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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

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Accelerating Big Data & Analytics Innovations through Public – Private Partnerships: Experiences and Results

  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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.
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. 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
  • 11. Innovation Project: Selected Results*: 11 Data Loading Performance Reporting Performance (simple / mid-complex queries): * for Case C – 3.5 million data sets):
  • 12. 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
  • 13. 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. 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