Data cleaning and harmonisation: A catalyst for innovation

Messy data remains a persistent challenge in modern drug discovery. The complementary processes of data cleaning and data harmonisation play a pivotal role in optimising workflows, each addressing distinct but interconnected challenges

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Scientific discovery has always relied on good, clean data. Bad data leads to irreproducible outcomes, problematic solutions, and, ultimately, the need to revisit and acquire better data. Now that AI-driven predictive models are becoming more commonplace in early drug discovery workflows, the importance of data that is accurate and consistent across entire research workflows has never been greater.

Messy data remains a persistent challenge in modern drug discovery, causing scientists to spend considerable time resolving inconsistencies in entity naming, catching mismatched database formats, and correcting errors. Machines struggle to handle the nuances of complex datasets because they lack the contextual understanding needed to interpret ambiguity and inconsistencies. Human scientists are tasked frequently with identifying then rectifying issues hidden in data that technology is not equipped to address sufficiently.

For drug discovery teams, messy data creates a ripple effect, leading researchers to design experiments and build models based on flawed assumptions or incomplete information, ultimately wasting valuable resources. This is where the complementary processes of data cleaning and data harmonisation play a pivotal role in optimising workflows, each addressing distinct but interconnected challenges.

Data cleaning: The process of having experts identify and correct errors, fill in missing values, and remove irrelevant information to ensure accuracy and reliability within individual datasets

Data harmonisation: The human integration of information from multiple sources, standardising and unifying it into a cohesive framework to enable seamless analysis, comparability, and collaboration

The key to successful cleaning and harmonisation lies in human curation.