Big Data in Pharma and Life Sciences – AI and Data Management Emerj - Artificial Intelligence Research and Insight

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for. AI and machine learning projects can take months to get off the ground. Many pharmaceutical companies don't start seeing an ROI for half a year or more after launching an AI product if they see one at all. As such, it's important for pharmaceutical companies to clean and store their data so that it's "machine-readable," ready for feeding into a machine learning algorithm when the time comes. This is likely to save them time and money (thousands even) on an AI product's initial integration, whether the company makes it in-house or purchases it from an AI vendor.