White Papers

Lifebit’s Approach to Data Standardisation

Written by Ingrid Knarston, PhD | Nov 7, 2022 11:02:20 PM

Lifebit’s Platform offers researchers and clinicians a data-driven view into the determinants of health and disease, facilitating clinical impacts and accelerating drug discovery. 

The Challenge: Lack of standardisation across global health data resources

Health data comes from a wide range of sources, including biobanks, clinical trials and electronic health records. With this diversity comes wide variability in how data is described and stored, which creates challenges for researchers preparing data for analyses.

Key challenges:

  • Inefficient and repetitive tasks
  • Coding and data expertise needed
  • Limited analysis potential

The Solution: Adopting a Common Data Model 

Common Data Models are being increasingly adopted across the healthcare industry to address the lack of data standardisation. Having datasets standardised to a consistent and accessible format means they can be easily merged and analysed for research.

Lifebit’s Data Transformation Suite

Fully integrated within Lifebit’s Platform, the Data Transformation Suite is a flexible set of pipelines that transform raw data to analysis-ready data using the OMOP Common Data Model.

Benefits:

  • Simplify data management: Achieve one unified model for all data types
  • Reduce time to insights: Less spent cleaning and pre-processing the data, more time spent gaining data insights
  • Enhance collaboration: Distributed datasets can be combined and analysed in the same manner
  • No data loss: All data transformed to OMOP
  • Flexible: Both client- and data-agnostic, the pipelines can be extended to suit your organisational needs
  • FAIRification of Data: Make your data Findable, Accessible, Interoperable and Reusable
  • Improve reproducibility: Enable reproducible research at scale

Find out how top 20 pharmaceutical companies are using Lifebit’s Data Transformation Suite to capture the transformational value of biobank data and prepare their data for research at scale.