Laura Sebastian-Coleman, Data Quality and Data Standards Center of Excellence Lead at Cigna, has worked on data quality in large health care analytic data warehouses since 2003.
Cigna is a global health service company dedicated to helping people improve their health, well-being and sense of security.
Laura has implemented data quality metrics and reporting, launched and facilitated data quality working groups, and contributed to data consumer training programs. She has led efforts to establish data standards and to manage metadata for large analytic data warehouses.
In 2009, she led a group of analysts at Optum in developing the original Data Quality Assessment Framework (DQAF) which is the basis for her book Measuring Data Quality for Ongoing Improvement (Morgan Kaufmann, 2013).
Click here to buy Laura’s book.
DQPVS 1: Data Quality Requirements: How to Define Measurable Characteristics of Data
To manage the quality of data, you must be able to measure it. To measure it, you need to define what you mean by quality data.
This presentation outlines an approach for defining measurable characteristics based on dimensions of quality.
It also discusses the relationship between data assessment and requirements definition, and the application of criticality and risk assessment to make better decisions about what data to measure and how to measure it.
DQPVS 2: Little Data in a Big Data World
In this presentation, data quality expert and author, Laura Sebastian-Coleman explores the data quality challenges in healthcare and their impact on Big Data initiatives.
- The promise of Big Data in Healthcare
- Challenges with the quality of healthcare data
- Academy health examples
- Science, commerce and data quality implications
- Butterfly effect in data quality
- Making good on promise of healthcare data analytics
- Recognising and reducing variation in data quality