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© 2011 Chevron Corp. All Rights Reserved

Case Study – Chevron’s Improve Data Quality Initiative

IBM Information Integration &

Governance Forum

March 10, 2011

Bev DemmelChevron Corporation

This document is intended only for use by Chevron for presentation at the 2011 IBM

Information Integration & Governance Forum. No portion of this document may be copied, displayed, distributed, reproduced, published, sold, licensed, downloaded, or used to create a derivative work, unless the use has been specifically authorized by Chevron in writing.

© 2011 Chevron Corp. All Rights Reserved 2

The Topics for Our Discussion Today

� A profile of Chevron

� An overview of Chevron’s Top 10

Information Types and the associated

projects for improving data quality

� What we are doing to improve our

organizational capability and to support

sustainability of our existing data quality

efforts

� Our measures of success

2Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

© 2011 Chevron Corp. All Rights Reserved 3

� 2nd largest integrated energy company in the United States

� 6th largest company in the world

� 65,000+ worldwide employees (includes service station personnel)

� 2.75 net million barrels of oil per day in 2009

� $19 Billion Net Income in 2009

� $26 Billion 2011 Capital and Exploratory budget

Chevron Is One of the Largest Integrated Energy Companies in the World

3Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

© 2011 Chevron Corp. All Rights Reserved 4

� 6,900+ employees and

contractors

� 30% of Information

Technology employees are

outside of US

� 64 global locations

� 4 infrastructure hubs: San

Ramon, Houston, London

and Singapore

� 4 specialized technical

computing sites: Houston,

San Ramon, Aberdeen and Perth

Technology Operations

Technology Hubs

Technical Computing Sites

* In some cases, one dot

designates multiple locations

Latin America& Caribbean

U.S. & Canada

Eurasia

Middle East

Europe

Africa

Asia-Pacific

Chevron Corporation Headquarters

Our Broad Information Technology Footprint Is Distributed Globally

4Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

© 2011 Chevron Corp. All Rights Reserved 5

The Case for Change:Data quality at Chevron is an important and escalating issue

5Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

Data is Growing Rapidly

� Chevron’s data doubles every two years � Employees spend 40-60% of their time on data access and quality issues

High Value Data is of Poor Quality

� To improve, we must work on standard definitions, systems of record, clear data

ownership and governance, and quality improvement.

� Multiple independent data quality efforts are taking place across the SBUs, but we

have been slow to address this from an enterprise perspective.

0

200

400

600

800

1000

2005 2006 2007 2008 2009

Terabytes of Data StorageTerabytes of Data Storage

Information Quality

Info

rma

tion

Va

lue

© 2011 Chevron Corp. All Rights Reserved 6

Our purpose is to improve our productivity and decision

quality through standardized, high value and high quality data

6Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

The objective of the Improve Data Quality initiative is to standardize the Top 10 Information

Types and for the Top 5, achieve assessment of high quality, by year end 2011.

Current State Desired State

Preparing information to make key decisions requires significant effort.

Organizations can quickly answer the most pressing business questions with high quality information.

Too much time spent managing information; everyone does it differently.

Leading practices and organizational capability exist to sustain and continuously improve data quality.

Data quality is someone else’s job to fix.

Managing data quality is an understood and accepted responsibility.

Only a select few organizations govern information standards and data quality.

Our most valuable information is governed through data governance councils.

GovernanceGovernance

AccountabilityAccountability

Standards and Sustainability

Standards and Sustainability

Decision Making

Decision Making

© 2010 Chevron Corp. All Rights Reserved 7

Headquarters

Production

Midstream

Refining

Marketing

Retail

Trading

Upstream

Downstream

MarketingExploration

Improving data quality allows us to get accurate and trusted answers to critical business questions

Equipment: Which types of equipment have the highest failure rates and in

what operating conditions do these occur? What is the spare part required?

What is the most cost effective source?

Spatial: Will new construction

collide with an older pipeline?

Which drilled wells lie in Angola

Block 14 Kuito Field?

Supplier Spend Activity: What is our total spend for third party waste

stewardship? What is our total spend with Halliburton?

Well: What types of well logs are available?

Are the formation picks correct for a well?

Which wells have been completed in which

reservoirs

over time?

Production: Which category of production

loss is most prevalent in

Gulf Of Mexico? Why?

Reserves: What is our

reserve replacement ratio?

Which wells are associated

with which reserves? What

is the risk associated with

P2 through P6 reserves?

Image Source: SAP, Improving Business Insight & Operational Excellence 2009

Supply Chain Customer Profitability: What are the highest

net profits per customer, region and

product within Downstream SBUs?

Which parts of our supply chain

require de-costing?

Marine and Pipeline Schedules: What product

commitments/sales are planned for the next 7 days that cannot

be met by equity product from the Pascagoula refinery? What is

the current terminal inventory?

Financial: What is the historic, current and projected spend by

General Ledger coding block?

Employee: What is our global employee headcount?

How effectively are we managing our total

compensation?

7Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

© 2011 Chevron Corp. All Rights Reserved 8

Improving data quality requires proven processes, tools and capabilities

8Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

The Building Blocks for Sustaining

Data Quality

� Organizational Capability, including Role

Definition and Training Curriculum

� Data Management Software Tools

� Data Quality Services

� Data Management Leading Practices

Repository

� Data Governance Starter Kit

� Enterprise Information Architecture

Methodology

© 2011 Chevron Corp. All Rights Reserved 9

New standards for a suite of Data Management software tools have been implemented

Data Management Data Management

Software ToolsSoftware Tools

Automating data quality and data management tasks through proven standard

software tools increases the accuracy and speed with which we can do our work.

Master Data Management

Meta Data Management

Data Quality Assessment

� Master Data Management tools provide a

controlled and consistent repository for basic

data that can be referenced or syndicated out for

use by many different applications. Master data

is key non-transactional business information

such as Customer, Product, Well, and Vendor.

� Meta Data Management tools provide a

repository to record and manage information

related to the data, to improve its usability, for

example, business definitions, business rules,

system of record, and where and how it is used.

� Data Quality Assessment tools analyze and

profile data for consistency, completeness and

adherence to defined business rules, and

provide mechanisms for cleansing incomplete or

inconsistent data based on those rules.

9Case Study – Chevron’s Improve Data Quality Initiative / March 10, 2011

© 2011 Chevron Corp. All Rights Reserved 10

Data Governance organizes and implements policies, procedures and standards for the effective use of an

organization's information and data assets

.

10Improve Data Quality Initiative Overview / October, 2010

The Data Governance Starter Kit is

designed to jump start the process of

establishing data governance for projects

or initiatives, and will provide:

� Definition of data governance

� Justification for data governance

� Governance processes

� Governance models

� Examples of Data Governance Councils,

charters and policies

� Decision rights and responsibilities

� Roles and relationships for data governance