Data Dialogs Interview: Motiga’s Kimberly Stedman
Today, we have an interview with Kimberly Stedman, data scientist at Motiga and one of our speakers at this week’s Data Dialogs conference. Here, she talks to us about the human factors in data, the importance of accurately communicating findings, and why more data isn’t always better.
Tell us a little bit about yourself – what role do you play at Motiga? What is your background?
I’m a data scientist at Motiga, a game startup in Seattle. I have an unusual background. I started as a field anthropologist, where I studied the cultural structures in developing countries. Flash forward 10 years, and I am now doing big data in the video games industry, where I am having the time of my life, and where I am mugged less than half as often.
We hear a lot about things like the Harvard Business Review‘s “Sexiest Job of the 21st Century.” What do you think is the biggest myth — or misnomer — about data science? What would you say to set the record straight?
I’d say the biggest myth about data science is that it’s going to be easy, and if it’s not, it’s because we need bigger, faster servers. The premise seems to be that if we just had more data, sooner, we’d have more value, sooner. But that’s not how it works; humans have to ask meaningful questions and explain the results to each other in a way that leads to a useful action. Our goal isn’t just to process data quickly. It’s to have an impact.
What will you be speaking about at Data Dialogs?
Companies have grown from a culture of not having much information about whether the stuff they were trying was working. That kind of thing embeds itself in habits, org structures, and decisionmaking rituals. It takes time and hard labor to change a culture, and until you do so, you’re just pouring those hosting fees into a black hole. At Data Dialogs, I’ll be speaking about the human factors in data. I’ll discuss organizational structures and wetware hacks that support the social dissemination, reception, and processing of the information that comes from our data.