Harnessing Big Data for Precision Medicine, Part 4

By removing the need for relational databases, we no longer have to decide in advance what kinds of questions we want to ask in our quest for precision medicine insights. Using the tags, we can pursue any line of inquiry, looking at all the data in a data lake, a new way of storing and managing data.

If we decide to ask a different type of question, we don’t need to tear down the database and laboriously custom-build another. We simply go back to the data lake and reframe our inquiry.

Our advanced analytics allow us to take this a step further. Using the tags in the data lake, the analytics let the data speak for itself, revealing the kinds of hidden patterns and connections that can be so valuable in precision medicine. Once researchers find potential insights, they can test them with a traditional hypothesis approach. But the key here is that they don’t have to start with the hypotheses. They don’t have to suppose what the answers are before they even ask the question.

One might think that consolidating large amounts of data in the cloud makes it less secure. But with the data lake, the opposite is true. With conventional databases, it is often difficult to determine who is accessing the data, and what they do with it across their systems. The more that organizations share their data, the more their risk grows.

The data lake solves this problem. Each piece of information in the data lake is put into an individual cell, along with its tags. In addition, each cell is embedded with visibility controls that tightly govern who has access to that specific piece of data, and under what circumstances.