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4 books on Data Quality Monitoring [PDF]
Updated: February 09, 2024 | 17 |
Books on Data Quality Monitoring are indispensable resources for Data Quality Monitoring startups, offering profound insights and strategic guidance in the ever-expanding field of data quality assurance and management. These resources provide a comprehensive understanding of data quality technologies, validation methodologies, and best practices, empowering startups with the knowledge and expertise required to develop robust, accurate, and scalable solutions. They delve into critical aspects such as data profiling, cleansing, and anomaly detection, enabling startups to create platforms that continuously monitor and ensure the integrity of data assets for businesses and organizations. Furthermore, these books often explore emerging trends like machine learning-based data quality assessment and integration with data governance frameworks, ensuring startups remain innovative and competitive in the ever-evolving data quality monitoring industry.
1. Data Quality Fundamentals
2022 by Barr Moses, Lior Gavish, Molly Vorwerck
If your product dashboards seem off, quarterly reports lack freshness, or your data set appears flawed, "Data Quality Fundamentals" is the solution you need. Addressing common issues faced by almost every team in a reactive and ad hoc manner, this book is a comprehensive guide for data engineering teams grappling with the "good pipelines, bad data" dilemma. Authored by Barr Moses, Lior Gavish, and Molly Vorwerck of the data observability company Monte Carlo, the book provides insights into tackling data quality and trust at scale. By sharing best practices and technologies employed by some of the world's most innovative companies, the authors guide readers on building more trustworthy and reliable data pipelines, conducting data checks, setting and maintaining data SLAs, SLIs, and SLOs, leading data quality initiatives, treating data services with production software diligence, automating data lineage graphs, and developing anomaly detectors for critical data assets. If you seek to enhance your data quality practices, this book offers a strategic and practical approach to address challenges and implement effective solutions.
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2. Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework
2012 by Laura Sebastian-Coleman
The book "Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework" offers a comprehensive guide to measuring and maintaining data quality over time. Beginning with fundamental measurement concepts, the framework explores over three dozen measurement types across five key dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Emphasizing continuous measurement rather than one-time activities, the book provides a clear and accessible approach that can be grasped by both business and IT professionals. Practical guidance on applying the Data Quality Assessment Framework (DQAF) within any organization is provided, enabling the prioritization of measurements and effective reporting of results. The book also includes strategies for utilizing data measurement to govern and enhance data quality, along with guidelines for integrating the framework into data assets. Readers will gain the ability to prioritize measurement types, determine their placement in data flows, and establish optimal measurement frequencies. Additionally, the book covers conceptual models for defining and storing data quality results, facilitating trend analysis. It is a valuable resource that empowers organizations to address specific business priorities and overcome data quality challenges through a technology-independent framework.
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3. Data Quality for Analytics Using SAS
2012 by Gerhard Svolba
"Dive into the realm of data quality for analytics with Gerhard Svolba's 'Data Quality for Analytics Using SAS,' a guide that leverages the robust capabilities of SAS to measure and enhance data quality. Focused on the crucial aspects of selecting the right data sources and ensuring data quantity, relevancy, and completeness, the book is structured into three parts. The first, a conceptual section, defines data quality with rich content, explanations, and examples. The second part demonstrates how to profile data quality status and improve it through analytical methods. The final section delves into the repercussions of poor data quality for predictive modeling and time series forecasting. Gain insights into using SAS for advanced data quality profiling and discover how SAS can elevate and refine your data quality practices. A valuable addition to the SAS Press program, this book is a comprehensive resource for anyone navigating the intricacies of data quality in the analytics landscape."
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4. Data Quality: The Accuracy Dimension
2003 by Jack E. Olson
"Data Quality: The Accuracy Dimension" delves into the assessment and enhancement of corporate data accuracy through the utilization of the data profiling method. In an era where corporate data holds increasing significance and its various applications continue to expand, the precision of data within information systems has become a pivotal objective for companies mindful of its profound impact on their overall performance. Jack Olson, an original developer of data profiling technology, introduces and demystifies data profiling, elucidating its role in the broader context of data quality. This book serves as an accessible and engaging introduction to the realm of data accuracy, enriched with real-world anecdotes. It not only provides a framework for data profiling but also discusses analytical tools pertinent to the evaluation of data accuracy. A valuable read for data management and IT professionals, as well as CIOs overseeing companies with substantial data assets.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Data Quality Fundamentals
2022 by Barr Moses, Lior Gavish, Molly Vorwerck
If your product dashboards seem off, quarterly reports lack freshness, or your data set appears flawed, "Data Quality Fundamentals" is the solution you need. Addressing common issues faced by almost every team in a reactive and ad hoc manner, this book is a comprehensive guide for data engineering teams grappling with the "good pipelines, bad data" dilemma. Authored by Barr Moses, Lior Gavish, and Molly Vorwerck of the data observability company Monte Carlo, the book provides insights into tackling data quality and trust at scale. By sharing best practices and technologies employed by some of the world's most innovative companies, the authors guide readers on building more trustworthy and reliable data pipelines, conducting data checks, setting and maintaining data SLAs, SLIs, and SLOs, leading data quality initiatives, treating data services with production software diligence, automating data lineage graphs, and developing anomaly detectors for critical data assets. If you seek to enhance your data quality practices, this book offers a strategic and practical approach to address challenges and implement effective solutions.
Download PDF
2. Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework
2012 by Laura Sebastian-Coleman
The book "Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework" offers a comprehensive guide to measuring and maintaining data quality over time. Beginning with fundamental measurement concepts, the framework explores over three dozen measurement types across five key dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Emphasizing continuous measurement rather than one-time activities, the book provides a clear and accessible approach that can be grasped by both business and IT professionals. Practical guidance on applying the Data Quality Assessment Framework (DQAF) within any organization is provided, enabling the prioritization of measurements and effective reporting of results. The book also includes strategies for utilizing data measurement to govern and enhance data quality, along with guidelines for integrating the framework into data assets. Readers will gain the ability to prioritize measurement types, determine their placement in data flows, and establish optimal measurement frequencies. Additionally, the book covers conceptual models for defining and storing data quality results, facilitating trend analysis. It is a valuable resource that empowers organizations to address specific business priorities and overcome data quality challenges through a technology-independent framework.
Download PDF
3. Data Quality for Analytics Using SAS
2012 by Gerhard Svolba
"Dive into the realm of data quality for analytics with Gerhard Svolba's 'Data Quality for Analytics Using SAS,' a guide that leverages the robust capabilities of SAS to measure and enhance data quality. Focused on the crucial aspects of selecting the right data sources and ensuring data quantity, relevancy, and completeness, the book is structured into three parts. The first, a conceptual section, defines data quality with rich content, explanations, and examples. The second part demonstrates how to profile data quality status and improve it through analytical methods. The final section delves into the repercussions of poor data quality for predictive modeling and time series forecasting. Gain insights into using SAS for advanced data quality profiling and discover how SAS can elevate and refine your data quality practices. A valuable addition to the SAS Press program, this book is a comprehensive resource for anyone navigating the intricacies of data quality in the analytics landscape."
Download PDF
4. Data Quality: The Accuracy Dimension
2003 by Jack E. Olson
"Data Quality: The Accuracy Dimension" delves into the assessment and enhancement of corporate data accuracy through the utilization of the data profiling method. In an era where corporate data holds increasing significance and its various applications continue to expand, the precision of data within information systems has become a pivotal objective for companies mindful of its profound impact on their overall performance. Jack Olson, an original developer of data profiling technology, introduces and demystifies data profiling, elucidating its role in the broader context of data quality. This book serves as an accessible and engaging introduction to the realm of data accuracy, enriched with real-world anecdotes. It not only provides a framework for data profiling but also discusses analytical tools pertinent to the evaluation of data accuracy. A valuable read for data management and IT professionals, as well as CIOs overseeing companies with substantial data assets.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
