Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. In this session, you’ll learn actionable ways to help your organization guard against the data quality challenges uncovered this past year and be better prepared to respond in the future.
Participants will learn:
- How data profiling and data quality assessments can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
- How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
- How defining data quality based on its measurable utility can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.