Skip to Main Content
Measuring Data Quality for Ongoing Improvement
book

Measuring Data Quality for Ongoing Improvement

by Laura Sebastian-Coleman
December 2012
Intermediate to advanced content levelIntermediate to advanced
376 pages
13h 56m
English
Morgan Kaufmann
Content preview from Measuring Data Quality for Ongoing Improvement
Appendix C

Completeness, Consistency, and Integrity of the Data Model

Purpose

This appendix discusses how to assess the completeness, consistency, and integrity of the data model and metadata associated with the data model as part of data quality assessment for a database (as described in DQAF Measurement Types 1–5) (See Table C.1.). To be meaningful, data requires context. In databases, the data model, including its metadata, contributes significantly to this context. If information in the model is incomplete or inconsistent, it introduces risk. Data consumers may make poor decisions about which data to use, and technical processes may not function as expected.

Table C.1

Measurement Types Related to the Completeness, Consistency, Integrity of ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

A Product Development Approach to Improving Data Quality

A Product Development Approach to Improving Data Quality

Data Science Salon
Data Stewardship

Data Stewardship

David Plotkin

Publisher Resources

ISBN: 9780123970336