Tackling key data science challenges at J&J

Plotted on a line chart, America's per capita consumption of mozzarella cheese apparently correlates with the number of civil engineering doctorates awarded annually. Ditto the divorce rate in Maine and per capita consumption of margarine.

Of course, common sense balks at such conclusions, neither of which are true.

Yet in the eyes of data scientists at Johnson & Johnson like Manoj Pandey, these humorous examples can teach valuable lessons about what he refers to as "the pitfalls of data science." Here's an inside look at some of those lessons, why they're such a key part of our approach to data science—and how they're helping us change the future of health.

Interpretability and visibility in data science

New diagnostic tools for patients with Alzheimer's disease, digital tools to detect and prevent dengue—we're driving health impact through data science in a number of bold and surprising ways. Take a look at some of the standouts from our inaugural Data Science Showcase for proof. 

But Manoj points out that as data science applications become more complex, issues of interpretability and visibility become increasingly paramount concerns.

Consider the following example: In the future, doctors might consult advanced machine learning algorithms in order to recommend the best course of treatment for patients. In that case, patients will surely want to know why those recommendations were made. And doctors will want to be able to validate their decisions, too.

"If we allow let these models to become 'black boxes,' we lose visibility into them," Manoj pointed out. 

So while we're doing groundbreaking work in data science at Johnson & Johnson, we're doing so with distinct priorities in mind. 

As Manoj explained, "We want to be able to not only extract valuable insights from machines, but also share why that decision has been made." 

Causation in data science

Another priority raised by data science has to do with causation—why a thing happens.

"While identifying a correlation is easy and generally useful in predictive models," Manoj said, "understanding causation is much more difficult. It requires us to experiment and have robust assumptions from the start."

As data science becomes a key component of investigative studies, for example, it requires us to take into account ethical considerations around data collection, study design, data analysis, the dissemination and application of findings and more.

"Data science holds tremendous power to generate insights in these studies," Manoj said. "But causality is key, particularly in terms of the conclusions these studies draw. And in the spirit of Our Credo, it's critical to have transparency in terms of where the data comes from, how and under what circumstances it was generated and so on."

Bringing critical controls to data science

The community of data scientists at Johnson & Johnson is keenly aware that reaching the right conclusions requires starting with the right approach.

As Manoj framed it, "One of the important questions is whether you start by identifying the problem you want to solve and then back-track to your data, or vice versa. In the latter case, that can make it challenging to design your approach in a way that fits the problem."

So we're taking steps to correct for that—for example, by rolling out new types of controls, which Manoj refers to as “gates," for data science projects.

"Gates help ensure we have sufficient data to solve the problem, and that we're always taking the right approach from the outset," he explained.

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