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Self-service BI for SAP and HANA – Dream or Reality?

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Gartner predicts that “analytics will be pervasive … for decisions and actions across the business.” Sounds like analytics nirvana with instant access for any analysis you want to do, in other words self-service BI. Is this dream or reality?

Join this webinar to find out how clouds like AWS or Azure are moving the industry close to this nirvana today through simple assembly of cloud services combined with the appropriate consumption model of these services.

We will demonstrate how easy it is to provision your high end SAP HANA Database right next to your BI Analytics tier.

Maybe we are closer to this nirvana than you think?

Listen to the full webcast here: http://bit.ly/2kfiJac

Publicado en: Tecnología
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Self-service BI for SAP and HANA – Dream or Reality?

  1. 1. Swen Conrad CEO, Ocean9 September 14, 2016 Self-service BI for SAP and HANA – Dream or Reality?
  2. 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. 3. How Gartner defines it January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 3 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. 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? January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 4 No More Middle-Man or Middle-Woman!
  5. 5. “… Approved … architecture and tools portfolio …” January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 5 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. 6. Case Study Self-service BI for Human Resources “… end users designing …their own … analyses …” January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 6
  7. 7. Co. wide migration to PC rendered HRIS end-of-live January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 7 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. 8. Focus on HR Team Training and Change Management January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 8 Path to Success
  9. 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 January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 9
  10. 10. January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 10 … continued • Teach related skills: Customized XLS training module • Agree on total time commitment
  11. 11. Happy HR Team + a CFO Quarterly Team Award  January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 11 Results speak for themselves!
  12. 12. Demo Simple Architecture enabling Self-service “… Approved … architecture and tools portfolio …” January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 12
  13. 13. “… Approved … architecture and tools portfolio …” January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 13 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. 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 January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 14 “Launching Taxi Service in NYC”
  15. 15. Some demo stats January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 15 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. 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? January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 16 Demo Playbook
  17. 17. January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 17
  18. 18. January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 18
  19. 19. Imagine the Possibilities! January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 19 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. 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 January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 20 Conclusion and Summary
  21. 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. January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved
  22. 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. 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. 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 1/24/2017 24 Resources Further resources are attached to the BrightTALK webinar here http://bit.ly/2cPdlr4
  25. 25. Q&A Please ask away January 24, 2017 © Copyright, 2016, Ocean9, Inc., All Rights Reserved 25
  26. 26. Thank you Swen Conrad, CEO swen@ocean9.io 650 889 9876

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