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Case Study

Global Biotech Pioneer Harnesses Stardog + Databricks to Power Insights and Drive Innovation

Location

Multinational

Industry

Biotech

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 Challenge

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.

"The organization drives this value by using the fabric with an ecosystem of tools, dashboards, and systems."

The Solution

With Stardog + Databricks, the organization created an enterprise data fabric that is connected, fluid, and democratized.

The Databricks Lakehouse Platform consolidates the data and creates a single environment for all types of users across the organization. Adding Stardog to Databricks provides a semantic layer that extends existing analytic and IT investments, enabling schema harmonization, data linking, and graph analysis. This unified business layer allows data from any point in the value chain to be used in business decision-making, reducing the cost of data marts while speeding discovery and insights.

"This connected data fuels diverse insights across a large and complex organization..."

The Results

The connected data fabric unlocks value from data, accelerating and enhancing business solutions. The combination of Databricks and Stardog allows for unified, cross-database queries with standards-based graph and SQL query languages. Analysts can use full-text search, geospatial data processing, and data quality constraints to improve the quality of their results without spending time on data wrangling. Stardog’s logical and predictive inferencing and graph analytics further enhances insights and allows for the discovery of new connections so that the organization can make faster and better decisions.

The data-centric enterprise data fabric promotes findability, accessibility, interoperability, and reusability. While centrally managing data in the data lake drives down storage costs, the need to relate data across data sources, maintain consistent identifiers, find data quality problems, and keep the organization on common definitions remains. Siloed data is consolidated and elevated into a knowledge graph with a semantic layer that can be accessed and understood by apps across the organization.

Data is linked across domains so that it can be brought together in the data fabric. This allows the organization to quickly answer critical questions. For example, data is often fractured in the pharma industry, starting at the identifier of individual products or materials. Manufacturing may then have different locations producing lots of medicine that relate that product to a supply chain. Using the enterprise data fabric, the organization can quickly assess the entire global supply and production chain for a drug to decide if production levels should be changed. Or, they can easily identify the downstream impacts of a raw materials shortage and make business-wide decisions about how to respond. By utilizing standards like Allotrope in the semantic layer, harmonization can occur during manufacturing quality assurance. This minimizes the change required to update reports based on equipment modifications and maintain a standardized view of the quality process.

This connected data fuels diverse insights across a large and complex organization, allowing for faster and more efficient regulatory filings, easier adverse effect identification and root-cause analysis, and improved risk mitigation from drug target and risk identification, to quality assurance, to customer and supply chain management. The organization drives this value by using the fabric with an ecosystem of tools, dashboards, and systems. This includes Tableau, Spotfire, data science notebooks, semantic search and disambiguation, custom integrations, and other commercial off-the-shelf software.

The Future

Building on the initial success of the data fabric, the organization is now working on expanding into an enterprise-wide platform for the consolidation and enrichment of data. The Stardog + Databricks infrastructure will become the foundation for all data projects, eliminating the need for any siloed repositories or analyses. This will enable new use cases, including Laboratory Information Management Systems to add automation and data integration capabilities, employee 360 to allow for improvements in talent management, training, and budgeting, and bill of materials to allow for improvements to manufacturing and production processes as well as further efficiencies in preparedness for regulatory processes. 

By continuing to expand the data fabric, the organization expects to see continued improvement in analyst efficiency and productivity, enabling more strategic business decisions, and ultimately enabling the delivery of more treatment options and better outcomes for patients.

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