Read e-book online Measuring Data Quality for Ongoing Improvement: A Data PDF

By Laura Sebastian-Coleman

ISBN-10: 0123970334

ISBN-13: 9780123970336

The info caliber overview Framework
shows you ways to degree and video display facts caliber, making sure caliber through the years. You'll begin with basic options of dimension and paintings your method via an in depth framework of greater than 3 dozen dimension varieties regarding 5 goal dimensions of caliber: completeness, timeliness, consistency, validity, and integrity. Ongoing dimension, instead of one time actions may also help your company succeed in a brand new point of knowledge caliber. This plain-language method of measuring information will be understood by way of either company and IT and gives useful assistance on find out how to practice the DQAF inside of any association permitting you to prioritize measurements and successfully record on effects. techniques for utilizing info dimension to manipulate and increase the standard of information and directions for utilizing the framework inside of an information asset are incorporated. You'll come away in a position to prioritize which size kinds to enforce, realizing the place to put them in a knowledge stream and the way usually to degree. universal conceptual versions for outlining and storing of knowledge caliber effects for reasons of pattern research also are integrated in addition to well-known enterprise requisites for ongoing measuring and tracking together with calculations and comparisons that make the measurements significant and aid comprehend traits and notice anomalies.

• Demonstrates the best way to leverage a expertise self sufficient facts caliber dimension framework in your particular company priorities and knowledge caliber demanding situations

• allows discussions among enterprise and IT with a non-technical vocabulary for information caliber dimension

• Describes find out how to degree info caliber on an ongoing foundation with primary size kinds that may be utilized to any scenario

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Extra info for Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework

Sample text

The adjective abstract means “existing in thought or as an idea but not having a physical or concrete existence” (NOAD). As a verb, to abstract has several definitions, all of which include the concept of separating things from each other—for example, abstracting an idea from its historical context, or producing a summary (an abstract) of a book or article. Data enable us to understand facets of reality by abstracting (separating) and representing them in summary form (an abstract) (Peirce, 1955, p.

The DQAF unpacks the dimensions of completeness, timeliness, validity, consistency, and integrity in order to define repeatable patterns through which specific measurements can be taken in a consistent manner. DQAF measurement types can be thought of as generic business requirements for taking repeatable kinds of measurements—just as a thermometer may be thought of as a generic means of measuring temperature. Discussions about data quality measurement describe dimensions of data quality, but they do not necessarily describe how to apply them.

But for someone who has orthopedic problems, a pair of shoes means two things: a left shoe that may need one kind of alteration to be usable and a right shoe that may need another kind of alteration. The second example is about the arbitrariness of representation. As any high school student who has written a research paper knows, a necessary part of research is compiling a bibliography. There are different conventions for presenting information about the sources used in research. The American Psychological Association (APA) represents books like this: Talburt, J.

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Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework by Laura Sebastian-Coleman

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