Data quality can make or break any data initiative or product. If you aren’t able to collect data that is accurate, or you have data sets that have varying structures, or are filled with typos and other issues caused by human error, then the chances drop drastically that your data models will be accurate, or even helpful.When it comes to healthcare, data quality can be an absolute nightmare. With so many different data sources, high data churn rates, and a lack of standardization in many different healthcare categories, it can seem impossible to make quality healthcare more easily accessible to people when they need it.Ribbon Health seeks to change that by using AI to improve the quality of healthcare data and create a data platform with actionable provider information including insurance coverage, prices, and performance.Today’s guests are Nate Fox, the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo, a former pediatrician who is now a data scientist at Ribbon Health.\Throughout the episode, we talk about why data quality in healthcare is messy, why having context around data is necessary to interpret and utilize it properly, how healthcare providers are improving their services because of platforms like Ribbon Health, how to tackle common data cleaning problems, and much more