The document discusses self-service business intelligence (BI) and its implementation using SAP HANA in the cloud. It begins with definitions of self-service BI and outlines a case study of migrating HR reporting to a self-service model. It then demonstrates setting up SAP HANA in the cloud with a large NYC taxi dataset and using Cloud9 Charts to analyze and visualize the data. The presentation concludes that enabling self-service BI requires giving business users tools to design their own analyses, using an approved architecture of simple cloud technologies, and training users to develop analytics skills.

1. Swen Conrad
CEO, Ocean9
September 14, 2016
Self-service BI for SAP and
HANA – Dream or Reality?

2. Little theory and lots of show and tell
• Self-Service BI – The Gartner definition
• Case study
• DEMO – Self-Service BI in action!
• Summary

3. How Gartner defines it
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Self-Service BI
“Self-service business intelligence is defined here
as end users designing and deploying their
own reports and analyses within an approved
and supported architecture and tools portfolio.”
Source: Gartner Glossary

4. “… end users designing …their own … analyses …”
• Haven’t we tried that before?
• Didn’t we just hire a few Data Scientists?
• How do we get there?
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No More Middle-Man or Middle-Woman!

5. “… Approved … architecture and tools portfolio …”
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Build a Simple Architecture!
Traditional Reporting
• Complex setup; ETL creates delays
• Little flexibility to technology or biz change
• High CAPEX expenditures
Cloud Self-Service BI
• Minimum setup and no ETL
• On-demand provisioning and consumption
• From data to insight for any data source
Data
Warehouse
Reporting
Fronted
ERP
CRM
CSV
files
Sensor
data
ETL
ERP
BI Frontend
CSV
files
Sensor
data
SAP HANA
in cloud
OBJECT
ATTRIBUTES
ID
META
DATA
DATA
CSV
files
CRM
On/off

6. Case Study
Self-service BI for Human Resources
“… end users designing …their own … analyses …”
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7. Co. wide migration to PC rendered HRIS end-of-live
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The Project: HRIS migration
• In Scope
– Replace existing capabilities at parity
• Out of scope
– All HRIS reporting since done via extracts
through centralized reporting function
• Reporting status quo/ later findings
– Complex & manual: 2 weeks from 4D data dump to quarterly Headcount report
– High effort to contextually “integrate” SAP HR w/out any business improvement
• Proposal and decision
– Build tailored HR reporting via assembly of existing SAP functions like SAP reporting tool
– Enable all of HR for Self-Service!

8. Focus on HR Team Training and Change Management
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Path to Success

9. Approach &
Principles
• Earned Executive
Support
• Design training plan to
enable T-shaped
Generalist knowledge
• Deliver tailored and
comprehensive training
to entire HR and FI
departments
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… continued
• Teach related skills:
Customized XLS
training module
• Agree on total time
commitment

11. Happy HR Team + a CFO Quarterly Team Award 
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Results speak for themselves!

12. Demo
Simple Architecture enabling Self-service
“… Approved … architecture and tools portfolio …”
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13. “… Approved … architecture and tools portfolio …”
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Build a Simple Architecture!
Traditional Reporting
• Complex setup; ETL creates delays
• Little flexibility to technology or biz change
• High CAPEX expenditures
Cloud Self-Service BI
• Minimum setup and no ETL
• On-demand provisioning and consumption
• From data to insight for any data source
Data
Warehouse
Reporting
Fronted
ERP
CRM
CSV
files
Sensor
data
ETL
ERP
BI Frontend
CSV
files
Sensor
data
SAP HANA
in cloud
OBJECT
ATTRIBUTES
ID
META
DATA
DATA
CSV
files
CRM
On/off

14. Which neighborhoods? Volumes? Pricing? Trips?
• Select appropriate data set
– NYC Taxi trips and fares – Found on Github
– 1.3 billion records
– Already stored in AWS Object Storage (S3), like many other data sets
• Provision reporting environment
– SAP HANA for absolute high performance with such a large set
– Start cloud system on-demand – No expertise required, 15 min startup time with Ocean9
– Load data set – Little expertise required, 60 min for entire process with Ocean9
– Find answers – Use Cloud9 Charts
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“Launching Taxi Service in NYC”

15. Some demo stats
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NYC Yellow Cab Taxi Trips and Fares
• Setup
– Database Engine: SAP HANA
– HANA-as-a-Service: Ocean9
– Reporting Front End: Cloud9 Charts
• Data set
– 1.231 billion rows
– 217 GB raw CSV data
– 34.8 GB full backup (HANA System + data)
– 60 minutes loading time from S3 to HANA
– 20 minutes restore from backup with Ocean9
• Schema
CREATE COLUMN TABLE nyc.yellow_taxi (
vendor_name char(3),
Trip_Pickup_DateTime TIMESTAMP,
Trip_Dropoff_DateTime TIMESTAMP,
Passenger_Count TINYINT,
Trip_Distance DOUBLE,
Start_Lon DOUBLE,
Start_Lat DOUBLE,
Rate_Code VARCHAR(10),
store_and_forward VARCHAR(10),
End_Lon DOUBLE,
End_Lat DOUBLE,
Payment_Type VARCHAR(10),
Fare_Amt REAL,
surcharge REAL,
mta_tax REAL,
Tip_Amt REAL,
Tolls_Amt REAL,
Total_Amt REAL
);

16. • Restore SAP HANA system from backup
• Show existing HANA system with NYC data
• Go to Cloud9 Charts
– Answer business questions for “Locean9 Cabs”
- Which neighborhoods have a lot of rides next to average right length or taxi fare?
- Which neighborhood has the best average tipping?
- Which neighborhood shows good growth in transportation numbers over time?
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Demo Playbook

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19. Imagine the Possibilities!
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What we just Showed You
Find Data
Start SAP
HANA System
Load Data
from S3 to
HANA
Connect
Cloud9 Charts
to HANA
Build Cloud9
Charts
dashboard
Explore data
and cash
relevant info
Restore
HANA with
Data from
Backup
Explore Data
in Cloud9
Charts
Duration: 60 min
- Time/effort varies
- NYC Taxi Data from
Github
Duration: 15 min
- Same time for any
SAP HANA system
- Powered by Ocean9
Duration: 60 min
- Create schema
in seconds
- Load data: 60 min
- Powered by Ocean9
Duration: 5 min
- Pre-defined
integration
- Via HANA System IP,
user, password
- Powered by Cloud9
Charts
Duration: 60 min
- Varies by dataset
complexity
- Powered by Cloud9
Charts
Duration: ongoing
- Explore live data
- Cash data for later
analysis and to share
with team
- Reconnect to live data
source any time
- Powered by Cloud9
Charts
Duration: 20 min
- HANA backup on S3
- Both system and business
data in one location
- Powered by Ocean9
Duration: ongoing
- Connection already existing
- Build on top of previous
analysis
- Powered by Cloud9 Charts
INITIALSETUP
LATER
ON-DEMANDUSE

20. • Self-Service BI is reality once you have …
– “… end users designing …their own … analyses …”
– “… approved … architecture and tools portfolio …”
– Combined with Simplicity and Speed
• Keys to meeting these goals
– Enable your business teams
- Hire new team members with business analytics skills
- Follow generalist training approach to develop T-Shaped Skill set
– Deploy a simple and universal technology foundation for answering analytic questions
- Assemble of the shelf cloud services and transition IT from “Built-to-order” to “Assemble-to-Order”
- Look for technical features like simple start, data-load, backup and stop features
- Look for OPEX based “pay as-you-go” business models
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Conclusion and Summary

21. Powering Digital Business
• Focusing on SAP, cloud and big data
• Combined team experience
– SAP and HANA – 37 years
– Cloud and AWS – 19 years
– IT operations and management – 22 years
• Passionate about
– Digital Transformation
– SAP HANA, big data, and IoT simplification, automation and operation
– Customer success
21
Ocean9, Inc.
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22. Polyglot Analytics
• Next generation Analytics platform as a Service
• Built for high volume, high velocity, multi-structured data sources
22
Cloud9 Charts, Inc.
January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved

23. Your Presenters Today
Swen Conrad, CEO Ocean9
Swen brings 20 years of business leadership in SAP and
cloud across consulting, IT management, marketing and
sales disciplines to Ocean9.
In 2002, he held IT operational responsibility for one of
Hewlett Packard’s ww SAP installations with $8billion in
annual transaction volume.
When at SAP he created the first unified solution for the
Business of IT, earning company wide recognition. Being
part of the highly successful SAP in-memory database
launch (SAP HANA), and later launching related AWS and
managed cloud offerings, he started shifting his full attention
to cloud.
In 2014, Swen rejoined HP where he has recently held roles
as SAP CTO as well as in cloud sales.
Swen has co-authored a book on IT Business Management
and is a frequent presenter at events.
Jay CEO, Cloud9 Charts
Jay founded Cloud9 Charts to the address the need for
an analytics platform specifically for modern data.
The fast changing database landscape requires a new
breed of solution designed from the ground up to
handle data across structured, unstructured and multi-
structured data sources.
Previously, Jay led product at Demandforce (sold to
Intuit), was a founding engineer at Goodmail and
Mowingo.

24. Related blog
https://www.ocean9.io/post/1-billion-rides-in-sap-hana
Demo dashboard
https://cloud9charts.com/d/1.1-Billion-NYC-Taxi-Dataset-Analysis
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Resources
Further resources are attached to the BrightTALK webinar here
http://bit.ly/2cPdlr4

25. Q&A
Please ask away
January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 25

26. Thank you
Swen Conrad, CEO
swen@ocean9.io
650 889 9876