together More Value Less Complexity More Choice Less Risk More Flexibility Less Cost Copyright 2009, Oracle and / or its affiliates. All rights reserved. 6

7 Best-in-class Oracle #1 for Retail Oracle #1 for Data Warehousing Market Size is $6.7 Billion with 14.6% Growth YoY 1 Copyright 2009, Oracle and / or its affiliates. All rights reserved. 7

8 Oracle Retail Data Model An Overview Oracle Retail Data Model RAC Partitioning OLAP Data Mining Compression Oracle Database Enterprise Edition Oracle Exadata Storage Industry Standard Compliant (ARTS) Embedded strong Retail expertise 3NF Logical Data Model Physical Data Model designed & pretuned 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

9 Oracle Retail Data Model Foundation for Business Information Flow Sell-Side Customer & Consumer Interaction In-Side Retailer Knowledge POS (Point-of-Sale) Web stores & Catalog Order Management Inventory Optimization Advertising & Promotions Customer Service Workforce Scheduling Personalized Marketing Demand Knowledge Sales Knowledge Consumer Knowledge Marketing Knowledge Buy-Side Advanced Planning & Scheduling (Demand Driven) Inventory Tracking Pricing Cost Forecasting Purchase Order Mgmt. Retail Partnerships Warehouse Mgmt. Product Knowledge Manufacturing/Sourcing Sales Forecasting Inventory Tracking Inventory Knowledge Data Warehouse Partners Suppliers Forecasting Knowledge Mfg Perf. Knowledge Sourcing Knowledge Distributors

10 Oracle Retail Data Model Foundation for Business Information Flow Store-side Retailer Knowledge Data Warehouse Marketing Knowledge Sales Knowledge In-side Consumer Knowledge Demand Knowledge Inventory Knowledge Buy-side Forecasting Knowledge Sourcing Knowledge Mfg Perf. Knowledge Product Knowledge Copyright 2009, Oracle and / or its affiliates. All rights reserved. 10

11 Oracle Retail Data Model Industry Coverage Grocery Department Stores Discounters Hard Goods Apparel & Footwear Soft Goods Convenience Stores Gas Stations Copyright 2009, Oracle and / or its affiliates. All rights reserved. 11

12 Oracle Retail Data Model Key Statistics Data Model Contents 650+ Tables and 10,500+ Attributes ( ARTS++ ) Industry-specific 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

13 Business Area Coverage Pre-Built Measures & KPIs Store Operations Point of Sale Loss Prevention Store performance, Shopper Conversion, Comparative Store Analysis Multi Channel, POS Flow Unusual Transactions, Hidden Patterns, Attribute Analysis Merchandising Merchandise Performance, Item-Basket, Fast & Slow Movers Inventory Category Management Workforce Management Customer Promotion Order Management Inventory State Analysis, Forecast out-of-stock and zero selling. Product Mix, Shelf Analysis, Customer Purchase vs. Syndicated Data Employee Utilization, SPIFF & Split Commission Analysis Clustering & Segment - Creation, Migration, Analysis Causal Factor, Halo Impact & Promotional Lift Integrated Analytic between e-commerce and Retail

14 Business Area Coverage Pre-Built Measures & KPIs Merchandising 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 Store Operations Store Operations 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 Category Management 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 Marketing 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

15 Oracle Retail Data Model Components Base Layer (3NF) Derived & Aggregate Layer Sample Reports

16 Oracle Retail Data Model Why multiple layers Intelligent Interactions (Data Mining) Is the product assortment optimal for all my regions? Fact-Based Actions (OLAP, Statistics) What are my potential out-of-stock situations? Predictive Value Performance Management (KPI, Guided Analytics) How is the business doing compared to last year? Compared to plan? Slice/Dice, Ad-hoc, Query, BI Tools What is my gross margin return on space? Analysis Forecasting Transactional Reporting How are my catalog and internet sales performing? 1 Reporting 2 Generation Step

17 Oracle Retail Data Model Automatic Data Movement Oracle Retail Data Model Lookup Reference 3NF Base Intra-ETL (Derived) Derived Intra-ETL (Aggregate) OLTP Systems Source ETL (Data Quality, Staging, Interface) Aggregate

18 Leveraging Data Warehouse Features Embedded as part of VLDB design, not an afterthought Partitioning Reference Architecture Advanced Statistics Partitioned Outer Join Frequent Item Set Ranking Lag / Lead Compression OLAP Data Mining 3-5x Storage Savings Time Series Forecasting Classification (ABN/Decision Tree) Association Rules (Apriori) Materialized Views User Choice SQL Rewritten

19 Differentiator: Smart Inventory Reports Out of Stock Forecast (using built-in Forecasting & OLAP cubes numerous methods supported) OLAP forecasting of sales & inventory to predict potential stock shortage See which forecasting method fits best Copyright 2009, Oracle and / or its affiliates. All rights reserved. 19

20 Differentiator: Smart Category Report Product Category Mix Analysis: Suggest Items/Categories to Merchandise Together using a Pre-built Mining Model 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?] Copyright 2009, Oracle and / or its affiliates. All rights reserved. 20

21 Why Oracle Retail Data Model? Top 10 Reasons 1 Retail expertise with best-in-class technology 6 Easily extendable & customizable model 2 ARTS based normalized data model 7 Usable within any retail environment 3 Modern and topical with retail depth and breadth 8 Designed and optimized for VLDB 4 Intelligent retail insight using OLAP & Mining 9 Automated data flow between components 5 Extensive business intelligence metadata 10 Reduced implementation risk

22 The Oracle Solution Set Complete, Integrated, Open EPM Workspace EPM Applications BI Applications Business Intelligence Foundation Middleware Oracle Retail Data Model Database HP Oracle Database Machine / Storage

23 For More Information Oracle Retail Data Model or oracle.com

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