As a healthcare organization, we rely heavily on data to guide critical decisions around patient care, resource allocation, and strategic planning. However, extracting value from our data depends on our ability to efficiently find, understand, and leverage the most effective datasets across the enterprise. Improving discoverability has been critical to enhancing data assets' reusability.
As your analytics capabilities mature, you quickly amass a wealth of datasets. But without discoverability, these data products lose their relevance, leading to duplicative efforts as teams create their data instead of reusing existing sources. For example, our client had multiple unconnected datasets around patient satisfaction scores stored in various places like our EHR system, survey platform, and ad-hoc trackers. It became clear that while they had invested in scalable data and self-service analytics, the user experience had become fragmented.
You can redesign the user experience in your Power BI service data hub to emphasize key discoverability elements:
- Intuitive Naming Conventions
Standardized, descriptive dataset names enable self-service exploration based on user needs. Previously, our client's patient survey data was disorganized, labeled only as "Survey Data" without any clear context. Now, thanks to established guidelines and models, contributors consistently name datasets more descriptively, such as "Patient Satisfaction 2021," providing clarity about the content of the files.
Detailed metadata tagging allows one to search datasets by business terms rather than technical jargon. Your data stewards are responsible for expanding metadata breadth to enhance findability. For instance, tagging the patient satisfaction datasets with attributes like "patient experience" and "loyalty" allows less technical users to locate them.
Curation features like user comments, usage metrics, and ratings help surface the most relevant and trustworthy data products. It allows users to leverage insights from colleagues rather than relying solely on descriptions. The patient satisfaction score dataset now has ratings and reviews highlighting its value in understanding care quality perceptions.
- Advanced Search Functionality
Enhanced search capabilities allow drilling down by use case, data owner, refresh frequency, and other attributes. It pinpoints the precise datasets that best meet users' analysis needs. Users can filter to find the most up-to-date or authoritative datasets on patient experience based on their goals.
By emphasizing discoverability, you can make great strides in enabling users to locate accurate, usable data sources to power analytics use cases. Reduced duplication and friction have yielded more impactful insights and accelerated knowledge sharing. With the tools now in place for teams to efficiently find relevant datasets, your users can focus their time on extracting insights rather than chasing data.