Asset Data Integrity Is Serious Business

Asset Data Integrity Is Serious Business
Robert S. DiStefano, Stephen J Thomas
224 Pages, Hardcover
Published: April, 2006

Asset Data Integrity Is Serious Business
by Robert S. DiStefano and Stephen J Thomas

If your asset data is not reliable, you need to convince the organization of the enormous potential that is locked away. To accomplish this, you need to understand the breadth of the problem and the value of solving it. A viable business case for action is needed-so let's get started!

Physical asset data integrity is a critical aspect of every business, often the most valuable asset on the balance sheet, yet it is often overlooked. The data that we have about our assets collectively creates information, provides for accurate analysis and facilitates sound business decisions. Without accuracy of asset data there is a strong potential for poor decisions and their negative consequences. This book will not only provide an appreciation of this fact, it will also provide a road map to achieving value out of something most CEOs, managers, and workers often overlook.

• Relies on the authors' decades of experience and hands-on expertise that cannot be obtained elsewhere.
• Includes an assessment tool enabling the reader to easily recognize areas of improvement once a problem is detected.
• Features a valuable practical "how to" information.
• Focuses on the entire management spectrum, allowing everyone to see the value of data integrity within the context of their own responsibilities.


Robert S. DiStefano, CMRP is Chairman and CEO of Management Resources Group, Inc. He is an accomplished executive manager with more than 30 years of professional engineering, maintenance, reliability, management, and consulting experience.

Steve Thomas has 40 years of experience working in the petrochemical industry. He has published six books through Industrial Press, Inc., and Reliabilityweb.com, the most recent being Asset Data Integrity is Serious Business and Measuring Maintenance Workforce Productivity Made Simple.


• The Business Case for Data Integrity
• Introduction to the Business Case
• Information Overload
• Searching for Data
• Retiring Baby Boomers
• The Brain Drain
• A Business Case Example
• Consistency or Lack Thereof
• The Data Integrity Corporate Entitlement
• Impact on Shareholder Value
• Overview
• Who Are The Stakeholders?
• Why We Wrote This Book
• Who Will Benefit?
• What You Will Learn
• Chapter Synopsis
• Let's Get Started
• Defining the Terms
• Data Elements
• Taxonomy and Why Is It Important?
• What We Are Looking for in Good Data
• The Downside of Poor Data Integrity
• A Word About Information Technology
• Understanding Data Is Just the Beginning
• About Life Cycles
• The Asset Life Cycle
• The Asset Data Life Cycle
• Why the Data Life Cycle is Important
• Roles and Responsibilities Within the Asset Life Cycle
• It Is Never To Soon To Start
• Life Cycle Links
• Life Cycles as a Foundation
• Task vs. Strategic
• The Data Integrity Transform
• Data Integrity Tasks
• Reactive Data Integrity
• Proactive Data Integrity
• From Reactive to Proactive
• Indirect Impacts
• Decisions Are Just the Beginning
• Indirect Inputs
• Indirect Outputs
• The Legal Umbrella
• Indirect Aspects of the Transform
• External Issues
• Outcomes and Impacts - Partners
• Outcomes and Impacts - Suppliers
• Outcomes and Impacts -Customers
• Outcomes and Impacts -Agencies
• Outcomes and Impacts - Public
• Outcomes and Impacts - Insurance Carriers
• The External Impacts Are Important
• The Implication for IT
• Implications to IT of a Modern Asset Data Management Practice
• The Advent of ERP Systems
• Master Data Management
• The Future
• Historical View
• What Is an Asset?
• Asset Classification
• Static Data vs. Dynamic Data
• The Differences Among Assets, Functional
• Locations and Functional Location Hierarchies
• Other Asset-Related Master Data
• Asset Master Data Structure and Formatting
• Ideal Asset Data Repositories
• Enterprise-Level vs. Plant-Level Asset Data Integrity
• The Model For Material
• What Is a Spare Part?
• Items Classification
• Static Data vs. Dynamic Data
• Ideal Item Data Repositories
• Enterprise-Level vs. Plant-Level Item Data Integrity
• Data Quality Dimensions - The Beginning
• The Approach to the Assessment
• The Initial Steps
• The Assessment-General Comments
• The Assessment Process
• Moving Forward
• Similar But Different
• Assessing Asset Data
• Assessing Material Data
• Data Strategy Session
• To-Be Taxonomy
• Primary Data Fields
• Class and Subclass
• Manufacturer or Supplier Name
• Asset-Model Number or Serial Number
• Material Items-Manufacturer or Supplier Part Number
• Attribute Templates
• Other Asset Data Fields
• The Goal-Quality Data for the Future
• After the Assessment
• Data Repair is Far from Simple
• Repair Problems
• Data Repair Strategies
• The Big Bang Approach
• Fix It As You Go
• The Line in the Sand-More on Sustainability
• Commitment to Doing the Work
• Data Governance - Insight to the Problem
• Shifting the Burden
• The Long Term Solution
• The Benefits of Data Governance
• The Jobs of Data Governance
• It's All About Policy and Controls
• Roles and Responsibilities
• When Should We Start?

Data Governance

Sustaining What Has Been Created

• The Need to Sustain
• Establishing Ownership
• Communication
• Process and Procedures
• Training
• Prepare for Data Growth
• Walking the Walk
• Quality Control and Quality Assurance
• Using Key Performance Indicators
• The Continuous Improvement Cycle
• Sustainability Is Not Optional
• Getting Started

The Business Case for Data Integrity

Plant Asset Information - A Keystone for Success

What is Data Integrity?

The Asset / Data Integrity Life Cycle

Data Integrity at the Task Level

Internal Outcomes and Impacts

External Outcomes and Impacts

Information Technology (IT) Problems and Solutions

Building an Enterprise-Level Data Integrity Model

Building an Enterprise-Level Inventory Catalog Data Integrity Model

Data Integrity Assessment

Assessment Details-Assets and Material Items

Asset Data Clean-Up and Repair

Data Integrity Is Serious Business

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