JD: The purpose of big data and analytics investments should be to generate more actionable insights in a better, faster, and cheaper manner. Yet, achieving these goals requires the arduous task of acquiring, ingesting, cleaning, refreshing, linking, and grouping massive amounts of data in relevant schemas. While these steps can be done manually, they become unmanageable when you have petabytes or exabytes of data, a quantity most suitable for machine learning. Our industry has been notoriously slow to adopt tools that automate data management. Additionally, we have become too complacent with piecemeal information, remaining blind to complete, sequenced patient journeys. With the rise of tokenization, linking patient-level data, at a scale required for machine learning, is more feasible than ever before.
Healthcare’s big data infrastructure must pave the way to enable predictive modeling, blazing-fast queries, and the delivery of precise insights. The new standard in advanced analytics includes automation and tokenization, opening the door for healthcare institutions to benefit from meaningful and actionable insights at an unprecedented scale.