Large healthcare institutions can create 50 petabytes (PB) of data annually. Factors such as using electronic health record (EHR) systems and high-resolution diagnostic images (CT scan, MRI, etc.) lead to this data rise. Up to 97% of this data is generally unused so it becomes worthless. Storing large volumes of unnecessary data (data hoarding) leads to data sprawl, data decay (data becomes outdated) and silos. If this problem remains unsolved then healthcare institutions’ data storage expenditure will rise significantly each year. Data minimization solves data hoarding. It ensures that only relevant data is collected and stored for as long as it is needed. Chief Data Officers (CDOs) and IT leaders at healthcare institutions can use data minimization to maximize the value of their data and save on data storage costs.
Why Data Minimization Is Difficult
Data minimization involves only collecting the relevant data for …