MIT Press
All organizations today confront data quality problems, both systemic and
structural. Neither ad hoc approaches nor fixes at the systems leve -- installing
the latest software or developing an expensive data warehouse -- solve the basic
problem of bad data quality practices. Journey to Data Quality
offers a roadmap that can be used by practitioners, executives, and students for
planning and implementing a viable data and information quality management program.
This practical guide, based on rigorous research and informed by real-world
examples, describes the challenges of data management and provides the principles,
strategies, tools, and techniques necessary to meet them.The authors, all leaders in
the data quality field for many years, discuss how to make the economic case for
data quality and the importance of getting an organization's leaders on board. They
outline different approaches for assessing data, both subjectively (by users) and
objectively (using sampling and other techniques). They describe real problems and
solutions, including efforts to find the root causes of data quality problems at a
healthcare organization and data quality initiatives taken by a large teaching
hospital. They address setting company policy on data quality and, finally, they
consider future challenges on the journey to data quality.
Pipino / Wang / Funk
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structural. Neither ad hoc approaches nor fixes at the systems leve -- installing
the latest software or developing an expensive data warehouse -- solve the basic
problem of bad data quality practices. Journey to Data Quality
offers a roadmap that can be used by practitioners, executives, and students for
planning and implementing a viable data and information quality management program.
This practical guide, based on rigorous research and informed by real-world
examples, describes the challenges of data management and provides the principles,
strategies, tools, and techniques necessary to meet them.The authors, all leaders in
the data quality field for many years, discuss how to make the economic case for
data quality and the importance of getting an organization's leaders on board. They
outline different approaches for assessing data, both subjectively (by users) and
objectively (using sampling and other techniques). They describe real problems and
solutions, including efforts to find the root causes of data quality problems at a
healthcare organization and data quality initiatives taken by a large teaching
hospital. They address setting company policy on data quality and, finally, they
consider future challenges on the journey to data quality.
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