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by Shannon Cutt
Getting Data Right
Introduction
1. The Solution: Data Curation at Scale
Three Generations of Data Integration Systems
Five Tenets for Success
Tenet 1: Data Curation Is Never Done
Tenet 2: A PhD in AI Can’t be a Requirement for Success
Tenet 3: Fully Automatic Data Curation Is Not Likely to Be Successful
Tenet 4: Data Curation Must Fit into the Enterprise Ecosystem
Tenet 5: A Scheme for “Finding” Data Sources Must Be Present
2. An Alternative Approach to Data Management
Centralized Planning Approaches
Common Information
Information Chaos
What Is to Be Done?
Take a Federal Approach to Data Management
Use All the New Tools at Your Disposal
Don’t Model, Catalog
Cataloging Tools
Keep Everything Simple and Straightforward
Use an Ecological Approach
3. Pragmatic Challenges in Building Data Cleaning Systems
Data Cleaning Challenges
1. Scale
2. Human in the Loop
3. Expressing and Discovering Quality Constraints
4. Heterogeneity and Interaction of Quality Rules
5. Data and Constraints Decoupling and Interplay
6. Data Variety
7. Iterative by Nature, Not Design
Building Adoptable Data Cleaning Solutions
4. Understanding Data Science: An Emerging Discipline for Data-Intensive Discovery
Data Science: A New Discovery Paradigm That Will Transform Our World
Significance of DIA and Data Science
Illustrious Histories: The Origins of Data Science
What Could Possibly Go Wrong?
Do We Understand Data Science?
Cornerstone of a New Discovery Paradigm
Data Science: A Perspective
Understanding Data Science from Practice
Methodology to Better Understand DIA
DIA Processes
Characteristics of Large-Scale DIA Use Cases
Looking Into a Use Case
Research for an Emerging Discipline
Acknowledgment
5. From DevOps to DataOps
Why It’s Time to Embrace “DataOps” as a New Discipline
From DevOps to DataOps
Defining DataOps
Changing the Fundamental Infrastructure
DataOps Methodology
Integrating DataOps into Your Organization
The Four Processes of DataOps
Data Engineering
Data Integration
Data Quality
Data Security
Better Information, Analytics, and Decisions
6. Data Unification Brings Out the Best in Installed Data Management Strategies
Positioning ETL and MDM
Extract, Transform, and Load
Master Data Management
Clustering to Meet the Rising Data Tide
Embracing Data Variety with Data Unification
Data Unification Is Additive
Data Unification and Master Data Management
Data Unification and ETL
Changing Infrastructure
Probabilistic Approach to Data Unification
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