July - 20209TECH OUTLOOKof a drug making it to market faster. Today's technological advances and the emerging role of the data scientist, are giving us new levels of analysis and the ability to predict outcomes in near real-time. Innovations such as machine learning and artificial intelligence are providing us with new algorithmic techniques that can assist in identifying patterns that support enhanced and automated decision making along the drug development path. For example, having historical data about a clinical trial's ability to recruit suitable patients can predict the probability of future recruitment success. The combination of qualitative and descriptive data now enables researchers to identify similar groups of patients who are best suited for a new trial. By knowing exactly who you're looking for, you not only shorten site selection and patient recruitment, you also increase the likelihood of positive results. Historically the gap between combining structured and unstructured data for clinical trial decision making has caused errors and delays in setting up and completing successful trials. However, with the new aggregation, storage and analysis solutions, combined with artificial intelligence and machine learning we can now detect trends and negative signals much earlier in clinical trials. Not only does this compress the critical path for drug development and improve safety, it increases our chances of creating a more robust pipeline of disease-treating therapies ­ all at a faster pace than ever before. However, even with all of our technological advances, the human role is still critical. In fact, with the growing amount of data and data types, a fairly new role has emerged--the data scientist or data revolutionist. As clinical researchers continue to focus their daily efforts on creating successful trials, the role of the data scientist is focused on directing overall clinical data quality and management activities that will support the progression of the drug development pipeline. This emerging role has responsibility for connecting the various dots across the organization to ensure the right data makes it into the right hands at the right time. While the role is defined based on each biopharmaceutical company's needs, ultimately the data scientist's job is to be a leader in both science and complex data management, quality and visualization from source to submission. So even as we continue to see more innovation in automating decision making, growing datasets and the management of the technological advances will still require a human touch. When we combine today's technological innovations and the new data scientist role, and apply them to the clinical world, we see a huge opportunity for positive impact. Not only does the insight from more data from different sources enhance the questions we ask of our clinical trials and fill a much needed disease-fighting pipeline of new drugs, it also allows us to uncover hidden relationships that can precipitate new hypotheses and provoke new, potentially life-saving questions that we never thought to ask before. With the right technology and people, new sources of data promise to speed, not slow, clinical trials allowing biopharmaceuticals to bring more life-saving drugs to market faster. There are tremendous challenges in aggregating, storing and preparing the data for high speed analysis because it is no longer just structured data
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