Book description
Discover how to achieve business goals by relying on high-quality, robust data
In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications.
The author shows you how to:
- Profile for data quality, including the appropriate techniques, criteria, and KPIs
- Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
- Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
- Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business
An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Table of contents
- Cover
- Title Page
- Copyright
- Foreword
- Preface
- Acknowledgments
- PART I: Define Phase
- PART II: Analyze Phase
-
PART III: Realize Phase
- CHAPTER 7: Reference Architecture for Data Quality
-
CHAPTER 8: Best Practices to Realize Data Quality
- INTRODUCTION
- OVERVIEW OF BEST PRACTICES
- BP 1: IDENTIFY THE BUSINESS KPIs AND THE OWNERSHIP OF THESE KPIs AND THE PERTINENT DATA
- BP 2: BUILD AND IMPROVE THE DATA CULTURE AND LITERACY IN THE ORGANIZATION
- BP 3: DEFINE THE CURRENT AND DESIRED STATE OF DATA QUALITY
- BP 4: FOLLOW THE MINIMALISTIC APPROACH TO DATA CAPTURE
- BP 5: SELECT AND DEFINE THE DATA ATTRIBUTES FOR DATA QUALITY
- BP 6: CAPTURE AND MANAGE CRITICAL DATA WITH DATA STANDARDS IN MDM SYSTEMS
- KEY TAKEAWAYS
- CONCLUSION
- REFERENCES
-
CHAPTER 9: Best Practices to Realize Data Quality
- INTRODUCTION
- BP 7: RATIONALIZE AND AUTOMATE THE INTEGRATION OF CRITICAL DATA ELEMENTS
- BP 8: DEFINE THE SoR AND SECURELY CAPTURE TRANSACTIONAL DATA IN THE SoR/OLTP SYSTEM
- BP 9: BUILD AND MANAGE ROBUST DATA INTEGRATION CAPABILITIES
- BP 10: DISTRIBUTE DATA SOURCING AND INSIGHT CONSUMPTION
- KEY TAKEAWAYS
- CONCLUSION
- REFERENCES
- PART IV: Sustain Phase
- Appendix 1: Abbreviations and Acronyms
- Appendix 2: Glossary
- Appendix 3: Data Literacy Competencies
- About the Author
- Index
- End User License Agreement
Product information
- Title: Data Quality
- Author(s):
- Release date: February 2023
- Publisher(s): Wiley
- ISBN: 9781394165230
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