Embed Size (px)
Citation preview
Accelerating Big Data & Analytics Innovations through Public – Private Partnerships: Experiences and Results
Prof. Dr. Alexander Mädche, University of MannheimDr. 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
3
Different Types of Big Data & Analytics Innovations
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 DevelopAdd-on
RECAP SAP UA EMEA
CONFERENCE 2014, Berlin
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 projectsEducation
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 MädcheUniversity of Mannheim | Business School | Institute for Enterprise Systems (InES)L 15, 1-6 | 4th floor | 68131 Mannheim | GermanyPhone +49 621 181-3606 | Fax +49 621 [email protected] | http://eris.bwl.uni-mannheim.de http://ines.uni-mannheim.de
Thank you for your attention!
Dr. Hendrik MethManager Business Warehouse Competence CenterBorgWarner IT Services Europe GmbH, Marnheimer Straße 85/8767292 Kirchheimbolanden / GermanyTel.: +49 63 52-403-5243 [email protected]
