Good data science isn't about finding answers to questions

I just finished reading an article over on Fast Company titled “How Designers Are Helping HIV Researchers Find A Vaccine.”  The story related in this article is a perfect example of what ‘good’ data science looks like.  The data scientists and designers worked together to build a platform that made it easy for anyone to dive into data sets, find answers – and more importantly – find more questions.

I’ve said it before – Good data science isn’t about finding answers to questions. Good data science is about setting up your data sets, processes and systems to allow you to find more questions.  As I’ve said before:

Big Data helps you find the questions you don’t know you want to ask.

The designers and data scientists working with the HIV data were working from a similar mindset. From the article:

“We’ve already harmonized the data . . . we’ve lined everything up, put it in the space, made it so you could ask questions you didn’t set out to ask,” says Dave McColgin, UX design director at Artefact. “You can sort of stumble into additional questions, if that makes sense.”

This is good data science.

These folks didn’t take the data and throw it into a data repository, set up processing systems and technologies and then keep everyone away from it. They didn’t hoard the data or the results of any analysis. They opened the data up to everyone to get multiple sets of eyes (and brains) on the data. They focused on data visualization to make it easy to understand and conceptualize the data. They started with the idea that they wanted to see more questions asked then answered. Again…this is good data science.

For those of you who are thinking about data initiatives or currently working with data, make sure you are building your systems and processes to find more questions than answers. Otherwise, you’ll be missing out on a good portion of the value of data science.