Services

Data Stewardship

Our Data Stewardship service helps life science organizations establish robust data governance frameworks, standardize data models, and ensure integrity across R&D, clinical, manufacturing, and quality systems. We align people, processes, and technology to enable AI readiness and seamless data integration—turning fragmented information into a strategic, insight-driven asset for the enterprise.

Case Studies

Harmonized Clinical Planning Data

 

Establishment of standard data definitions, taxonomies, and meta data across functional planning teams to enable centralized reporting.

Insights

Challenge: A pharmaceutical company had not established data definitions, taxonomies, meta data, etc. for its functional planning teams of pipeline assets with every team using its own flavor of data for planning timelines. This created siloed reporting, stakeholder confusion on terminology, and an inability to scale reporting infrastructure.

Approach: Scimitar partnered with cross-functional planning teams to standardize planning templates, data definitions, clinical asset taxonomies, and required meta data to drive reporting for key business questions.

Impact: After data harmonization was achieved, centralized reporting was able to be developed and efficiently scaled to provide holistic insights across the development pipeline to facilitate data driven budget, resource allocation, and long-range planning decisions.

Scalable Resource Management

 

Establishment of data definitions, taxonomies, and meta data across a R&D organization to enable scalable resource planning and analytics.

Insights

Challenge: A pharmaceutical R&D organization with 500+ resources was struggled to allocate resources to their pipeline and understand which teams were not be efficiently utilized. They had no standardized data infrastructure in place resulting in manual, cumbersome reporting and stakeholder confusion when comparing plans across teams.

Approach: Scimitar collaborated with R&D leadership to define standard work taxonomies, organizational taxonomies and standard work and resource meta data with definitions across the portfolio of archetype work that functional teams manage. This enabled the development of a resource management and reporting capability.

Impact: The R&D leadership team was able to efficiently understand the work teams are performing, their utilization, and how they align to strategic priorities. Bottlenecks could be forecasted and work readjusted to meet priority timelines.

Data Quality and Alignment Through Master Data Management

 

Implementation of a centralized MDM platform to unify portfolio and partner data, improving data quality, downstream reporting, and enterprise alignment.

Insights

Challenge: The client’s data across its clinical trial operations and portfolio existed in silos. Master data management solutions were identified as needed but required bridging with the business and IT functional groups to be successful.

Approach: Scimitar led cross-functional business workshops to gather requirements and assess downstream system impacts. We designed and oversaw testing across regression, functionality, and data validation streams, supported users with training and SharePoint onboarding resources, and executed a targeted change plan with stakeholder messaging, newsletters, and go-live communications. A “hypercare” support model ensured smooth adoption and tracked resolution of early-stage issues.

Impact: The new MDM system enabled standardized, high-quality data across partner and portfolio domains. Users gained clarity in data definitions and improved confidence in dashboard outputs. The enterprise saw faster onboarding, fewer data conflicts, and a foundation for future releases aligned to industry standards and AI-readiness efforts.