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Practical Data Quality

Practical Data Quality

By : Hawker
4.9 (13)
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Practical Data Quality

Practical Data Quality

4.9 (13)
By: Hawker

Overview of this book

Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating. Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives. By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.
Table of Contents (16 chapters)
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1
Part 1 – Getting Started
6
Part 2 – Understanding and Monitoring the Data That Matters
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10
Part 3 – Improving Data Quality for the Long Term

Part 2 – Understanding and Monitoring the Data That Matters

Organizations have a vast quantity of data – and data volumes are growing all the time. A key part of a data quality initiative is understanding which data is worth your time and effort and provides the greatest benefit. To understand this, data quality professionals need to start with all the different stakeholders connected to the data and establish what they all need.

Once this is understood, stakeholders can communicate their business strategy and how it links to data and surface the challenges that data issues cause them. Data quality rules can thus be derived so that bad data can finally be identified.

Once you have completed this part of the book, you will understand how to optimize engagement with stakeholders so that the right rules are identified, and a suite of data quality reports accurately reflects the data quality position and trends over time.

This part comprises the following chapters...

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Tech Concepts
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Programming languages
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Practical Data Quality
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