Where does art fit in the world of data science?
Data artists — the conveyers of great data science wisdom — are rapidly emerging as an in-demand profession, and data science leaders are eager to bring them into the fold. However, don't be so quick to hire the first data artist that shows up on your doorstep, especially if you already have a good data science team in place. Data artists can be a great addition to your data science team, though be careful and intentional when integrating them with other data scientists.
Not so fast, Picasso
A data artist is a data professional who specializes in data visualizations, infographics, and other ways to graphically communicate the sophisticated data analyses that your data scientists are brewing. The function they perform is vitally important to your strategy, especially if you're incorporating big data analytics into a product targeted to the larger masses. Your data scientists may do great work, but if nobody understands what they're talking about, there's no way to market their discoveries.
Traditionally this job would be performed by the more creative data scientists on your team, yet now we're seeing professionals that specialize in data visualization; this presents an interesting option for the data science leader.
Although adding a data artist to the team sounds like a good idea, I wouldn't jump right into hiring the first one that shows up. A data artist can be very disruptive to a data science team, especially a team that feels they already have their data visualization needs covered. I've seen this happen in a number of marketing operations, where the graphic artists steer clear from the analysts. They don't intend to irritate each other, but they do. There are personality differences. Analytics tend to be precise and structured, whereas creatives tend to be open and ambiguous. Analytics need some alone time, whereas, creatives like to brainstorm out loud with others.
The biggest issue is that data scientists don't like to be explained by others — everything already makes sense in their own heads, so for some specialist to come in and present it differently is not only unnecessary in their opinions but unsettling.
The ideal candidate
For these reasons, it's important to be careful about screening in the right data artist. Your potential data artist should have more of a data background than a graphics background. Because of the sudden demand, we're seeing data artists come from a lot of different places. Some of them are artists who are trying to apply their skills to data visualization. It's unlikely that someone like this will integrate well with your existing data science team. Some data artists, however, started with a background in mathematics and/or computer programming and realized that data visualization is a natural extension of what they already know. These are the ideal candidates for your existing data science team.
Remember, it's not about having a great portfolio of graphics — it's more about how well they integrate with the existing members of your data science team. I'd rather have an average data artist who works great with the team, than a fantastic data artist who (through no fault of their own) doesn't mesh well with the team. Once the artist is screened in, have your change leader work with your new data artist on collaborating with the other data scientists. It's important that data visualization becomes a team exercise and not something that's farmed out to the data artist's corner. In the end, the data scientists should feel like the data artist helped them extend their own thinking instead of twisting all their data around into something that's not accurate.
Finally, have the data artist increase the capability of the entire team by sharing their knowledge. Data scientists love to learn new skills and, given the right circumstances, will welcome the opportunity to build their resume.
A data artist can be an important addition to your portfolio of data science talent, but choose wisely. When introducing a data artist to an existing team of data scientists, make sure you choose someone who truly understands data science, and not someone who is just learning how to play with numbers. Also, make sure the candidate is a good team player that works with the team to create great visualizations, and not a creative genius that works in isolation.
Take some time today to assess your data visualization capabilities and consider hiring a data artist — as long as the fit is right. Otherwise, you may have to find a creative way to get this person out of the team.
- Data visualization: When it's the wrong tool for the job
- Five Web-based Apps to help you visualize big data
- Personality clashes stalling your data science team? Try the Myers-Briggs Type Indicator
John Weathington is President and CEO of Excellent Management Systems, Inc., a management consultancy that helps executives turn chaotic information into profitable wisdom.