Appendix Quick References

Facts do not cease to exist because they are ignored.

– Anonymous

This appendix presents some of the key ideas from the methodology in an at-a-glance format. They are perfect for hanging on your cube or office wall or putting in a notebook when you need a fast reminder of ideas presented in more detail elsewhere in this book. See website at www.books.elsevier.com/companions/9780123743695 for downloads of printable versions of these items.

The Framework for Information Quality

1. Business Goals/Strategy/Issues/Opportunities. The “Why.” Anything done with information should help the business meet its goals.
2. Information Life Cycle. Use POSMAD to help remember the information life cycle:
   • Plan—Identify objectives, plan information architecture, and develop standards and definitions; many activities associated with modeling, designing, and developing applications, databases, processes, organizations, and the like.
   • Obtain—Data or information is acquired in some way; for example, by creating records, purchasing data, or loading external files.
   • Store and Share—Data are stored and made available for use.
   • Maintain—Update, change, manipulate data; cleanse and transform data, match and merge records; and so forth.
   • Apply—Retrieve data; use information. Includes all information usage such as completing a transaction, writing a report, making a management decision, and completing automated processes.
   • Dispose—Archive information or delete data or records.
3. Key Components. Four key components affect information quality.
   • Data (What)—Known facts or other items of interest to the business.
   • Processes (How)—Functions, activities, actions, tasks, or procedures that touch the data or information (business processes, data management processes, processes external to the company, etc.).
   • People and Organizations (Who)—Organizations, teams, roles, responsibilities, or individuals.
   • Technology (How)—Forms, applications, databases, files, programs, code, or media that store, share, or manipulate the data are involved with the processes, or are used by the people and organizations.
4. Interaction Matrix. Interaction between the Information Life Cycle phases (POSMAD) and the four Key Components.
5. Location (Where) and Time (When and How Long) Note: The top half of the framework, along with the first long bar, answers the interrogatives of who, what, how, why, where, when, and how long.
6. Broad-Impact Components. Additional factors that affect information quality. Lower your risk by ensuring that components have been discussed and appropriately addressed. If they are not addressed, you are still at risk (RRISCC) as far as information quality is concerned.
   • Requirements and Constraints
   • Responsibility
   • Improvement and Prevention
   • Structure and Meaning
   • Communication
   • Change
7. Culture and Environment. Take into account to better accomplish your goals.

Source: Copyright © 2005–2008 Danette McGilvray, Granite Falls Consulting, Inc.

Figure A.1 • The Framework for Information Quality (FIQ).

Source: Copyright © 2005–2008 Danette McGilvray, Granite Falls Consulting, Inc.

The POSMAD Interaction Matrix in Detail

The POSMAD Interaction Matrix is part of the Framework for Information Quality. Figure A.2 contains sample questions in each cell of the matrix to indicate the interaction between the phases of the POSMAD Information Life Cycle and the four Key Components—data, processes, people/organizations, and technology—that impact information quality.

Figure A.2 • POSMAD interaction matrix detail—sample questions.

Source: Copyright © 2005–2008 Danette McGilvray, Granite Falls Consulting, Inc.

POSMAD Phases and Activities

The acronym POSMAD is used to help remember the six phases—Plan, Obtain, Store and Share, Maintain, Apply, Dispose—in the Information Life Cycle. Table A.1 describes the activities and provides examples of them within each of the life cycle’s phases as they apply to information.

Table A.1 • POSMAD Information Life Cycle Phases and Activities

Data Quality Dimensions

A Data Quality Dimension is an aspect or feature of information and a way to classify information and data quality needs. Dimensions are used to define, measure, and manage the quality of the data and information. Table A.2 contains a quick reference list of the 12 data quality dimensions used in The Ten Step process.

Table A.2 • Data Quality Dimensions

Business Impact Techniques

Business Impact Techniques use qualitative and quantitative measures for determining the effects of data quality on the business. Table A.3 contains a quick reference list of the eight Business Impact Techniques used in the methodology—Ten Steps to Quality Data and Trusted Information™.

Figure A.3 shows a continuum of the relative time and effort to determine business impact for each technique from generally less complex and taking less time (technique 1) to more complex and taking more time (technique 8).

Table A.3 • Business Impact Techniques

*The phrases ROI or return on investment are often used in a general sense to indicate any means of showing some type of return on an investment. ROI in technique 8 refers to the formula for calculating return on investment.

Figure A.3 • Business impact techniques relative to time and effort.

Overview of The Ten Steps Process

The Ten Steps process is the approach for assessing, improving, and creating information and data quality. The steps that need to be used are shown in Figure A.4 and described in the box on the next page.

Figure A.4 • The Ten Steps process.

Source: Copyright © 2005–2008 Danette McGilvray, Granite Falls Consulting, Inc.

Definitions of Data Categories

Data categories are groupings of data with common characteristics or features. Table A.4 includes definitions and examples for major data categories. These definitions were jointly created by Danette McGilvray, author of Executing Data Quality: Ten Steps to Quality Data and Trusted Information™ and Gwen Thomas, president of the Data Governance Institute.

Table A.4 • Definitions of Data Categories