The era of data-driven R&D is motivating investment in technologies such as machine learning and natural language processing to provide deeper insights into new drug development strategies. Despite major advances in technology, many computational approaches struggle to deal with the complexity and variability of unstructured scientific language.
One fundamental of data science remains unchanged: the accuracy and reliability of results are both critically dependent on clean, high quality data.
However, the data cleansing and annotation work required to achieve clean, high quality data can be costly, often prohibitively so. For example, data scientists spend almost 80% of their time as ‘data janitors’, collecting, cleaning, formatting and linking data, and only 20% of their time actually analysing data.
Furthermore, for most data scientists, data preparation is the least enjoyable part of their role. This presents a significant risk: when people spend a significant part of their time on a task they don’t enjoy, mistakes are bound to occur.
For most Pharmaceutical companies, extracting insight from heterogeneous and ambiguous data remains a challenge, consuming a significant amount of the time of their already constrained data scientist resources.
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