Accelerating Big Data & Analytics Innovations through
Public – Private Partnerships: Experiences and Results
Prof. Dr. Alexander Mädche, University of Mannheim
Dr. Hendrik Meth, BorgWarner IT Services Europa GmbH
Walldorf, September 11th 2015
SAP University Alliance EMEA Conference

Agenda
2
Agenda
1 Public Private Partnerships for Big Data Innovations (Mädche)
2 Innovation Prototyping: BW on HANA Performance Analysis (Meth)
3 Experiences & Lessons Learned (Mädche)
2

Different Types of Big Data & Analytics Innovations
3
SAP HANA
Platform for
Big Data
Extend Existing Transactional
& Analytical Stack of SAP
Develop Innovative Intelligent
Applications
Other Big Data
(Analytics)
Technologies
Existing Transactional & Analytical Stack (ERP, DWH, …)
Custom
Develop
Add-on

Public – Private Partnerships in the context of Big Data Innovations have huge
potentials: Universities get access to real-world problems and data, private
organizations establish networks and get access to state-of-the-art knowledge.
Public – Private Partnerships have the potential to enable and establish new forms of
networked innovations.
Public – Private Partnership (PPP) for Big Data & Analytics
Innovations
4
Public Private
Technology
Providers
Consulting Service
Providers
Corporate UsersBig Data
Innovation Lab
Big Data
Innovation Center

Extending and Building PPP Innovation Networks:
The SAP Big Data Innovation Lab
5
In the last year we have extended and accelerated the innovation network with a
consulting service provider and first corporate users:
Public Private
Technology
Providers
Consulting Service
Providers
Corporate UsersBig Data
Innovation Lab
Big Data
Innovation Center
• We established a cooperation with
a well-known consulting service
provider.
• We have carried out first
innovation projects with corporate
users. Results of a finalized
innovation project in cooperation
with BorgWarner will be
presented.

Cooperation Concept with Consulting Service Provider
6
• Leverage Big Data & Analytics infrastructures to extend the
existing SAP stack as well as to deliver analytics pilot
innovation applications with real-world data in cooperation
with consulting service provider clients.
• Execute dedicated research projects in cooperation with
consulting service provider and its clients and deliver joint
publications in the form of research and white papers
Research
&
Innovation
• Embed „Analytics Challenge“ into M.Sc. lecture on Business
Intelligence
• Run joint bachelor / master thesis projects
Education

Agenda
7
Agenda
1 Public Private Partnerships for Big Data Innovations (Mädche)
2 Innovation Prototyping: BW on HANA Performance Analysis (Meth)
3 Experiences & Lessons Learned (Mädche)
7

Introduction
• BorgWarner is one of the leading automotive suppliers in the world.
• Engine and Drivetrain Systems
• Worldwide operations and customer base
• Large SAP Business Warehouse 7.01 implementation, following
layered scalable architecture (LSA), e.g. see Sales Architecture:
8
• Challenges:
 Data Loading
performance
 Reporting
performance

Innovation Project: Setup-1
• Main research question behind the study: Can the potential
performance improvements of SAP HANA be realized in a data
and modelling and reporting setup comparable to BorgWarner’s
system landscape ?
• Compare three variants with regards to data loading / reporting
performance
 Model-A: SAP BW 7.3 on relational database using LSA modeling approach
 Model-B: SAP BW 7.3 on SAP HANA database using LSA modeling approach
 Model-C: SAP BW 7.3 on SAP HANA database leveraging HANA-optimized
modelling
9

Innovation Project: Setup-2
• Create a data model similar to our existing environment
• Utilize real-world data from BorgWarner along three cases:
 Case A: 1 million records
 Case B: 2 million records
 Case C: 3.5 million records.
• Create different types of representative queries (for reporting)
• Run 5 different iterations
• Provide infrastructures in Big Data Innovation Center Magdeburg
(BW on HANA / BW on relational database) and run evaluation in
controlled lab environment.
10

Innovation Project: Selected Results*:
11
Data Loading Performance
Reporting Performance (simple / mid-complex queries):
* for Case C – 3.5 million data sets):

Agenda
12
Agenda
1 Public Private Partnerships for Big Data Innovations (Mädche)
2 Innovation Prototyping: BW on HANA Performance Analysis (Meth)
3 Experiences & Lessons Learned (Mädche)
12

Experiences & Lessons Learned
• Private-Public Partnerships leveraging a partner network covering
different roles and competencies help to drive big data innovations
forward.
• Various types of legal, security and compliance aspects remain the
key inhibitor for running big data innovation projects => Template
contracts, tool support (e.g. for data randomization), etc. is required
• Big Data Innovation extension scenarios may require complex
system landscapes (HANA, ABAP Stack, BW, …); costs tend to
become higher than expected
• Professional installation / delivery support from Big Data Innovation
Center is really required and very helpful.
13

14
Prof. Dr. Alexander Mädche
University of Mannheim | Business School | Institute for Enterprise Systems (InES)
L 15, 1-6 | 4th floor | 68131 Mannheim | Germany
Phone +49 621 181-3606 | Fax +49 621 181-3627
maedche@es.uni-mannheim.de | http://eris.bwl.uni-mannheim.de
http://ines.uni-mannheim.de
Thank you for your attention!
Dr. Hendrik Meth
Manager Business Warehouse Competence Center
BorgWarner IT Services Europe GmbH, Marnheimer Straße 85/87
67292 Kirchheimbolanden / Germany
Tel.: +49 63 52-403-5243
HMeth@BorgWarner.com