In Memory Computing for Agile Business Intelligence

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Presentation of SAP's latest in-memory technology Hana, presentation to School of Information and Service Economy of Aalto University Helsinki, Prof. Matti Rossi, presentation includes links to demo systems and explains how to apply for access to a real SAP Hana system.

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  • Good morning everyone, my Name is Markus Alsleben and it ’ s a great pleasure to be here at Aalto University to talk about In Memory Computing for agile business intelligence. We should have plenty of time, so if you have any question along the way, feel free to ask.
  • This is today ’ s agenda, we have approximately three hours and I believe that we should cover most of the theoretical foundation in the first 1.5 hrs, then have a short break and continue afterwards with the introduction of SAP Hana, SAP ’ s in memory data base and several live demonstrations.
  • Every product must contain mobile access & strategy
  • An ever increasing amount of data is People talk about “ long data ” not only manage big amounts of data, but also to ensure longetivity.
  • There is clearly a trend to use mobile devices, even to a point that they are replacing conventional desktops. In SAP every product has to have mobile incorporated and must be mobile enabled. Mobile solutions enhance productivity and allow access to information anytime and anywhere.
  • One of the classic mobile business scenarios is sales force automation, in which the sales executive goes out to the customer and: - takes customer orders directly on a mobile device - checks availability to promise, or replacements for discontinued items - records his sales pipeline (opportunities, prospects, etc.) - plans a visit - etc. Here we see a typical day of a mobile enabled worker.
  • Neoclassical economics and decision theory often give the impression that decisions are always rational and consistent. As you as experts of business intelligence will be major decision supporter, I'd like to provide a word of caution. I therefore have selected five researchers that provide different perspectives on decision theories. I hope that you keep this in mind when preparing the next business case or business model for your company.
  • This is from an interview with Hasso Plattner one of the founders of SAP. Working for IBM in the early 1970s, they moved from customer to customer to always develop the same finanical accounting application, so that one day they thought about developing a standard product that could be used at every customer with minimal customisation effort instead of complete redevelopment. - The birth of the standard business application software product. However as Hasso points out, many of the initial design ambitions had to abandoned along the way.
  • Starting in the early 1990, SAP R/3 was designed in a three tier structure, where all software modules of the ERP system would run on a database server, application server and client computer to allow scalability for larger installations with 1000s of users. Around the year 2000, however, the internet boom made it necessary for SAP to open the ERP system to the internet and provide internet based functionality as e.g. catalog based buying via webbrowser, web stores as sales channel etc. SAP at that time couldn't accommodate the additional functions in the existing ERP product due to different release cycles and data structures, thus leading to separate systems that were connected to the core ERP system via interfaces/data replicators. Overtime additional products were developed outside the ERP system as standalone or connected systems, leading to ever complex landscapes.
  • Here we see a typical three tier ERP System with an attached data warehouse system. As you see data replication is required to ensure that the data warehouse system has the most recent data available. After data import, the data is stored in data cubes that have the reporting dimensions and characteristics that the queries that are run by the business user (e.g. CFO, CEO CxO) require. As you can see this design has several advantages and disadvantages. At a time when processing power and memory was expensive surely a feasible design. BUT.... technology and especially user expectations have changed !!!!!!!!
  • Hard to imaging that you would send a post card to google, as your parents did to wait 30 days for the printout of the search result. So the expectation of users these days is instant search results, as provided by Google and others. Allowing a "Trail and error" approach to find the right answers. Web searches however, sift through indexed data with only relevant data being presented. Business Applications however require complex aggregations, data thus needs to be prepared before it can be presented to the user. Data also originates from different sources (Finance/Materials Management/HR, external Data.)
  • When we look at traditional Business Intelligence Architectures, multiple steps are required before the business user can run analytics. Data need to be copied from the operational OLTP system, limitations: copy windows getting smaller, Data cleansing is required, as often data quality of OLTP systems is limited and often leading to inconsistencies in an analysis. (COO-CDM example cleansing of SAP's CRM System - conflicting definitions.) Data Cubes need to be predefined by developers to ensure that the right dimensions/characteristics of the data can be stores. Time intensive, requires IT specialists - often the bottleneck in running flexible analytics. Queries need to be pre-written, similar to a small IT project with User requirements, development, testing etc. - making ad-hoc queries and simulations difficult if not impossible.
  • Typical Database Structures of OLTP applications have highly normalized formats, to avoid redundancies, use less memory and reduce dependencies and speed up inserts. They however need percalculated aggregate views for performance reasons. Basically a design constraint from a time where both processing power and memory were expensive. Columnar Storage does not normalise data, as agregates are calculated on the flight, normalization is not required as memory is available in abundance and effective compression reduces the required memory by 30-50%.
  • - Unified Location Data Cube contained the 12 defined internal and external KPIS with location information to allow “ slice and dice ” by single locations or groups of locations. - Data cube provided SAP Location Dashboard (Webbased tool), the Excel Based Evaluation Tool (DSS) and the ARCGIS Desktop Version with data for analysis and decision making. - The web based tool was received enthusiastically by stakeholders including the worker ’ s council as an intuitive way to visualize and analyze the global setup of SAP. - The Evaluation Tool allows managers to compare different locations through a scoring model based on the data provided by the unified location data cube while the professional grade ARCGIS Desktop Software allows more comprehensive geospatial analysis and creation of specific maps.
  • In Memory Computing for Agile Business Intelligence

    1. 1. management|consultingIN-MEMORY COMPUTING FOR AGILE BUSINESS INTELLIGENCE Dr. Markus Alsleben CEO Alsleben Ltd.
    2. 2. AGENDA management|consulting Self Introduction Trends in the Global IT industry The Pretense of Knowledge The Journey towards In Memory Computing Introducing SAP Hana - In Memory DB SAP Hana - Live Demonstrations Q&A
    3. 3. COMPANY INTRODUCTION management|consulting Founded in 2008 by Dr. Markus Alsleben, Alsleben Ltd. provides management consulting and professional services critical for companies engaging in the high velocity Chinese marketplace. At Alsleben Ltd. we believe that quality advisory in the context of high velocity environments can only be successful through a solid scientific foundation. Management research projects are therefore an integral part ofDr. Markus Alsleben our practice incorporating latest research into unique client solutions. OurCEO Alsleben Ltd. affiliation with prestigious research institutions and corporations enables us to utilize the latest knowledge base for your management consulting projects with Alsleben. Ltd. implementing next practice today. Affiliations Selected Clients Our services include: •Management Consulting and Training Services: Since 2008 Alsleben Ltd. has worked together with leading multinational companies across various industries in China and around the world to design and implement strategies, change organizations and conduct training services that deliver results. •Information Technology Advisory: Business without powerful IT support is impossible in todays hyper competition. Designing and implementing IT Strategies and ERP Systems provides the competitive edge sustainable success for your China operations. •Human Resources: World-class talent acquisition and management are key capabilities of successful enterprises in China. Alsleben Ltd. provides talent management solutions that let you win the war for talent in China.
    4. 4. BIO management|consulting Alsleben Ltd.2008 - today CEO Management Consulting, Hong Kong Lead Management Consultant Location Strategy & Management Project SAP AG2008 - 2010 Designing and Implementing SAPs global Location Strategy. Germany Spatial reorganization and optimization of R&D at SAP. Vice President SAP Labs China2006 - 2008 Corporate development and execution of growth strategy for Shanghai development locations in China. Vice President - Consulting Director North Asia SAP China2000 - 2006 Consulting head for Greater China with more than 150 Beijing consultant, delivering SAP implementations. KPMG Consulting1997 - 1999 Senior SAP Consultant for Logistics now Bearing Point & o.tel.o Telecom, Germany
    5. 5. PUBLICATIONS management|consulting Creating Dynamic Capabilities R&D Network Management for Globally Distributed Research and Development in the Software Industry SAP: Establishing a Research Centre in China Harvard Business Publishing - Case Study
    6. 6. management|consultingTRENDS IN THEGLOBAL IT INDUSTRY
    7. 7. GLOBAL IT TRENDS - HYPE CURVE management|consulting Big Data Cloud Mobile Source: Gartner, 2012.
    8. 8. GLOBAL IT TRENDS management|consulting CLOUD COMPUTING BIG DATA The exponential growth in data across allCloud computing provides “convenient industries requires newon-demand technologies for:network access to a sharedpool of configurable computingresources that can be quicklyprovisioned and released with minimal • Data Sourcingmanagement effort or service provider and Storageinteraction.”1 The various subsets of • Data Integration andcould computing as SaaS, PaaS, Iaas Transformationor more generic XaaS provide cost to generate new insightseffective and high available computing • Data Analysis and and opportunities. Classificationresources with near to unlimitedscalability. MOBILE COMPUTING The increasing penetration of connected mobile phones and tablet computers allows new context based services as e.g. location based services, augmented reality and rapid data collection e.g. for traffic analysis. Always on mobile devices allow quick communication and collaboration. By 2013, more than 15 billion devices will be connected to the Internet using a mobile device.Source: Mell, p. and Grance, t. the nIst definition of cloud computing. Special Publication 800-145, 2011; http:// csrc.nist.gov/publications/nistpubs/800-145/sp800-1
    9. 9. CLOUD COMPUTING HYPE CURVE 2012 management|consulting
    10. 10. 10 n management|consultingPrefix 10n Decimal Scale 0 1 onedeca 110 ten 4k Memoryhecto 2100 hundred Apollo Guidancekilo 31,000 thousand Computer 1 Terabytemega 61,000,000 million equals 210giga 91,000,000,000 billion single sided DVDstera 121,000,000,000,000 trillion 2.5 Petabyte Wallmart’speta 151,000,000,000,000,000 quadrillion annual Data 295Growth Exabyteexa 181,000,000,000,000,000,000 quintillion estimatedzetta 211,000,000,000,000,000,000,000 sextillion complete humanyotta 241,000,000,000,000,000,000,000,000 septillion knowledge in 2007 880 Yottameter 7.9 Zetabyte diameter of est. amount of observable universe digital data by 2015
    11. 11. BIG DATA IS NOT ONLY BIG... management|consulting Source: SAP 2012.
    12. 12. Business Rational of Mobile EnterpriseComputing management|consulting Source: SAP 2012.
    13. 13. A day in the life with mobile analytics suite management|consulting Source: SAP 2012.
    14. 14. management|consulting THE PRETENSE OF KNOWLEDGEFriedrich August Hayek Herbert A. Simon Nassim Nicholas Taleb
    15. 15. SOCIAL SCIENCE ≠ PHYSICAL SCIENCE management|consulting “It seems to me that this failure of the economists to guide policy more successfully is closely connected with their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences - anFriedrich August HayekNoble Laureate in Economics 1974 attempt which in our field may lead to outright error. [...] Unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena, the aspects of the events QUANTITATIVE to be accounted for about which we can get quantitative data are necessarily RESEARCH limited and may not include the important ones. While in the physical sciences it is generally assumed, probably with good reason, QUALITATIVE RESEARCH v that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all theMIXED-METHODS circumstances which will determine the outcome of a process, for reasons which I RESEARCH shall explain later, will hardly ever be fully known or measurable. [...] [Using Mathematical techniques] has led to the illusion, however, that we can use this technique for the determination and prediction of the numerical values of those magnitudes; and this has led to a vain search for quantitative or numerical constants.” SOURCE: http://www.nobelprize.org/nobel_prizes/economics/laureates/1974/hayek-lecture.html
    16. 16. BOUNDED RATIONALITY: “I KNOW THAT I DON’T KNOW” management|consulting In Economics the so called Neoclassical school postulated rational decision making of the “homo oeconomicus” with perfect information available.Herbert A. Simon Uncertainty about the future and costs in acquiring information in the present were not considered part of rational decision theory. However do uncertainty and costs limit the extent to which agents can make a fully rational decision, thus they possess only “bounded rationality” and must make decisions by BOUNDED “satisficing,” or choosing that which might not be optimal but which will makeRATIONALITY them happy enough. vSATISFYICING The internal organization of firms and the external business decisions thereof did not conform to the Neoclassical theories of “rational” decision-making. POLITICAL Bounded rationality is used to designate rational choice that takes into BEHAVIOR account the cognitive limitations of both knowledge and cognitive capacity. Bounded rationality is a central theme in behavioral economics. It is concerned with the ways in which the actual decision-making process influences decisions. Theories of bounded rationality relax one or more assumptions of standard expected utility theory”. SOURCE: WIKIPEDIA.ORG
    17. 17. SH.... HAPPENS management|consultingNassim Nicholas Taleb LUCID FALLACY vHINDSIGHT BIAS SURPRISE DON’T BE THE TURKEY
    18. 18. SH.... HAPPENS management|consulting Until 1697 all known Swans were white, so that the existence of a black swan was considered impossible, until the discovery of Australia and with it the discovery of black swans.Nassim Nicholas Taleb Nasim Nicholas Taleb defines a black swan event as a surprise (to the observer), one that has a major effect, and after the fact is often inappropriately rationalized with the benefit of hindsight explaining: LUCID FALLACY •The disproportionate role of high-profile, hard-to-predict, and rare events that are beyond the realm of normal expectations in history, science, finance, and technologyHINDSIGHT BIAS •The non-computability of the probability of the consequential rare events using scientific v methods (owing to the very nature of small probabilities) 1.The psychological biases that make people individually and collectively blind to DON’T BE THE TURKEY uncertainty and unaware of the massive role of the rare event in historical affairs “Fat Tail Mitigation strategies Distributions” • Built robustness agains black swan events, exploit white swan events • Avoid modeling based on normal distributions as risk is typically NOT normal distributed ! • “Avoid being the Turkey” - turn around black swan into white swan events. SOURCE: WIKIPEDIA.ORG
    19. 19. SH.... HAPPENS management|consulting Then one morning Deadalus said to Icarus:Nassim Nicholas Taleb “Now Son, we are ready to leave this island for good. We LUCID shall fly home to Athens. But although you are now quite FALLACY good at flying, you must not forget that it can be very dangerous. So listen to my instructions and be sure toHINDSIGHT BIAS follow them to the letter. At all times follow me, for I will v find the way home. Do not veer off on a different flight DON’T BE THE TURKEY path, or you will soon be lost. And do not fly too low, or your wings will fill with moisture from the waves and they “Fat Tail Distributions” will become too heavy you will sink down. Nor should you fly too high, or the sun will heat the wax and your wings will fall apart. Have you understood all that I have said?”
    20. 20. SOLID DATA IS NOT EVERYTHING management|consulting Political behavior is an important contingency in enterprises. Strategic Management is not a mere planning problem as intended strategies are often not implemented as planned and deliberate strategies emerge over time.Kathleen Eisenhardt Clay Christensen Preconditions of political processes: POLITICAL •diverging interests among organizational members BEHAVIOR •limited amount of resources available to satisfy all such interests. RESOURCE ALLOCATION v •Decisions with non-determined outcome PROCESS •The larger the available decision space the more political decisions tend to become, as outcomes require coalitions, DYNAMISM negotiations and tactics between participants in the political process. While political processes typically negatively correlate with profitability in high velocity environments, they can be a source of corporate renewal that leads to higher profitability. Resource Allocation Process SOURCE: Christensen, C. M. & Dann, J. B. (1999). Process of strategy definition and implementation. Harvard Business Publishing. Eisenhardt, K. M. & Bourgeois, L. J. B. (1988). Politics of strategic decision making in high-velocity environments: Toward a midrange theory. Academy of Management Journal, 31(4), 737-770. Schreyögg (2008). Organisation - Grundlagen moderner Organisationsgestaltung [Organization - Foundations of modern organizational design] (5th Edition ed.). Wiesbaden: Gabler.
    21. 21. management|consultingThe Journey towardsIn-Memory Computing
    22. 22. THE ROAD TO IN-MEMORY COMPUTING management|consulting George E. Moore SOURCE: SINGULARITY.COM
    23. 23. ORIGINS OF OLTP AND OLAP management|consulting “Relational database systems have been the backbone of business applications for more than 20 years. We promised to provide companies with a management information system that covers the core applications, including financials, sales, order fulfillment, manufacturing, as well as human resources, which run from planning through business processes to individually defined Hasso Plattner analytics.However, we fell short of achieving this goal. The more complex businessrequirements became, the more we focused on the so-called transactionalprocessing part and designed the database structures accordingly. These systemsare called OLTP (Online Transactional Processing) system. Analytical and financialplanning applications were increasingly moved out to separate systems for moreflexibility and better performance. These systems are called OLAP (OnlineAnalytical Processing) systems.” Plattner, H. (2009). A common database approach for oltp and olap using an in-memory column database. In Proceedings of the 35th sigmod international conference on management of data.
    24. 24. SAP’s product landscape circa 2000 - 2005 management|consulting Advanced Business Planner & Warehouse Optimizer (APO) (BW) Supplier Customer Relationship Relationship Management ERP Management (SRM) (CRM) Logistics Mobile Platform Execution
    25. 25. OLTP AND OLAP ARCHITECTURES management|consulting OLTP - THREE TIER ERP SYSTEM OLAP - DATA WAREHOUSE SYSTEM Data Cubes Architectural Benefits Architectural Challenges (+) Performance due to dedicated system (-) More Expensive through additional hardware (+) Independent / No single point of failure (-) Double work for data cleansing, uploading, cube design, report writing (-) Upload Windows often not sufficient in large scale installations. Adopted from: Plattner, H. & Zeier, A. (2012). In-Memory data management: Technology and applications. Springer
    26. 26. USER EXPECTATIONS HAVE CHANGED management|consulting “At the University of Potsdam, I got bored with the presentation of traditional enterprise v software and the students didnt like it much, either; they wanted something more modern, more like Google.” Hasso Plattner Traditional Business Analytics In-Memory Business Analytics Source: google-classic.com
    27. 27. TRADITIONAL DATA WAREHOUSEVS. IN-MEMORY ANALYTICS management|consulting OLD WAY NEW WAY SOURCE: SAP
    28. 28. WHY DO WE NORMALIZE AT ALL ? management|consulting Normalized Database Form (De-)Normalized Database Form Flat File SOURCE: http://www.codinghorror.com/blog/2008/07/maybe-normalizing-isnt-normal.html
    29. 29. SAP HANA - HIGH LEVEL ARCHITECTURE management|consulting Plattner, H. & Zeier, A. (2012). In-Memory data management: Technology and applications. Springer
    30. 30. COLUMNAR VS. ROW BASED STORAGE management|consulting Source: Plattner, H. & Zeier, A. (2012). In-Memory data management: Technology and applications. Springer
    31. 31. TECHNOLOGIES BEHING IMDB management|consulting Source: Plattner, H. & Zeier, A. (2012). In-Memory data management: Technology and applications. Springer
    32. 32. IMDB: RADICALLY SIMPLIFYING ENTERPRISE APPLICATIONS (e.g. SAP ERP FINANCIALS) management|consulting Accounting Document Accounting Document Items Header Future Table Structure in SAP Current Table Structure in SAP ERP Finance ERP Finance (Vision) Source: Plattner, H. & Zeier, A. (2012). In-Memory data management: Technology and applications. Springer SOURCE: Plattner, H. (2009). A common database approach for oltp and olap using an in-memory co lumn database. In Proceedings of the 35th sigmod international conference on management o f data.
    33. 33. BUSINESS BENEFITS (TCO) management|consulting On the fly financial aggregation, e.g. closing according to different accounting standards (US-GAAP, IAS, etc), financial applications faster and less complex. Provision of on-demand scenarios and analytics allow frequent run of simulations and establish higher business agility. Simplification of overall IT landscape (one application server instead of server farm with dedicated application servers) resulting in less power consumption, cooling etc. - The solution is easier to setup, scale and change. Less complex software, through reduction of software layers resulting in less maintenance and administration costs. Allows the creation of innovative business solution for on the spot decision making that were previously not feasible - online personalised discounts.
    34. 34. DYNAMIC CAPABILITIES management|consultingCompetitive Advantage based on organizational resources or capabilitiesis not sustainable in high velocity environments, Dynamic Capabilitiesthus become a critical differentiator for successful global enterprises. Micro-foundations of Dynamic Capabilities (Teece, 2009, p. 49) Source: Teece, D. J. (2009). Dynamic capabilities and strategic management. Oxford: Oxford University Press.
    35. 35. Case Study: SAP Location Strategy & Management management|consulting
    36. 36. THE FUTURE OF DATA DRIVEN MANAGEMENT:THE MANAGEMENT COCKPIT management|consulting support@v2softlogic.com SOURCE: Controlling - Zeitschrift für die erfolgsorientierte Unternehmensführung, Vol. 18, June 2006, p. 311-318
    37. 37. management|consultingSOURCE: Controlling - Zeitschrift für die erfolgsorientierte Unternehmensführung, Vol. 18, June 2006, p. 311-318
    38. 38. management|consultingINTRODUCINGSAP HANAIN MEMORY DB
    39. 39. management|consultingSAP HANALive Demonstrations
    40. 40. YOUR PERSONAL SAP HANA CLOUDDEMO management|consulting SAP HANA VISUAL INTELLIGENCE HANA Studio http://www.saphana.com/welcome
    41. 41. YOUR PERSONAL SAP HANA CLOUDDEMO management|consultingHow to get access to your personal SAP Hana Test Drive System? 1) Sign up with the SAP Community Network (SCN) at http://scn.sap.com/welcome
    42. 42. YOUR PERSONAL SAP HANA CLOUDDEMO management|consulting2) Navigate to http://scn.sap.com/docs/DOC-28191, read the document and sign up via the link at the bottom of the page 3) Accept the T&Cs 4) Confirm you data 5) Follow the instructions you have received in your email
    43. 43. Now it’s your turn... SAP HANA Web access management|consulting PROFITABILITY ANALYSIS SALES COCKPIT CENSUS DATA WITH GIS INTEGRATION http://www.saphana.com/welcome
    44. 44. Use Case: Profitability Analysis management|consulting PROFITABILITY ANALYSIS Profitability Reports in the SAP ERP Controlling Module (CO-PA) are what managers are most interested in to analyze profitability, over time, by region, product group and customer segments. Traditionally these reports have a very long run time in large enterprises. This web based example shows the CO- PA Accelerator in which CO-PA data structures are copied into Hana. This web based example with a real backend Hana system allows account manager, regional sales manager and sales director to review critical profitability information. http://www.saphana.com/welcome
    45. 45. Use Case: Sales Cockpit management|consulting SALES COCKPIT Regular reviews of the Sales Pipeline and analysis of sales performance are critical for Sales Executives to safeguard revenue generation for the enterprise. Recent data is critical for territory planning, account reviews and definition and implementation of marketing strategies. Traditionally this data resides in SAP CRM and reports have a very long run time in large enterprises. This web based example with a real backend Hana system allows to assume the roles of senior sales director and vice president of sales reviewing sales pipeline and sold revenue. http://www.saphana.com/welcome
    46. 46. Now it’s your turn... SAP HANA Web access management|consulting Governments all around the world need accurate data for provision of public services, benefits, taxation and infrastructure. This SAP Hana application combines the power of in-memory computing with a Geographical Information System to immediately visualize census data with changes of the map. It also allows the analysis and breakdown of census data byCENSUS DATA WITH GIS INTEGRATION various dimensions. This web based example with a real backend Hana system allows to analyze annonymised real US Census data in a geographical context. http://www.saphana.com/welcome
    47. 47. TYPICAL DBA REQUIREMENTS management|consulting QUESTIONS & ANSWERS
    48. 48. THANK YOU management|consulting

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