Increased analyst efficiency
Fast & efficient analytic builds in the semantically enriched lake house allow analysts to create more value driving analysis more quickly
Increased analyst productivity
Enterprise capabilities for search and disambiguation boost productivity levels across the board
Improved QA & issue identification
Reduced time and effort to build manufacturing QA reports enables team to better identify issues and take action more quickly
A multinational biotech firm uses Stardog + Databricks to create a connected data fabric that allows them to unite data across the entire enterprise, driving business and decisions for current problems and unanticipated needs in the future.
The organization realized that the types of problems they were looking to solve had changed. The most pressing challenges were becoming more and more cross-functional, involving data and people from across the company. At the same time, the volume of data was increasing dramatically.
Although the necessary data existed, it was functionally siloed. It was difficult or impossible to unify and analyze the huge amounts of data across the drug development and commercialization life cycle, which limited sustained value realization. Data wasn’t always visible or accessible across groups, limiting usage and leading to redundancies. Additionally, data was not always technically or semantically interoperable, complicating cross-functional data aggregation and hampering analysis.
Even in cases where analysts could bring data sources together, they needed to stitch together various tools to meet different use cases. This time-consuming process created complexity and made collaboration difficult. This limited insights and slowed progress, hampering the organization’s mission of delivering new, life-changing treatments to patients who need them.