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Nigel Tebbutt Profile - Fin Tech PDF

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Nigel Tebbutt Profile - Fin Tech PDF

  1. 1. Reliance Oil and Gas - Global Energy Trading Roll-out – (from 2009t to 2010) Reliance Oil and Gas. Business and Technology Portfolio Manager – Business Architecture to Technical Solution - from strategy through to delivery. Nigel successfully engaged with the stakeholders and drove the end-to-end architecture, delivering a common shared vision and developing a successful solution strategy to enhance trading performance by creating a more efficient JVA & ETRM environment – framework-driven enterprise risk management (CLAS, COSO and Outsights) E2E Energy Market Data integration.. Amphora Symphony Trade and Risk transactional platform was integrated interactively in Real-time with back-office SAP Financials supported by Real-time Analytics for interactive Trading, Risk and Settlements', Performance Management and Compliance – Enterprise Governance, Reporting and Controls British Energy (now EDF) Trading Roll-out – Gloucester (from 2006 to 2007) British Energy - Power and Energy Trading – Nigel managed Agile Development Teams delivering Enterprise Services accessing the Allegro Energy Trading Platform within the British Energy Trading and Sales Segment - Energy Trading and Risk Management Business Transformation Programme. This involved running Requirements and Design Workshops with Stakeholders, Subject Matter Experts, Domain Specialists and Technical Design Authorities BP International Global SAP Roll-out – Sunbury (from 2005 to 2006) BP International – Shipping and Trading / Refinery and Marketing Segments. Financial Analysis and Cost Management, Systems Accounting and Enterprise Governance, Reporting and Controls - Petroleum Inventory Valuation / Hydrocarbon Value Chain Management. Nigel reported to the Director of Planning and Strategy, Refinery and Marketing Segment, under the Process Fitness Programme – a $50bn initiative over 10 tears for global technology change and business transformation. After a massive Merger and Acquisition phase by BP International (Amoco and ARM in the USA) a global Process Fitness Programme was introduced to deliver post-merger re-structuring, consolidation, rationalisation and integration. BP Budget Holders were issued with a Cost Challenge – to maintain Business Value and Contribution whilst reducing costs in real terms by 20% over 3 years. JPMorgan Chase Global Asset Management Roll-out – (from 2001 to 2002) JPMorgan Chase – Global Investor Services – Enterprise Portfolio Architect Nigel worked within the Technical Support Group in order to develop a coherent approach for Enterprise Data Architecture delivery for the Global Investor Services business, He designed information landscapes and roadmaps for legacy transition - supporting both Service-Oriented and Component-Based views. Nigel provided consultancy and advice services for distributed Messaging and Middleware technologies (IBM MQSI) to the Asset Management Programme, a major business transformation initiative, and was responsible for the quality and fitness for purpose of the Component Libraries (Service Catalogues) and logical and physical database design - as well as synchronisation of the Relational Design (Data Model) with the Class Diagram (Object Model). The programme featured an Internet front end with intelligent agents & alerts, driving data integration with SWIFT via a back-office Asset Management System (AMS) – a COTS package for fund managers interactive management of Investment Portfolios Relevant Experience • Global SAP ECC6 IS/Oil and Gas Financials implementations • SAP solution design - Global Templates & Design Patterns, • Business process design and improvement roll-outs • Architecture, design and SAP Project team management Functional Expertise Professional Background Mr Tebbutt is a Finance, Planning and Strategy Consultant and Portfolio Manager working in Financial Technology He has over 7 years experience in Fin Tech providing deep and broad expertise within this Business Sector – from both a Business Service Line and Software Product Line perspective. Mr Tebbutt has deep expertise in Energy, Oil & Gas - with 5 years in Upstream roles supported by a further 5 years in Finance, Planning and Strategy – including Physical and Economic Reservoir Modelling, Hydrocarbon Value Chain Management, Petroleum Inventory Valuation, JVA & ETRM. His effective role is Portfolio Manager, providing Financial Technology expertise and working with the business to deploy fit-for-purpose integrated Digital Fin Tech solutions. Most Recent Role Hitachi Nuclear – UK Horizon Programme • ENERGY – ECONOMIC MODELLING and LONG-RANGE FORECASTING • Nigel architected and designed Forecast Energy Demand, Supply and Cost / Price Models – for Economic (Forecast Demand / Supply + Cost / Price) and Physical Commodity / Futures / Derivatives Models using large scale Data Warehouse Structures for both Historic and Future values (+/- 50 years closing prices for Power Contracts contrasted with Physical Gas (LPG + LNG) and Petroleum (all grades of crude) . Name: Nigel Tebbutt • 5 years experience in Oil Upstream industries (including 2 years in offshore roles.) • 10 years experience in Oil & Gas Downstream / Utilities (including 5 years in Finance, Planning, Strategy and JVA, with 3 Global SAP roll-outs.) Industry Experience Insert Photo CAREER SUMMARY
  2. 2. ENERGY TRADING AND RISK MANAGEMENT (ETRM) EXPERIENCE SAP HANA
  3. 3. CANDIDATE EXPERIENCE • Knowledge of Physical Energy, Commodity Markets and Financial Derivatives – Schlumberger and BP. Substantial expertise in Physical / Financial Traded Instruments - Energy (Electricity, Coal, Oil and Gas / Carbon Offset Trades) / Commodities / Futures / Complex Derivatives - including two years Upstream as a Petroleum Geologist (Research, Exploration and Production) and five years Downstream - Front Office (Trading and Risk), Middle Office (Shipping / Refining / Transport ), and Back Office (Settlements, Compliance, Funds, Liquidity and Treasury) as well as extensive Finance, Planning and Strategy experience - including five years as a Group Accountant. Expert at Energy Market Data, 3rd Party Integration and Trade Reporting – APEX / GV8 / ICE. • Commodity Trading Consulting in Front, Middle, Back Offices - including Oil and Gas Logistics. Substantial experience in Business Processes (Business Service Lines) and Enterprise Solutions (Software Product Lines) in the Front Office (Trading and Risk), Middle Office (Shipping / Refining / Transport ), and Back Office (Settlements, Compliance, Funds, Liquidity and Treasury) as well as Finance, Planning and Strategy - including five years as a Group Accountant. Expert at Business Process / Use Case / Scenario – Design and Development • Integrated Trading Systems Solution Architecture experience – Allegro versions 5-7 at British Energy - still the only UK Allegro Implementation. Expert - Microsoft BizRalk C# .NET Framework Lean / Agile / Scrum Architecture, Design and Development Team-leading / Portfolio Management. Amphora Symphony and SAP HANA Financials, Treasury and Risk Management (TRM) at Reliance Oil & Gas • Ability to lead clients though all functional phases of implementation - Planning and Executing end-to-end Software Development Lifecycle / Portfolio Management. • Ability to perform software prototyping, demonstrations and training to all user groups Expert - Requirements Capture, Business Process / Use Case / Scenarios - Design, Prototyping and Demonstration • Ability manage stakeholder expectations - Expert at Client / Stakeholder Management – Expert - Client and Stakeholder Management Communications Strategy and Benefits Realisation Management ETRM Business Experience Management Experience ETRM Planning Methodology: - 1. Understand business opportunities and threats – Business Outcomes, Goals and Objectives 2. Understand business challenges and issues – Business Drivers and Requirements 3. Gather the evidence to quantify the impact of those issues – Business Case 4. Quantify the business benefits of resolving the issues – Benefits Realisation 5. Quantify the changes need to resolve the issues – Business Transformation 6. Understand Stakeholder Management issues – Communication Strategy 7. Understand organisational constraints – Organisational Impact Analysis 8. Understand technology constraints – Technology Strategy ETRM Delivery Methodology: - 1. Understand success management – Scope, Budget, Resources, Dependencies, Milestones, Timeline 2. Understand achievement measures – Critical Success Factors / Key Performance Indicators / ROI 3. Produce the outline supporting planning documentation - Business and Technology Roadmaps 4. Complete the detailed supporting planning documentation – Programme and Project Plans 5. Design the solution options to solve the challenges – Business and Solution Architectures 6. Execute the preferred solution implementation – using Lean / Agile delivery techniques 7. Report Actual Progress, Issues, Risks and Changes against Budget / Plan / Forecast 8. Delivery, Implementation and Go-live ! Solution Experience Trade and Risk Software : - 1. SunGard Zainet 2. OpenLink Endur 3. Amphora Symphony 4. Allegro Standard Risk Frameworks: - 1. COSO 2. Outsights 3. The Three Horizons 4. Eltville Model / Future Management Framework Treasury and Settlements Software : - 1. SunGard Quantum 2. OpenLink Findur 3. SAP HANA BW / BI / BO 4. SAP ECC8 Financials (FI/CO) 5. SAP ECC8 Corporate Financial Management (CFM) 6. SAP ECC8 Treasury and Risk Management (TRM)
  4. 4. Targeting – Map / Reduce Consume – End-User Data Data Acquisition – High-Volume Data Flows – Mobile Enterprise Platforms (MEAP’s) Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica Smart Devices Smart Apps Smart Grid Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting – Data Delivery and Consumption News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM – Data Discovery and Collection – Analytics Engines - Hadoop – Data Presentation and Display Excel Web Mobile – Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load – Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast data replication – Data Management Tools DataFlux Embarcadero Informatica Talend – Info. Management Tools Business Objects Cognos Hyperion Microstrategy Biolap Jedox Sagent Polaris Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox – Data Warehouse Appliances Ab Initio Ascential Genio Orchestra Social Intelligence – The Emerging Big Data Stack
  5. 5. Joint Venture Accounting (GAAP / IFRS) Expertise Business Work Stream Activities Produce and Publish JV Business Programme Plan and Work-stream Plans JV Agreement - JV Partners bound by Contract which establishes Joint Control JV Agreement - Joint Risk & Reward - Jointly Controlled Operations, Assets and Entities Set up Joint Venture Heads of Agreement – Contractual Terms and Conditions Set up Special Purpose Vehicle (SPV) for the new Joint Venture entity Joint Venture Life-cycle Management - Benefits Management - Cost / Savings Models Joint Venture Life-cycle Management - Finance Plan and Payment Management Joint Venture Life-cycle Management - Partnership Calls and Alternative Funding Establish Accounting Policies and Procedures – GAAP / IFRS Establish Organisational Structure – People, Places and Policies Establish Requirements Catalogue and Issues Register Establish Business Architecture – Documents, Data Flows and Processes Produce and Publish JV Architecture Roadmap and Enterprise Models Design Joint Venture Business Operating Model (BOM) Design Chart-of-Accounts, Project Structure and Financial Object Types Define JV Partner Disbursement / Reimbursement Routines Define Responsibility Accounting / Profit / Cost Centres Objects Define Business Hierarchies, Organisational and Responsibility Structures Define Account Hierarchies, Posting Methods and Period-end Rules Define JVA Master Data Sets - Global Reference Data Energy Supply Value Chain – KPI’s and Business Process Management (BPM) DECC / OFGEM and BoE / FSA Compliance, Regulatory Reporting and Controls Technology Work Stream Activities Produce and Publish JV IS / IT Programme Plan and Work-stream Plans Joint Venture Project Management - Benefits Management - Cost / Savings Models Joint Venture Project Management - 3rd Party / Strategic Vendor Management Joint Venture Project Management - Implementation Planning and Go-live Design Solution Options – SAP FI, CA, BW, BO, SEM, EPM, SSM, HANA Design JVA Solution Architecture – Global Templates and Design Patterns Design JVA Solution Architecture - High Level Design Design JVA Solution Architecture - Detailed Specification Populate Chart-of-Accounts, Project Structure and Financial Object Types Populate Joint Venture Master Data Sets - Global Reference Data Integration with internal data sources – SAP NetWeaver MDM and Pi Integration with external data sources – Partner Systems Integration with 3rd-party Market Data Providers - SWIFT, APEX, ICE, GV* etc. Set up Accounting Periods – Months, Quarters and Annual Set up Accounting Buckets – Plan, Forecast, Budget and Actual Set up P&L and BS Report Formats and define Report Content Set up Offset and Control Accounts for Allocations and Apportionments Set up Recurring Journal Entries for Allocations and Apportionments Set up Responsibility Accounting / Profit / Cost Centres Objects Set up Business Hierarchies, Organisational and Responsibility Structures Set up Account Hierarchies, Posting Methods and Period-end Rules User Acceptance Testing / Validation and Verification / Parallel-run and Cut-over Operational Acceptance Testing / Go-live and Post-implementation Review Petroleum Inventory Valuation and Hydrocarbon Value Chain Management Expertise Petroleum Inventory Valuation and Hydrocarbon Value Chain Analysis Methods - discovers exactly where Business Value is being created (and destroyed.....) by analysing the inputs and outputs of each and every Enterprise Business Process – and then allocating the Business Value generated (or lost) to the nominated Business Process Owner (for Stakeholder Value and responsibility accounting). This technique is based on Value Mapping – that is, plotting Stakeholder Value generated against the level of Internal Investment required, at the appropriate Business Process aggregation level – and then may be further analysed within the SAP Business Hierarchy – Projects, within Profit or Cost Centres, within a Strategic Business Unit (SBU), within a Segment, within the overall Oil and Gas Enterprise. ACCOUNTING EXPERIENCE
  6. 6. UPSTREAM OIL and GAS BUSINESS SEGMENTS DOWNSTREAM DOMAIN Research Exploration Production Shipping Trading Refining Marketing Retail Head Office Future Management Sustainability Futures Geological Prospecting and Petrology Reserve Location: Digital Carbon Fields of the Future Enhanced Oil / Gas Recovery Shipping Capacity Forecasting Strategic Foresight and Future Management Hydrocarbon Economic Forecasting Demand / Supply Future Energy Landscape Future Retail Landscape Government - Future Energy Policy Regulation and Legislation Strategy and Planning Hydroelectricity, Solar, Wind and Water Turbines Tidal Power Geothermal CHP Bio-fuels Petrology Reservoir: - Assessment and Yield Prediction Advanced Petrology Reservoir Modelling and Exploitation Hydrocarbon Value Chain Planning & Portfolio Management Risk Management Frameworks - Outsights - COSO - IFRS Hydrocarbon Value Chain Planning & Portfolio Management Customer Experience and Journey Customer Loyalty Strategy Retail Proposition, Customer Offer, Experience and Journey Governance, Reporting and Controls - CLAS / COSOS - GAAP / IFRS - SOX / COBIT Business Operations Generation Portfolio Research and Strategy Petrology Reservoir Mapping, Analysis and Sub-Surface Modelling Economic Modelling and Enhanced Recovery Techniques Hydrocarbon Value Chain & Petroleum Inventory Valuation Financial Markets and Traded Instruments Hydrocarbon Value Chain & Petroleum Inventory Valuation Customer Relationship Management Hydrocarbon Value Chain Supply Chain Management Statutory and Regulatory Compliance Joint Venture Accounting JVA Architecture Asset and Environment Management Architecture Geological Mapping, Analysis and Modelling Architecture Smart Grid Infrastructure Architecture IDEX MVNO / VPN Platforms ETRM - Energy Trading and Enterprise Risk Management Architecture CRM Contact and Campaign Architecture Supply Chain, EPOS, Retail Merchandising Architecture Enterprise Performance Management - DWH / BI - Analytics - Data Mining Solution Architecture Asset and Environment Management Solution Design Well-logging and Core Data Management Smart Grid Information Management MVNO / VPN Grid Network Design ETRM - Energy Trading and Enterprise Risk Management Market Data and Processes CRM Contact and Campaign Management Supply Chain , EPOS, Retail Merchandising Document Management Financials / Accounting HR / Talent Management Systems Design Plant, Building, Site and Environment Management Systems GIS Mapping and Spatial Analysis Geologic Data Management Systems Energy Data Collection and Aggregation - MVNO / VPN Energy Data Management Trading and Enterprise Risk Management Systems: - Allegro Amphora Endur Zainet CRM Systems Sales Systems Supply Chain Retail Systems CRM Systems SAP IS Retail SAP IS Utilities SAP IS Oil & Gas SAP HANA SAP FI CA SSM SD SEM BI BW IBM FileNet, ECM Infrastructure Management SCADA Network Infrastructure SCADA Network Monitoring and Control Smart Device Infrastructure Management Digital Oilfields of the Future Standardised Terminating Equipment On-demand Computing and Shared Services IT Risk Management IT Demand / Supply Model Shared Services Virtualisation, Automation, Business Continuity On-demand Computing and Shared Services Multi-media Channels and Fulfilment Desktop Services Client Inventory, Provisioning, Help Desk and Support Key Basic Industry Sector Familiarity / Understanding Good Segment Understanding / Previous Experience Current Segment / Business Unit Knowledge ENERGY, OIL AND GAS EXPERIENCE
  7. 7. SMACT 4D Digital Technology Telematics The Internet of Things (IoT) – Smart Devices, Smart Apps, Wearable Technology, Vehicle Telemetry, Smart Homes and Building Automation
  8. 8. Financial Technology – Governance
  9. 9. Financial Technology – Data Categories
  10. 10. Adapting to the New Regulatory Environment • Technology has dramatically advanced the trading of financial instruments over the past two decades. During the last twenty years, the practice of “open outcry” trading has been replaced by electronic trading platforms for all equity, bond and currency markets – with the sole and notable exception of the London Metals Exchange. • This shift has fundamentally changed the way these markets behave and has led to higher trading volumes. Regulatory changes have also played a role in the increasing use of automated trading and asset management processes and electronic exchanges. Today, new regulations are poised to accelerate this trend, bringing even larger trading volumes and diminished cost-of-business to the huge derivatives market., amongst other areas. • The proliferation of technology is certain, and as regulation forces more transactions onto electronic platforms, most financial market participants will need to change the way they operate. This reality poses both challenges and opportunities. To successfully navigate the new environment, market participants will need to adapt strategies and determine how to best leverage current advances in Financial Technologies (Fin Tech).
  11. 11. Adapting to the New Regulatory Environment
  12. 12. • For many banks, achieving their enterprise risk management goals will require a radical new approach to managing not only risk data – but all of the huge volumes of internal and external data stored and accessed by the bank. Why does this appear so hard to achieve? There are many fundamental challenges to overcome. The focus and functions of finance and risk are different and, over time, every business area and risk group – trading, risk, finance, settlements, treasury - has developed its own set of systems, tools and processes to manage their own specific requirements. • As an example, a finance focus includes planning and budgeting, financial reporting (which implies via general ledger data hierarchies, either a balance sheet and asset- centric view, or an income statement and profit-centric view), responsibility accounting (accounting for individual responsible managers and their cost and profitability targets),. • A risk focus includes asset liability management, specific risk types such as trade (micro- economic) risk, market (macro-economic) risk, credit, and operational risk (which imply a portfolio or segment-centric view and data hierarchies), loss forecasting, and economic capital and Capital Adequacy (Liquidity Risk) Rules such as Solvency II (insurance)and Basle II (banking) regulations. The data requirements for these areas differ widely in terms of the data elements and data attributes themselves - as well as data reliability - history, granularity and data quality. With all of these differing data requirements and scenarios, the situation is further compounded by data for each function being typically trapped in silos, hiding firm-wide risk accumulations. Risk in the New Regulatory Environment
  13. 13. • Inconsistent risk and portfolio definitions, asset valuations and master reference data also can exist across different parts of the firm. Few standards have been established for data quality management , and data governance models are often inadequate. Risk systems do not allow for proper analysis of firm-wide exposure across the full range of risk dimensions, and counterparties and models generate incorrect forecasting of potential outcomes. Financial systems do not store risk-related attributes that are essential (for example, risk ratings or collateral information in commercial banking). • New and exciting data management philosophies, approaches and architectures have emerged to address the increasingly complex, pervasive, extensive and interconnected data storage and processing challenges – enabling banks to move forward on risk and finance integration. First there are a few fundamental steps to take. Banks must adopt new data management tenets that remediate the deficiencies in traditional approaches. • Recent advances in new and emerging technologies including Graphics Processor Units (GPUs) and Solid State Drives (SSDs) – powering in-memory performance acceleration in analytics and cloud computing – are making these challenges far easier to overcome. Quantitative (data-centric) risk modelling involving thousands of intensive Monte Carlo computation cycles – is now de rigueur in Econometrics, Trading and Risk Management. Risk in the New Regulatory Environment
  14. 14. SAP HANA Analytics Methodology
  15. 15. Executive Summary - The Management of Uncertainty • It has long been recognized that one of the most important competitive factors for any organisation to master is the management of uncertainty. Uncertainty is the major intangible factor contributing towards the risk of failure in every process, at every level, in every type of business. The way that we think about the future must mirror how the future actually unfolds. As we have learned from recent experience, the future is not a straightforward extrapolation of simple, single-domain trends. We now have to consider ways in which the possibility of random, chaotic and radically disruptive events may be factored into enterprise threat assessment and risk management frameworks and incorporated into decision-making structures and processes. • Managers and organisations often aim to “stay focused” and maintain a narrow perspective in dealing with key business issues, challenges and targets. A concentration of focus may risk overlooking Weak Signals indicating potential issues and events, agents and catalysts of change. Such Weak Signals – along with their resultant Wild Card and Black Swan Events - represent early warning of radically disruptive future global transformations – which are even now taking shape at the very periphery of corporate awareness, perception and vision – or just beyond. These agents of change may precipitate global impact-level events which either threaten the very survival of the organisation - or present novel and unexpected opportunities for expansion and growth. The ability to include weak signals and peripheral vision into the strategy and planning process may therefore be critical in contributing towards the organisation's continued growth, success, well being and survival.
  16. 16. BI / Analytics Systems – New Horizons • Using Emerging Technologies such as in-memory Data Warehouse Appliances coupled with Real-time and Predictive Analytics Engines - we can now achieve so much more than we could ever do before with just simple after-the-event Historic Reporting..... • Real-time and Predictive Analytics are transforming the way that Business Managers are able to think, plan and operate. Based firmly on a foundation of In-Memory “Big Data” Computing technology, and an extended Time dimension from Past (Historic) through Present (Real-time) into Future (Predictive) Data - there is now a very new paradigm for enterprise information management, which supports the three key business reporting timeline requirements: - DEVICE INFORMATION TIMELINE PURPOSE Data Warehouse Appliances Historic Data Past Historic Reporting Real-time Analytics Engines Current Data Present Real-time Analytics Predictive Analytics Engines Forecast Data Future Predictive Analytics MODELLING HORIZON RESULTS RANGE (years) TIMELINE DATA TYPE FISCAL PERIOD AGGREGATION Financial Management Previous, Current, Planned 5 - 7 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative Strategic Management Previous, Current, Planned 5 - 15 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative Future Management Previous, Current, Planned 50 - 200 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative
  17. 17. BI / Analytics Systems – Vendor Comparison APPLICATION CATEGORY VENDOR COMPONENTS SAS SAP JEDOX USER INTERFACE Mobile Enterprise Application Platforms MEAPs Sybase Unwired Platform (SUP) Mobile Apps Data Presentation & Display GUI SAS Add-In for Microsoft Office Enterprise Portal Excel, Web Graphic Visualisation BLOBs Enterprise Guide, BI Dashboard, SAS/Graph PowerPoint ENTERPRISE SERVER Database Server Servers Base SAS Software SAP BW, BO, BI SQL/Server Application Server Servers SAS Enterprise Business Intelligence Server HANA OLAP Server Data Warehouse Appliance Fast Data SAS Scalable Performance Data Server (SPDS) BW, BO, BI, HANA Accelerator Analytics Engines Big Data Hadoop, “R” Hadoop, Pentaho PERFORMANCE ACCELERATION Massive Parallelism GPUs Accelerator In-memory Processing SSDs HANA Accelerator INFRASTRUCTURE SOFTWARE Database Management Relational Sybase SQL/Server System (DBMS) Columnar Sybase Vertical Unstructured Autonomy Autonomy MDDB (Cubes) Base SAS Software Ultra-fast Data Replication Propagation Sybase SSIS
  18. 18. BI / Analytics Systems – Vendor Comparison APPLICATION CATEGORY VENDOR COMPONENTS SAS SAP JEDOX USER INTERFACE Mobile Enterprise Application Platforms MEAPs Sybase Unwired Platform (SUP) Mobile Apps Data Presentation & Display GUI SAS Add-In for Microsoft Office Enterprise Portal Excel, Web Graphic Visualisation BLOBs Enterprise Guide, BI Dashboard, SAS/Graph PowerPoint IINTEGRATION SOFTWARE Data Management ETL Information Map Studio HANA Studio ETL, SSIS, Pentaho Application Integration Enterprise Service Bus SAS windowing environment SAS Web OLAP Viewer for Java SAS Web OLAP Viewer for.NET NetWeaver PI Process Integrator Jedox Connecter for SAP, BizTalk Connectors and Adaptors Data Access SAS/CONNECT, SAS/ACCESS SAS Library Engines and Remote Library Services Jedox Connecter, SSIS Development Tools Programming SAS/AF, SAS/SCL, SAS/ASSIST “R” C#, DOT.NET Framework Business Hierarchies Modelling and Design Facts and Dimensions Data Integration Studio BW / BO Universe NetWeaver MDM SAP HANA Studio OLAP Server ENTERPRISE SOFTWARE Data Analysis and Reporting Reporting SAS Enterprise Business Intelligence Server Crystal Reports / Business Objects OLAP Server / Excel Business Intelligence BI Base SAS Software BI / BO / BW OLAP Server Information Management OLAP OLAP Cube Studio “R” OLAP Server Statistical Analysis SAS/STAT, Stat Graphics Data Mining Enterprise Miner SAP Analytics SQL/Server Analytics Analytics SAS/INSIGHT SSM OLAP Server, SSAS Financial Consolidation Controlling FI, CO, BPC / BHP OLAP Server Enterprise Performance Management Planning SAS Strategy Management SEM / EPM OLAP Server Scenario Planning and Impact Analysis Simulation BPS OLAP Server
  19. 19. Business Intelligence Systems Methodology STAGE STAGE DURATION PROCESS STAGE DELIVERABLES VENDOR DELIVEABLES CLIENT OUTCOME Elapsed Client Input Requirements Discovery Requirements Discovery Workshops Requirements Analysis Business Modelling Requirements Catalogue Business Architecture Business Roadmap Vendor RFI Request for Product Information - Vendor Response Business Architecture Delivered Solution Options Solution Options Workshops Solution Options Document Requirements to Solution Mapping Requirements Mapping Document Solution Options Document Solution Roadmap Vendor ITT Tender Document Solution Options Delivered Solution Mapping Delivered Recommendations, Blueprint, Pilot and Proof-of-concept Vendor Product Demonstration Workshops Business Case Cost / Benefits Analysis Programme Planning Vendor Product Evaluation - Balanced Scorecard Cost / Benefits Model Solution Architecture Programme Plan Vendor RFP Vendor Product Demonstrations Proposal Document Solution Architecture Delivered Business Case Delivered Cost / Benefits Stream Defined Programme Plan Delivered Agile Delivery Iterative, Incremental Lean / Agile Delivery Business Intelligence Data and Processes Best Practice and Quality Assurance BI / Analytics Capability
  20. 20. Business Intelligence Systems Methodology SAP HANA BI / Analytics Systems Planning Methodology: - • Understand business opportunities and threats – Business Outcomes, Goals and Objectives • Understand business challenges and issues – Business Drivers and Requirements • Gather the evidence to quantify the impact of those issues – Business Case • Quantify the business benefits of resolving the issues – Benefits Realisation • Quantify the changes need to resolve the issues – Business Transformation • Understand Stakeholder Management issues – Communication Strategy • Understand organisational constraints – Organisational Impact Analysis • Understand technology constraints – Technology Strategy SAP HANA BI / Analytics Systems Delivery Methodology: - • Understand success management – Scope, Budget, Resources, Dependencies, Milestones, Timeline • Understand achievement measures – Critical Success Factors / Key Performance Indicators / ROI • Produce the outline supporting planning documentation - Business and Technology Roadmaps • Complete the detailed supporting planning documentation – Programme and Project Plans • Design the solution options to solve the challenges – Business and Solution Architectures • Execute the preferred solution implementation – using Lean / Agile delivery techniques • Report Actual Progress, Issues, Risks and Changes against Budget / Plan / Forecast • Delivery, Implementation and Go-live !
  21. 21. Amphora Symphony – ETRM and beyond
  22. 22. Energy Trading and Risk Management • Integrated trade and risk management – a collaborative approach focused on ETRM market leadership through total asset control. Amphora Symphony solutions handle every aspect of the energy commodities lifecycle (physical and derivative products) around the world.....
  23. 23. Reservoir Simulation  The Grid System  The Well Model  Conservation Equations  Geological Mapping, Log Data and Spatial Analysis  Reservoir Modelling and Typological Characterization o Aquifers o Salt Domes  Model Initialisation o Data Load Runs o Model Initialisation Runs o Model Tuning Runs o History Matching Runs  Recovery Forecasting and Prediction o Monte Carlo Simulation o Scenario Planning and Impact Analysis  Exploitation Modelling o Depletion Options o Recovery Extend o t Extraction Rates Reservoir Exploitation  Economic Modelling for Oil & Gas Production  Geological Science  Transient Well Logging  Open Hole Logging  Production Logging  Subsurface Reservoir Geology  Exploration Geophysics  Reservoir Mapping  Reservoir Modelling  Heavy Oil Technology  Enhanced Recovery Techniques o Water Injection o Gas Injection  Enhanced Oil and Gas Recovery Operations o Water flooding o Reservoir Analysis o Recovery Prediction o Injection Design  Gas displacement o Reservoir Analysis o Recovery Prediction o Injection Design • Future Management - Modelling and Forecasting Future Outcomes • Energy Oil and Gas conglomerates use Forecast Demand, Supply and Cost / Price Models to help forecast the price of Energy (Energy Cost / Price Curves) over very long periods (up to 50 years). This information is needed to help drive long-term infrastructural investment decisions – such as opening up expensive remote, difficult or hazardous Oil Fields. Modelling usually begins by running Workshops in which the Physical (Commodity / Reservoir Exploitation) and Economic (Forecast Demand, Supply and Cost / Price) Models We start with Physical (Geological) and Conceptual (Economic) Model Design as Systems are envisioned, discovered, elaborated, scoped, architected and designed using very large scale GIS Mapping and Spatial Analysis (sub-surface modelling) and Data Warehouse Structures - containing both Historic (up to 20 years daily closing prices for LPG and all grades of crude) and Future values (daily forecast and weekly projected price curve details, monthly and quarterly movement predictions, and so on for up to 20 years into the future. . EXPLORATION and PRODUCTION EXPERIENCE
  24. 24. Energy Trading and Risk Management
  25. 25. HYDROCARBON VALUE CHAIN EXPERIENCE
  26. 26. Trading and Risk Management
  27. 27. Market Risk • MARKET RISK • Market Risk = Market Sentiment – Actual Results (Reality) • The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook, burst on to the scene five years ago and have since grown into internet giants. Facebook has over 900 million active members and Twitter over 250 million, with users posting over 2 billion "tweets“ or messages every week. This provides hugely valuable and rich insights into how Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels – and so is also a source of real-time data that can be “mined” by super-fast computers to forecast changes to Commodity Price Curves Info-graphic – Apple Historic Stock Data Analysis..... • Investors and traders around the world have accepted the fact that financial markets are driven by “greed and fear”. This info-graphic is an example of the kind of correlation we see between historic stock price and social media sentiment data. A trading advantage can arrive if you spot a significant change in sentiment which is a leading asset price indicator. Derwent Capital Markets are pioneers in trading the financial markets using global sentiment derived from large scale social media analysis.
  28. 28. Apple Historic Stock Data Analysis Info-graphic using “Big Data” MARKET RISK = MARKET SENTIMENT – ACTUAL RESULTS (REALITY)
  29. 29. Financial Markets around the world are driven by “greed and fear”..... Derwent Capital Markets – Market Risk = Market Sentiment – Actual Results (Reality)..... • Derwent Capital Markets used Twitter to figure out where the money is going - just like that. A hedge fund that analyzed tweets to figure out where to invest its managed funds closed its doors to new investors last year – after just one month in which it made 1.86% Profit – Annual Projection 21% reports the Financial Times. “As a result we made the strategic decision to re-use the Social Market Sentiment Engine behind the Derwent Absolute Return Fund – and invest directly in developing a Social Media on-line trading platform” commented Derwent Capital Markets founder Paul Hawtin, Mood states – “greed and fear”..... • These two mood states are primitive human instincts which, until now, we've struggled to accurately quantify. Social networks, such as Twitter and Facebook, burst on to the scene five years ago and have since grown into internet giants. Facebook has over 900 million active members and Twitter over 250 million, with users posting over 2 billion "tweets“ or messages every week. This provides a hugely valuable and rich source of real-time data that can be “mined” by super-fast computers..... • Derwent Capital Markets - the sentiment analysis provider launched by Paul Hawtin in May 2012 following the dissolution of his "Twitter Market Sentiment Fund", sold yesterday to the highest bidder at the end of a two-week online auction. The winning bid came from a Financial Technology (Fin Tech) firm, which Hawtin declined to name. Hawtin had set a guide price of £5 million ($7.8m), but claimed at the start of the auction process that any bid over and above the £350,000 ($543,000) cash he had invested would represent a successful outcome..... CFD Trading, Spread Betting and FX Trading using “Big Data”
  30. 30. Event Risk • EVENT RISK • Black Swan Event = extreme event with Low Probability and High Impact • A 'Black Swan' Event – is an extreme, rare and unexpected occurrence or event, with low probability and high impact - difficult to forecast or predict, with outcomes and consequences deviating far beyond the normal expectations for any given situation – Nassim Nicholas Taleb - Finance Professor, Author and former Wall Street Trader. Market Risk = Market Sentiment – Actual Results (Reality) • The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook, burst on to the scene five years ago and have since grown into internet giants. Facebook has over 900 million active members and Twitter over 250 million, with users posting over 2 billion "tweets“ or messages every week. This provides hugely valuable and rich insights into how Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels – and so is also a source of real-time data that can be “mined” by super-fast computers to forecast changes to Commodity Price Curves
  31. 31. Weak Signals Wild Cards, Black Swans Wild Card Strong Signal Random Event Weak Signal Communicate Discover Understand Evaluate Random Event Strong Signal Weak Signal Wild Card Black Swan Runaway Wild Card Scenario Stock Market Panic of 2008
  32. 32. Trigger D USA Sub-Prime Mortgage Crisis Trigger F CDO Toxic Asset Crisis K ETrigger K Sovereign Debt Crisis BTrigger I Money Supply Shock CTrigger H Financial Services Sector Collapse DTrigger G L ATrigger J Credit Crisis Global RecessionDefinition of a “Black Swan” Event • A “Black Swan” Event is an event or occurrence that deviates beyond what is normally expected of any given situation and that would be extremely difficult to predict. The term “Black Swan” was popularised by Nassim Nicholas Taleb, a finance professor and former Investment Fund Manager and Wall Street trader. • Black Swan Events – are unforeseen, sudden and extreme change events or Global-level transformations in either the military, political, social, economic or environmental landscape. Black Swan Events are a complete surprise when they occur and all feature an inordinately low probability of occurrence - coupled with an extraordinarily high impact when they do happen (Nassim Taleb). “Black Swan” Event Cluster or “Storm” Stock Market Panic of 2008 Black Swan Events
  33. 33. Big Data – Products The MapReduce technique has spilled over into many other disciplines that process vast quantities of information including science, industry, and systems management. The Apache Hadoop Library has become the most popular implementation of MapReduce – with framework implementations from Cloudera, Hortonworks and MAPR
  34. 34. “BIG DATA” – my own special areas of technical expertise Targeting – Map / Reduce Consume – End-User Data Data Acquisition – High-Volume Data Flows – Mobile Enterprise Platforms (MEAP’s) Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica Smart Devices Smart Apps Smart Grid Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting – Data Delivery and Consumption News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM – Data Discovery and Collection – Analytics Engines - Hadoop – Data Presentation and Display Excel Web Mobile – Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load – Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast data replication – Data Management Tools DataFlux Embarcadero Informatica Talend – Info. Management Tools Business Objects Cognos Hyperion Microstrategy Biolap Jedox Sagent Polaris Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox – Data Warehouse Appliances Ab Initio Ascential Genio Orchestra
  35. 35. Load-Map-Shuffle-Reduce Process Load Map Shuffle Reduce
  36. 36. Informatica / Hortonworks VIBE
  37. 37. HDFS MapReduce Pig Zookeeper Hive HBase Oozie Mahoot Hadoop Distributed File System (HDFS) Scalable Data Applications Framework Procedural Language – abstracts low-level MapReduce operators High-reliability distributed cluster co-ordination Structured Data Access Management Hadoop Database Management System Job Management and Data Flow Co-ordination Scalable Knowledge-base Framework Apache Hadoop Component Stack “BIG DATA” – my own special area of Business expertise
  38. 38. Hadoop Framework Distribution Libraries FEATURE Hortonworks Cloudera MAPR Open Source Hadoop Library Yes Yes Yes Support Yes Yes Yes Professional Services Yes Yes Yes Catalogue Extensions Yes Yes Yes Management Extensions Yes Yes Architecture Extensions Yes Infrastructure Extensions Yes Library Support Services Library Support Services Catalogue Job Management Library Support Services Catalogue Hortonworks Cloudera MAPR Catalogue Job Management Resilience High Availability Performance
  39. 39. Manufacturer Server Configuration Cached Memory Server Type Software Platform Cost (est.) SAP HANA 32-node (4 Channels x 8 CPU) 1.3 Terabytes SMP Proprietary $ 6,000,,000 Teradata 20-node (2 Channels x 10 CPU) 1 Terabyte MPP Proprietary $ 1,000,000 Netezza (now IBM) 20-node (2 Channels x 10 CPU) 1 Terabyte MPP Proprietary $ 180,000 IBM ex5 (non- HANA configuration) 32-node (4 Channels x 8 CPU) 1.3 Terabytes SMP Proprietary $ 120,000 Greenplum (now Pivotal) 20-node (2 Channels x 10 CPU) 1 Terabyte MPP Open Source $ 20,000 XtremeData xdb (BO BW) 20-node (2 Channels x 10 CPU) 1 Terabyte MPP Open Source $ 18,000 Zybert Gridbox 48-node (4 Channels x 12 CPU) 20 Terabytes SMP Open Source $ 60,000 Data Warehouse Appliance / Real-time Analytics Engines
  40. 40. SalesForce.com – a Cloud Platform CRM / CEM Business Solution The Cone™ - Lifestyle Understanding Customer Management (CRM / CEM) Social Intelligence Campaign Management e-Business Big Data Analytics The Cone™ Customer Loyalty & Brand Affinity The Cone™ Smart Apps
  41. 41. The Cone™ – Digital Marketing Data Streams into Revenue Streams….. • Digital Marketing is the communication, advertising and marketing of brands, products and services via multiple digital channels and channel partners in order to reach out to, contact and connect, on the most intimate terms, with the widest possible range of consumers. Through the exploitation of Digital Media we can initiate and maintain engaging Social Conversations. • Digital Marketing extends key Brand Messages across every digital platform, from simple internet marketing to mobile, broadcast and social media channels – yielding Social Intelligence data in order to discover actionable Marketing Insights – which in turn convert digital Data Streams into Revenue Streams • The key objective of Digital Marketing is to reach out to, contact and connect directly with carefully selected consumers – so that we create strong, lasting and durable relationships in order to promote key brand, category and product messages to targeted consumers and thus develop a tangible, valuable. very real and distinct brand / category / product interest, following, affinity and loyalty
  42. 42. Social Intelligence – Profiling and Analysis Fanatics - 10% Enthusiasts - 20% Casuals - 30% Indifferent - 40% The Cone™ – Profiling & Analysis The Cone™ Brand Loyalty & Affinity
  43. 43. The Cone™ - Eight Primitives Primitive Problem / Opportunity Business Domain System Function Software Product Who ? Who are our Customers ? Party - People / Organisations CRM / CEM SalesForce.com - Customer Management What ? What are they saying about us ? Social Media / Communications Social Intelligence Google Analytics, Anomaly 42 Why ? Why - their Interest / Behaviour / Motivation / Aspirations / Desires ? Brand Identity / Loyalty / Affinity / Offers / Promos’ Marketing, Campaign Management Predictive Analytics / Propensity Modelling Where ? Where do they Live / Work / Shop / Relax ? Places - Location GIS / GPS Geospatial Analytics When ? When do they contact / buy products from us ? Time / Date Sales Transaction Multi-channel Retail / Mobile Platforms How ? How do they contact and connect with us – Media / Telecoms Channels ? Communications Channel • Mobile • Internet • In-store Multi-channel Retail / Mobile Platforms Which ? Which Brands / Ranges / Categories / Products ? Retail Merchandising Product Catalogue IBM Product Centre / Stebo / Kalido Via ? Via Business Partners / 3rd Party Channels ? Sales Channel Retail Channel / Outlet Amazon, E-bay, Alibaba
  44. 44. Event Dimension Party Dimension Geographic Dimension Motivation Dimension Time Dimension Media Dimension Cone™ MEDIA FACT WHO ? WHAT ? WHERE ? HOW ?WHEN ?WHY ? • Indifferent • Casuals • Enthusiasts • Fanatics • Radio Show • Television Show • Internet Advert • Campaign • Offer • Promotion • Pre-order • Purchase • Download • Playlist • Booking • Attendance • Advert / Publicity • Posting / Blog • Facebook • LinkedIn • Myspace • Twitter • YouTube • Xing • Region / Country • State / County • City / Town • Street / Building • Postcode • Person • Organisation Product Dimension WHICH ? • Category • Label / Artist • Album / Track • Tour / City / Arena • Merchandise Channel Dimension VIA ? • Channel / Partner • In-store • Internet Service • Mobile Smart App (Spotify etc.) Advert / Publicity Type Sales Channel Posting / Blog Source / Type Subject Location Media Event • Awareness • Interest • Need • Desire Motivation Customer Time / Date Version 2 – Media Co’s The Cone™ - Eight Primitives
  45. 45. Social Intelligence – Streaming and Segmentation Social Interaction Brand Affinity Geo-demographic Profile Experian Mosaic – 15 Groups (Streams), 66 Types (Segments) Hybrid Cone – 3 Dimensions The Cone™ – Streaming & Segmentation The Cone™ Brand Loyalty & Affinity
  46. 46. The Cone™ - Converting Data Streams into Revenue Streams Salesforce Anomaly 42 Cone Unica End User BIG DATA ANALYTICS SOCIAL MEDIA E-Commerce Platform FULFILMENT Sales Orders The Cone™ Brand Loyalty & Affinity SalesForce CRM Geo-demographics • Streaming • Segmentation • Household Data SOCIAL CRM Households Insights InsightsInsights Anomaly 42Unica Offers and Promotions People and Places Campaigns Social Intelligence • User Content and Blogs • Social Groups and Networks EXPERIAN
  47. 47. Social Intelligence – Actionable Insights Brand Affinity Social Interaction Geo-demographic ProfileExperian Mosaic – 15 Groups (Segments), 66 Types (Streams) Hybrid Cone – 3 Dimensions Fanatics - 10% Enthusiasts - 20% Casuals - 30% Indifferent - 40% The Cone™ Brand Loyalty & Affinity The Cone™ – Actionable Insights
  48. 48. Social Intelligence – Split-Map-Shuffle-Reduce Process Split Map Shuffle Reduce Key / Value Pairs
  49. 49. The Cone™ - CAMPAIGN Social Intelligence – CAMPAIGN MANAGEMENT
  50. 50. The Cone™ – CYCLE Salesforce Anomaly 42 Cone Unica End User BIG DATA ANALYTICS Cone™ Brand Affinity Campaign CRM Insights InsightsInsights SALES PEOPLE DEMOGRAPHICS Household Data SOCIAL INTELLIGENCE User Content, Social Groups and Networks Offers and Promotions People & Places Streaming & Segmentation The Cone™ – CYCLE
  51. 51. Social Interaction How consumers use social media (e.g., Facebook, Twitter) to address and/or engage with companies around social and environmental issues.
  52. 52. Geo-demographics - “Big Data” • The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ structure of groupings provides an important technique for many statistical and analytic applications. Cluster analysis on the basis of profile similarities or geographic distribution is a method where no prior assumptions are made concerning the number of groups or group hierarchies and internal structure. Geo- demographic techniques are frequently used in order to profile and segment populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes - along with many other shared characteristics and common factors.....
  53. 53. Split-Map-Shuffle-Reduce Process Split Map Shuffle Reduce Key / Value Pairs
  54. 54. Apache Hadoop Component Stack HDFS MapReduce Pig Zookeeper Hive HBase Oozie Mahoot Hadoop Distributed File System (HDFS) Scalable Data Applications Framework Procedural Language – abstracts low-level MapReduce operators High-reliability distributed cluster co-ordination Structured Data Access Management Hadoop Database Management System Job Management and Data Flow Co-ordination Scalable Knowledge-base Framework
  55. 55. Hadoop Related Component Stack YARN Drill Millwheel Hadoop Resource Scheduling Data Analysis Framework Data Analytics on-the-fly + Extract – Transform – Load Framework MatLab R Data Acquisition and Analysis Application Development Toolkit Statistical Programming / Algorithm Language Flume Sqoop Scribe Extract – Transform - Load Extract – Transform - Load Extract – Transform - Load
  56. 56. Big Data / Data Science Extended Component Stack Autonomy Vertica MungoDB Ambari Vibe Splunk Unstructured Data DBMS Columnar DBMS High-availability DBMS High-availability distributed cluster co-ordination High Velocity / High Volume Machine / Automatic Data Streaming High Velocity / High Volume Machine / Automatic Data Streaming Talend Extract – Transform - Load Pentaho Data Reporting on-the-fly + Extract – Transform – Load Framework
  57. 57. SSD SSD (Solid State Drive) – configured as cached memory / fast HDD Big Data / Data Science Extended Infrastructure Stack CUDA CUDA (Compute Unified Device Architecture) GPGPU GPGPU (General Purpose Graphical Processing Unit Architecture) IMDG IMDG (In-memory Data Grid – extended cached memory) Mathematica Mathematical Expressions and Algorithms StatGraphics Statistical Expressions and Algorithms FastStats FastStats (numerical computation, visualization, and programming) Pivotal Pivotal Big Data Suite – GreenPlum, GemFire, SQLFire, HAWQ
  58. 58. Hadoop Framework Distributions FEATURE Hortonworks Cloudera MAPR Open Source Hadoop Library Yes Yes Yes Support Yes Yes Yes Professional Services Yes Yes Yes Catalogue Extensions Yes Yes Yes Management Extensions Yes Yes Architecture Extensions Yes Infrastructure Extensions Yes Library Support Services Catalogue Job Management Resilience High Availability Library Support Services Catalogue Job Management Library Support Services Catalogue Hortonworks Cloudera MAPR Performance
  59. 59. Telco 2.0 “Big Data” Analytics Architecture
  60. 60. • SAP is a Growth Company. SAP wishes to elevate itself to become a trusted innovator for all of their customers – whether it’s achieving business outcomes, simplifying everything through the cloud or driving business efficiency and growth using Mobile and In-memory Computing. • Industry Focused. In 2013 SAP was global the market leader for supplying ERP application software across 25 different Industry Sectors – and will continue to increase its Industry Sector focus to make SAP HANA the standard business platform for world-class Industry Sector applications and process execution. • The Digital Enterprise. SAP grew its mobile, cloud and in-memory computing businesses heavily in 2013 and will continue to strengthen its transition into products supporting the Digital Enterprise area even more so in 2014. BIW (Business Information Warehouse) and ECC6 (ERP Central Components version 6) Business Suite – will ultimately be fully integrated into Cloud, Mobile and SAP HANA High-availability Analytics in-memory computing platform environments. • Key Technology Platforms and Industry Sector areas for SAP in 2014 include the following: - 1. Digital Healthcare 2. Multi-channel Retail 3. Financial Technology Industry SectorsTechnologies 1. Cloud Services 2. The Mobile Enterprise 3. In-memory Computing SAP – Outlook for 2014 SAP HANA version 2 EXPERIENCE
  61. 61. • Patient Experience and Journey – Patient Administration and Billing – Patient Relationship Management • Clinical Delivery – Clinical Treatment and Care • Digital Imaging – (MRI / CTI / X-Ray / Ultrasound) • Robotic Surgery – (Microsurgery / Remote Surgery) • Patient Monitoring – (Clinical Trials / Health / Wellbeing) • Biomedical Data – (Data Streaming / Biomedical Analytics) • Emergency Incident Management – (Response Team Alerts) • Epidemiology – (Disease Transmission / Contact Management) – Enterprise Healthcare Mobility (Mobile Devices / Smart Apps) • Activity Monitor – (Pedometer / GPS) • Position Monitor – (Falling / Fainting / Fitting) • Sleep Monitor – (Light Sleep / Deep Sleep / REM) • Cardiac Monitor – (Heart Rhythm / Blood Pressure) • Blood Monitor – (Glucose / Oxygen / Liver Function) • Breathing Monitor – (Breathing Rate / Blood Oxygen Level) • Care Collaboration – Connected Care – Referral Management Healthcare: - SAP Solution Roadmap SAP HANA version 2 EXPERIENCE : – Digital Healthcare
  62. 62. • SAP HANA is a new Database Appliance hosting a Hardware and Software bundle (SAP software powered by INTEL core technologies with Veola Garda SSD In-memory Architecture). Introduced in late 2010 – HANA initially focused on Real-time Analytics – processing vast quantities of data on the fly. SAP HANA now address many of the challenges facing customers needing to make instant Management Decisions using very large data volumes. • The SAP HANA Appliance was massively developed and further extended in 2012 to support the many upcoming user requirements for processing Very Large Scale (VLS) data volumes in the realm of real time analytics. SAP AG, together with INTEL, has expended massive effort in order to meet the emerging challenges of the Real-time world – optimising Enterprise Resources in manufacturing, financial services, healthcare, national security, etc. • SAP HANA presents a novel opportunity for businesses that needs instant access to Real-time Data for analytic models that drive automated processing and Intelligent Agents / Alerts for instant decision-making. SAP HANA also allows users to federate external data sources (ERP / CRM databases, message queues, Data Warehouse Appliances, Real-time Data Feeds Internet Content and Click-stream Processing) with their Analytics Engines.
  63. 63. SAP HANA Overview
  64. 64. SAP HANA Applications and Analytics In its current form, SAP HANA (Version 2) can be used for five fundamental types of System Template: - 1. Agile Data Mart for supporting Real-time Analytics 2. SAP Business Suite Application Accelerator 3. Primary Database for SAP NetWeaver Business Warehouse 4. Development Platform for new end-user applications. 5. SAP Rapid Deployment Solutions (RDS) Analytics– The Major Categories of Real-time analytics for which HANA is optimised: - – Operational Reporting – real-time insights from transaction systems such as SAP ERP Applications or third-party solutions from IBM, Oracle or Microsoft. – Data Warehousing (SAP NetWeaver BW on HANA) – BW customers can run their entire BW application suite on the SAP HANA Platform. – Predictive and Text analysis on Big Data – To succeed, companies must go beyond focusing on delivering the best product or service and uncover customer/employee /vendor/partner trends and insights, anticipate behaviour and take proactive action from predictive insights into ERP transaction data. – Core process accelerators – HANA accelerate business reporting and enterprise performance management by powering ERP, Data Warehouse and Data Mart Accelerators, – Planning and Optimization Apps – SAP HANA excels at applications that require complex, interactive planning and scheduling in real-time with ultra-fast results, – Sense and Response Apps – These applications offer real-time insights from “Big Data” such as global markets data and newsfeeds (Automatic Trading) , remote sensing and monitoring data from Intelligent Buildings and Smart Homes smart meter data (energy demand / supply optimisation), satellites, drones and fixed HDCCTV cameras (optical recognition) Electronic point-of-sale (EPOS) data, social media data, global internet content (Market Sentiment) , Streamed Biomedical Data ,for Clinical Trials, Emergency Response and much more besides..... SAP HANA version 2 EXPERIENCE
  65. 65. BW powered by HANA • In this scenario, SAP NetWeaver Business Warehouse (BW) uses the SAP HANA appliance software as the primary database. Having the data stored in columns in the main memory means that measures, or columns, can be read much faster, and totals and averages can be calculated quickly – even for vast numbers of data records. InfoProviders designed specifically for SAP HANA, such as DataStore objects and InfoCubes optimized for SAP HANA, further accelerate the loading and analysis of data in BW, since complex and performance-intensive processes, such as activating DSO requests, can be done in the SAP HANA appliance software itself. SAP HANA as a data mart • In this deployment scenario, the SAP HANA appliance software is used alongside an existing database. Operational data from SAP or non-SAP systems can be replicated to the SAP HANA database using the SAP LT Replication Server or SAP BusinessObjects Data Services. Whereas SAP BusinessObjects Data Services is used to set up complex processes to extract, transform, and load data, the SAP LT Replication Server brings about a trigger-based replication of all relevant tables using Sybase ultra-fast Database Replication. When data is inserted or updated in the ERP system, it is automatically transmitted to the SAP HANA database so that it is available for almost real-time reporting. Data in the SAP HANA appliance software is accessed using information models such as attribute, analytic, and calculation views - which can be created using the SAP HANA (Eclipse) studio. Agile Data Mart for supporting Real-time Analytics • This System Template has advantages of (1) being completely non-disruptive to the existing application landscape and (2) providing an immediate, focused solution to an urgent business analytics problem. Example Application Scenarios for a stand-alone Data Mart supporting Real-time Analytics include: - – Sales Analysis Data Mart – Traded Instrument Data Mart – Smart Meter Reading Data Mart SAP HANA version 2 EXPERIENCE
  66. 66. SAP HANA version 2 • Using Emerging Technologies such as in-memory Data Warehouse Appliances with Real-time and Predictive Analytics Engines - we can now achieve so much more than we could ever do before..... • Real-time and Predictive Businesses are transforming the way that they think, plan and operate. Based firmly on a foundation of In-Memory Computing technology, and an extended Time dimension from Past (Historic) through Present (Real-time) into Future (Predictive) Data - there is now a very new paradigm for enterprise information management, which supports the three key business reporting requirements: - DEVICE INFORMATION TIMELINE PURPOSE Data Warehouse Appliances Historic Data Past Historic Reporting Real-time Analytics Engines Current Data Present Real-time Analytics Predictive Analytics Engines Forecast Data Future Predictive Analytics MODELLING HORIZON RESULTS RANGE (years) TIMELINE DATA TYPE FISCAL PERIOD AGGREGATION Financial Management Previous, Current, Planned 5 - 7 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative Strategic Management Previous, Current, Planned 5 - 10 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative Future Management Previous, Current, Planned 50 - 100 Past, Present, Future Actual / Forecast Day, Week, Month, Quarter, Annual Atomic and Cumulative SAP HANA version 2 EXPERIENCE
  67. 67. SAP HANA Planning Methodology: - • Understand business opportunities and threats – Business Outcomes, Goals and Objectives • Understand business challenges and issues – Business Drivers and Requirements • Gather the evidence to quantify the impact of those issues – Business Case • Quantify the business benefits of resolving the issues – Benefits Realisation • Quantify the changes need to resolve the issues – Business Transformation • Understand Stakeholder Management issues – Communication Strategy • Understand organisational constraints – Organisational Impact Analysis • Understand technology constraints – Technology Strategy SAP HANA Delivery Methodology: - • Understand success management – Scope, Budget, Resources, Dependencies, Milestones, Timeline • Understand achievement measures – Critical Success Factors / Key Performance Indicators / ROI • Produce the outline supporting planning documentation - Business and Technology Roadmaps • Complete the detailed supporting planning documentation – Programme and Project Plans • Design the solution options to solve the challenges – Business and Solution Architectures • Execute the preferred solution implementation – using Lean / Agile delivery techniques • Report Actual Progress, Issues, Risks and Changes against Budget / Plan / Forecast • Delivery, Implementation and Go-live ! SAP HANA Methodology
  68. 68. SAP HANA Architecture Overview
  69. 69. APPLICATION CATEGORY VENDOR SAS SAP JEDOX USER INTERFACE Mobile Enterprise Application Platforms MEAPs Sybase Unwired Platform (SUP) Mobile Apps Data Presentation & Display GUI SAS Add-In for Microsoft Office Enterprise Portal Excel, Web Graphic Visualisation BLOBs Enterprise Guide, BI Dashboard, SAS/Graph PowerPoint ENTERPRISE SERVER Database Server Servers Base SAS Software SAP BW, BO, BI OLAP Server Application Server Servers SAS Enterprise Business Intelligence Server HANA Accelerator Data Warehouse Appliance Fast Data SAS Scalable Performance Data Server (SPDS) BW, BO, BI, HANA Accelerator Analytics Engines Big Data Hadoop, “R” Hadoop, Pentaho PERFORMANCE ACCELERATION Massive Parallelism GPUs Accelerator In-memory Processing SSDs HANA Accelerator ENTERPRISE SOFTWARE Data Analysis and Reporting Reporting SAS Enterprise Business Intelligence Server Crystal Reports / Business Objects OLAP Server / Excel Business Intelligence BI Base SAS Software BI / BO / BW OLAP Server Information Management OLAP OLAP Cube Studio “R” OLAP Server Statistical Analysis SAS/STAT, Stat Graphics Data Mining Enterprise Miner, SAS/INSIGHT Analytics SSM OLAP Server, SSAS Financial Consolidation Controlling FI, CO, BPC / BHP OLAP Server Enterprise Performance Management Planning SAS Strategy Management SEM / EPM OLAP Server SAP HANA Applications
  70. 70. SAP HANA Architecture
  71. 71. • SAP HANA is a new Technology Appliance Coupled with Hardware and Software bundle (Intel Architecture powered by SAP In memory Technology). Introduced in to the market late 2010, initially focusing on Analyzing Huge volume of DATA in real time. It Address the whole challenge what customers are facing with extreme volumes of data to make Management Decisions Quicker than Never before. • The Appliance has fine-tuned Very Aggressively in 2012 It meets most of the challenge in the Real- time world. SAP to gether with INTEL, has deployed Huge resources to meet upcoming challenges in the real time world. You may call it analysing your health, managing your resources, Prevention of crime etc., Making us to run our live Happier Like Never Before. • Data in real-time provides a completely unique capability for businesses that require instant access to their information. In addition, SAP HANA allow users to federate external data sources (including CEP engines, message queues, tick databases, traditional relational databases, and OData sources)

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