Nigel Tebbutt Profile - Fin Tech PDF

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

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CAREER SUMMARY

ENERGY TRADING AND RISK MANAGEMENT (ETRM) EXPERIENCE

SAP HANA

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)

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

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

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

SMACT 4D Digital Technology

Telematics The Internet of Things (IoT) – Smart Devices, Smart Apps, Wearable Technology, Vehicle Telemetry, Smart Homes and Building Automation

Financial Technology – Governance

Financial Technology – Data Categories

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).

Adapting to the New Regulatory Environment

• 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

• 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

SAP HANA Analytics Methodology

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.

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

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

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

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

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 !

Amphora Symphony – ETRM and beyond

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.....

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

Energy Trading and Risk Management

HYDROCARBON VALUE CHAIN EXPERIENCE

Trading and Risk Management

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.

Apple Historic Stock Data Analysis Info-graphic using “Big Data”

MARKET RISK = MARKET SENTIMENT – ACTUAL RESULTS (REALITY)

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”

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

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

Trigger D

USA Sub-Prime Mortgage Crisis

Trigger F

CDO Toxic Asset Crisis

K

E Trigger

K

Sovereign Debt Crisis

B Trigger

I

Money Supply Shock

C Trigger

H

Financial Services Sector

Collapse

D Trigger

G

L

A Trigger

J

Credit Crisis

Global Recession Definition 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

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

“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

Load-Map-Shuffle-Reduce Process

Load Map Shuffle Reduce

Informatica / Hortonworks VIBE

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

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

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

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

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

Social Intelligence – Profiling and Analysis

Fanatics - 10%

Enthusiasts - 20%

Casuals - 30%

Indifferent - 40%

The Cone™ – Profiling & Analysis

The Cone™ Brand Loyalty & Affinity

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

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

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

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

Insights Insights

Anomaly 42 Unica

Offers and Promotions

People and Places

Campaigns

Social Intelligence • User Content and Blogs

• Social Groups and Networks

EXPERIAN

Social Intelligence – Actionable Insights

Brand Affinity

Social Interaction

Geo-demographic Profile

Experian 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

Social Intelligence – Split-Map-Shuffle-Reduce Process

Split Map Shuffle Reduce

Key / Value Pairs

The Cone™ - CAMPAIGN

Social Intelligence – CAMPAIGN MANAGEMENT

The Cone™ – CYCLE

Salesforce

Anomaly 42

Cone

Unica

End User

BIG DATA

ANALYTICS

Cone™ Brand Affinity

Campaign

CRM

Insights

Insights Insights

SALES

PEOPLE

DEMOGRAPHICS Household Data

SOCIAL INTELLIGENCE User Content, Social Groups and Networks

Offers and Promotions

People & Places

Streaming & Segmentation

The Cone™ – CYCLE

Social Interaction

How consumers use social media (e.g., Facebook, Twitter) to address and/or engage with companies around social and environmental issues.

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.....

Split-Map-Shuffle-Reduce Process

Split Map Shuffle Reduce

Key / Value Pairs

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

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

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

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

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

Telco 2.0 “Big Data” Analytics Architecture

• 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 Sectors Technologies 1. Cloud Services 2. The Mobile Enterprise 3. In-memory Computing

SAP – Outlook for 2014

SAP HANA version 2 EXPERIENCE

• 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

• 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.

SAP HANA Overview

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

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

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

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

SAP HANA Architecture Overview

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

SAP HANA Architecture

• 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)

into their analytic models in order to further amplify the utility.