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Books > Computing & IT > General theory of computing
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Executing Data Quality Projects - Ten Steps to Quality Data and Trusted Information (TM) (Paperback, 2nd edition)
Loot Price: R1,680
Discovery Miles 16 800
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Executing Data Quality Projects - Ten Steps to Quality Data and Trusted Information (TM) (Paperback, 2nd edition)
Expected to ship within 10 - 15 working days
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Executing Data Quality Projects, Second Edition presents a
structured yet flexible approach for creating, improving,
sustaining and managing the quality of data and information within
any organization. Studies show that data quality problems are
costing businesses billions of dollars each year, with poor data
linked to waste and inefficiency, damaged credibility among
customers and suppliers, and an organizational inability to make
sound decisions. Help is here! This book describes a proven Ten
Step approach that combines a conceptual framework for
understanding information quality with techniques, tools, and
instructions for practically putting the approach to work - with
the end result of high-quality trusted data and information, so
critical to today's data-dependent organizations. The Ten Steps
approach applies to all types of data and all types of
organizations - for-profit in any industry, non-profit, government,
education, healthcare, science, research, and medicine. This book
includes numerous templates, detailed examples, and practical
advice for executing every step. At the same time, readers are
advised on how to select relevant steps and apply them in different
ways to best address the many situations they will face. The layout
allows for quick reference with an easy-to-use format highlighting
key concepts and definitions, important checkpoints, communication
activities, best practices, and warnings. The experience of actual
clients and users of the Ten Steps provide real examples of outputs
for the steps plus highlighted, sidebar case studies called Ten
Steps in Action. This book uses projects as the vehicle for data
quality work and the word broadly to include: 1) focused data
quality improvement projects, such as improving data used in supply
chain management, 2) data quality activities in other projects such
as building new applications and migrating data from legacy
systems, integrating data because of mergers and acquisitions, or
untangling data due to organizational breakups, and 3) ad hoc use
of data quality steps, techniques, or activities in the course of
daily work. The Ten Steps approach can also be used to enrich an
organization's standard SDLC (whether sequential or Agile) and it
complements general improvement methodologies such as six sigma or
lean. No two data quality projects are the same but the flexible
nature of the Ten Steps means the methodology can be applied to
all. The new Second Edition highlights topics such as artificial
intelligence and machine learning, Internet of Things, security and
privacy, analytics, legal and regulatory requirements, data
science, big data, data lakes, and cloud computing, among others,
to show their dependence on data and information and why data
quality is more relevant and critical now than ever before.
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