Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live. You can subscribe to the DataTalk podcast on iTunes, Google Play, Stitcher, SoundCloud and Spotify.
This data science video and podcast series is part of Experian’s effort to help people understand how data-powered decisions can help organizations develop innovative solutions and drive more business.
To keep up with upcoming events, join our Data Science Community on Facebook or check out the archive of recent data science videos. To suggest future data science topics or guests, please contact Mike Delgado.
In our recent #DataTalk, we spoke with Emily Courey Pryor, Executive Director of Data2X, an initiative of the United Nations Foundations, dedicated to improving the quality, availability, and use of gender data in order to make a practical difference in the lives of women and girls worldwide.
Here’s a complete transcript:
Mike Delgado: Hello, and welcome to Experian’s Weekly Data Talk, a show featuring some of the smartest people working in data science today. Today, we’re very excited to chat with Emily Pryor. She’s the Executive Director of Data2X, an initiative of the United Nations Foundation which is dedicated to improving the quality, availability and use of gender data to make a practical difference in the lives of women and girls around the world. Emily previously served as a senior director of women’s economic empowerment for the United Nations. Emily, it’s a pleasure to have you as our guest today.
Emily Pryor: Thank you so much for having me. Looking forward to the discussion.
Mike Delgado: Could you briefly share your journey, what led you to begin working with the United Nations and data, and specifically gender data?
Emily Pryor: Absolutely. I started my journey on the data front as a data user. My background and training is in public health, and so I did the normal biostatistics training and such that all of us do, but I approached it from thinking about health policy and how you apply data from a policy but also a programmatic lens. That led to my journey, thinking about it not just on the data production side or the data analysis side, but also how having data translated into appreciable impact on programs and policies.
Mike Delgado: Before I discovered your Twitter account and then going to Data2X, I had no idea about data inequality and what an impact it makes. Before crossing your social channels and digging into your website, it was totally new to me. Can you tell the story of the Data2X initiative, what you’re focused on, why it matters?
Emily Pryor: First of all, I should thank you for finding us and for coming upon our website and our social channels. I think that’s such a huge part of the challenge here and then also the opportunity is just trying to help people learn about it and help people learn something new and question some of the things they thought they knew and some of what best practice is. In terms of the journey we’ve been on and the journey with Data2X, there’s certainly a history here in this field, especially as the women’s rights community and the gender equality community intensified their efforts even several, 20, 30-plus years ago.
It’s not a new conversation, but there’s a lot of new energy that’s coming into play here. We got our start in 2012. At the time, the U.S. State Department, which was then led by Secretary of State Hillary Clinton, was interested in this topic. And they were approaching it also from a data use side. They were policymakers. They were being confronted with any number of policy questions, development and diplomacy questions, and they realized they did not have the data they needed to make informed decisions. And part of the reason was there was a hole in terms of the data availability. There were huge gaps across every sector, across health and education, and economics and political participation, and peace and security.
There were gaps in terms of having information on women and girls relative to boys and men, and they wanted to see that change. So through a combination of leadership from the U.S. government and from a private foundation, the William and Flora Hewlett Foundation, there was the impetus for Data2X to be created. And it’s been an amazing journey to get to build it from scratch and try to figure out how we not only articulate what the gender data gaps are, put it all in one place in a way that different groups can understand, but also as a next step try to figure out how to act upon what those gaps are, how we close those gaps.
Then an additional step, of course, very importantly, is how we help people understand and then help people do something about it and get the right level of political will and energy behind it so that people like you and your listeners are saying, “Wait a second. There is a real data equality problem. What’s our role in that, and how can we change that?”
Mike Delgado: Emily, can you share why this gap exists, why there’s disparity, and why there’s data around men and boys but there’s a lack of data around women and girls?
Emily Pryor: Sure. I’ll give one example from my own awakening process. As I said before, I came at this from a data use perspective, and my training is in public health. One of the things I remember several years ago learning about and being frankly really shocked by in the health community was that there was a real lack of inclusion of women in clinical trials and in understanding major health problems like cardiovascular disease, which is something we know is a huge health concern for women. But historically there was not a lot of inclusion of women in study design, and part of the reason for that was hormonal fluctuations and that made it a little bit harder to conduct clinical trials. As a result, there was no data available, and there was a missed opportunity in terms of understanding how that impacted care and early detection of some of these issues. That was something I just remember as a graduate student learning about and internalizing what that means and how that played out over the life course.
That’s a good example, and it’s something to know that can apply across a variety of areas. To your question about why some of these biases exist. The first step is acknowledging that data systems surveys, the way these things are carried out, they’re a product of people, right? And there are certain assumptions and entrenched biases we might all carry around. I think there are wonderful intentions around data work, but perhaps we need to stop and question the way that some of the underpinnings of the systems and the questions and the way they’re formed can impact the type of information we get back about women and girls but also frankly about men and boys. It’s about having gender data — being able to see experiences about women and girls and men and boys and being able to also see those globally and comparatively among countries and regions.
Mike Delgado: That’s a really good explanation. Like I said, this was something brand-new to me. I never realized the gender data inequality that exists out there that leads to all this bias. Maybe not even intentionally — just that the data’s not there, right? There’s no access to it, so then when they’re doing research or when they’re doing analytics, there’s going to be bias just because the data’s not present.
Emily Pryor: Right. Also, thinking about some of the social norms that might play into the data collection process. There are often, in terms of some of the very large surveys that will happen at the country level, a lot of these will be household surveys, and when an enumerator is coming to a household, then they have a situation where the people who are answering the question are not the girls and women in the household. The right person may not be answering the question, maybe it’s the man answering the question. Maybe sometimes it’s a woman answering a question, but regardless, who answers the question and how they frame their answers impacts the data you have and then how you act upon that data. That’s an important awareness for any data scientist to be thinking about.
Another factor is that, around the world, as we all know, girls and women are much more likely to have restricted mobility and access to technology, and that also has an impact on their data lives and the kind of information they can share about their lived experiences with the data community and the data for the decision-making community as well.
Mike Delgado: Emily, do you find that there are certain … I’m just thinking about certain parts of the world where women are not treated as fairly, and it must be very, very difficult in certain regions to even get that type of data, because like you said, the man’s answering the phone, the man is giving the answers, and maybe you’re not even able to get in touch with women and girls in certain regions. Could you talk a little bit about the disparity around the world?
Emily Pryor: Sure. Some of the areas around the world where there are some of the most entrenched gender data gaps and challenges are also where some of the difficult gender norms are also present. So, there’s definitely an association there between those two factors. I would also say, though, that some of why these gaps exist is that, and where we’ll have either biased data or no data at all, is in some cases because, as I would think a lot of your listeners know, data collection is not an inexpensive proposition. It costs money to collect data.
So there’s a major resourcing issue around data collection at the global level and within countries. And when you don’t have the proper level of funding of human resources within statistical offices and the financial resources that are needed, then you’re not going to have that data. That not only applies to gender data, but it applies across topics and in terms of general development data collection. That’s really important to highlight as well. This isn’t only about these countries having a bias against gender data.
Mike Delgado: That’s right.
Emily Pryor: But I think the opposite in many ways is true. In many of the countries and with people I know who lead national statistical offices within countries, there’s a real willingness to do more on gender data and other forms of data, but the resourcing is not necessarily there. That’s something that can’t be underemphasized and needs to be addressed.
Mike Delgado: No doubt. The amount of money that’s involved, the teams that are involved in data collection — all that plays a huge factor, not just the area you’re trying to reach. Those are all good points. What sorts of data are you trying to collect to help resolve this inequality?
Emily Pryor: One of the things Data2X is is a civil society platform on gender data and on building gender data partnerships. So there’s a lot of work we do on the technical side of working to bring partners together who do the data collection. We’re a small group. We’re not the size of a statistics office within a country or some of the big multilateral and UN agencies with whom we work, like the International Labor Organization or the U.S. Statistics Division or UN Women or the World Bank. These are global entities that are the data producers and that work also with country governments. We all as a global community rely on their work and their expertise to produce the kinds and the breadth of data we need for decision-making.
So part of what we do is bring those groups together around solving a particular technical challenge around data gaps. For instance, one of the kind of data gap topics we’ve been very focused on is women’s economic participation. There’s a tool and a resource on our website you’ve probably seen, but it’s a table — one of the very first things we did was map out all the global gender data gaps that existed across different sectors. We came up with 28, and within sectors the area where there was the most gaps comparatively was in the economic sector, which is really interesting when we bridge back to the earlier part of our conversation about bias and why this bias exists. A lot of the way that labor statistics and employment statistics and those surveys were conceived was at a time when the perception was not about women as economic actors.
There’s been a movement by the international community of trying to recast and reframe the way we think about women’s economic participation, and that requires work with a lot of the organizations I mentioned before — with the International Labor Organization, which does so much of the international leadership on labor statistics; with the World Bank, which does massive survey collection around the world; working with the FAO, the Food and Agricultural Organization, on how agricultural surveys are conducted. So it requires working with all those groups to try to figure out some practical ways to improve the data collection that occurs in a way that more accurately and adequately reflects the way that women participate in economies.
Mike Delgado: For those listening to the podcast or watching the video after the live broadcast, I want to encourage everyone to check out Data2X.org. Find out about all the different activities happening there. A lot of exciting things that Emily’s referring to, the tools that are there, the resources that are there, go check it out. Again, the URL is Data2X.org. Fascinating work. Can you share, Emily, another use case of how gender data inequality has hurt society?
Emily Pryor: Sure. There are so many examples. Starting at a top level, when we are collecting and using biased data or when we’re not using gender data to make decisions that are relevant to women’s and girls’ lives, we just have the wrong information, full stop. If we don’t have the accurate information from the get-go, as we’re building programs and policies, then that’s a real problem. There’s a very foundational issue here. And it can hinder policymaking and decision-making in every sector. We were just talking about women’s participation in the economy.
If we as a global community, but also at the individual country level, are trying to increase women’s economic participation, or just increase economic participation in general in a country, and we recognize that one group is somehow missing from an economic strategy, but we don’t actually know how women are participating in the economy, we’re not going to be able to build the right kind of economic policies to encourage greater participation and lead to greater opportunities for various people within a country.
If we don’t have accurate and complete data on violence against women and girls, which can happen for a variety of reasons … To bridge back to what we were discussing earlier on cost, one of the challenges with something like an area of data collection on violence is that it’s a very sensitive area on which to collect data, and it requires specialized training and specialized enumerators. It’s often going to require same-sex enumerators. In the case of women and girls, ensuring you have a pipeline of female enumerators who are ready to take this on is important. So there’s a cost issue, there’s a cost barrier, which then leads to that data not being collected, which then leads to not being able to make proactive policies to ensure a reduction in violence against women and girls. Those are just a couple of examples.
Mike Delgado: Thinking about violence against women and girls, that data is so sensitive. I’m curious about how these different data science teams are helping to protect that data because it is so sensitive and you want to protect everyone who’s involved in these studies. Can you speak a little bit about how this data is protected and secured?
Emily Pryor: Sure. In the case of the organizations that are doing a lot of the large-scale work on this, on the violence piece, we’re not doing that work, so I don’t want to put words into the mouths of those experts, but what I can say is that it’s a big topic of conversation and there’s a lot of very real concern, awareness and action on that front. There are legal, regulatory, technical aspects. There are protocols in place that have to be among a variety of factors at the country level, at the multilateral level, at the global governance institutions, academics, who are utilizing. So there’s a lot of work going on in that area and a very high level of concern and awareness and sensitivity to the issues of privacy and anonymity and data. There are a lot of other people who are specifically in the violence data collection space who could also comment further.
And just to point out one of the things I think is really interesting and a challenge that we have certainly our eyes wide open on is that in the case of gender data, one of our things we always say is we want more and better gender data. But what comes along with that is, in our quest for more and better, we have to be very careful — “we” the royal we, not just Data2X, but all of us as a data community — of what that means, and more and better with discipline, with protocols, with awareness about what that means.
Mike Delgado: What’s also really difficult is with that really sensitive data, especially about violence against women, a lot of that is very difficult to retrieve, not only because of finding the women, but having them open up and talk to you about these issues. These are usually issues no one wants to talk about. They don’t want to raise their hand that they want to be talked to about this subject. That’s also a difficult situation because there are so many women who are being abused, but they’re not raising their hands. I’m thankful we have movements like #MeToo that are helping to build awareness. The #MeToo campaign has been for me shocking to see the amount of violence and sexual harassment that’s been going on, but this is obviously prevalent. It’s been going on for a very, very long time, and campaigns like #MeToo are helping to expose it. Now the data collection aspect involves how we begin to pull in this data to make this useful.
Emily Pryor: And actionable.
Mike Delgado: And actionable.
Emily Pryor: Yes, exactly. I couldn’t agree more, and I think it’s absolutely the case on violence. But I also think it’s also the case around a lot of the issues where we have data gaps. Mental health is a great example of this. The data gaps on mental health for women and men are huge, and part of the reason is the data’s difficult to collect. It’s not easy for people to talk about. There’s just a real gulf there that needs to be filled, and the same even on certain kind of topical issues that are quite relevant within certain communities. Obstetric fistula is a great one. It’s a devastating obstetric injury that occurs in developing countries. The data on it, despite a lot of efforts, is notoriously poor, and the reason is because it’s really hard. People don’t want to talk about it. So it’s hard to collect accurate information on a large scale.
Mike Delgado: I’m even thinking culturally, different parts of the world, like certain subjects are just not discussed, like certain tragic things.
Emily Pryor: Including here.
Mike Delgado: Right. Same here. It’s like you don’t discuss it.
Emily Pryor: It’s important to keep that in mind. Here in the U.S., there are subjects we don’t want to talk about. I think part of why #MeToo has transfixed us is because some of these things were just not a part of the public discourse and culturally in some cases. I want to underline that gaps exist and cultural norms exist not only in other places in the world, but within our own country as well.
Mike Delgado: Can you share some of the successes Data2X has had with working with different organizations to help bring more gender equality in this world?
Emily Pryor: Absolutely. One of our big goals when we were getting started was to socialize the concept of bias and of gender data and what it is, what it means and why it’s important. I’ve watched that change since starting up Data2X a few years ago. I’ve watched more people want to talk to us and want to understand what we do as just a back-of-the-envelope calculation, but also a lot of that has been from having so many committed people in multilateral organizations — UN agencies, for instance — as well as at the country level who are really onboard and are ready to act. A lot of data producers who want to work together with us. And then going along with that, a lot of high-level champions who are engaging.
Melinda Gates made a pretty famous speech in May 2016 on this topic. Her Majesty Queen Rania has also championed the issue of gender data, helping to promote awareness about it and getting people to act — people at the highest levels, policymakers and decision-makers. Christine Lagarde has made some wonderful comments on the importance of gender data within the financial system. It’s that combination of trying to socialize the concept, getting people thinking about it, and combining that with some smart partnerships on the technical and data production side, as well as thinking about the advocacy side, the messengers, how we get this word out where influencers and decision-makers are acting.
In terms of some other Data2X-specific work, which people can see on our website, we work on a variety of topics in a variety of areas with lots of amazing partners. One of the things we’re quite proud of and excited about that might be relevant for much of your audience is that since our earliest days, we’ve done work on how big data can be utilized for gender analysis. And this is particularly exciting because when we were first mapping out the gender data gaps, some of these gaps are pernicious and persistent, and there’s a realization that some of the official statistics and official data collection, it’s going to take some time for those systems to ramp up to collect data on mental health. But perhaps there are ways that new data technologies and sources can be utilized to close some of those gaps and complement official statistics so they can close those gaps more quickly.
And we were really interested in this because it’s just a fascinating topic. We all want to close gaps more quickly, but also I’m interested in it, having worked in gender equality for a long time, because I’m used to being in a situation where I’m saying, “Wait. Don’t forget about gender. Don’t forget about gender equality in this.” I see big data as an interesting opportunity for this combination of big data and gender data as a way for us to be a leader rather than “a don’t forget about us,”. As an area where it’s exciting kind of creativity challenge for data scientists to think about how we can do this in a new sort of way. We have a big data for gender challenge we launched last year, and we had 10 award winners, 10 prize project winners.
Mike Delgado: Oh, really?
Emily Pryor: Yeah. That’s on our website. You can check it out. The projects are working from anywhere from satellite imagery to social media.
Mike Delgado: Wow.
Emily Pryor: It’s exciting. They’re working on a variety of topical areas. It’s a great thing we’re doing. I’m very proud of that along with many of our other streams of work, too.
Mike Delgado: That’s awesome. So we have a lot of data scientists who are tuned in to this show. Can people go to the website and offer their services?
Emily Pryor: People should definitely come to our website, and you can also always find us on, if you want to send a note to us, we’re firstname.lastname@example.org. We’re quite active on social media. We’re @data2x, so definitely ping us that way. It’s a conversation. We’d love to keep that going. I think another area that would be really interesting for your community is that we just launched a project earlier this month, and it’s open until March 8th. It’s called Gender Data Impacts. One of the things we’ve realized is that there’s socializing the problem and getting the word out and getting people interested in it, but for long-term change you also have to be able to show people what changes if you have gender data at the policy or program level. We have some great examples of that in a few areas, but we know there are more.
And the challenge has always been that they don’t exist in one place, and it can be hard to articulate them, especially because one of the issues is that there are huge gaps in data. So it’s hard to show the impact of something that doesn’t exist, but there are examples. So we put this call out in partnership with DevX and with Open Data Watch to try to find a way to centralize some of these stories and promote them and push them out there. You have to be able to show how having data was a key driver of action, how it had an appreciable impact. All that information’s online as well within your data scientist community. If people have great examples of that and how gender data specifically led to an impact, we would love to hear from folks.
Mike Delgado: That’s awesome. Well, Emily, thank you so much for sharing your insights with our community. I want to encourage everyone who’s listening to the podcast or watching the video to definitely check out data2x.org. So many things going on, so many things to learn there, so make sure to check it out. It’s very cool to hear about these challenges you guys have out there, Emily. For those who are new to Data Talk, we have this show every single week, and if you want to learn more about upcoming shows or past episodes, you can always search for Data Talk on Google or the short URL is just ex.pn/datatalk. Emily, thank you again for being our guest. It was an honor talking to you and awesome to hear about all the work you’re doing to help improve our world with better gender data.
Emily Pryor: Thank you so much for having me.
Mike Delgado: Take care.
Emily Pryor: You, too.
Emily Courey Pryor is Executive Director of Data2X. Hosted at the UN Foundation, Data2X promotes more and better gender data and its use to advance gender equality and womens empowerment. Alongside launching Data2X in 2013, Emily built and managed the UN Foundations research program on Womens Economic Empowerment, and served as Senior Advisor to Girl Up, a for girls, by girls campaign. Emily has also worked in the private sector, for biotech firm Gilead Sciences, and for the American Red Cross Headquarters. She received her MPH from the University of Michigan and BA from the University of Florida. You can follow her on LinkedIn and on Twitter.
Check out our upcoming live video big data chats.