Selfservice BI for SAP and HANA Dream or

Self-service BI for SAP and HANA – Dream or Reality? Swen Conrad CEO, Ocean 9 September 14, 2016

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

Self-Service BI How Gartner defines it “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 December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 3

No More Middle-Man or Middle-Woman! “… 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? December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 4

Build a Simple Architecture! “… Approved … architecture and tools portfolio …” Traditional Reporting Cloud Self-Service BI • Complex setup; ETL creates delays • Little flexibility to technology or biz change • High CAPEX expenditures • Minimum setup and no ETL • On-demand provisioning and consumption • From data to insight for any data source BI Frontend Reporting Fronted Data Warehouse SAP HANA in cloud CSV files ETL ID On/off CRM META DATA OBJECT ERP CRM CSV files ERP DATA Sensor data ATTRIBUTES December 5, 2020 CSV files Sensor data © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 5

Case Study Self-service BI for Human Resources “… end users designing …their own …

The Project: HRIS migration Co. wide migration to PC rendered HRIS end-of-live • In Scope • Out of scope – Replace existing capabilities at parity – All HRIS reporting since done via extracts through centralized reporting function • Reporting status quo/ later findings – Complex & manual: 2 weeks from 4 D 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! December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 7

Path to Success Focus on HR Team Training and Change Management December 5, 2020

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 December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 9

… continued • Teach related skills: Customized XLS training module • Agree on total

Results speak for themselves! Happy HR Team + a CFO Quarterly Team Award December

Demo Simple Architecture enabling Self-service “… Approved … architecture and tools portfolio …” December

Build a Simple Architecture! “… Approved … architecture and tools portfolio …” Traditional Reporting Cloud Self-Service BI • Complex setup; ETL creates delays • Little flexibility to technology or biz change • High CAPEX expenditures • Minimum setup and no ETL • On-demand provisioning and consumption • From data to insight for any data source BI Frontend Reporting Fronted Data Warehouse SAP HANA in cloud CSV files ETL ID On/off CRM META DATA OBJECT ERP CRM CSV files ERP DATA Sensor data ATTRIBUTES December 5, 2020 CSV files Sensor data © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 13

“Launching Taxi Service in NYC” 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 (S 3), 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 Ocean 9 Load data set – Little expertise required, 60 min for entire process with Ocean 9 Find answers – Use Cloud 9 Charts December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 14

NYC Yellow Cab Taxi Trips and Fares Some demo stats • Setup – Database Engine: – HANA-as-a-Service: – Reporting Front End: • Schema SAP HANA Ocean 9 Cloud 9 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 S 3 to HANA 20 minutes restore from backup with Ocean 9 December 5, 2020 CREATE COLUMN TABLE nyc. yellow_taxi ( vendor_name char(3), Trip_Pickup_Date. Time TIMESTAMP, Trip_Dropoff_Date. Time 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 ); 15 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved

Demo Playbook • Restore SAP HANA system from backup • Show existing HANA system with NYC data • Go to Cloud 9 Charts – Answer business questions for “Locean 9 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? December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 16

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What we just Showed You LATER ON-DEMAND USE INITIAL SETUP Imagine the Possibilities! Load Data from S 3 to HANA Start SAP HANA System Find Data Connect Cloud 9 Charts to HANA Build Cloud 9 Charts dashboard Duration: 60 min Duration: 15 min Duration: 60 min Duration: 5 min - Time/effort varies - NYC Taxi Data from Github - Same time for any SAP HANA system - Powered by Ocean 9 - Create schema in seconds - Load data: 60 min - Powered by Ocean 9 - Pre-defined - Varies by dataset integration complexity - Via HANA System IP, - Powered by Cloud 9 user, password Charts - Powered by Cloud 9 Charts Restore HANA with Data from Backup Explore Data in Cloud 9 Charts Duration: 20 min Duration: ongoing - HANA backup on S 3 - Both system and business data in one location - Powered by Ocean 9 - Connection already existing - Build on top of previous analysis - Powered by Cloud 9 Charts December 5, 2020 Duration: 60 min Explore data and cash relevant info Duration: ongoing - Explore live data - Cash data for later analysis and to share with team - Reconnect to live data source any time - Powered by Cloud 9 Charts © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 19

Conclusion and Summary • 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 December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 20

Ocean 9, Inc. 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 Io. T simplification, automation and operation – Customer success December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 21

Cloud 9 Charts, Inc. Polyglot Analytics • Next generation Analytics platform as a Service • Built for high volume, high velocity, multi-structured data sources December 5, 2020 © Copyright, 2016, Ocean 9, Inc. , All Rights Reserved 22

Your Presenters Today Swen Conrad, CEO Ocean 9 Jay CEO, Cloud 9 Charts Swen brings 20 years of business leadership in SAP and cloud across consulting, IT management, marketing and sales disciplines to Ocean 9. Jay founded Cloud 9 Charts to the address the need for an analytics platform specifically for modern data. In 2002, he held IT operational responsibility for one of Hewlett Packard’s ww SAP installations with $8 billion 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. The fast changing database landscape requires a new breed of solution designed from the ground up to handle data across structured, unstructured and multistructured data sources. Previously, Jay led product at Demandforce (sold to Intuit), was a founding engineer at Goodmail and Mowingo.

Resources Related blog https: //www. ocean 9. io/post/1 -billion-rides-in-sap-hana Demo dashboard https: //cloud 9 charts. com/d/1. 1 -Billion-NYC-Taxi-Dataset-Analysis Further resources are attached to the Bright. TALK webinar here http: //bit. ly/2 c. Pdlr 4 12/5/2020 24

Download the Slides for free at: https: //www. ocean 9. io/content-download/self-service Thank you Swen Conrad, CEO swen@ocean 9. io 650 889 9876