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Explore a preview version of Data Quality Assessment right now. Imagine a group of prehistoric hunters armed with stone-tipped spears.
Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies.
Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task.
Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organizing rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline.
Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners.
Skip to main content. Start your free trial. Data Quality Assessment by Arkady Maydanchik. Book Description Imagine a group of prehistoric hunters armed with stone-tipped spears. Table of Contents Product Information.
Table of Contents Chapter 1 Causes of data quality problems 1. Initial Data Conversion 1. System Consolidations 1. Manual Data Entry 1. Batch Feeds 1. Real-Time Interfaces 1. Data Processing 1. Data Cleansing 1. Data Purging 1. Changes Not Captured 1. System Upgrades 1. New Data Uses 1. Loss of Expertise 1. Data Quality Assessment 2. Data Cleansing 2. Monitoring Data Integration Interfaces 2. Project Team 3. Project Plan Overview 3. Planning Phase 3. Preparation Phase 3. Loading Data to Staging Area 3.
Gathering General Meta Data 3. Implementation Phase 3. Data Profiling 3. Designing Data Quality Rules 3. Fine-Tuning Phase 3. Introduction to Attribute Domain Constraints 4. Attribute Profiling 4. Optionality Constraints 4. Attribute Format Constraints 4. Valid Value Constraints 4.
Relational Data Model Basics 5. Identity Rules 5. Reference Rules 5. Cardinal Rules 5. Introduction to Historical Data 6. Currency Rules 6. Retention Rules 6.
Continuity and Granularity Rules 6. Timeline Patterns 6. Value Patterns 6. Data Quality Rules for Event Histories 6. Event Dependencies 6. Event Conditions 6. Introduction to State-Dependent Objects 7. Identifying State-Dependent Entities 7. Profiling State-Transition Models 7.
State and Terminator Profiling 7. State-Transition Profiling 7. Action Profiling 7. Conclusion 7. Rules Derived from State-Transition Diagrams 7. Domain Constraints 7. Transition Constraints 7. Timeline Constraints 7. Continuity Rules 7. Duration Rules 7. State Duration Profiling 7. Cumulative Duration Rules 7. Advanced Rules 7. Action-Specific Attribute Constraints 7. State-Specific Attribute Constraints 7.
Introduction to Attribute Dependency Rules 8. Redundant Attributes 8. Derived Attributes 8. Partially Dependent Attributes 8. Attributes with Conditional Optionality 8. Correlated Attributes 8. Identifying Dependencies through Analysis 8. Gathering Expert Knowledge 8. Investigating Data Relationships 8. Data Gazing 8. Identifying Dependencies through Data Profiling 8. Value Affinity 8. Value Correlation 8. Value Clustering 8. Identifying Dependencies Across Data Sources 8.
Step 1 — Identifying Secondary Data Sources 8. Step 2 — Qualifying Secondary Data Sources 8.
Data Quality Assessment
Data Quality Assessment. Arkady Maydanchik. Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies.
How to Create a Data Quality Scorecard by Arkady Maydanchik
Data Quality Assessment. Arkady Maydanchik. Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed.
This tutorial focuses on how to create a state dependent data quality rules process for your data quality management programme. This is an essential and often overlooked aspect of data quality improvement initiatives. Data quality scorecard is the centerpiece of any data quality management program. It provides comprehensive information about quality of data in a database, and allows both aggregated analysis and detailed drill-downs. A well-designed data quality scorecard is the key to understanding how well the data supports various reports, analytical and operational processes, and data-driven projects. It is also critical for making good decisions about data quality improvement initiatives.