Information systems architecture for the Oil and Gas industry

IBM Sales and Distribution Thought Leadership White Paper

Information systems architecture for the Oil and Gas industry

Chemicals & Petroleum

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Information systems architecture for the Oil and Gas industry

Overview Oil and gas companies continue to address the information challenges associated with digital oilfield implementations by focusing on optimizing operations, productivity, efficiency and safety. Managing the realities of lower commodity prices, capital scarcity, ongoing productivity deficiencies and community scrutiny requires a deft balance—one which innovative technology solutions can help achieve. Driving value from information has long been seen as central to the mission of optimized production management, improved maintenance and reliability, better supply chain management and increased safety. But with technologies changing as quickly as they do, how should organizations best tailor a path through information management, big data, analytics and business intelligence? This paper lays out an architectural response to this question, providing a descriptive primer for information systems architecture for the oil and gas industry, aimed at business users, IT architects and solution designers.

People, process and technology This paper addresses technology and systems architecture, as well as the critical change management elements needed for people to embrace new or changed technologies, and for the business to integrate these technologies into their standard processes. For a consistent operational model to succeed, change needs to be appropriately designed and implemented. The change management elements include structures such as project management offices, centers of competency and others, and the detailed change management work required to document, standardize, refactor and optimize business processes. It is perhaps even more critical to plan and execute based on the required business sponsorship, stakeholder management and benefits realization to deliver technology-enabled transformations.

People Benefits

Sponsors Operating Model

Process

Technology

Stakeholders

Figure 1. High-­level Operating Model

Information systems architectural approach To begin planning the technologies required for information management and analytics for an oil and gas company, we must first understand the types of systems and data that we are trying to manage and analyze. There are many methods of depicting the high-level systems architecture of oil and gas companies. One of the most widely accepted methods is the ISA-95 standard, an international standard for developing an automated interface between enterprise and control systems. For example, Figure 2 shows the ISA-95 architectural levels (from Level 0 at the bottom, which represents the production process itself, to Level 4 at the top, representing business planning and logistics) for the upstream oil and gas industry. Systems across the levels store and process data in varying execution windows.

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ISA-95 Level 4

Business planning and logistics (ERP) Production scheduling

Materials scheduling

Delivery and shipping

Inventory management

Months, years, decades

Multidimensional Structured

3

Olap Data warehouse Data marts

Operations Management

ISA-95 Level 2

ISA-95 Level 1

Performance and remote monitoring

Integrated Operations

Fleet, rail and shipping

Production Execution

Monitoring, Supervising & Automated Controls Facility Monitoring

Digital Control

Process Control

Historians

Analytic approaches

Asset management

Exploration

Data types

Supply chain management

Execution window

ISA-95 Level 3

Health, safety and environment

Sub-second

Unstructured Semistructured

Big Data Hadoop Real-Time

Sensing and Manipulation Sensors

Actuators

Networks

Production Process ISA-95 Level 0

Drill

Manage Water Port

Pipeline Ship

Plant Customer

Figure 2. Conceptual Architecture for Upstream Oil and Gas

A sensor at Level 1 may produce data every second or more frequently, while an enterprise planning system may work on a monthly, yearly or even 30-year basis. Data at the higher levels is also more likely to be structured such as in accounts, assets, schedules, whereas data at the lower levels is more likely to be unstructured, such as with video, images or text. These variations in time intervals and structure mean that different analytical approaches are necessary at different levels. Traditional OLAP, data warehousing and data marts are more applicable to the higher levels. Some of the newer big data and real-time capabilities offer additional value at the lower levels.

The 4 Vs of data Velocity, volume, variety and veracity are critical characteristics of data. The conceptual architecture in Figure 2 also introduces the concept of execution windows (shown in the Execution Windows arrow), or data velocity. The velocity with which data must be processed, ranging from real-time or near-real-time to monthly or yearly data, is one aspect to consider when choosing how best to store data. Velocity also influences the decision of which analytical approach (shown in the Analytic Types arrow) is most appropriate for gaining insight into that data. The volume of data raises another key consideration, in terms of small requirements for master data, to petabyte-scale requirements and beyond for video, geospatial, seismic and historic data.

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Information systems architecture for the Oil and Gas industry

Variety is also important, as some storage and analytics approaches are best at processing specific types of data whereas others provide capabilities to integrate and correlate various forms of data. Data varieties may include text, images and many other types of inputs. Figure 2 illustrates how data types may be managed and analyzed with different analytic approaches. Veracity refers to the accuracy, validity or quality of the data. Data veracity considerations include how much the data has been or needs to be cleansed, that is, made to be valid, accurate, complete, consistent and uniform. This is important because a data error in a real-time, transient data feed may be able to be ignored. However, data errors in a permanent, enterprise master data record could be costly to remediate. Figure 3 depicts these four key aspects of data.

Information systems Velocity, volume, variety and veracity determine which types of data storage and analytics processes are most appropriate for the data types found at the various levels of the oil and gas industry architecture.

Velocity

Data in Motion

Figure 3. The 4 Vs of Data

Volume

Data at Scale

For example, enterprise resource planning (ERP) data is typically transactional and is best represented by a relational, normalized data structure. Data may need to be processed in real time, as with transactions, or longer execution windows, in up to 30 year plans. ERP data volumes may be large but not extreme, such as gigabytes or more, and there are a variety of data types to store. A different type is stored in each table. Transactional consistency is important and data must conform to the relational schema defined. For example, a maintenance schedule cannot be entered for a piece of equipment that does not exist. This type of data is therefore best stored in a relational database management system, which may or may not use inmemory features for fast read/write capabilities. In order to perform analytics on this data, it is typically necessary to extract it to a multi-dimensional OLAP data warehouse to optimize read access and report on aggregations across groups of data. Operational data, on the other hand, is typically non-transactional, transient and may be semi-structured or totally unstructured. It is likely that such data needs to be processed in real time or “right” time, depending on the underlying operational equipment.

Variety

Data in Many Forms

Veracity

Data Uncertainty

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Operational data volumes can be extremely large (petabytes and beyond), particularly for seismic, image and video data, and in cases where years of historic data needs to be stored to perform predictive analytics. Variety is limited however. For instance, there aren’t hundreds of tables, but it might be necessary to integrate different types of data. An example of this is when integrating real-time data from an equipment sensor with video data from a nearby camera.

ISA-95 Level 4

Managing veracity can initially be less complex. Data cleansing is often limited to ensuring the data matches corporate master entities and removing “noise on the curve.” This type of data is often best stored in historians and other flat files or file formats specially designed for the data types in question. To analyze the data, however, particularly for large amounts of historical data, big data storage facilities such as NoSQL and Hadoop databases are often the best approach. These ideas are shown in Table 1.

Data

Structure

Volume

Velocity

Variety

Master data

Relational

Megabytes

Daily

Assets, corporate structure

Enterprise transactional

Relational

Gigabytes

Real-time to yearly

Assets, budgets, schedules, accounts, HSSE

Email

Unstructured text

Terabytes

Real-time

MIME

Documents

Unstructured text

Terabytes

Daily

Documents, spreadsheets, presentations, plans

Engineering data

Relational, ontologies, CAD, 3D

Terabytes

Daily

ISO15926, ontologies, CAD, 3D

ISA-95 Level 2

Operational semistructured

Semistructured

Terabytes

Real-time

Time series, well logs

ISA-95 Level 1

Operational unstructured

Unstructured

Petabytes

Real-time to yearly

Logs, videos, web logs, geospatial, seismic, text, images

ISA-95 Level 3

Table 1. Information Systems Considerations for Data Types

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Veracity ●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

ACID required Highest data cleansing requirements ACID required Data cleansed to map to relational schema Addressee validation Minimal requirements

Data cleansed to map to master data Data cleansed to map to master data Data cleansed to map to master data

Storage ●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

Analytics

Master data management platform

OLAP, enterprise data warehouse

Relational database In-memory database

OLAP, enterprise data warehouse

Email system

NoSQL database

Enterprise document management system

NoSQL database Hadoop database

Engineering data warehouse

Any

Historians

Hadoop database

Flat files, specialist file formats

Hadoop database

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Information systems architecture for the Oil and Gas industry

Information repositories The considerations for different types of storage mechanisms and analytical approaches for the various data types mean that a number of information repository capabilities are required across the enterprise. Table 2 provides a summary of the most important capabilities and their strengths and weaknesses. It also shows the underlying data storage techniques. Commonly used products are listed in the Examples column.

Repository Master data platform Relational database In-memory database Email system

Enterprise document management system Engineering data warehouse Historians

Structure ●● ●

Relational

Strengths

Weaknesses

Volume

Velocity

Storage

Master data

Anything else

Gigabytes

Real-time

●● ●

On disk

Examples ●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

Relational Columnar

Transactions

Columnar

Proprietary

Proprietary

Reports and analytics

Terabytes

Fast read/write for structured data

Large volumes

Terabytes

Email

Anything else

Document management

Real-time

●● ●

On disk

●● ●

●● ●

Real-time

●● ●

In memory

●● ●

●● ●

Anything else

Terabytes

Terabytes

Real-time

Real-time

●● ●

On disk Archive to tape

●● ●

On disk

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

●● ●

Relational Ontologies

Engineering data and documents

Anything else

Terabytes

Batch or real-time

●● ●

On disk

●● ●

●● ●

●● ●

●● ●

Key-value pairs

Time series

Anything else

Terabytes

Real-time

●● ●

On disk

●● ●

●● ●

OLAP, enterprise data warehouse NoSQL database Hadoop database

MOLAP Cubes Columnar DBs DW Appliances

Large volumes, fast retrieval, analytics

Transactions semi-structured and unstructured data

Petabytes

●● ●

JSON

Key-based retrieval, write performance

Reporting

Petabytes

Real-time

●● ●

●● ●

MapReduce

Inexpensive storage of semi- and unstructured data

Fast retrieval

Petabytes

Batch

●● ●

●● ●

●● ●

●● ●

Table 2. Information Systems Considerations for Data Repositories

Batch

●● ●

●● ●

●● ●

On disk In memory On appliance On disk

MPP file system

●● ●

●● ●

●● ●

SAP Netweaver MDM IBM InfoSphere MDM IBM DB2 MS SQL Server IBM DB2 BLU SAP HANA IBM Lotus Notes MS Exchange

IBM FileNet EMC Documentum MS Sharepoint

Bentley Intergraph Aveva OSISoft PI Honeywell Phd IBM Cognos SAP HANA IBM Netezza

●● ●

IBM Cloudant

●● ●

IBM BigInsights

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Big data and analytics Information technology (IT) organizations have traditionally achieved strong implementations of ERP, document management and other systems at the higher levels of the conceptual architecture. Operations technology (OT) departments, on the other hand, have focused on the process control domain at the lower levels of the architecture. The current trend of IT and OT convergence means that these parts of the business come together to drive additional value from integrating and analyzing operational data. As we have discussed, traditional relational databases and data warehouses are less appropriate for storing and analyzing operational data, so a new architecture and approach is needed for big data and analytics. This new architecture for oil and gas introduces additional capabilities and concepts. Real-time analytics is a key new capability. That means analytics are run against each instance of data as it happens, rather than extracting batches of data to be analyzed later in a data warehouse. Applying this real-time analytics paradigm leads to new and enhanced capabilities to address critical questions: ●●

●●

●●

●●

What is happening currently? Why is it happening? What should we do? What could happen next?

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Perhaps most importantly, real-time analytics help answer the question: “What did I learn and what would be best to do next time?” To address these questions, the new architecture enables new capabilities: ●●

●●

●●

●●

●●

Discovery and exploration, often in conjunction with real-time visualization and alerts, which are used to understand the current state of operations Reporting and analysis, often root cause analysis, which is used to understand the behavioral characteristics of operations Predictive analytics, which provides the capability to understand future possibilities Decision management, which helps support the actions taken to move forward Cognitive computing, best exemplified by IBM® Watson™ technologies, which helps users plan future best outcomes based on learned operational behavior

Figure 4 highlights real-time processing and analytics as a key function.

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Information systems architecture for the Oil and Gas industry

New/Enhanced Applications

All Data

What is happening? Discovery and exploration

Deep Analytics data zone Operational data zone

Landing, Exploration and Archive data zone

EDW and data mart zone

What action should I take? Decision management

What could happen? Predictive analytics and modeling

Real-time Data Processing and Analytics

Information Integration and Governance Systems

Security

Storage

On premise, Cloud, as a Service Figure 4. Conceptual Architecture for Big Data and Analytics

What did I learn, what’s best? Cognitive

Why did it happen? Reporting and analysis

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Logical architecture The associated logical architecture for big data and analytics is depicted in Figure 5. Here, streaming computing technology is introduced to provide real-time or near-real-time analytics capabilities. Data integration is highlighted, and the needs for data governance, event processing and security and business continuity are also addressed.

Data Sources

Shared Operational Information

ISA-95 Level 2 ISA-95 Level 1

Data Acquisition

ISA-95 Level 3

Discovery and Exploration

What is happening?

Deep Analytics and Modeling

Analysis and Reporting

Why did it happen?

Analysis and Reporting

Predictive Analytics and Modeling

What could happen?

Decision Management

What action should I take?

Planning and Forecasting

What did I learn? What’s best?

Analytical Sources

ISA-95 Level 4 Data Integration

Landing, Archive and Exploration

Integrated Data Warehouse

Actionable Insight

Streaming Computing Governance Security and Business Continuity Management

Figure 5. Logical Architecture for Big Data and Analytics

An example of this architecture is shown in Figure 6. In this simple example, asset, such as equipment, information is captured from asset management and maintenance, repair and operations (MRO) inventory systems and integrated with historian data from sensors in the field to provide analytics and optimized planning and scheduling. Data adaptors are used to capture the data and transport it to an integration bus. From there, it can be loaded into a staging area (Hadoop in this instance) and the enterprise

data warehouse. Predictive analytics can be performed on both of these sources, depending on the level of information required. From historic data, asset performance is reported on, and events are analyzed using causal temporal analysis; asset failures are predicted, and optimized maintenance plans and schedules are produced. Data can also be used in real time mode, processing time series data from historians in a streaming computing engine and invoking the predictive analytics capabilities to provide realtime predictions of impending failures and other scenarios.

10 Information systems architecture for the Oil and Gas industry

Data Sources Inventory Management

ISA-95 Level 3

Asset Management

ISA-95 Level 2

Asset Historians

ISA-95 Level 1

Asset Sensors

Data Adaptors and Transport

ISA-95 Level 4

Assets

Shared Operational Information

Inventory

Asset Performance

Analytical Sources Integration Bus

Hadoop Staging Area

OLAP Enterprise Data Warehouse

Actionable Insight

Predictive Analytics Reporting and Analysis

Failure Analysis Predictive Asset Maintenance Maintenance Planning

Time Series Processing

Streaming Computing

Real-time Analytics

Maintenance Scheduling

Figure 6. Architecture Example

Hadoop and HANA Hadoop and SAP HANA solutions work side-by-side to provide an overall enterprise big data solution. Many IBM customers use Hadoop alongside the IBM Netezza® data warehouse appliance in a similar pattern. HANA can best be used to store high-value, frequently used, structured data—the type of data that comes from ERP and other corporate systems. Hadoop, on the other hand, lends itself to the world of unstructured and semi-structured data found in operational systems, including time series, web logs, geospatial, seismic, text, images, and video data. Holding these volumes of data in memory, as HANA does, would have little value. Hadoop uses large clusters of commodity servers to provide a cost-effective solution to the problem of analyzing and gaining insights from big data. The analytics performed on data in HANA and Hadoop are also different and complementary. With its in-memory, columnar structures, HANA is well-suited to high performance OLAP analytics, aggregating and summarizing data. An example would be forecasting production output by month, well or joint venture partner. Hadoop’s MPP file system and MapReduce programming model is better suited to large-scale batch processing, and the predictive analytics that can be the result of finding correlations and patterns among the data. An example would be analyzing years of sensor data from electric submersible pumps to detect anomaly patterns and predict future failures.

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The IBM Hadoop solution Apache Hadoop is an open source project. There are, however, distributions of Hadoop provided by a number of software vendors including IBM. The IBM InfoSphere® BigInsights™ Hadoop solution offers a number of distinctive advantages over using open source Hadoop. These include: ●●

●●

●●

●●

●●

●●

●●

Cost savings—A study by the International Technology Group of high-impact applications in six companies showed cost savings of 28 percent over three years for the use of the InfoSphere BigInsights analytics platform over open source Hadoop. Cost calculations included licensing, support and personnel. Stability and support—Hadoop and the open source software stack it relies on are currently defined in a set of at least 25 Apache sub-projects and incubators, which are supported only by the open source community. BigInsights offers an IBM-supported solution to ensure the stability of IBM clients’ analytics applications. Predictive analytics support—InfoSphere BigInsights provides interoperability with IBM SPSS® software, one of the industry’s leading predictive analytics solutions. Streaming computing—BigInsights contains a limited-use license for the IBM InfoSphere Streams advanced analytics platform, used for real-time, big data analytics. SPSS predictive models can be embedded into the InfoSphere Streams platform to provide real-time predictive analytic scoring on data streamed from meters, sensors, gauges and more. BigSheets, BigSQL and Big R—BigSheets provides a spreadsheet-like user interface for big data exploration and visualization. IBM BigSQL provides an SQL programming interface to Hadoop, reducing the need for scarce, expensive MapReduce programmers. IBM InfoSphere BigInsights Big R provides a library of functions for integrating the popular statistical computing language “R” with BigInsights. IBM Accelerator for Machine Data—This tool provides libraries to speed the development of analytic solutions for data from smart meters, factory floor machines and sensors in vehicles, among others. Other considerations—Interoperability, resiliency and manageability are also challenges with open source Hadoop. These are addressed by the InfoSphere BigInsights platform.

12 Information systems architecture for the Oil and Gas industry

Logical architecture capabilities Finally, in Figure 7 the logical architecture is elaborated. It details the capabilities required across the oil and gas enterprise for a successful big data and analytics rollout.

Transactional Data Application Data

Operations Data Sources Machine and Sensor Data

Monitoring, Supervising and Automated Controls Sensing and Manipulation

Real-Time Analytical Processing

Corporate Data Sources Third-Party Data

Image and Video Enterprise Content Data

Social Data

Internet Data

Shared Operational Information

Corporate Governance

Master and Reference

Content Hub

Data Integration Data Ingestion

Delivery and Visualization

Rules Management

Analytical Sources Data Warehousing Data Warehouse

Accelerators (In-Memory)

Data Marts

Analytical Appliances

Initial Stage ODS

Cubes

Time Persistent

Sandbox

Data Quality Clean Staging Transformation

Actionable Insight Decision Management

Metadata Catalog

Extract and Subscribe

Hadoop and Exploration

Load Ready

Landing

Exploration

Load

Archive

Indexing

Streaming Computing Predictive Analytics

IT Governance

Reporting, Analysis and Content Analytics

Data Publish

Planning, Forecasting

Scorecards, Dashboards

Budgeting

Query and Analysis

Reporting

Storytelling

Collaboration

Alerting, Monitoring

Data Access

Data Caching

Data Delivery

Discovery and Exploration Annotation

Data Virtualization

Data Federation

Public Clouds

Figure 7. Logical Architecture Capabilities for Big Data and Analytics

Data Link

Real-Time Insights

Appliances

Search

Reporting, Analysis and Content Analytics Predictive Analytics

Text Analytics

Simulation

Optimization

Data Mining

Correlations

Governance

Information Governance

IT Security, Privacy & Recovery

Real Time Decision Management

Data Services

Real-Time Analytical Processing Data Integration

Data Security & Privacy

Private Cloud

Activity Hub

Information Access

Semantic Layer

Operations Management

Data Sources

Data Acquisition (Extract, Replicate, Copy) & Application Access

Business Planning and Logistics (ERP)

Security & Business Continuity Management Custom HW Solutions

Platforms

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Information systems architecture implementation

value

No company will begin the journey of implementing its information systems architecture from a blank slate, so the architecture diagrams presented here can best be viewed as desired outcomes or end-state artifacts. Depending on the current maturity of the company, a tailored implementation roadmap for the relevant capabilities can be put in place.

Implement field instrumentation for surveillance of critical points in production system

1

Integrate operations and enterprise data for a crossfunctional view

A traditional maturity model, shown in Figure 8, can also be used to illustrate the current state of maturity and perform the gap analysis to the desired state in order to plan the transformation roadmap.

Intelligent operations Monitor critical performance factors and enable rapid response

3

2

Questions such as “Can we integrate ERP data with our sensor data?” and “Can we do analytics on data as it occurs?” and “Do we have optimally performing, cost-effective repositories for all of our data?” will help to clarify the current level of maturity.

Intelligent

Integrated

Instrumented

Maturity Figure 8. Information Systems Architecture Maturity Model

Predictive operations Enable proactive management of production system

Optimize resource recovery Model & implement systemic changes to enhance profitability and realize full value

5

4

Predictive

Optimized

14 Information systems architecture for the Oil and Gas industry

IBM offers a unique value proposition IBM presents a unique value proposition to oil and gas clients to help them use leading industry information systems technologies to improve productivity, profitability and safety. IBM does this through: ●●

●●

●●

●●

●●

●●

Consulting services and systems integration to help plan and deliver on the people, process and technology elements of any information system or big data and analytics implementation A robust, enterprise-class relational database management system, such as with IBM DB2® software, which offers relational, columnar and in-memory data storage and retrieval A comprehensive set of technologies for big data repositories, including Hadoop distribution with InfoSphere BigInsights, a Netezza data warehouse appliance, and IBM Cloudant™, a NoSQL cloud service. A comprehensive set of technologies for big data analytics, including IBM real-time streaming capability with the InfoSphere Streams platform, predictive analytics with SPSS software, decision management and optimization software with the IBM ILOG® Optimization Decision Manager, and the Watson family of cognitive technologies Software that provides the governance, lifecycle management, archiving and security of all levels of enterprise data Specialized infrastructure, optimized for the performance and scalability of big data and analytics workflows, including those associated with Hadoop (both open-source and using BigInsights), the Netezza data warehouse appliance, and with SAP HANA

Cloud solutions provide the flexibility to scale costs with usage, shift expenses from capital to operational and quickly realize business benefits. IBM cloud solutions support public, private and hybrid approaches and consumption models, from software as a service (SaaS) to platform and infrastructure as a service (PaaS). Additional examples of IBM cloud services include: ●●

●●

Mobile solutions that provide information systems engagement and analytical insights for workers in the field. Systems support and management services for customized cloud-optimized IaaS on-premises support or managed services on installations hosted by IBM.

IBM Business Partners are a broad range of independent software vendors, open-source developers and systems integrators. These companies provide the deep ecosystem required to deliver enterprise-scale big data and analytics.

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Glossary of relevant terms ACID

Set of properties that guarantee database transactions are processed reliably, with atomicity, consistency, isolation and durability

Columnar

Database management system that stores data as columns rather than as rows, as does the relational schema

DW appliance

Integrated compute, storage, memory and software pre-optimized for data warehousing on a single node

Hadoop

Open-source framework for storage and processing of large-scale data on clusters of commodity hardware using a massively parallel file system such as a Hadoop Distributed File System

ISA-95

International standard for developing an automated interface between enterprise and control systems

ISO15926

Standard for data integration and exchange for process industries

JSON

JavaScript Object Notation—an open standard used to transmit key-value pairs

Key-value pair

A pair tuple:

MapReduce

Programming model used to store and search data in the Hadoop framework

MIME

Internet standard for email, headers and attachments

MPP file system

A distributed file system that supports massive parallel processing (MPP)

NoSQL

Not only SQL storage for semi-structured and unstructured data, such as non-relational data

OLAP

Online analytical processing

Ontology

Hierarchy of concepts within a domain, often represented in web ontology language (OWL)

Relational

Data structured according to the relational calculus, such as relationally normalized data

Semi-structured data

Data that can be structured according to a schema other than relational. Examples are XML and time series.

Structured data

Data that can be structured according to a relational schema

Time series

Series of key-value pairs for a given function over time:

Unstructured data

Data that is schema-less

Well logs

Depth-dependent time series for geological formations in a well or borehole

For more information To learn more about information systems architecture for the oil and gas industry, please contact your IBM representative or IBM Business Partner, or visit the following website: ibm.com/industries/chemicalspetroleum/?lnk=msoIS-cape-usen

About the author Ross Collins is Chief Technical Officer, Natural Resources Sector, IBM Australia - Chief Technical Officer, Chemicals & Petroleum, IBM

© Copyright IBM Corporation 2014 IBM Corporation Sales and Distribution Group Route 100 Somers, NY 10589 Produced in the United States of America November 2014 IBM, the IBM logo, ibm.com, Netezza, InfoSphere, DB2, Lotus Notes, Cognos, Cloudant, BigInsights, Watson, SPSS, and ILOG are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at ibm.com/legal/copytrade.shtml Microsoft, Windows and Windows NT are trademarks of Microsoft Corporation in the United States, other countries, or both. Other product, company or service names may be trademarks or service marks of others. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data discussed herein is presented as derived under specific operating conditions. Actual results may vary. It is the user’s responsibility to evaluate and verify the operation of any other products or programs with IBM products and programs. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. Please Recycle

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