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You too can be a sexy data unicorn — and other lessons from my first DataDive

Here are 5 surprising things I learned while covering a DataDive hackathon at Google’s NYC headquarters

On opening night, data scientist Shubin Li, a Data Ambassador for DataKind, talks with volunteers about the projects they can work on. (Photos by Alison Bert)
On opening night, data scientist Shubin Li, a Data Ambassador for DataKind, talks with volunteers about the projects they can work on. (Photos by Alison Bert)

Alison Bert is Executive Editor of Strategic Communications at Elsevier. (Photo by Tim Lamorte)That was me with the camera, taking your picture and pestering you to explain stuff when you emerged from your data scrums. Last month, more than 100 of you spent a weekend at Google headquarters in Manhattan, creating technology solutions for organizations that make sure citizens are included in international development.

It was a DataDive by DataKind, a global nonprofit organization that is "harnessing the power of data science in the service of humanity." Their DataDives unite tech-savvy volunteers with nonprofits to develop solutions that help the organizations achieve their missions.

With the Elsevier Foundation as a sponsor and several of my Elsevier colleagues volunteering, I was there to do live coverage. After all, my expertise is journalism, and even though I write about my company’s data science and technology initiatives, I certainly had nothing to contribute directly to the DataDive.

Or did I? That leads to my first takeaway.

1. There’s a place for everyone – even those of us with “non-technical” skills.

Many of these volunteers were experts in data science, coding, statistical analysis, and other aspects of technology. That’s usually the case at a hackathon, so I never pictured myself actually participating in one.

But then I heard the organizers inviting people with “non-technical skills” to work alongside these experts. The skills they mentioned included project management, manually classifying data sets, research, Q&A testing and documentation. That got me thinking of other ways us “non-technicals” could contribute.

The leaders from the nonprofits were great storytellers, helping us visualize what it would be like if developers showed up unexpectedly and caused us to lose our homes and livelihoods. That put the whole weekend in context; it was why your work mattered. And at the end, reps from each team gave a presentation summing up their team’s accomplishments and what remained to be done. It reminded me that there will always be a need for good communication. During the event, someone could act as a team liaison, providing updates on what different parts the team are accomplishing and what they need. And someone could tweet about the event, creating buzz for a cause people may want to contribute as volunteers or donors.

Another thing I noticed was that volunteers were at different stages of their careers. There were undergraduate students, new graduates, rookies and seasoned professionals. In some cases, less experienced volunteers worked alongside pros, doing more tasks that were more basic but just as necessary. So no matter what your level of experience, there’s a valuable role you can play in a DataDive.

2. “We all need to step up our data analysis skills.”

Jonathan Zimmerman (at right) is Associate Director of Customer Insights for Elsevier. He's using his analytical skills on the Accountability Counsel project.

Some people I met do data analysis as part of their jobs. But here, they had a chance to apply those skills in a different context and learn from the people around them. That was the case for Jonathan Zimmerman, who has been using his analytical skills for years at Elsevier, currently as Associate Director of Customer Insights. Jonathan pointed out that the work he's been doing at the DataDive was different from his daily work and therefore presented a new challenge. And that's one reason he signed up:

It's about seeing if I could expand my horizons in data analysis. At Elsevier, we're really dealing with survey data from customers, and this is a whole different thing.

With Elsevier becoming more and more focused on data analysis, we all need to step up our data analysis skills. Now, when I walk through the office, every single person is looking at data. That wasn't the case when I started working here.

That’s certainly been my experience at Elsevier. Our company has transformed from being a publisher to an information analytics business specializing in science and health. Sure, we still publish scholarly research, but our technologists and data scientists create tools and platforms that help people find the information they need for their work – and make sense of it.

Even on my Global Communications team, data is a key part of our work; for example, we analyze usage metrics to determine what kind of communication people find engaging and relevant. So after the DataDive, I talked to my own manager about how to improve data analysis and reporting skills.

Participating in a DataDive seems like a great way to hone your skills and get new experience while contributing to a great cause.

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3. You can be a sexy data unicorn – but only after doing the grunt work.

Dr. Jake Porway, founder and Executive Director of DataKind, congratulates participants on their progress, saying that much of data science involves data cleanup.

The founder and Executive Director of DataKind, Dr. Jake Porway, is a keen data scientist himself. So when he took the stage before lunch on the first full day and told the teams how impressed he was with their progress, he was serious, even though most people were still at the data-preparation stage.

When I asked him about that, he told me something revealing:

Any good data science project is mostly data cleanup. Everyone thinks they're going to be the sexy data unicorn, but then you just discover you're the data janitor.

It's like you need to build a house, but first you have to go shopping at the Home Depot with the lights off. You don't know what's in the data until you upload it onto your computer and start analyzing it.

Of course there are many more levels to data science, and it can even rise to the level of an art, as Dr. Jabe Wilson, Elsevier’s Consulting Director of Text and Data Analytics, illustrated in his presentation for Bio-IT: Zen and the Art of Data Science Maintenance.

But as in any undertaking, foundational work is key to a successful outcome.

It reminds me of when I was a professional musician. People would see me performing classical guitar recitals or teaching students at the university, and they would assume I was in a seriously glamorous profession. What they didn’t see were the hours a day of practice, which did not involve performing Bach sonatas in an evening gown. In reality, I would practice scales and play musical passages so slowly they were unrecognizable – yes, while wearing sweat pants with an elastic waistband. Or I would put my instrument down and analyze the music phrase by phrase to figure out the underlying structure and how to best convey the drama and nuance.

In the end, if I did my job well and put in the hours, the result would be powerful and compelling – just like the work of data scientists. (Just check out what the data scientists here accomplished in the space of a weekend!)

So I’m going to dare to tweak Jake’s statement: If you put in the work as data janitor, you can be that sexy data unicorn.

4. Even in a hackathon, there’s still a place for paper.

: A project champion uses a bullet journal to update project tasks.

Almost everything’s digital these days, and it’s great to be green, but it can be hard to do certain things online – like drawing diagrams on the fly or keeping your entire life on track. For the latter, I recently starting using that brilliant millennial invention – the bullet journal. I’m not going to go into detail other than to say that it’s not about the notebook but the system.

So during a data team meeting, I was thrilled to see the project champion whip out his bullet journal to update the project tasks and make sure everyone was on track.

As the event progressed, I also noticed that teams were posting diagrams and flow charts, like this one by the team that was helping the Accountability Counsel monitor local complaints again international development.

Here's a chart their team created to show the complaint lifecycle.

5. DataDives are challenging (and expensive) to stage.

Participants work in front of a screen that displays the DataDive sponsors (left to right): Dr. William Gunn, Director of Scholarly Communication at Elsevier; Cherisse Thomas, who just graduated with an MBA in data analytics; and Johal Baez, a data analyst for JP Morgan.

With all the volunteers involved, I had always assumed that hackathons paid for themselves.

But that’s far from the truth.

There’s the facility itself and the charge for security and other personnel. There are caterers to pay and clean-up to be done. And while the event itself takes place over a weekend, DataKind staff spend three to four months preparing to set the event and projects up for success. Magdalen Sangiolo, DataKind’s Business Development Manager, told me her colleagues take great care in scoping projects in the months leading up to the event, creating a list of very clear deliverables for the volunteers. They also audit and clean all data sets so DataDive volunteers can “dive right” in and start working towards the deliverables.

“Data scientists don’t just show up and start hacking away at problems or random ideas,” she said. “Rather DataKind is dedicated to producing results with our NGO partners that we collaboratively identify, explore and vet ahead of time.”

For these reasons, DataKind relies on donations and sponsors. At this event, the Elsevier Foundation was one of five sponsors, the others being 11th Hour Project, American Airlines, Google Cloud and Teradata. Meanwhile, the beautiful space was donated by Google.

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