SUMMA™ enables the integration of large real-world patient data sources into one repository or Data Lake. The platform has the capability to ingest these RWD assets and standardization of these disparate data into a common data model (e.g. CDISC, OMOP).
What sets SUMMA™ apart from other data lake providers is the emphasis placed on data harmonization and curation with processes such as:
This configurable Data lake creates cohesiveness across entie organization by breaking down silos and supporting a “Center of Excellence” approach for uniform and accessible enterprise-wide data utilization.
Creating one central repository with automated functions allows for many operational efficiencies in an organization:
Given the volume and intricacies of disparate data sources, Machine Learning (ML) and Artificial Intelligence (AI) are necessary to reveal patterns in large, complex data not visible to the human eye.
SUMMA™ automates these advanced techniques across large volumes of linked data, leading to broader impact in less time to establish new patterns and insights for clinical discovery. These patterns can lead to more targeted outcomes while decreasing cost, time and resources.
SUMMA™ can effectively solve a variety of clinical challenges: