Theresa Ancick and Sharad Varshney discuss the types of data quality implementation, which to focus on first, and common mistakes to avoid.
0:00 Introductions
2:11 Types of data quality implementation
8:42 Reactive data quality: process, cost, and criticality
15:45 Proactive data quality: involving the business and remediation context
23:00 Q: How do you involve the business in data quality and make them stakeholders?
26:46 Q: How does the tool identify the root cause of the problem and the owner of the issue?
29:08 Q: Who is involved in deciding criticality, and how do you map impact?
32:24 Q: Who “owns” the data? How can IT not be the owner of data if they control it?
34:28 Fit for consumption data
39:55 Anomaly detection
44:50 Measuring data quality
46:50 Q: What is the general structure of the data quality team?
48:28 Q: When do you conduct data quality scoring?
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