This document summarizes a guest lecture given by Joshua Fletcher on SAP Business Intelligence. The agenda included introductions, an overview of SAP, their analytics and information management platform, and industry trends like mobile analytics, in-memory databases, and predictive analysis. Fletcher has 12 years of experience with BI tools and is now a BI architect. He discussed SAP's analytics platform and trends in mobile BI, in-memory databases like SAP HANA, and predictive analytics using SAP Predictive Analysis. He demonstrated the capabilities of SAP's mobile BI, HANA, and predictive analysis software and provided useful links for further information.

1. University of Western Australia
Guest Lecture
SAP Business Intelligence
Joshua Fletcher
SAP Mentor
16th October 2013

2. Agenda
1.
2.
3.
4.
Introduction
SAP Overview
SAP's Analytics & IM Platform
Industry Trends
– Mobile Analytics
– In-memory Databases
– Predictive / Statistical Analysis
5. Questions & Answers

3. Introduction
• Twelve years experience with BI tools
• Recently spent seven years as a
Principal Consultant and Team Lead (up
to 10 consultants) including recruiting
graduates
• Now contracted to BHP Billiton Iron Ore
as a BI Architect in their new BICC
• Experienced across the lifecycle of
Business Intelligence and Enterprise
Information Management

4. SAP Mentor Initiative

5. SAP Mentor Initiative
• SAP Mentors are the top community
influencers of the SAP Ecosystem
• Most of the ~130 mentors work for
customers or partners of SAP
• All of them are hands-on experts of an SAP
product or service, as well as excellent
champions of community-driven projects
• Focus is on engagement, co-innovation and
advocacy

6. Channels
dslayer.net
@dslayered
dslayered
Unprofessional
journalism at its
finest
Recorded by a
bunch of guys in
the SAP BI
community
Podcasts on
product news,
technology usage
and interviews with
other BI people

7. Who is SAP?
• SAP is the world leader in enterprise applications in
terms of software and software-related service
revenue
• More than 238,000 customers in 188 countries
• More than 65,500 employees – and locations in
more than 130 countries
• A 41-year history of innovation and growth as a true
industry leader
• Annual revenue (IFRS) of € 16,22 billion
• Listed under the symbol "SAP" on stock exchanges,
including the Frankfurt Exchange and NYSE
• SAP recently celebrated it’s 40th anniversary (video)

8. SAP’s Analytics Platform
Gartner Magic Quadrant 2013
Business Intelligence
Gartner Magic Quadrant 2013
Data Warehouse

9. Information Management

10. Trend 1 – Mobile Analytics
•
•
•
1 in 3 Americans now own a tablet (Mashable Jun
2013)
Australians have the most tables per capita in
study across 16 countries (SMH Dec 2012)
What are the hot areas for mobile BI?
–
–
–
–
•
Self-service report & dashboard consumption
Spatial analysis
Exploration of information
Simple ad-hoc analysis
Most vendors have mobile apps that are free to
download and use trial data with

11. Mobile BI Demonstration

12. Trend 2 – In-memory Databases
• Gartner note 60 vendors who provide
forms of in-memory databases (IMDB)
• Major vendors who now support (or have
announced planned support) include:
–
–
–
–
SAP with HANA
Oracle with Oracle DB In-memory Option
IBM with IBM DB2 Blu
Microsoft with SQL Server 2012 Hekaton
• Some vendors are adding to existing
tech, others are building new

13. SAP HANA
• Natively in-memory with MPP architecture
• Designed to support OLTP & OLAP
workloads simultaneously (write
individuals record while querying billions)
• Unstructured & structured data storage
• High compression with column & row store
• On-premise or cloud deployment options

14. SAP HANA Capabilities
•
•
•
•
•
•
Advanced calculation/semantic layer
Unstructured text analytics
Statistical algorithms
Data quality algorithms
Spatial querying supported
Smart Data Access allows querying of
Hadoop, Sybase IQ and other databases

15. SAP HANA – More Than a DB

16. SAP HANA Performance
3 billion scans per second per core
12.5 million aggregates per second per core
1.5 million inserts per second

17. SAP HANA Performance
• 1 PB Performance Benchmark
–
–
–
–
100 Nodes, 100 TB in DRAM
10 Years of Sales & Distribution Data
1.2 trillion Rows (330 Million transactions / day)
Ad-hoc Simple Queries (e.g. Month Report)
• 430ms – 647ms, Drill-down: 142ms, Complex Queries
(e.g. YoY report): 1.2s – 3.1s
– Query Throughput (Queries per Hour)
• 7,547 for 1 stream, 57,202 for 10 streams, 112,602 for
60 streams

18. SAP HANA Demonstration

19. Trend 3 - SAP Predictive Analytics
• Desktop analyst tool which provides data
acquisition, cleansing, statistical analysis and
visualisation capabilities
• Integrates with local R engine or pushes
analysis to HANA and Predictive Algorithm
Library (PAL)
• Able to handle big data by leveraging HANA’s
capabilities
• New possibilities such as McLaren F1 (video)

20. Predictive Needs & Examples
• Anomalies – what anomalies or
groupings/clusters exist?
• Forecasting – how does historical information
translate to future performance?
• Relationships – are there correlations in data,
or opportunities to cross-sell or up-sell?
• Key Influencers – what are main influencers
of customer satisfaction or customer churn?
• Trends – what are emerging trends or
sudden step changes that will impact
business?

21. Integration with R Library
• Open source statistical programming
language with over 3,500 packages and
ability to write your own functions
• Used by growing number of data analytics
in industry, government, consulting and
academia
• Free, comprehensive and many learn at
college or university

22. HANA Synergies
• Leverage complimentary capabilities of
Predictive Analytics and HANA PAL
• Integrated and optimised for
interoperability, enabling combination of
real-time and operational analytics, access
to big data, and predictive capabilities
• If it's available through HANA, it can be
used for data mining and predictive
analysis: gain real-time access to BPC,
BW, ERP, Analytic Applications and more

23. Common Algorithms
• Association (Apriori)
– Find frequent item patterns in transactional
datasets ie market basket analysis
• Clustering (K-Means)
– Cluster observations into related groups
• Decision Trees (CNR Tree)
– Classify observations into groups and predict
discrete variables

24. Common Algorithms
• Neural Network (MONMLP Neural Network)
– Forecast, classify and undertake statistical
pattern recognition
• Outliers (Nearest Neighbour Outlier)
– Find patterns in data that aren’t expected
• Regression (Exponential Regression)
– Finds trends in data
• Time Series (Single Exponential Smoothing)
– Smooth (trend) or forecast time series data points

25. SAP Predictive Analysis Demonstration

26. Useful Links – Mobile BI
•
•
•
•
Explorer iPad App
Mobile BI iPad App
Lumira Cloud Free
Lumira Desktop Personal Edition Free

27. Useful Links – HANA
• SAP HANA Startup Program
• Access Your Own HANA Server
– Free 30 day trial
– Cloud development access (Amazon EC2
PAYG)
• HANA Academy
• NBA Statistics (powered by HANA)

28. Useful Links – Predictive
• SAP Predictive Analysis Trial
• Try R School
• Free book ‘Learning Statistics with R’

29. Useful Links – General
• SAP Mentor Initiative Introduction
• Diversified Semantic Layer

30. Any questions?
You can reach me on:
@josh_fletcher
josh@geek2live.net