Efficiently Maximizing Retail Value Across Distributed Data

Customer Use Case: Efficiently
Maximizing Retail Value Across
Distributed Data Warehouse Systems
Klaus-Peter Sauer
Technical Lead SAP CoE EMEA at Teradata
Agenda
1
HEMA Company Background
2
Teradata Overview
3
Why HEMA choose Teradata
4
The Implementation
5
Summary
2
A new store in the Netherlands in 1926
3
Facts



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Brand awareness in the Netherlands 100%
4.4 million customers per week
Daily number of visitors on www.hema.nl: 50.000
HEMA sells a sausage every 3 seconds
(10 million a year)
 One out of three Dutch boys wears
HEMA underwear
 One out of five Dutch women
wears HEMA bra
4
Distinguished style
 This is one of our strongest USP’s
 Together with low price and high quality
5
Formats
High traffic
XL
AA / D
HEMA
international
6
6
Hema.nl
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7
Agenda
1
HEMA Company Background
2
Teradata Overview
3
Why HEMA choose Teradata
4
The Implementation
5
Summary
8
Teradata – Company Overview
Teradata Corporation

Founded in 1979

2010 Revenue: $1,936M

8,000 Associates in 70 countries

Global Leader in Enterprise Data
Warehousing
2011 Magic Quadrant
Data Warehouse DBMS
> Independent since Oct 2007
> S&P 500 Member, listed NYSE (TDC)
> First TB+PB DWH on Teradata
> Database Technology, Analytic
Solutions, Consulting Services

Since 1999 #1 Position
in “Gartner’s Leader’s Quadrant
in Data Warehousing”

Teradata Key Offerings
Teradata DBMS
Teradata MPP Platform
The Magic Quadrant is copyrighted January 2011 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a
marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by
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Teradata SAP Partnership Overview
Business Objects Partner since 1995
 320+ joint customers globally, across industries
 Teradata Advisory Group
 Business Objects is included in both BI and Data Integration
portfolios
SAP NetWeaver Partner since 2004
 Teradata is committed to the SAP NetWeaver platform to
provide better, seamless integration between SAP applications
and Teradata.
 Teradata certified SAP NetWeaver Interfaces.
 Teradata SAP integration development lab in
San Diego.
 Teradata CoE SAP to support the field organizations.
 Teradata SAP Integration Lab EMEA in Prague.
 Teradata Office at SAP Partner Port Building in Walldorf.
Partner Port Building in Walldorf
10
Teradata Virtual Access for SAP
Teradata Extract and Load Solution
Teradata Supply Chain Accelerator
Jan 2008
Nov 2006
> Using Virtual Info Cubes to access data held in
Teradata
> Easily combine SAP and non-SAP data in BW
queries
Jun 2007
Oct 2005
SAP NW Integration Products
> Use Open Hub to load data from BW to
Teradata
> Easy extraction of SAP data into Teradata
environment
> Use Teradata to power SAP Demand Planning
Solution
> Faster, more frequent planning cycles using
greater detail and history
Teradata JMS Universal Connector
> Teradata Active Data Warehouse for SAP
> Message-Bus Integration with SAP NetWeaver PI
11
Agenda
1
HEMA Company Background
2
Teradata Overview
3
Why HEMA choose Teradata
4
The Implementation
5
Summary
12
HEMA Expansion
 We became Holland’s favorite and we still are!
1926 2 stores
1940 24 stores
1970 95 stores
1985 193 stores
1995 242 stores
2011 +550 stores
in the Netherlands, Belgium, Germany, France, Luxembourg
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13
Expansion is key to HEMA …
…but that puts pressure on HEMA supply chain
Consequence
 New formats do not always fit in the current model
 Local influences (store level) become more important
Conclusion: new Supply Chain model is required:
 Demand driven
 Based upon local influences
 Management by Exception
Teradata selected to support HEMA strategy:
 DCM application
 SAP BW integration
14
Challenges
Demand Chain Management
 New Demand Chain (DCM) application on
Teradata chosen as foundation of HEMA’s
new Supply Chain model
 Analysis did show, that most of the data
needed to feed the DCM application already
stored in SAP BW
 Potential Data duplication issue raised
SAP Business Warehouse
 Fast data SAP BW volume growth expected
 Query performance issue with SAP BW on
Oracle perceived
15
Strategy and Project Rules
 Leverage the Teradata DCM investment also to
solve SAP BW (Oracle) performance issue
 Avoid data redundancy - “Single version of the
truth”
 Data scope: Sales and Stock subject area
(~50% of SAP BW data)
 (Re-)Use current SAP BW ETL / Reporting
 Keep or improve query performance
 Performance test halfway the project!
16
Agenda
1
HEMA Company Background
2
Teradata Overview
3
Why HEMA choose Teradata
4
The Implementation
5
Summary
17
SAP BW at HEMA
usage
data




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 About 500 HQ users +
Distribution Center Users
 All shops in all countries(550+)
 Monday morning peak
sizing
Sales (per day-article-plant)
No receipts
Stock (article-week-shop)
Remote cube to R/3 (actual stock)
Article movements
Financial data (pca, cca, sl)
used tools
 2,5TB+ data at this moment
 150+ InfoCubes
 1000+ report queries
 BEX (Web) Analyzer
 BEX Report Designer
 BEX Broadcasting
18
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Implementation in a nutshell
1. Teradata infrastructure implementation and set up
2. Integration Teradata and SAP BW:
– Data flows from SAP BW to Teradata via SAP OpenHub
and Teradata TELS
– Queries get data out of Teradata via Teradata TVAS
3. Implementation Teradata DCM on top of Teradata DW
SHS
DCM
(SAP HEMA Store)
Stock / Sales /
SAP Retail
(ECC 6.0)
SAP
BW
Master Data
TVAS
Daily replenishment
order proposals
19
Teradata
DW
Teradata Virtual Access Solution
 TVAS allows SAP BW End-users to run reports
against data which is physically stored in
Teradata only.
 TVAS avoids data duplication and
ETL implementation.
 TVAS gives SAP BW End-users high
performance access to detailed data in
Teradata.
 TVAS key functionality is a Teradata specific
SQL generator.
 TVAS runs on SAP NetWeaver Java
Application Server and supports multiple BW
instances including SAP Java load balancing.
 TVAS supports multiple Teradata systems and
Teradata query banding.
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 Reduced Cost
 Improved Performance
 Increased Business Value by
more fresh and detailed data
TVAS Use Cases
Illustrative
21
HEMA Solution Architecture
Teradata Complements SAP BW
22
Illustrative
Step 1: Simplify the Data Model
Basic Design Idea – Store once, use many!
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Illustrative
Step 2: Initial Data Load
• Load historical info available from 2006
– Sales Data
– Stock Data
– Master Data
• Method:
– Export from SAP BW to a Flat File
– Import in Teradata with Loader
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Step 3: Data Mapping
SAP BW Virtual Provider to Teradata (TVAS GUI)
25
Step 4: Daily ETL
Embedded in existing HEMA/CapGemini
environment, use of:
– BMC Control-M scheduling
ETL
– Export : via SAP BW export via Open Hub
– Load: via Teradata Load Solution (TELS) and
FTP/Teradata loader: load SAP BW data in
Teradata Staging Area
– Transform: via Teradata SQL: update Data model
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BW & Teradata in Production
Results & Findings




Query performance improved significantly
Users do not complain (so much) anymore
Very stable environment
New queries developed to combine SAP and DCM data
Previous response time
Current timings
(average)
A
< 10 sec
2x faster
B
10 < > 60 sec
2x faster
C
60 < > 300 sec
10x faster
D
> 300 sec
24x faster
Group
27
27
Agenda
1
HEMA Company Background
2
Teradata Overview
3
Why HEMA choose Teradata
4
The Implementation
5
Summary
28
Implementation Summary
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




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No difference for BW End-User
Substantial performance improvement
Store once, use many
Simplified Data Model and structures
Implementation with a small team in 4 months
Cost savings on storage & maintenance
Compare before and after
– More users and more usage
– More historical data on the system
– More data requested in the reports
29
Looking forward
Teradata role for HEMA is changing…
SAP BW
Teradata
 Commodity reporting
 Special reporting
 Large group of users:
Stores and Head office
 Special Head office users
only
 Aggregated data
 Detailed data
 Data hub to Teradata
 Data supply to BW for
non SAP data
 SAP Merchandise and
Assortment Planning
 POS Data
 Web Data
30
Contact
Klaus-Peter Sauer
Technical Lead SAP Program
Europe – Middle East – Africa
Teradata GmbH
Altrottstr. 31
69190 Walldorf / Germany
Tel:
+49 (0) 6227 / 733 511
Mobile: +49 (0) 172 / 8238 665
Fax:
+49 (0) 89 / 3221 1974
Klaus-Peter.Sauer@teradata.com
Teradata.com