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SAP Big Data Forum 2013 keynote Henry Cook - SAP

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  • 1. Big Data Forum: HANA ‘The Why’ Strategy, History and differentiation of HANA. Why and how it was invented and why it is different. Henry Cook, SAP Database and Technology, Centre of Excellence, EMEA 10th October 2013
  • 2. © 2013 SAP AG. All rights reserved. 2
  • 3. The Motivating Idea Source: In-Memory Data Management: An Inflection Point for Enterprise Applications. Hasso Plattner Alexander Zeier ISBN 978-3-642-19362-0 © 2013 SAP AG. All rights reserved. 3
  • 4. The History of HANA Source, IBM Red Paper; SAP In-Memory Computing on IBM eX5 Systems January 2012, IBM International Technical Support Organization © 2012 SAP AG. All rights reserved. 4
  • 5. The History – 6 Years Ago © 2013 SAP AG. All rights reserved. 5
  • 6. Processors have become much, much faster © 2013 SAP AG. All rights reserved. 6
  • 7. To gain the full performance benefits we now focus on optimising at the memory, not the disk level © 2013 SAP AG. All rights reserved. 8
  • 8. Memory is now Affordable, and Getting More so all The Time Memory cost has been falling significantly Whilst more expensive than disk is now affordable for main storage Whole companies data can now be held economically Memory 2012 = disk 2000 Cost of storage vs. cost of throughput Source: In-Memory Data Management: An Inflection Point for Enterprise Applications. Hasso Plattner Alexander Zeier ISBN 978-3-642-19362-0 © 2013 SAP AG. All rights reserved. 9
  • 9. Phone Analogy
  • 10. Processor Memory Latencies © 2013 SAP AG. All rights reserved. 11
  • 11. A Useful Analogy CPU MAIN MEMORY (RAM) - LOCAL LONDON SHOP (30m) L1 CACHE, TABLE (1m) DISK – BREWERY , MILWAUKEE USA (6,000,000m) L2 CACHE KITCHEN FRIDGE (3m) L3 CACHE THE GARAGE (9m) © 2013 SAP AG. All rights reserved. 12
  • 12. One useful technique; Columnar Data Stores Columnar data store benefits conceptual view     A 10 € B 35 $ C 2 € D 40 € Optimizes load of data to CPU High data compression Very fast data calculation / aggregation Makes use of real-life tables behavior (few fields filled, few updates) E 12 $ Can be joined with row-based data mapping to memory organize by row A 10 € B 35 $ C 2 € D 40 € E 12 $ organize by column A B C D E 10 35 2 40 12 € $ € € $ memory address © 2013 SAP AG. All rights reserved. 13
  • 13. The HANA Project – Techniques © 2013 SAP AG. All rights reserved. 14
  • 14. Where We are Today © 2013 SAP AG. All rights reserved. 15
  • 15. Where We Are Today - System Diagram © 2013 SAP AG. All rights reserved. 16
  • 16. SAP HANA Platform for Big Data SAP HANA Platform Exploration, Dashboards, Reports, Charting, Visualization Machine Learning & Predictive Native HANA Apps & Services HANA In Memory Hadoop Planning & Simulation Store & Process Transactional Graph Analytical Smart Data Access Spatial Extended Storage (IQ) Tiered Storage (Hot-warm-cold) Text, Social Media Processing Ingest Consume Process © 2013 SAP AG. All rights reserved. ESP Replication Framework Data Services Roles, security, governance, compliance, audits Applications Landscape management Analytics Modeling & lifecycle management Consume IM 17
  • 17. What This Means for Business © 2013 SAP AG. All rights reserved. 18
  • 18. Hasso - In Conclusion © 2013 SAP AG. All rights reserved. 19
  • 19. SAP HANA Delivering across all 5 dimensions of modern decision-processing Deep Complex & interactive questions on granular data Broad High Speed Big data, many data types Fast response-time, interactivity Real-time Recent data, preferably real-time © 2013 SAP AG. All rights reserved. Simple No data preparation, no preaggregates, no tuning 20
  • 20. Summary: Smarter, Faster, Simpler  HANA is not just a database  Fundamental rethink of how to build information systems  It solves (at least) three fundamental problems of traditional systems   Eliminating app / data platform processing inefficiencies   Access to full processing speed of modern CPUs Enabling multiple engine / multiple programming paradigms within a single platform It enables benefits on 5 dimensions  Broader data to enable a wider scope to problems  Deeper data for more detailed analysis  Faster Processing  Real Time working both for transactional working and currency of data  Simplicity – allowing radically simpler approaches and landscape simplification © 2013 SAP AG. All rights reserved. 21
  • 21. Thank You! Q&A Henry Cook HANA and Database Technology, CoE SAP AG SAP (UK) Limited, Clockhouse Place, Bedfont Rd. ,Feltham,, Middlesex, TW14 8HD United Kingdom Henry.Cook@sap.com T +49-6227-7-40070
  • 22. The Value of HANA Henry Cook, October 2013 SAP Database and Technology, Centre of Excellence, EMEA
  • 23. SAP HANA Delivering across all 5 dimensions of modern decision-processing Deep Complex & interactive questions on granular data Broad High Speed Big data, many data types Fast response-time, interactivity Real-time Recent data, preferably real-time © 2013 SAP AG. All rights reserved. Simple No data preparation, no preaggregates, no tuning 24
  • 24. The Value of HANA Current Situation 25
  • 25. Latency Batch - Hours Interactive - seconds Train of thought, Instant feedback Quicker Current Situation 26
  • 26. NongFu Spring CPG Industry (Bottled water and beverages) – Sales Reporting >2,300x faster to Product: Agile Datamart, BW on HANA produce freight settlement report Business Challenges  Poor decision making due to slow speed and lack of accurate data  Slower response time to competition, growing business needs 5-10x reduction in BW query response Technical Challenges  Big data: takes more than a day to organize huge amounts of point-of-sale (POS) data  Lack of coordination between sales, marketing, production, and logistics 33% reduction in database size “ ” Benefit  Real-time decisions regarding the company’s long-term development, improving efficiency and lowering costs  Real-time shipping fee calculation and sales order analysis We are so satisfied with the operation and speed of the entire BW on HANA implementation. According to end users' feedback, the speed of their standard BW reports is very fast and has been improved greatly. In addition, the running of the whole production system is stable with fast speed of the data extract, ETL, and incremental updates. CIO NongFu Springs © 2012 SAP AG. All rights reserved. 27
  • 27. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Current Situation T-Mobile 28
  • 28. T-Mobile USA, Inc. Telecom – Measure effectiveness of Marketing Campaigns $10-25 savings per Product: Agile Datamart win-back subscriber potentially billions of additional revenue per year Business Challenges  Absence of rapid insights lead to inefficiencies and potential revenue loss  Proliferation of offers/micro-offers makes offer performance management increasingly strategic  High acquisition costs and intense competition in a saturated market 56x faster insights performing churn analysis Technical Challenges  Big data cannot be analyzed efficiently and accurately 2 Billion+ records for 21M customers analyzed in 3 hours (Vs. 1 week earlier) Benefit  Enable targeted micro-offers in real time  Real-time business decisions to maximize revenue by channel  Refine sales offers leading to increased adoption rate and profitability from their customers “ ” SAP HANA is enterprise ready and can handle information from multiple sources at T-Mobile for targeted offers to more than 21 million customers via channels such as retail stores, customer care centers and eventually SMS. Since marketing campaigns are frequent and precise to a segment, it requires rock solid performance in addition to speed of analysis with SAP HANA to provide offers on the fly for improved customer adoption, profitability and retention. Jeff Wiggin, VP Entreprise Information Technology , T-Mobile USA, Inc. Based on the rapid analytics that we’re performing on SAP HANA, we are now able to quickly fine tune our current and future campaigns to improve the customer adoptionSAP AG. All rights reserved. increase profitability . Alison Bessho, Director, Enterprise Systems Business Solutions, T-Mobile USA rate, reduce churn and © 2012 29
  • 29. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More T-Mobile More Often Deeper Broader Current Situation 30
  • 30. Provimi (acquired by Cargill) CPG industry – Animal nutrition solutions Product: COPA RDS, Ops Rpt RDS v2 500k Euro working capital reduction within a week Business Challenges  Lower profitability due to poor decision making and demand forecasting  Lower business growth: sales dependent on IT for analytics 40% speed-up of monthly close from 5 to 3 days Technical Challenge  Speed up monthly closing process and increase process quality Benefits  Increase profitability in Sourcing by making real-time decisions in purchasing raw materials based on demand forecasting 15,000x faster analysis and insight  Increase profitability in Sales by deciding real-time on commercial proposals based on up-to-date margin analysis  CO-PA report 10 hours on ECC down to 2.4 seconds on HANA  All company codes and 2 years of data. 15,000x faster analysis and insight “ ” 3-3-3 for SAP HANA: in 3 days, we successfully completed a proof of concept using our own CO-PA data; in 3 weeks, we were live and running; and within 3 seconds, we now run any CO-PA analysis. Rogier Jacobs, CIO at Provimi © 2012 SAP AG. All rights reserved. 31
  • 31. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More More Often T-Mobile More Timely BigPoint, SKM In Real-time Deeper Broader Current Situation 32
  • 32. Bigpoint Gaming Industry – Predictive Game Player Behavior Analysis 5,000 events per Business Challenges second loaded onto SAP HANA (not possible before)  Increase conversion rate from free  paying player  Increase the average revenue per paying player  Decrease churn – keep paying players playing longer Technical Challenges 10-30% increase in revenue per year  Leverage real-time data processing in SAP HANA and classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players  Analyze vast amounts of historical and transactional data to forecast player behavior patterns Interactive data analysis leading to improved design thinking and game planning “” Benefits  Real-time insights  Per player profitability analysis and understand player behavior  Increase data volume and processing capabilities to communicate more personally to individual game players At Bigpoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-innovation project with SAP HANA, using Real Time Offer Management Bigpoint, we hope to increase revenue by 10-30%. Claus Wagner, Senior Vice President SAP Technology, Bigpoint © 2012 SAP AG. All rights reserved. 33
  • 33. Strategic Condition Management (SKM) powered by SAP HANA® Control and optimize your rebates and bill back conditions with gicom’s solution „Strategic Condition Management“  Achieve higher quantity scales  Leverage your cross supplier analysis  Simulate your profit on all levels Problem  Being able to see the effect of contract conditions during contract negotiations Solution  Ad-hoc simulation and profit forecast allow real-time strategic procurement decisions   Purchasing volume and rebates are transparent real time  Effects on the profit of discount changes and purchasing volumes can be simulated and are transparent immediately Calculation and simulation of net-net-prices and real margin on the lowest level (Article per store per day) with      Purchase Price and all discounts Sales price per day per article and its margin per day Competitor sales prices Grouping those calculations on any goods group or supplier level or any other criteria What if Scenarios   How to negotiate my discounts to get the best result? What happens with my total profit if I shift my purchase volume from branded goods to private brands partly/totally? Business Case Elements    Exert more detailed, informed control Profitability improved X% Negotiator productivity
  • 34. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More More Often T-Mobile More Timely BigPoint, SKM In Real-time Deeper Broader Current Situation 35
  • 35. Thank You! Q&A Henry Cook HANA and Database Technology, CoE SAP AG SAP (UK) Limited, Clockhouse Place, Bedfont Rd. ,Feltham,, Middlesex, TW14 8HD United Kingdom Henry.Cook@sap.com T +49-6227-7-40070
  • 36. The Value of HANA – Financial Industry Henry Cook, October 2013 SAP Database and Technology, Centre of Excellence, EMEA
  • 37. SAP HANA can deliver value in a number of different ways Deep Complex & interactive questions on granular data Broad High Speed Big data, many data types Fast response-time, interactivity Real-time Recent data, preferably real-time © 2013 SAP AG. All rights reserved. Simple No data preparation, no preaggregates, no tuning 38
  • 38. The Value of HANA Current Situation 39
  • 39. Latency Batch - Hours Interactive - seconds Train of thought, Instant feedback Quicker Current Situation 40
  • 40. Deep Banking customer Customer Nordea Cost © 2013 SAP AG. All rights reserved. Broad Speed Compliance Real Time Menu Simple 41
  • 41. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Current Situation BofA, LRM 42
  • 42. Deep Customer Broad Speed Compliance Cost Real Time Bank of America: Simplifying the Company and Reducing Risk through Financial Excellence Bank of America was able to migrate 120M records of profitability data to HANA and see 56X reporting improvement. Simple
  • 43. Project Efficiency – ~60% reduction Current Mode of Operation (CMO) 6 Weeks Define Traditional RDBMS Mth 1 (column-store in-memory DB) 4 weeks Define • Replicate rather than ETL • • Avoid physical model (46 layers/ & transformations) Single Modelling Tool (Power Designer) 3-4 weeks Rework Mth 3 2 weeks Backload 4 weeks Volume Test Mth 5 Mth 4 Aug-08 2 weeks Tune 2-3 weeks Report Mth 7 2 weeks Implement Mth 8 Sep-08 ~3 months project Jun-09 Jul-08 4 weeks Develop/Test/Rework unlimited data! 1-3 days Backload 1 day Tune Less Development • No index (re)-build • Virtual Model  Activate replication rather than ETL • • No need to change physical data model (e.g. aggregations) • Replication or ETL can go 50X faster (e.g. BW PoC)  No physical layers Less Testing • No embedded calculation  Replication easier to test • • 2 weeks Report D’ment & Volume Test • Higher self-service/analysis means less reports to build No need to renew sematic layer 1-2 weeks Implement • Virtual Model  Easily transported  Faster reload (no intermediate physical layers, in-memory) Only need to set parameters  Fewer transformations • Faster, Iterative test/fix/test • © 2013 SAP AG. All rights reserved. 3 weeks Test Mth 2 Future Mode of Operation (FMO) SAP HANA ~7 month project 4 weeks Develop 2 days data Model-driven development Menu 44
  • 44. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More BofA, LRM More Often Deeper Broader Current Situation 45
  • 45. Deep Banking customer Customer Customer Money Usage Analysis Cost Business Scenario Speed Broad Compliance Real Time Process Flow Customers  Offer customers the ability to perform financial analysis on their account(s)  Increase customer loyalty by providing value added services that make their lives easier and provide better access to their financial information  Customers can not just look up their account information but analyse their finances and spend; e.g. by type of spend, type of income, trends over time etc.  Customers can use mobile devices to do money management and financial planning. Simple 100k’s, 1m’s Back Office Systems Challenges  MyQ&A Value Proposition  Customer Complexity, large variety of data that has to gathered and made available  Data modeling, how to categorize data, how to provide aggregated information, which aggregates to provide  Volume of data  Volume of customer interactions – available to hundreds of thousands or millions of customers © 2013 SAP AG. All rights reserved.      Increase customer loyalty, diminish churn by providing services that bind the customer to the bank. Be able to support the volumes of data Support for large volumes of customers Most of all, be able to do this without complex design. These services are likely to evolve rapidly and our simplicity of approach supports this rapid change and thus maximises the requirements that can be met. Do this more cost effectively than with traditional technology Menu 46
  • 46. Provimi (acquired by Cargill) CPG industry – Animal nutrition solutions Product: COPA RDS, Ops Rpt RDS v2 500k Euro working capital reduction within a week Business Challenges  Lower profitability due to poor decision making and demand forecasting  Lower business growth: sales dependent on IT for analytics 40% speed-up of monthly close from 5 to 3 days Technical Challenge  Speed up monthly closing process and increase process quality Benefits  Increase profitability in Sourcing by making real-time decisions in purchasing raw materials based on demand forecasting 15,000x faster analysis and insight  Increase profitability in Sales by deciding real-time on commercial proposals based on up-to-date margin analysis  CO-PA report 10 hours on ECC down to 2.4 seconds on HANA  All company codes and 2 years of data. 15,000x faster analysis and insight “ ” 3-3-3 for SAP HANA: in 3 days, we successfully completed a proof of concept using our own CO-PA data; in 3 weeks, we were live and running; and within 3 seconds, we now run any CO-PA analysis. Rogier Jacobs, CIO at Provimi © 2012 SAP AG. All rights reserved. 47
  • 47. Deep Financial Customer Customer AOK QUICK FACTS AOK is the largest of the150 statutory health insurance funds in Germany. Around 24 million people are insured with 12 regional AOKs. More than 53,000 AOK employees based in over 1,200 offices ensure that their members receive all the services they require: fast, competent and with a minimum of bureaucracy  Revenue: € 61.2 Billion  Number of employees: >53.000  SAP solutions in use  ERP, CRM, SRM  NetWeaver BW, HANA  SAP for Insurance (Claims, Collections& Disbursements) Broad Speed Compliance Cost Real Time Simple Reasons for Change HANA is a strategic choice for AOK to massively improve their analytical potentials. There are several use cases that drive this development:  Ability to handle huge (and yet increasing) amounts of data  Improvement of analysis performance and analysis quality  Implementation of new analysis possibilities Key Drivers  Premium is no more USP for statutory health insurers. Differentiation through special services is needed.  Increasing focus on providing preventive medical services and disease management programs. This requires powerful analysis tools of patient data in combination with data from different sources (e.g. hospitals, pharmacies).  Delivery of best medical treatments while cutting costs Implementation Approach  Close interaction between AOK Systems and SAP Field Service The high expectations of SAP HANA have already been surpassed significantly during the proof of concept in 2011. . (Udo Patzelt, AOK Systems at SAP Sapphire Madrid) © 2013 SAP AG. All rights reserved. Link to Customer Video: video-aok-and-sap-set-new-standards http://www.saphana.com/docs/DOC-1577 48
  • 48. Deep Banking customer Customer Banco de Galicia Cost QUICK FACTS Founded in 1905, Banco Galicia is one of the largest private sector banks in the Argentine financial system and a leading financial services provider in the country. As a universal bank, Banco Galicia offers a full spectrum of financial services to over 4.2 million customers, both individual and corporate. Total Assets: 35.298 Million Pesos  Number of Employees: 3900  SAP Solutions used:  HANA  Sybase Unwired Platform  Banking Services  Business Intelligence  Process and Access Control  Planning and Consolidation  Profitability and Cost Mgmt. © 2013 SAP AG. All rights reserved. Speed Broad Compliance Real Time Simple Reasons for Change  Banco Galicia faces challenges around the time and cost required to obtain historical information about customers, products, and transactions  Bank leaders identified some hard requirements: customer credit information must be more readily available for internal reporting purposes  By legal regulations, the bank has to keep two years information on defaulting customers, and be able to pull that info on the spot while doing credit rating analysis.  Banco Central de la República Argentina (BCRA) audits, debtor information must reside in a unique information system accessible by current business intelligence tools. Key Drivers  Facilitate BCRA compliance at Banco Galicia  Provide greater flexibility in the way the company searches for new information  SAP HANA technology will power significant performance improvements  SAP mobility solutions will help Banco Galicia continue its policy of delivering innovative solutions to its customers 49
  • 49. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More More Often BofA, LRM More Timely Tax and Finance In Real-time Deeper Broader Current Situation 50
  • 50. Liquidity Risk Management SAP HANA results in a very great reduction in end to end process time 220 Mio Cash Flows Traditional 4000x faster End to End: 48 hours HANA 60+ minutes Standard End to End: 1 second HANA From 2 days down to 10 min 10 mins 10 sec => Speed not for the sake of speed but to create tangible benefits © 2013 SAP AG. All rights reserved. Menu 51
  • 51. Deep Banking customer Tax and Finance Reporting Customer Speed Broad Compliance Cost Real Time Simple Adjustments Accounting System (SAP) Business Scenario  Extraction of data and reporting on accounting data for tax management, governance and regulatory reporting  Adjustments have to be made to the accounts, then reports prepared  Often it is necessary to go through multiple cycles before numbers and reports can be finalised. SAP BW Report (1hr) Frequently have to respond to urgent requests  Extract (4hrs)  Currently takes 4 – 6.5 hours to make adjustments, extract data and then report on it  HANA 5 seconds Adjustments Real Time Report takes over an hour to run  On viewing reports may realise more adjustments can be run  Multiple cycles can take more time than is available e.g. 8am deadline © 2011 SAP AG. All rights reserved. HANA Report 2 secs Business Objects Report Value Proposition Extraction done 2x per day, takes 4 or more hours  Report Traditional 4-6.5 hrs Accounting System (SAP) Current Challenges MS Access  Productivity – cycles take much less elapsed time  Avoidance of fine and reputational risk by being able to perform multiple cycles within available timescale.  Confidence in results, by being able to perform many cycles quickly and report with low latency it is easier to check results prior to releasing reports so confidence in reports is higher. 52
  • 52. Agile, Fast to develop, Easier to change, More requirements met = more benefit Latency Batch - Hours No Prebuild Interactive - seconds Train of thought, Instant feedback Quicker Do More More Often BofA, LRM More Timely Tax and Finance In Real-time Deeper Broader Current Situation 53
  • 53. Thank You! Q&A Henry Cook HANA and Database Technology, CoE SAP AG SAP (UK) Limited, Clockhouse Place, Bedfont Rd. ,Feltham,, Middlesex, TW14 8HD United Kingdom Henry.Cook@sap.com T +49-6227-7-40070
  • 54. BACKUP SLIDES
  • 55. HANA is designed to be more than just a Database Preconfigured Analytical Appliance Other Applications MDX SAP BusinessObjects SQL SQL ■ In-Memory software + hardware (HP, IBM, Fujitsu, Cisco) BICS In-Memory Computing Engine Software ■ ■ Modeling Studio Real-time Data Replication via Sybase Replication Server ■ SAP HANA Data Modeling and Data Management Data Services for ETL capabilities from SAP Business Suite, SAP BW and 3rd Party Systems Components In-Memory Computing Engine © 2012 SAP AG. All rights reserved. 3rd Party Calc Engine ■ Predictive Analytics ■ SQL Script / Calc Engine Language Text Engine Planning Engine Spatial ■ Business Function Library ■ SAP Business Suite SAP NetWeaver BW Column Store ■ Data Services ■ ■ Real–Time Replication Services Row Store ■ Row & Column Storage ■ ■ Calculation and Planning Engine Persistance (logging, recovery) , ACID transaction integrity 56
  • 56. BACKUP: USEFUL QUOTES
  • 57. “The future is already here – it's just not evenly distributed” William Gibson The Economist, December 4, 2003
  • 58. “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” Roy Amara Institute for the Future.
  • 59. “If I had asked my customers what they wanted they would have told me that they wanted a faster horse” Henry Ford … on disruptive technologies
  • 60. BACKUP: USEFUL ANALOGIES

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