Much of what we do at Data Tapestry is helping our clients gain an understanding of their own data. If you work at a large organization, data access can be limited. This means you may not know what questions to ask of your data or what you can measure with your data. Hillary Rivera walks us through how she partnered with multiple stakeholders to gain insight from a variety of data sources.
Can you tell us about your background and current role at Data Tapestry?
I started out my career thinking I would eventually go to medical school, but I quickly realized I wanted to work in analytics. I studied public health and worked in healthcare data management for a while. Then I went back to school to study business analytics at UT. Since then, I’ve enjoyed working in ecommerce and software and technology. Now I’m back in healthcare working as a data scientist at Data Tapestry.
Tell me about how your project started with the client.
I had a unique experience with my client because I had two very disjoint responsibilities. One was to automate a report that showed how many dollars were being spent on physician liability claims. The other was to identify how physician stipend dollars were being allocated to physician leadership and to see if that allocation could be optimized based on facility volume.
How was each project different?
The report automation piece was very straight forward. I would receive a spreadsheet of physician claims and apply some filters to the data based on the parameters defined by the stakeholder.
The stipend dollar analysis was much more involved because it required us to define what we would consider as a stipend. There were also many subject matter experts involved because
there was no definitive knowledge of where all the data elements were located.
Can you describe your relationship with your stakeholder and how you managed the project to ensure forward progress?
For the stipend project, we had a weekly check in with most of the subject matter experts and the business partners. We discussed progress and defined tasks needed for forward progress. Outside of that, I would meet with my business partner more frequently since we were both learning where all the data elements lived and how they tied together.
Describe the nature of the deliverable you provided and how it helped the client.The physician claim report ended up turning into a data validation tool. So, the client ultimately wanted to reconcile what she was receiving from one system with what was being ingested by their data engineering team. For the stipend analysis, I delivered a master data set in which all the stipend dollars could be traced back to an individual and then to a facility. I also provided a script that provided a descriptive look at the relationship between dollars and facility volume.