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
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. 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. 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. 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. SMACT 4D Digital Technology
Telematics
The Internet of Things (IoT) – Smart Devices, Smart Apps, Wearable
Technology, Vehicle Telemetry, Smart Homes and Building Automation
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).
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. • 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
16. 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.
17. 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
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
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
19. 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
20. 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
21. 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 !
23. 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.....
24. 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
30. 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.
31. Apple Historic Stock Data Analysis Info-graphic using “Big Data”
MARKET RISK = MARKET SENTIMENT – ACTUAL RESULTS (REALITY)
32. 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”
33. 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
34. 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
35. 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
36.
37. 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
38. “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
41. 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
42. 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
43. 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
44. 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
45. 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
46. Social Intelligence – Profiling and Analysis
Fanatics - 10%
Enthusiasts - 20%
Casuals - 30%
Indifferent - 40%
The Cone™ – Profiling & Analysis
The Cone™
Brand Loyalty & Affinity
47. 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
48. 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
49. 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
50. 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
51. 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
52. Social Intelligence – Split-Map-Shuffle-Reduce Process
Split Map Shuffle Reduce
Key / Value Pairs
53. The Cone™ - CAMPAIGN
Social Intelligence – CAMPAIGN MANAGEMENT
54. 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
55. Social Interaction
How consumers use social media (e.g., Facebook, Twitter) to address and/or engage with companies around social and environmental issues.
56.
57. 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.....
59. 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
60. 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
61. 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
62. 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
63. 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
66. • 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
67. • 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
68. • 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.
70. 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
71. 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
72. 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
73. 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
75. 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
77. • 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)
