Since the early-19th century, professionals from nearly every field have held meetings to educate, exchange ideas, and network with their peers. But today it can be difficult for event organizers to determine which processes are still effective, which are dated and should be retired, and which need improvement. At Convening Leaders 2014, Masters Series speaker Hilary Mason — who recently became the first data scientist in residence at Accel Partners, a venture and growth equity firm, after serving as chief scientist at the URL-shortening service Bitly since 2009 — will encourage meeting professionals to think differently when it comes to data, and discuss ways to mine and analyze information to advance their conferences and careers.
As co-founder of DataGotham, an annual conference in New York City for data professionals, Mason understands firsthand what it’s like to interpret attendee responses and construct a better meeting based on the results. Additionally, she is an active member of New York Mayor Michael Bloomberg’s Technology and Innovation Advisory Council, helping the mayor discover ways to foster growth in the local technology community. She’s spoken to groups about everything from email hacking to the history of machine learning.
At Convening Leaders, Mason will help attendees “make better decisions with data,” taking a look at the amount of data that’s available today and the ways in which people are now able to source and construe this intelligence. “I’m a data optimist,” Mason recently told Convene, “in that I think there’s a huge amount of potential for all businesses that we still have yet to explore with data.”
Are there basic ways in which all professionals should be thinking about data?
It’s hard to answer that question without context. Over the last five years, a huge amount of data has become available. It’s suddenly cheaper to keep data than it is to throw it away. It’s a very simple technical change that’s gotten us into a situation I don’t think anyone expected, where businesses and professionals now have access to a huge amount of data, but don’t necessarily yet have the tools or processes or organization to really make use of it optimally. So the way professionals today in 2013 should be thinking about data is, when you’re trying to make a decision where you think the data might be able to guide or inform the decision you make, make sure you have access to and understand the data that’s available to you.
You are the co-organizer of DataGotham, an annual conference in New York City for data professionals. Where did the idea for the event come from? How has it evolved?
I’ve run an informal, semi-private group of data practitioners in New York for about four years, where we meet up for beers every couple of months. I started doing that because when I started at Bitly, I was the only data person at my company and I was sort of lonely, so I thought, wouldn’t it be fun if other people in the same situation could get together and talk with people who do the same sort of thing? Over the years, [attendance has] grown; it’s now over 300 people.
We wanted to create an event with two goals, and the first goal is to help people who care about data in New York connect with each other, and lots of relationships and jobs have come out of that. The second goal was to make a point to the rest of the world that if you want to do or see this kind of interesting work with data, it’s here in New York.
What kinds of data do you collect about DataGotham attendees, and how do you use it?
It’s funny you ask that question, because we’ve really just been getting the basics. We find out if attendees are local or not local, what company they work for, what they’re hoping to get out of [the event], and whether they’re hiring. We have a very large group of students attending and then a large group of companies who are looking to hire those students, so we want to make sure we connect them.
Last year we did a couple of visualizations after the fact of how people were tweeting about the event. We did find out that a lot of people were following along who were not physically present at the event, which was good, because one of our goals was to get the word out beyond New York.
Are there basic data-science tools with which everyone should be familiar? Can you recommend software or tools that would help meeting professionals specifically better collect and use data?
The tool doesn’t matter nearly as much as the mindset. What you actually have to do is, when you’re in the process of making a decision, stop and say, “Is there data that I can access that can inform this decision in a useful way or give me some context?” And if the answer is yes, then just make a little extra effort to grab that data and try and ask some questions of it. That said, if you want to do some more intense data analysis, there are packages, like Tableau Software, that are designed to be used by people really committed to data analysis.
What should meeting professionals avoid when it comes to data analysis?
One of the big misconceptions that I see often is that data will solve your problems for you, and actually people are sometimes afraid of starting to use data because they feel like it will make them look bad or it will take away some of their agency, when the truth is actually the opposite. The example I like to use for that is that A/B testing [testing one feature against another and going with the one that consumers respond to best] is a great tool for figuring out which of two options you should decide to use, but it will not tell you what A and B should be in the first place. Data should be used to question your assumptions and help you, once you’ve already been creative, decide how to deploy things. You shouldn’t just assume, “The data will run my business for me.” It can’t do that.
What is the role of written surveys and questionnaires, compared with more empirical data?
That’s really a question about the difference between qualitative and quantitative data, and both are useful. I find that most people using surveys and questionnaires are trying to get a picture of context. In business, you try to make decisions that are going to take place in a world that you have a fairly small amount of insight into. When you are surveying your customers, what you’re trying to understand is what effect your decisions actually had on them.
I’m generally more of a fan of the quantitative data that you can actually [collect] in a way that’s implicit — so you’re not asking people to think about something, you’re just observing what they do and changing your behavior accordingly But surveys and questionnaires are hugely useful and they’ve been used for 50 years for this purpose, in helping to inform the context around an event. “Are people actually happy?” — that’s the kind of question you can ask.
In what areas do you see meeting professionals potentially most benefiting from data science?
The opportunity I would look for is a place to question old assumptions. The meeting industry has been around for a really long time, and I would surmise that there are business practices that have developed in the industry for historical reasons that can now be questioned and taken advantage of. I would look into the behaviors that people do just because that’s the way it’s done or everybody does it this way, and see if there’s data that can show whether those are actually good behaviors or whether there’s something that a clever meeting organizer could take advantage of to really boost the quality or lower the cost of their event.