Abstract

HDSR’s Founding Editor-in-Chief, Xiao-Li Meng, recently met with Tim Ritchie, President of the Museum of Science (Boston, Massachusetts) for a dynamic and insightful conversation that touched on many topics pertaining to the role of museums in society, particularly in the context of data science, AI, and fundraising.

A nonprofit leader with broad experience in science, education, law, and advocacy for people with disabilities, Ritchie’s vision for the Museum of Science extends beyond traditional exhibits, aiming to transform visitors into users, providing them with learning resources and tools, and “awesome experiences.” He also emphasized the importance of leveraging data to enhance fundraising efforts and tailor museum experiences. The pair discussed the potential for museums to serve as platforms for community-driven content creation and partnerships, and most importantly for enhancing the public trust in (data) science. 

This interview is part of HDSR’s Conversations with Leaders series.

HDSR includes both an audio recording and written transcript of the interview below. The transcript that appears below has been edited for purposes of grammar and clarity with approval from all contributors.

Xiao-Li Meng: [00:00:00] So, Tim, it’s a great pleasure to have you on this episode of Conversation with Leaders, especially after the really enlightening tour of the Museum of Science in Boston that you recently very kindly arranged and led. The experience was not only impressive but also really surpassed my expectations, which were based on memories from over a decade ago. Since taking the helm in February 2020, you have evidently accomplished a great deal. For our listeners who may not be familiar with the responsibility of a museum president, could you shed some light on your role? Specifically, how does data science contribute to your strategic planning and daily operations?

Tim Ritchie: [00:00:37] Well, it’s a pleasure, Xiao-Li, and great to be on this show with you. So, when I came in particular, in February of 2020, then in March, we had to shut the museum down. And then the question was, could a place-based institution be able to do its mission? Because that’s what the job of the president is. The president’s job is to make sure that we can do our mission, our vision, and enact our strategy and that sort of thing. And what happened during COVID[-19] was that we were able to reach the world digitally. And right now, we spun up a good team. And a couple of years later, we’re reaching about 200 million people a year now online. But it was a commitment to the digital part of what we do. And what was enlightening about that was how much data we get from our digital reach. We get far more data, from how people are receiving our programs digitally than we do on-site. Now, we do have a giant commitment to getting data for what the visitor experiences on-site and in schools. But what we found, when I got here through our digital reach, is that we have a much better sense of our demographics, how far we reach in terms of, you know, other countries and other states, how long people use our programs, whether they actually take an action on the basis of them. So, the gift of COVID[-19], so to speak, under my leadership here was that we were able to spin up a real focus on our digital part, which actually increases our data so that we’re able to use them to make decisions about what’s working and what’s not.

Xiao-Li Meng: [00:02:16] Well, thank you. You just reminded me that you took on the job at the most difficult time. So, let’s follow up on that question. Since you have been at the helm, the Museum of Science has seen some remarkable exhibitions and programs. Could you share some of the highlights and what made them so popular? On the flip side, are there any initiatives that did not quite meet expectations? If so, how did you leverage data to understand the cause and formulate the lessons learned?

Tim Ritchie: [00:02:43] Yes, so as a broad matter, I would say that one of the strengths of the Museum of Science is our research and evaluation team. And we have actually created a service for other science centers so that they can collect data in a program that’s called Coves, which is all about visitor services and understanding how things are going. So, we had a pretty deep bench in terms of our commitment to research and evaluation and data on the on-site experience. I think what has been really dramatic is how do we expand that to our other two platforms, which is the in-school experience, where we collect lots and lots of data from the five million students we reach a year. And then how do we do that online? And I’ve already alluded to the online part. I would say we have had to increase our commitment to better systems underlying that. So, for instance, our CRM [customer relationship management] system was really not a very good one, and we went to a more robust commitment to Salesforce [our new CRM System] and a really robust application of Salesforce. We’ve had to add other systems that will help us get data more quickly. So, a variety of commitments to digital things, simple ones like Smartsheet even, which enable us to communicate easily across the institution. There is one big experiment that I’m not particularly happy about yet, but I believe can be great, which is kind of the holy grail for many science centers: an immersive experience that is also interactive, that can also be changed quickly. And so if you have someone in an immersive experience, you can see what they’re doing, you can see how they interact. But the problem is that most of these things are really just movies in a box. So we wanted to create something that was much, much, much more interactive. And we’ve created three such experiences, but they’ve all been too expensive to duplicate. So we’re trying to figure out how to drive down the cost of creating these immersive experiences that are also interactive, because we will get reams of data on how the visitors are interacting within those particular kinds of exhibits, which will then, of course, help us to develop better exhibits down the line.

Xiao-Li Meng: [00:05:17] Yes, I remember during that tour, I think we experienced, you know, some of those. And, I’m curious when your team gets all these data, I can imagine how much data you have. Do you have an in-house team to analyze them and how they draw the conclusion? How do you take advantage of the conclusions they draw to inform you?

Tim Ritchie: [00:05:38] Yes. So, it’s a large team, Xiao-Li. We probably have 15 people on that team. Now, once again, we’re helping other science centers around the country do the same thing. So it’s a service we provide, but it definitely impacts exhibit design. So we learn from one thing and we try to apply it to the next. So exhibit design for sure. And then a closely related but somewhat different analysis is program design. So there’s one thing to have an exhibit. It’s another thing to do a program on the floor. So we get lots and lots of feedback on how visitors are responding to that. And that changes how we do presentations. And then finally, on the digital side, for instance, we get so much data back that it has really improved our YouTube channel, our Instagram channel, and TikTok, and so forth. And so because we can see what people are responding to or not responding to, it’s a little bit like an old school A/B testing where we’ll do one thing one way and another thing another way. And it’s pretty clear. So our followers on YouTube have shot up. I forget how many followers we have, but I think it’s over 200,000 people now that are following us on YouTube. And that's increased a good bit recently because we’ve paid attention to the data about what’s working and what’s not.

Xiao-Li Meng: [00:06:56] I see. That’s fascinating. You mentioned you have a team of 15 people; you know, my department does not have 15 faculty members and we are still trying to increase that. And I want to have another follow-up question here because, again, our readers and listeners are mostly data scientists, and many of them probably have not thought about working for a museum. What kind of people do you recruit? Who are they? What are the skill sets you need?

Tim Ritchie: [00:07:22] The people on that data team really pretty much were educators and generalists who developed the ability to do the visitor assessment. There are definitely one or two of them that are true statisticians. I mean, that’s what they did and that’s what they did in college. And now they’re applying it in a cultural context. But our kind of assessment is really not very advanced. It’s more just going through the work, of assessing the functional equivalent of surveys. So it’s not rocket science, like, literally you might have in some of the rocket science type programs in the community. But it is people who love education. They probably loved math. They were good at the statistics side and wanted to combine the two.

Xiao-Li Meng: [00:08:14] So it sounds like you might have a job for me if I get tired of Harvard.

Tim Ritchie: [00:08:19] You come on over, Xiao-Li.

Xiao-Li Meng: [00:08:21] All right, I would actually. When I walked in to the museum, I said to myself, “Wow, this is not what I had remembered.” I was just fascinated by it. I was so pleased to have that opportunity to visit, you know. Actually, this goes to the next part so well. As you know, we have been talking about a potential collaboration between the Museum of Science and Harvard Data Science Review. As you know, we have a shared commitment, for example, to increase science education, the general literacy, whether it’s on the science more broadly or specifically on data science. So given our previous discussions on potential collaborations, could you inspire our listeners by outlining some possible ways in which the museum education body and the research community can work together toward these shared objectives of increasing science literacy, a better informed society, and avoiding misinformation?

Tim Ritchie: [00:09:13] So I’m going to step way back as I think about answering that question as sort of a fundamental thing that happens in museums. You should not leave a museum without saying at some point, ‘Oh, I had no idea.’ ‘Oh, things aren’t what they seem.’ That’s kind of the whole point. And so you learn about electricity because you had no idea, or you learn about a piece of art even. Well, data is hugely important to help people realize what reality really looks like. Data is what helps us live evidence-based lives. Data is what helps us realize that oh, we thought X, but the truth is Y. That’s hugely important. Just as truth is central to ordered liberty, so it is to museums. This is all the stuff of data, and it’s all the stuff of how do you live evidence-based lives. That is very much under assault right now. People really want to live on the basis of their preexisting ideas, their dogma about the way the world should be. And I think data and evidence helps us create the world as it is. It helps us greet reality as a friend. And that’s a very important thing for a science center to do. And it’s a very important kind of partnership we could have with you, because the kind of work you do is helping people understand the truth about things, and that’s always surprising to people. And it’s usually good news even when it starts out as bad news. Bad news is good news because you can do something about it. And at least you’re going forward on the basis of the truth. And that’s precious and it’s valuable. And I would love to think that we could do some collaborative work in the seasons to come.

Xiao-Li Meng: [00:11:33] Oh, absolutely. The one thing I feel these days, particularly running Harvard Data Science Review and having been a professor for so long, it always surprises me that increasingly so these days we have to emphasize that we’re seeking the truth, because it’s always given, right? When I study those things, studying statistics is always about using the data to help to discover the truth. Right now, these days, as you alluded to, we almost have like, ideologically driven truths, you know, whatever that means. It’s just really hard to imagine how we evolved to this point. And as you said, museums have such a big presence in changing people’s perspectives. And so does the education body. We all can obviously work together. Now, speaking of which, I want to ask you particularly because I know a museum is one place we always take our kids to, right? Museum of Science in particular, I remember my kids, we took them, and they always got very excited. What are you doing for these young kids? I know you attract them as early as possible. But how have data science, AI, whatever these things are, helped you to think about improving the amazing things you have been already doing and what’s coming for this young age group?

Tim Ritchie: [00:12:48] It’s a great question and I think that you can think of other places where kids go, it fires their imaginations. You think about Disney, or you think about Pixar and you think about these wonderful places, and they all kind of begin with this ancient truth that wonder leads to wisdom. I mean, Socrates said that, and we know that it’s true that the path to the head lies to the heart, and you could use a different word instead of wonder, you could call it awe or awesome. So, we want people to come here and believe that science is awesome, and we want them to believe that they are awesome and that their ability to use science can make for an awesome future. I know that sounds very colloquial, but that’s kind of the bottom line. So how do you do that? Well, one is through a big exhibit like the Theater of Electricity that is truly awesome. Or a big environment like a planetarium or IMAX. But then there are more specific exhibit experiences. So, we’ve just created a beautiful new exhibit, which we opened during COVID[-19] called Engineering Design Workshop. Engineering Design Workshop is full of tech. It’s full of data-related things where you are solving a problem, and you develop your problem-solving capability or your creative confidence. So, for instance, in one of them, we introduce kids to data. It’s called Dive and Splash, and they have to create a mechanical thing and then measure digitally and see how far down it goes in this digital water tank or how big of a splash occurs. And they’re measuring and making things all the time. And they can measure how slowly their apparatus goes down to the surface of Mars. So, data is kind of plugged into that exhibit because it’s a way to build that person’s creative confidence. It’s like, oh, I got a I got a five on this one, but I want to try to get a seven, a certain kind of measurement that I can improve upon. So, we build that into as many things as we can. But in terms of exhibit design, data itself is kind of interesting to visitors. Another good example of that is this year where we’re focused on climate change. This is the year of the Earthshot at the Museum of Science. Well, so much of the information about climate change has to do with the data on the facts. I mean, are we at 1.5°C above preindustrial levels? So, these are all things that people have to understand and what they mean. Or if we talk about carbon capture, what does it mean to pull things out of the atmosphere or CO₂ emissions? So, I could go on and on and on about the many, many ways that is important to a great science center exhibit.

Xiao-Li Meng: [00:16:08] To follow up on that, I want to run an idea by you. I visited not too long ago the National Museum of Mathematics in New York. Do you envision someday that there might be a museum of data science and AI, or should they be always a part of a museum of science? Do you see a more of a specialized museum? Would that be something useful?

Tim Ritchie: [00:16:33] I think it would be very useful. I mean, I do think big science centers like ours should have streaks on data, streaks on AI that go through everything. But just like mathematics should streak through a lot of the stuff we do, having a more niche exhibit that really gets you plunging down into it makes a lot of sense to me. I think that keeping up with the tech on all of these places is a little bit hard, but a small niche museum that could then be kind of a service of others. So, for instance, the Museum of Mathematics that you’re talking about, they actually serve as wonderful consultants to the rest of us on exhibits about mathematics. And they are really plugged in. They come to conferences, and we all know we can count on them if we want to talk with them about math in our museums. So a smaller niche museum that can then provide that service to other museums, that actually makes a lot of sense. It could be a lot of fun.

Xiao-Li Meng: [00:17:30] Well, now you’ve encouraged me. People have been asking me, ‘Now you have founded this journal, what do you do after this?’ I can now I say, ‘I’ll create a museum.’ Speaking of which, I want to ask you a more personal question. I understand that your background is actually in legal study. You had the JD. I don’t think many people know what does it take to become a president of a museum? What is the career path? And, if I ever need to become a museum president, what skill sets should I learn? What should I learn from you?

Tim Ritchie: [00:18:03] I would say if you polled all my colleagues and investigated the other CEOs pasts, they’re all very, very different. My path to becoming the president of the Museum of Science really started when I left my law practice to work in a big public housing community in Birmingham, Alabama, to do education programs for kids who are very much on the wrong side of the opportunity gap. And they were very talented but did not have any opportunities. But I was able to see that they were great at things like their computers and their cell phones, and that the talent level was very high, even though the public schools that they went to were not very good. And I realized that, oh, wait a second, hands-on science learning is a great way to get young people over the opportunity gap. And so my path went from working in public housing with kids and education to helping the group that was starting a new science center in Birmingham, to be a part of that effort. And then eventually I went to graduate school and I came back. Well, also, the path to leadership in my case was about awareness of the importance of science centers to the opportunity gap. And you will see that some people come into museums that way. Some people will come in through the science route, no doubt about it. And some might come through something like the fundraising route or the administrative route. There is one common denominator, and the common denominator is a deep passion for people to have access to high-quality science learning, a deep compelling passion to get that to the world. And so I think the path really isn’t something specific like your training. It is more is your heart and your soul moved by getting high-quality science learning to everybody? And if it is, then after that, the leadership challenges are all the same. There it’s about how you manage people. It’s how you do programs, how you have positive cash flow, how you have your board behind you, how you create an iconic experience. They’re all the same. Whether you’re running a university or you’re running a science center, I think they’re essentially the same.

Xiao-Li Meng: [00:20:29] That’s incredibly encouraging. So, I see that myself, I might have a chance. I do share the passion and I’m learning all these skills, you know, so who knows? You encouraged me.

Tim Ritchie: [00:20:39] You definitely have a chance. I wouldn’t put it past you.

Xiao-Li Meng: [00:20:43] Okay. Well thank you, thank you, seriously. Now let’s talk about something you mentioned multiple times. As we know, passion is incredibly important. We are all driven by passion. But in the end, when we want to make something happen, we need the funding. As you’ve already mentioned, providing museum experiences can be incredibly expensive. Clearly the Museum of Science has built a very effective fundraising team. And so I want to ask you again because this is a data science–driven conversation, how much of these data analytics help you to build this fundraising team? Is there anything you can share with other nonprofit organizations? These days I know everybody wants money, and there are lots of things about, you know, how can we engage donors, get more funding, make funding sustainable, and so on. So anything you can say to enlighten other nonprofit organizations, that would be great.

Tim Ritchie: [00:21:43] Yes. And I will get to the point about data in a second, because at one level, just running the institution of a science center is very similar to running any other nonprofit. The sales or the fundraising part of what we do, or the earned revenue part, or the membership part is very similar to what you would have in the for-profit community as well. And I actually think museums don’t use data enough, but I think I like to go into kind of three areas you’re talking about. So funding would be on the contributed side, which I think is what you are getting at. How do you get people to contribute? But it’s also on the earned side. How do you get people to either buy a ticket or become a member? And then it’s finally on the endowment side, which is how do you create an enduring institution because people want to leave that as a legacy? And the data is different in each case. With respect to the contributed side, I can boil all of fundraising down to one sentence. So here you go. For anybody who wants to know what fundraising is about. Here it is. ‘People give to what they value when they’re asked by somebody they trust.’ That’s it.

Xiao-Li Meng: [00:22:58] That’s great insight.

Tim Ritchie: [00:23:00] And it has three components. Do they value it? Were they asked? And did they trust them? So then you think about high-quality science learning and you think about Boston. Well, it’s an almost unlimited sea of people that value high-quality science learning. So there’s a giant market out there. So if you wanted to apply data to that, the question is, do you have a big enough pool? Well, you have a giant pool. And I think that we are not using our data enough to know our community, because this is a community that values what we do, high-quality science learning. The second thing is, do you ask them, and this is really where the data comes in. And this is where you learn a lot from places like public media and other places that have much more broad-based funding campaigns. And I don’t think we’re doing enough of that. And then the third component. Are they asked by somebody they trust? For us right now, that’s particularly easy because people trust the Museum of Science and we’re beloved. We could lose that because institutions do lose trust. You can see that in higher education right now, where there’s a tremendous amount of disappointment in and concern about higher education. But there’s not that about the Museum of Science. So on the fundraising side, I actually think we need to have much more courage about a broad-based campaign, like the kind of things you see in public media. And then I could go on and on about members’ insights. That’s why we’re making such a big commitment to our CRM system. Like, what do our members actually value, peering into that and expanding that. And then on the endowment. Well, that’s a much more kind of person-to-person kind of conversation. But even on the earned side, people buy what they value when they hear a message that they trust. So I think we need to do more about understanding our audiences, much, much more about understanding our audiences so that we can reach them with what we know that many of them value.

Xiao-Li Meng: [00:25:16] I think your emphasis on value is just incredibly important. Is there any particular new initiative you have in terms of how to assess what people value?

Tim Ritchie: [00:25:27] That’s a really difficult question. You know, Steve Jobs famously didn’t like these kinds of data-driven assessments ahead of time. He was like, I know what people want. I’m just going to make it and you’re going to like it. We do some testing and we have a basic sense, but there are a variety of firms that actually do this for cultural institutions.

Xiao-Li Meng: [00:25:50] I see.

Tim Ritchie: [00:25:51] And there’s a woman whose name is Colleen Dilenschneider. She has a blog called Know Your Own Bone. Know your own community. She’s quite effective in helping places like ours understand visitor trends. And there are organizations that help you understand trends in visitor services and what people are liking.

Xiao-Li Meng: [00:26:14] I see.

Tim Ritchie: [00:26:15] And so that’s helpful to us as we think about designing exhibits in the future and what the what the future is going to require. Having said that, we know what they want more than anything else is to have an awesome experience and that almost is subject neutral. So we are making a very big commitment this year on climate change. We’re calling it the year of the Earthshot. And we know we can make the Earth awesome. And we know we can create awesome experiences because the Earth is awesome. And then next year, we’re not doing a whole lot of visitor survey on this, we’re going to devote to the year of being human, the year of being human, and we’re going to focus on our bodies and our minds and our communities and our innovations. We know that within that broad category of the year of being human and the various things we can do, we can make that an awesome experience. Now drilling down and making all of them awesome. That is the main thing people want. There are some other things we know they want. We know they want a social experience with their families. And so these are the things that sort of at the core, we know we have to do well. It has to be awesome and has to be a wonderful social experience. And then there are other really, really, really practical things like we know they want a great cafe, we know they want a clean place, and we know they want to be treated with respect, and we know they want culturally relevant programing. So there are some things that are just primal, and then there are other things that are very specific about whether it’s going to hit or not. And that’s where we need the help of places like these visitor studies groups.

Xiao-Li Meng: [00:28:00] Thank you. That is awesome. A description of what you do. I’m learning the word awesome. I have to use it a lot more because I do see exactly what you mean. How people, you know, have that awe feeling. And you mentioned about this year is the Earthshot. That reminds me to talk to you a little bit about the museum scene internationally. I think we talked about, given that the Museum of Science in Boston is one of the largest in the country, that you’re providing service to many others. And when I visit other countries, I tend to visit more art museums, as museums of science are not as preeminent as they are here. I just want to get your sense. What’s the scene like and what you can learn from other countries or what they can learn from you?

Tim Ritchie: [00:28:49] It’s a really important question because science really is the world’s common language. The Museum of Science is very involved with the Association for Science and Technology Centers [ASTC] consisting of 650 members, about 350 science museums alone. And I happen to be the chairman of the board of that organization right now. ASTC is the North American version of that. But I’m also involved with the European version, which is called Ecsite, also a very large group, but not as big as the ASTC. But then there’s an Asian one, mainly East Asian, but not just East Asian called ASPAC [Asia Pacific Network of Science & Technology Centres]. And then there’s one in South America called RedPOP [Red de Popularización de la Ciencia y la Tecnología en América latina y el Caribe / Latin American and Caribbean Network for the Popularization of Science and Technology]. The reason I bring this up is that we are all connected, but it hasn’t been until recently that we have seen the technological ability to do things together like we can now. So, for instance, ASTC, the organization where I’m the chair, we have started something called seeding action, which is to try to get all of our members to do collective things with respect to climate change. But you could also imagine other issues like public health, for instance, where if we were all connected, we could actually move very quickly on public health–type issues, workforce development, things that relate to generational poverty. So I’m a big fan of the power of science centers to work together to take on some of the big issues that the world faces from the public science communication angle.

Xiao-Li Meng: [00:30:30] I’m really glad to hear that. I know you have all these the grand visions. In recent years, I have engaged with some collaborators from Sweden, working on using data science to predict, for example, a poverty index in Africa, and addressing global poverty issues. That is truly, as what my collaborator will always say, a planetary-scale issue. So you have to get involved. Well, our conversation can continue for another hour or so but I’m mindful of your time and thank you so much. But before I end with the final question, I want to ask if there is anything else you would like to share with the readership of HDSR that we haven’t touched on? Anything on the data or AI? Any wisdom that you would like to share with them?

Tim Ritchie: [00:31:19] What I’d like your listeners to think about is kind of a different vision of what a museum can be. And hopefully we’re becoming that as well. But I’d like a museum not to be a place you visit. I like it to be a place you use, like you use a library. You don’t visit a library; you use a library because it has resources for you. And so if you could use the Museum of Science, because we did have data visualization things, for instance, that were updated all the time. So for instance, during the upcoming eclipse, we’re going to have streaming data where people can see where the eclipse is going to be, where you want to go for that kind of thing. It would show up on our website as well, but I think that we can push ourselves to think about, becoming a place that’s not as focused on exhibits, for instance. But it’s focused on tools. Tools that could become exhibits, for sure, but they could become many different things. So once we have those tools, then we can invite the community in to do the programing. And I would love to have partnerships from your visitors, from you, from others, so that the intellectual capital of this community starts flooding through our museum, so that you are very much a part of the content that goes out to the world. Because when that happens, especially in a place like Boston, but the truth is, every place there’s a science center, the best asset they have is nothing that lies within the science center. It’s the intellectual capital of the community around it. When we push ourselves to create the conditions for your listeners, you and others just start pouring content through the museum, then we become a much more active community asset so that people like you in the future will start to say, ‘How can I get my message through the Museum of Science? How can I use the Museum of Science to reach the world?’ And one place where we are doing a lot of that right now is with AI. So we have a very strong relationship with a bunch of colleagues at MIT working on AI. They call us quite a lot, and we start doing lots of programing where they are the content. We are just the convener. We’ll apply that then to public health, apply that then to material sciences or workforce development. Apply that to government where the government has a really big message it wants to get out about some issue, and they start to think, ‘Oh, I can get that message out to the Museum of Science or the science center of my community, because that’s how they have all the tools to reach the world.’ I think that’s a different vision of what a museum can be and a very activist vision, but one that also preserves the notion of museum as common ground.

Xiao-Li Meng: [00:34:12] Well, thank you so much. You are clearly an awesome leader. And for this awesome conversation now, I can’t help but to give a plug for you. I know you’re running for an Overseer at Harvard?

Tim Ritchie: [00:34:24] Yes.

Xiao-Li Meng: [00:34:26] And I would encourage all the alumni listening to this conversation or who read HDSR to vote for you because Harvard clearly can use some awesome leadership here. Thank you very much. Appreciate it.

Tim Ritchie: [00:34:39] Thank you very much, Xiao-Li. It’s a real pleasure to be with you, and I look forward to working with you in the seasons to come.

Disclosure Statement

Tim Ritchie and Xiao-Li Meng have no financial or non-financial disclosures to share for this interview.

©2024 Tim Ritchie and Xiao-Li Meng. This interview is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the interview.