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Master data management: getting your house in order
Kathleen Petri Seiler1, Nicole E Bodycombe, Ted Hawkins
1The Broad Institute, Chemical Biology Program, Cambridge, MA 02140, USA.
Combinatorial Chemistry & High Throughput Screening
|June 3, 2011
Summary
Structured data management using master data (MD) is crucial for small-molecule science. Implementing master data management (MDM) enhances data quality and experimental power in drug discovery.
Area of Science:
- Small-molecule science
- Drug discovery
- Cheminformatics
Background:
- High-throughput techniques in small-molecule science necessitate structured data management.
- Drug discovery involves a mix of high- and low-throughput experiments.
- Current data management lacks standardization, hindering complex data analysis.
Purpose of the Study:
- To discuss how master data management (MDM) techniques enhance scientific data quality and utility.
- To present a model for master data storage and exploitation.
- To highlight practical applications and benefits of MDM in small-molecule drug discovery.
Main Methods:
- Utilizing master data (controlled terminology for materials and methods) for structured experimental recording.
- Implementing MDM techniques at project, laboratory, and institutional levels.
- Developing a model for master data storage and exploitation.
Main Results:
- Consistent MDM increases experimental power by connecting data across the discovery lifecycle.
- MDM enables computational analysis of assay results, reducing manual effort.
- MDM enhances the quality and utility of scientific data.
Conclusions:
- MDM techniques are vital for improving data quality and enabling complex analyses in small-molecule science.
- Structured data management through MDM facilitates novel connections and reduces costs in drug discovery.
- Consistent implementation of MDM across research settings is key to realizing its full potential.
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