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. This leaves organizations unprepared for future events, like COVID-19, that require a rapid enterprise-wide analytic response. What are some actionable ways you can help your organization guard against the data quality challenges uncovered this past year and better prepare to respond in the future? Join Taylor Larsen, Director of Data Quality for Health Catalyst, to learn more. What You’ll Learn - How data profiling and data quality assessments, in combination with your data catalog, 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 (i.e., data represents information that supports better decisions) can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.