Datasets related to the biopharmaceutical industry are often provided by third-party vendors rather than being collected by the company itself. Generally, raw datasets obtained from vendors are designed to be efficient, rather than optimally structured for data visualization. In this case, the data was split across multiple files, some acting as reference tables and some as transactional tables. These two types of files needed to be cross-referenced, cleaned, and manipulated for reporting purposes. As a result, the integration of multiple data sources to produce enhanced analytics reports presented a challenge.
Seeking to create analytics dashboards, our biopharmaceutical client acquired several datasets from a third-party data provider for the healthcare sector. For these dashboards, the client required the integration of several other data sources at different levels of granularity, all with the third-party datasets. The dashboards needed to visualize data in an intelligible way, presenting different levels of detail for the leadership team and managers. The leadership team who tend to use data at a higher level required executive dashboards with KPIs and other aggregated data to understand high-level trends. Managers, on the other hand, needed more granular operational dashboards to understand and gain insights for their specific department or team’s performance.
Optimus SBR Data conducted sessions with stakeholders to understand their objectives, requirements, and datasets in-scope. Once the requirements were verified with the client, several ETL (Extract, Transform & Load) workflows were created with Alteryx Designer to consolidate multiple data sources. In turn, this process transformed data into a structure for optimal consumption in Tableau, the client’s data visualization tool. By using defined business rules, the workflows were designed to combine data sources with different granularity and produce standardized data outputs for visualization in Tableau.
Optimus SBR Data created several Tableau dashboards that provided users the ability to track performance. The result was a “slice and dice” capability, where data can be used for ad-hoc analytical purposes. Because the size of the output is so large, optimizing dashboard performance was imperative. The transformation of data in the back-end combined with the application of best practices for Tableau dashboard development resulted in excellent loading time.
