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A culture for doing good things with data: The why, what, how, and who of a progressive culture for data in public health

6 Redux: Who, how, what, and why

I have written about some ways that CDC and other organizations could better support a culture for doing good things with data, especially in view of fast-moving methods, tools, and technology. At CDC, we have a shared mission, a commitment to public service, and an intense, pragmatic need to draw on expertise across disciplines in multifaceted teams. We should surely hire great talent. But we also need to tend to staff who are already on board.

For our data generation, we must foster technical skills and a mastery of technique that allows scientists to extract information from data; we must foster intellectual virtues, including practical wisdom, that guide both inquiry and self-learning, and that enable scientists to ask good questions and to line up tools to answer those questions rigorously with data; and, we must foster a culture of mentoring, peer support, and advocacy in a community of practice that empowers data science learners and doers to keep up with fast-moving methods, tools, and technology.

Who: Everyone who wants to do good things with data should get to make the effort, as long as they are rigorous and accountable.

How: At the individual level, data science calls for technical skills in computation and data analysis and nontechnical skills to keep inquiry directed toward learning from data and to deal with obstacles. At the collective level, it calls for a progressive culture that supports putting those skills to use for doing good things with data.

What: Data science studies how to learn from data by combining analytic, computational, and subject-matter methods to connect the whole life cycle of data, subject to norms of scientific quality and analytic rigor.

Why: Foremost, data science is about learning from data. Data science helps us to keep up with fast-moving methods, tools, and technology for learning from data of all structures, sizes, shapes, and speeds.

A progressive culture remains rooted in history and continues to learn from old data in new ways, and it anticipates the future and handles evolving demands. Cultivating a progressive data culture in the present will best position the field of public health as ever ready to learn from and act on data.