SAP Academic Conference Americas 2013
Analytics: From Big Data to Insight
Bjarne Berg, Lenoir-Rhyne University
Adrian Gardiner, Georgia Southern University
February 2013
Influence Next-Gen Leaders
Agenda
A Tipping Point?
 The rise of big data and nearly infinite computing power
Implications for Business Education
 Why educators need to get on board
SAP and Analytics
 Outlining SAP’s analytic software stack
Teaching Using SAP HANA
 SAP HANA will be a ‘game changer’ for business computing
 SAP University Alliances’ curriculum in SAP HANA
Teaching Analytics
 SAP University Alliances’ curriculum in: SAP NetWeaver Business Warehouse,
SAP Crystal Reports, SAP Crystal Dashboard Design, & SAP BusinessObjects 4.0
© 2013 SAP AG. All rights reserved.
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Innovation Starts Here
SAP HANA Powers Innovation Across SAP’s Solutions
Applications
Analytics
Mobile
Database &
Technology
Cloud
Powered by SAP HANA
SAP University Alliances provides curriculum aligned with SAP’s Innovation
Platform…including Analytics, Mobile, and SAP HANA…so students are
prepared with skills needed by industry.
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A Tipping Point?
The rise of big data and nearly infinite computing power
A Tipping Point?
2012: a tipping point in business technology?
Gartner: “Nearly there or have now arrived”
Analytic insight and computing power are nearly infinite and cost-effectively
scalable.
Big data and global scale computing at small prices.
 Gartner's 2012 Hype Cycle for Emerging Technologies
Applications powered by SAP HANA
Recent SAP HANA benchmark: 100 billion records analyzed in 300ms.
SAP Business Suite powered by SAP HANA.
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Google Trends
Key term: Big Data
Key term: Analytics
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Big Data
October 2012
The amount of business data is increasing
exponentially. We are entering the age of ‘big
data.’ It has been estimated that 90% of all data
is less than two years old.
 IBM 2012: Bringing Big Data to the Enterprise
Some foresee a ‘revolution’ in management.
 Harvard Business Review, Oct. 2012: ‘Big Data: The
Management Revolution’
Micro-case: Social media Klout publishes
measures of online influence. Analyzes 1 - 12
billion data points daily.
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Analytics
Discovery and communication of meaningful patterns in data.
Applied to business data, to describe, predict, and improve business performance.
Analytic applications often favor data visualization to communicate insight.
Intelligent data
Behavioral data, sensor data, transactional data, market research, …
Questions (e.g., marketing):
Who are my core customers?
What drives their behavior?
Can I predict change?
Can I segment?
What has led up to this point?
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What is Holding us Back?
Disk speed is growing slower than all other hardware
components, while the need for speed is increasing
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Implications for Business
Education
Why educators need to get on board
Implications for Business Education
“The United States alone could, by 2018, face a shortage of 140,000 to 190,000
people with deep analytical skills as well as 1.5 million managers and
analysts with the know-how to use the analysis of big data to make effective
decisions.”
 McKinsey & Co., 2011
“If you are looking for a career where your services will be in high demand,
you should find something where you provide a scarce, complementary
service to something that is getting ubiquitous and cheap. So what’s getting
ubiquitous and cheap? Data. And what is complementary to data? Analysis.
So my recommendation is to take lots of courses about how to manipulate
and analyze data: databases, machine learning, econometrics, statistics,
visualization, and so on.”
 Hal Varian, Chief Economist at Google and emeritus professor at the University of
California, Berkeley
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Implications for Business Education
Departments are changing their names
e.g. University of Sydney’s Dept. of Business Analytics (formerly: Operations
Management and Econometrics).
Innovative degree programs are being launched
e.g., MS in Predictive Analytics (Northwestern University); MS in Analytics (NCSU)
“Now nobody has a data-science department; in 30 years every school on the
planet will have one.”
•
Pat Gelsinger, CEO, EMC Corp., 2012
Something big indeed is going on!
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SAP and Analytics
Outlining SAP’s analytic software stack
SAP Analytics Software Stack
simplified
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SAP Analytics Software Stack
Data Foundation Layer
Data provisioning
Information Access Layer
Supporting end-user analysis, modeling and decision making
Tools for generating & communicating insight
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Teaching Using SAP HANA
SAP HANA will be a ‘game changer’ for business computing
SAP University Alliances’ curriculum in SAP HANA
Teaching Using SAP HANA
We are in ramp-up mode of the SAP HANA curriculum, and had a workshop with 20
professors this fall as part of the first course offerings.
Today, we have material available for exercises involving data loading, table creation, building
views and reporting in SAP HANA.
These are step-by-step instructions that students can complete in a guided manner.
Professors can quickly learn the material by doing the exercises themselves, or by attending
the SAP HANA workshop tomorrow
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Teaching Focus for SAP HANA
SAP HANA can be taught from many perspectives:
Analytics (Business Intelligence)
 Predictive modeling, data mining, data visualization, reporting, analysis, enterprise
performance management, scorecarding, benchmarking, dashboards etc.
Data Warehousing
 Dimensional modeling, ETL, data cleansing, data architecture, administration.
Technical and Database design
 May include as part of a database class to learn how to model and organize data inmemory (row and column stores), creating indexes, views and loading data.
Or simply as a part of a business, CS or IS course.
Professors and students are encouraged to explore
learning resources at saphana.com
© 2013 SAP AG. All rights reserved.
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SAPHANA.com
Also:
•
•
•
http://www.saphana.com/community/resources/hana-academy
Use Cases
Test
Drive/Demos
More on
HANA’s
architecture
Quick Demo of a SAP University Alliances’ SAP HANA
Exercise for Students
In this short 4 min. demonstration, we show how a
student can complete one of the SAP HANA exercises
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Demo of Analytics on SAP HANA – 780 million rows
In this short 5 min. demonstration, we show 3
dashboard examples running on SAP HANA
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HANA Textbook Resources
In Memory Data Management: Technology and Applications – Hasso Plattner
SAP HANA Essentials – Jeffrey Word
SAP HANA: An Introduction - Bjarne Berg & Penny Silvia

For desk copies: SAP Press – Jon Kent (jon.kent@sap-press.com)
Teaching Analytics
SAP University Alliances’ curriculum in: SAP NetWeaver Business Warehouse,
SAP Crystal Reports, SAP Crystal Dashboard Design, & SAP BusinessObjects 4.0
Teaching Data Foundation with SAP BW
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Teaching Data Foundation with SAP BW
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The Value of Learning SAP NetWeaver Business
Warehouse
Curriculum promotes understanding of:
System aspects of data foundation
•
Architecture: data flow into, within, and out of the data warehouse; ETL
The need for data warehousing:
•
Vis-à-vis SAP ERP’s reporting capabilities
Data for decision making usually comes from many sources
Importance of data quality & data transformation
Data modeling
OLAP
Role of metadata
Query design: BEx Query Designer
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Teaching Experiences: SAP NetWeaver Business
Warehouse
SAP University Alliances’ curriculum is mature
SAP NetWeaver Business Warehouse can be technical
Ideally attend summer workshop
Target group: more technical students – e.g., IS, CS
Students ideally have completed a course in SAP ERP
Tight integration of data/metadata between SAP ERP and SAP BW
Database knowledge not absolutely necessary
But may wish to spend time on understanding OLAP concepts – star schema
Never actually see database (BW provides a layer of abstraction over database
technology)
SAP NetWeaver Business Warehouse has data mining functionality
See ‘Analytics Applications and Global Bike, Inc.’ workshop
Predictive Analysis may be coming shortly
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Teaching the Information Access Layer
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Information Access
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Different Tools for Different User Groups
Professionally
Informed
Information
Consumers
Target group
Executives
& Managers
Crystal
Reports
Business
Analysts
Technically
Capable
Limited
Interactive Experience, Responsiveness
Guided
Source: Kramer, R. (2011). SAP Business Intelligence.
© 2013 SAP AG. All rights reserved.
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SAP Crystal Reports
Sophisticated report formatting
Pixel perfect reports
Templates and wizards speed up report creation
Secure, large scale distribution of reports
Connectivity to any data source
Teaching:
Enterprise reporting
Understanding links to data foundation
Encourages power-user DIY approach
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Different Tools for Different User Groups
Professionally
Informed
Information
Consumers
Target group
Executives
& Managers
Crystal
Reports
Dashboards
&
Visual
Intelligence
Business
Analysts
Technically
Capable
Limited
Interactive Experience, Responsiveness
Guided
Source: Kramer, R. (2011). SAP Business Intelligence.
© 2013 SAP AG. All rights reserved.
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SAP Crystal Dashboard Design
Personalised Flash-based dashboards
Pre-built components, skins, maps,
charts, gauges, and selectors
Empower business users with
interactive information
Teaching:
Eye-candy for students: leads to broader
discussion of visualization
Monitor KPIs, metrics & exceptions
Powerful “what if” analysis
Ability to drill down
Understanding linkage to data foundation
© 2013 SAP AG. All rights reserved.
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Different Tools for Different User Groups
Professionally
Informed
Information
Consumers
Target group
Executives
& Managers
Crystal
Reports
Dashboards
&
Visual
Intelligence
Web
Intelligence
Business
Analysts
Technically
Capable
Limited
Interactive Experience, Responsiveness
Guided
Source: Kramer, R. (2011). SAP Business Intelligence.
© 2013 SAP AG. All rights reserved.
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SAP BusinessObjects Web Intelligence
Self service analysis and reporting
Flexible formatted reports with built-in
analysis features
Aimed at business users
Report designers with limited technical expertise
Interactive report creation
Connectivity to any data source
Universe design
Teaching:
Answering ad-hoc questions
Role of semantic universes
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Different Tools for Different User Groups
Professionally
Informed
Information
Consumers
Target group
Executives
& Managers
Crystal
Reports
Dashboards
&
Visual
Intelligence
Web
Intelligence
Analysis
(BEx)
Business
Analysts
Technically
Capable
Limited
Interactive Experience, Responsiveness
Guided
Source: Kramer, R. (2011). SAP Business Intelligence.
© 2013 SAP AG. All rights reserved.
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Analysis - OLAP/Office (Excel, Powerpoint and Web)
Explore multi-dimensional data sets & drill
down into details
Comprehensive range of business
and time calculations
Visualization, exception highlighting
Teaching:
Heavily integrated with SAP BW & Query
Designer (teach together)
Replacing older BEx
© 2013 SAP AG. All rights reserved.
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Different Tools for Different User Groups
Professionally
Informed
Information
Consumers
Target group
Executives
& Managers
Crystal
Reports
Dashboards
&
Visual
Intelligence
Explorer
Web
Intelligence
Analysis
(BEx)
Business
Analysts
Technically
Capable
Limited
Interactive Experience, Responsiveness
Guided
Source: Kramer, R. (2011). SAP Business Intelligence.
© 2013 SAP AG. All rights reserved.
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SAP BusinessObjects Explorer
Search and navigation tool for casual users to access business data
About quickly finding an answer to a pressing question, rather than generating
intricate multi-dimensional charts
Search, explore and visualise large data volumes via BWA / SAP HANA
Guided analysis and smart visualisations
Limited training required
US Government is using to
improve transparency
•
e.g., Recovery.gov
Teaching:
Dimensionality
Links to data foundation
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Different Tools for Different User Groups
Opinion of the authors
Pick one or two tools to teach first. Keep it simple
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SAP HANA – What does it look like?
The SAP HANA system is hosted and your University does not need to
maintain HANA hardware.
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Thank You!
Contact information:
Bjarne Berg
Associate Professor, Computer Science
bergb@lr.edu
828-328-7258
Adrian Gardiner
Associate Professor, Information Systems
agardine@georgiasouthern.edu
912-4787479