Automated Data Harmonization Suite for Clinical Research Integration
Website: https://www.elucidata.io/ Elucidata leverages its platform, Polly to augment the quality of data in pre-clinical drug discovery. It curates multi-omics and assay data to make them ML-ready or analysis-ready. Our exceptional multi-disciplinary team of experts use Polly’s powerful curation engine to harmonize a diverse array of data-types, curate metadata and process data consistently at affordable costs while maintaining information-richness. We are one of the only companies to offer a tech-enabled approach to multi-modal data curation that serves the life science industry. Polly’s technology and experts have helped R&D teams arrive at multiple validated drug targets across immunology, oncology, and metabolomic disorders. Currently, 25+ research organizations, including 4 of the largest 10 pharma companies are using Polly and its allied solutions to accelerate their discovery programs. Many other data-driven healthcare companies also use Polly to process, harmonize and store public or in-house biomedical data. Address: 114 Sansome Street, Suite 250 San Francisco, CA 94104 Phone No: 9716140329 Contact Email: info@elucidata.io
Data Harmonization Challenges
Data harmonization is the process by which fragmented datasets are transformed into standardized formats. In clinical research, it enables effective integration and analysis of data. However, this process is challenging and can slow down research and impact outcomes.
Current Industry Problems
Clinical data is often dispersed across platforms like electronic health records (EHRs), biobanks, and clinical trial repositories. One of the most pressing issues in data harmonization is differences in conventions, terminologies, and data formats between data sources. For instance, a biomarker might be referred to by different names or units in separate datasets or a single dataset might include inconsistent units of measurement (e.g., mg/dL vs. mmol/L) or incomplete metadata. This introduces errors and delays during the harmonization process and prevents meaningful comparisons across studies or institutions. Data silos exacerbate these challenges by restricting access and hindering collaboration across institutions. These silos make it difficult to aggregate data for cross-study analyses, limiting the ability to develop comprehensive insights or patient profiles.
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