Tell us how you found yourself at the HOPE Program, and any context around the role data and AI have played in the work that you do. 

When I moved from Colombia to the United States in 2017, I was looking for a job where I could help change the world. My first impulse was the United Nations, but someone suggested that I look for something in the nonprofit sector. I was fortunate to land at the Bedford Stuyvesant Restoration Corporation, the first community development corporation in the country. I started as a financial counselor and worked my way up to director. After several years, I had the opportunity to apply for a job as a chief of staff at the HOPE Program, where I could apply my skills to their initiatives in workforce development, with particular emphasis on the green economy. 

The HOPE Program was looking for someone data-oriented; My experience as Director of Program Compliance meant that working with data was part of my everyday responsibilities, ensuring we met reporting and compliance requirements. That background helped me secure the role. 

Once at HOPE, I started to evaluate the data to align with our new strategic plan, Home Of Prosperity and Empowerment, and with a focus on growth at our 40th anniversary. When you’re making decisions, you need good data! 

And data leads naturally to AI. The need to develop and deploy AI was something that we started to hear from our partners. Funders were suggesting that we consider AI tools to be more efficient in conducting research for grants and for grant writing. At first, everybody was familiar with only those tools that took notes during a meeting. Learning how to engage with generative AI tools like ChatGPT, Gemini, or Copilot was something new.  

We tried to start to bring it into our daily work, but we were not fully sure what we should do, given our concerns about data privacy. We sought guidance from private companies as well as other nonprofits on how to handle these concerns, and landed on creating a data policy as a first step. From practice to policy, data is the bedrock of a lot of my work here. 

Are you seeing widespread AI adoption in the social sector more broadly? 

Very recently, I attended a meeting with about 15 nonprofits. Before the meeting, they surveyed the group to ask where they stood in their AI journey, and I was surprised that only 7% reported actively using AI in their day-to-day work. This is not a statistically significant sample, but an example of what I see. Everybody’s trying to figure out the right tools to use and, most importantly, the right way to use them without compromising privacy. We understand that transparency is essential, and the importance of privacy for our participants and our community. That’s the context in which we, and many other nonprofits, are operating. 

We realize that change is coming. I remember when the adoption of the internet became widespread, and how much it changed work and the world. AI is going to change and take away a lot of existing behaviors. And create new ones: AI tools and agents are becoming collaborators in daily work. We need to figure out how to become more efficient, and also how to retain the human touch and insight. We’re going to use AI, but we want to be sure that we are doing it the right way.

What problem are you trying to solve at the HOPE Program, and where do data and AI fit in?

The HOPE Program helps New Yorkers build sustainable futures. We serve adults over 18 years old: 95% of our participants are from low-income communities; 90% are BIPOC, Black, Indigenous people of color. Around 50% of our participants have faced homelessness. We also work with a lot of formerly incarcerated people, a focus that we are known for in this sector.  

One HOPE priority–which I really admire–is that we serve communities that are highly impacted by environmental issues. For example, right now we are working in the South Bronx, where environmental, economic, and social disparities are all high.  

Data is a way to help us figure out how we should move forward to solve the systemic challenges we face. Now we’re taking that data and putting it into AI tools to help us deeply research how this data can affect what is happening in the world. That plays a role in steering our direction.  

Here’s a specific example: the US federal government recently changed its funding priorities. Our task was to figure out how to navigate these rapid changes without an expert on hand. We were able to use AI to check all the executive orders and understand the potential impact on the services our organization provides, particularly the training focused on the environmental or green sector. Using AI helped us understand the impact.  

Were there any unexpected blockers to your career entry or progression, or your move into this field?  

Language was more of a barrier than I expected. When I applied for that first job in the US, my initial phone interview in English was a big challenge. As someone who enjoys networking and spends a lot of time with people, I was frustrated—but it reminded me to continue working, to improve my professional English, and to build a network here.  

Living in New York has helped. There is a love for immigrants, for people coming from different places around the world. Practically everybody has an accent, and that helped me gain confidence. 

What community of people or resources bolsters your work today? 

I am supported by the community of people we serve.  

The community connection is powerful if you deeply believe in the mission of the organization that you’re working with or working for. I believe in what we do, absolutely. Maybe I’m not facing the same issues and challenges that our community faces, but as an ally, I try to see how my skills can help this community.  

I also have benefited from cohort work with people solving similar problems with data and AI. When I was with the Restoration Corporation, we participated in the data.org Data Maturity Assessment (DMA) cohort funded by Microsoft. We spent six months learning together about different topics and reviewing our results. We had a lot of interesting findings about our data culture, and it helped us shift the organization in the right direction. Almost 15 months later, I asked a former colleague how it’s going with the data culture. Apparently, it has really continued because of this cohort work. That is affirming. 

Which skills—not necessarily related to data and AI—have helped you in your career?

I would say soft skills, which might seem counterintuitive because this is a workforce development organization, and we are training people to become better in their jobs. I always say the only thing that is going to make a difference is the soft skills and how you interact with people. You can learn a tool, you can learn the job responsibilities, but how you interact with others and show respect is critical. So much is about empathy and understanding.  

When the soft skills are missing, it makes a difference. For example, we noticed during COVID how young people started to become very attached to technology, and how they were missing interactions with others. There was a high cost: after COVID, they didn’t know how to be in spaces with other people. Soft skills matter. 

What advice do you have for someone interested in doing this work? What have you seen as differentiators for success?

Focus on what you want and where you believe you can make a difference in the challenges you want to solve. Be unafraid of taking on new opportunities, because if you’re afraid, you might be setting yourself up for failure. Gain some learning from each new opportunity—even a work experience that is not the right path can set you on a different, right direction for you.  

In my view, what really sets people apart today is the ability to bring together analytical, creative, and critical thinking. With so much information out there and AI now part of the process, it comes down to staying curious and asking the right questions. More than ever, we need to be thoughtful, ethical, and discerning about the outputs we get, whether they come from AI or any other advanced technology. 

What do you see emerging as the next big thing in data and AI for social impact?

In my case, I’m very focused on equity in data and AI and would like to see that emphasis become more common. Every time I’m analyzing data, I’m trying to evaluate an equity component, because I believe that it’s the only way to make decisions that are focused on social impact. The same is true with AI, which is becoming a collaborator. 

Increasingly, we are applying data and AI to better understand and improve green metrics. For example, reducing heat in buildings and realizing energy savings. Getting and acting on good metrics is something that we want to expand. We are starting to use AI to get some specific points that maybe we are not able to collect, but we are able to compare. We want to use more data and AI in the coming months to improve not only our participants’ outcomes but also those of their families and communities.  

We are committed to sharing those findings. Sometimes we make decisions based on the data we see, but in the end, we are serving the communities. It is important to share findings and ensure that the community understands your work and the metrics behind it. Their input is valuable. Maybe the community can help you to identify gaps and opportunities – a new metric, a new outcome, or another service that the community needs. 

What’s your don’t miss daily or weekly read?  

I spend a lot of time in two apps: The New York Times and Masterclass. These keep me up to speed on news and help me continue to work on soft skills. Masterclass helps me explore topics like how to become a better communicator and personal growth. A lot is changing all around us, but we can always work on ourselves. 

The post Pathways to Impact: Julian Stillman appeared first on data.org.

You had a wide range of diverse work experiences before pursuing a career in social impact. What led you to this work?

My career started in computer science, so I naturally went into the tech sector, in startups. That spanned a period of eight and a half years across numerous companies—some succeeded, some failed. The transition towards social impact occurred when I had the realization that, despite the energetic and innovative nature of the startup community, for me, it was missing an essential ingredient around purpose. That led me to pursue an alternative path.

I believe that everything happens for a reason. Right around that time, a door opened by way of the Global Leadership Fellows Program with the World Economic Forum. The program provided an opportunity to get engaged in social entrepreneurship. The subcommittee I was part of looked at social entrepreneurship in East and Southeast Asia. The role entailed bringing these passionate and innovative social entrepreneurs into the broader World Economic Forum convenings. Our goal was to enable the cross-pollination of ideas between global CEOs and leaders and the social entrepreneurs driving change on the ground. 

Forging those connections was a hugely invigorating experience. And it was such an exciting time to be doing this work, with Mohammad Yunus just getting recognized for his results in microfinance.

I took all that as a very clear sign to contemplate a career shift toward social impact.

You’ve held many roles at AVPN, and most recently have been leading the ImpactCollab there. What are you working on with data and AI?

At AVPN, we start with the premise that we are essentially surrounded by capital. When we talk about engaging in social impact, it necessarily means deploying that capital to support non-profit organizations to deliver on their mission and to grow.

The challenge is connecting the two worlds: the abundance of capital needs to find a fit with social impact organization (SIO) leaders, who all need capital to support their work. This is a shared need for both nonprofit organizations and social entrepreneurs.

We ask ourselves: how do we best engage that capital? How do we get the people making decisions around capital to take notice of the important work happening in social impact? How do leaders at SIOs, from both data and communication perspectives, gather and present the right information to attract funding?

We have found that in many instances, the SIOs’ case for support is constructed with just one funder in mind, like the Gates Foundation. Hundreds, if not thousands, of other possible, well-resourced supporters would be interested in the nature of an SIO’s work—so we think a lot about how we gather the data to get all the right pieces of information to the right audiences so capital can flow.

Bridging this gap between the organizations that need the resources and the people who actually have the resources is what we’re focused on with ImpactCollab.

Here’s another place we’re seeing a role for data and AI: language. Within the Asian context, we’ve got an additional layer of challenge around languages. In most of Asia, the dominant language is not English: it’s Chinese, Japanese, or something else. So when Asia-based SIOs are trying to fundraise, often they are translating themselves into English with a particular donor profile in mind. That approach impairs communication to all the other potential local supporters who could appreciate the nature of their work.

Next, imagine the people with the wealth being subdivided into different communities of culture and language with all the nuance that entails. 

That’s where LLMs, in particular, can play an interesting and important role. First, they allow a different way to navigate the taxonomy of areas of social impact. For example, in this sector, we sometimes use very specific terms to describe our work. If you’re in climate-related work, you’ll talk about mitigation and adaptation. To the person on the street, those terms are just jargon. That person would say, “I want to do something for the environment,” but they might not frame it in terms of mitigation or adaptation.

An LLM actually allows for natural language to be used, for someone to present an interest and then connect to a potential opportunity to act or support. With technology, you can even layer the language translation layer on top of that. So that’s something we’re looking to build quite extensively into ImpactCollab.

It’s great because a lot of these technologies are actually now available out of the box. With so many AI competitors, we don’t have to build these capabilities from scratch; we’re just tapping into what’s being produced at such a fast pace. We want to ensure we do this work responsibly and also take full advantage of emerging technology to deploy capital to social impact areas where it’s needed most.

Were there any blockers to your career entry or career progression?

I’m both a Stoic and an optimist, so when something crops up in my path, I tend to take that as a sign and a challenge rather than as a barrier.

Over the years, I have embraced the challenge of adapting to new cultures. I grew up in Singapore, went to the UK for my undergraduate degree, and then to the US for grad school. 

I am very grateful for those experiences: diving into what people were like in each region and local community, and learning what motivates them—how they work. I also saw that as a way to develop myself personally, and to see life from different perspectives.

What community of people or resources bolsters your work?

For me, it’s always been the social entrepreneur community. And for me, that’s less a formal affiliation or group, and more my community of friends and colleagues who have the social entrepreneur mindset.

Very different people can embody this mindset, wearing different hats and holding different titles. You can find them in a nonprofit, in government, or in a corporation. My personal affinity is to the group that thinks in a socially entrepreneurial fashion. That’s the group of friends and stakeholders that energizes me, and that I proactively look to cultivate over time.

Thinking longer term, I always want to be active in this space—even as I grow old and retire. I can’t imagine myself doing nothing. I want to be surrounded by people with the social entrepreneur mindset, intent on change. I intentionally cultivate a network of my peers, and also those who are 20 and 30 years younger than I am, to ensure I can stay engaged and connected to this work.

Given the breadth of your education and roles you’ve held, you possess a wide range of skills beyond technical ones. Which skill has offered the greatest return in your career?

I’ve mentioned Stoicism before—it’s a mindset and philosophy that has hugely benefited me: Keeping an open mind around different perspectives, especially when you’re in times of high stress or pressure.

We at AVPN often find ourselves in those situations because we work among Type A employees who charge ahead. Sometimes, that pressure can build up. But you don’t want it to become a conflict. That can happen without cultivating a more accommodating mindset, where you seek to understand why people said what they did, or why people reacted the way they did.

I rely on the Stoic mindset, which I try to inculcate in the team. It has benefited me tremendously, as I appreciate a broad range of perspectives and approaches. Those different approaches add value to my individual contribution, the organization’s work, and overall, they make us more effective as humans.

What advice do you have for someone new to the field who is interested in doing this work?

I’d suggest first exploring volunteering—dipping their toe into the social impact space, assuming they are holding some other job. Engaging as a volunteer is an excellent introduction to some of the practical realities on the ground and the multifaceted challenges that are typically at play. 

It’s also important for them to reflect upon what makes sense for themselves and the issues that speak to them on a very personal basis. I’ve found that this has to work for each of us, personally, to be sustained on this journey of discovering our own purpose and place in society and life.

To take Singapore as a context, we’ve created a very robust process of getting young people through school and then into jobs. Once they have those first roles, that’s typically when they get to contemplate what it might mean to work at organizations on the ground that serve a broader purpose beyond individual self-interest. It’s then that they engage in a peer group to share experiences and learn together. It doesn’t matter whether you’re a fresh grad, a mid-career person, or even a retiree—finding what compels you is useful to unpack as you begin to work in the social sector.

What is the next big thing you see in data and AI for social impact?

I’ll focus on data and AI enabling a major cultural shift. In the Asian context, where AVPN focuses, there is such diversity of cultures, practices, and languages. The shift I anticipate and hope to see is a greater melding of cultures and mindsets across the region. As I said, so much of this work and communication is translated into an English medium, and as a result, we are seeing only a very thin slice of what is actually happening on the ground. 

Now, with AI, we can facilitate better translation, as well as a much richer understanding of semantics between cultures and practices. We’re able to expand from that very thin slice into a much broader view. You can begin to understand, for example, why Koreans approach social impact the way they do from a historical or possibly even religious perspective. Why do the Chinese do it that way? Why do the Indonesians do it that way?

Today, we are looking at the surface of understanding these differences, because all the work is being converted to English, and that’s how we’re sharing information. But if we are able to leverage tech to go into the true essence of our practices, similarities, and differences, then I think that there is a tremendous opportunity to learn from one another across this region. And then the same would be applicable globally; AI will enable greater understanding and fuel culture change.

It makes me think of a locksmith who tells you, “don’t make a key from a copy of a key—always go back to the master key.” Are you saying that moving everything in and out of English relegates you to an inferior copy or understanding?

Exactly. Because we’ve gone through translation layers over time—translations created and interpreted by people, and that’s inherently imperfect. I really love your key to a key analogy.

What is your don’t-miss daily or weekly read? How do you stay informed and not overwhelmed?

I’m going to go back to Stoicism again. 

I read Meditations by Marcus Aurelius—his reflections from his time as the emperor. I find it provides a valuable perspective beyond the news of the world and of the development sector. These readings help me stay grounded. 

The post Pathways to Impact: Kevin Teo appeared first on data.org.

Can you tell us about yourself and about your work at the intersection of tech and health?

I’m an ER physician and the founder of Link Health, which is focused on helping eligible patients enroll in cash assistance benefit programs. At Link Health, we’ve unlocked a way to have artificial intelligence help do that for thousands of Americans. 

If you’ve worked in—or even been in—an emergency department, you understand that our healthcare system is not functioning. You can only do so much work as an emergency physician until you begin to think, “There’s gotta be a better way to do this. There’s a better approach than just jumping in the river and trying to save the drowning person.” Eventually, somebody has to go upstream and figure out why all of these people are drowning in the first place. 

My work is really focused on solving this problem; throughout my career, it has taken multiple shapes. For example, it’s been helping people who are in healthcare waiting rooms be able to vote. Through our initiative, Vot-ER, we help people register to vote while they’re waiting to be seen. We believe that through the power of the vote, they can create a healthier and more robust healthcare system.

And that work also includes what we’re doing here with people and technology at Link Health. We’re trying to figure out how to use someone’s time in the waiting room to get them connected to the cash assistance benefit programs that they are already eligible for. The data on who is eligible is all there, but we need to make people aware and get them connected. Through that program, we’re proud to have helped over 3,300 patients enroll in vital federal benefit programs, ranging from rental and cash assistance to contributions toward a child’s 529 college savings account. Altogether, that’s more than $4.4 million in financial support distributed over the past two years. And we’re just getting started.

Were there any unexpected blockers or pivots in your career journey?

I came to medicine from a low-income community. I had never been on the clinical side of a healthcare setting, so I quite frankly didn’t know what I was getting myself into. By the time I was in my third year at Harvard Medical School (HMS), which is when you do your clinical rotations, I felt very disappointed. I think I would use the word “heartbroken” about the way that the healthcare system works.

There were things that I witnessed as an idealistic 23- and 24-year-old that broke my heart, and many of them had to do with the way that we treated patients who were either low-income or uninsured. So I had a decision to make: do I stay in this field and continue committing the harm, or do I head in a different direction? And that’s when I left HMS for two years.

During that time, I went to the Harvard Kennedy School of Government to learn how government works. I don’t think I actually learned how government works; instead, I found more questions to ask. After that, I worked in politics for the Governor of Vermont, and that was an eye-opening experience in learning how to get things done. And then I came back and did my residency at Massachusetts General Hospital; I was committed to medicine, but to doing it a different way. This career detour improved my work as a physician and a changemaker.

You spoke of coming to medicine from a low-income community. How has that been an advantage or a disadvantage in your career?

I think that the great gift of those who live on the margins of society is that they learn how to survive on the margins of society. If you are then put at the center of society, where it’s a resource-rich environment, you can see clearly what those resources can bring. 

You learn on the margins that you have to practice survival-based efficiency, where every decision has to be made with the knowledge that you may not get this chance again. So when you move to a place where there are more resources, it’s like Disney World. You learn to spot the opportunities quickly. I think it’s a real advantage.

Were you able to identify the kind of changes and solutions you felt were needed when you returned to medicine? 

As a 20-something-year-old in medical school, I definitely didn’t understand what the solutions were. I just knew how bad the problem was. I know that there is a really wide health/wealth gap for Medicaid patients that I see here in Massachusetts. With an average income of $22,000 a year for Medicaid enrollees, how are we expecting these patients to be healthy? People are wrestling with the skyrocketing prices for groceries and with putting gas in their tanks. 

I don’t promise to know what the solutions are now–just what some of them could be. 

My experience working at the White House taught me that there is some money available: federal and state programs that many low-income patients are eligible for but aren’t accessing. And that planted the idea for Link Health as one possible solution.

We don’t necessarily have to overhaul the whole system–although one could argue that probably some part of that is necessary. But we can optimize what we have today, using new technologies and existing dollars.

Our first iteration of Link Health required throwing a lot of humans at the work. And we still have a workforce: today, we have almost a hundred certified patient navigators whom we’ve trained to go out in those waiting rooms and enroll people in these programs. But the reality is that we’re never not going to scale our impact like that. That’s why the work that we are doing with the AI2AI Challenge award from data.org and the Mastercard Center for Inclusive Growth is so important. If we can leverage artificial intelligence using large language models, we can more effectively blanket clinical spaces with an invitation to check if you are eligible for these programs, and then help you with the process of enrolling. We are learning that a great deal can be done with a very well-written algorithm. With this technology, we don’t need to rely on human intervention alone, but can focus on the places where humans in the loop are critical in aiding patients with their applications. 

Your medical degree and your data and AI vision clearly inform your contribution to social impact. Which other skills have offered the greatest return in your work — which abilities have really supercharged your career pathway?

The thing that comes to mind right away is one specific thing I learned during my time at the Kennedy School: the adaptive leadership framework. I have no disclosures here; this framework simply changed my leadership perception and practice. Interestingly, it’s taught by a physician, actually a psychiatrist. 

Here’s how I understand it: in medicine, you have all these houses of medicine, these different specialties. There are different ways to be a physician: an ear, nose, and throat doctor; a dermatologist; a rheumatologist, and each of these has their specific practices and ways in which they are a doctor. It took me going to the Kennedy School to realize that leadership is like that, too: there are lots of ways to exercise leadership.

The adaptive leadership framework holds that leadership is not a position: it’s a verb. Leading is an exercise, and in a well-run organization, every person in that organization is empowered to try to do the work of pushing the organization towards solving its real challenges–and not shying away from the reality of the challenges. The framework also taught me how to think politically about coalitions and build partnerships that are mutually beneficial to do the work that the community needs.

What advice do you have for someone who is new to the data and AI for social impact field? 

The things that I would share right up front: you have to fall in love with the problem that you’re solving, not the solution that you are creating. You need to deeply understand the contours of the problem and why it exists before you settle on a solution. 

For example, when you try to address a problem, you have to make sure you understand the secondary consequences of that solution. Who stands to lose from you addressing or fixing this problem? The system is currently benefiting from the way the problem exists. You need to understand why that is.

It helps to have an understanding of community organizing. That’s the framework that I’m speaking to you from. As a community organizer, you’re not saying this is the solution that we need to move forward with. Instead, it’s more like orchestration. The piece of advice that I would share about tackling a problem is shifting from a mindset of “I have the solution, and I need to persuade people to go with it.”

The second piece of advice that I would share is this: you need to be using artificial intelligence yesterday. If you are not, you will be subsumed. It is an incredibly important resource, and I’ll leave it at that. Concretely, we do this through traditional skilling, but we also have watch parties for an hour every other week. For example, I’ll do a session where I am sharing my screen and showing you how I use ChatGPT’s new operator program, or giving an example of how deep research works. It can be as little as 10 or 15 minutes of an example, but it sparks learning and conversation. And then you can unlock the creativity of the team; people will give you ideas on ways to use this tool to maximize your productivity far and beyond what you could come up with alone. I really like the gelling that happens when we share and compare notes.

What’s your don’t-miss read?

The Politico Pulse daily newsletter is very, very good. It’s into the appropriate amount of detail on healthcare legislation.

The post Pathways to Impact: Dr. Alister Martin appeared first on data.org.

What drew you to work in data and digital product development? How did you find your way to where you are today?

It’s been a long road, and it all started with a brand-new master’s program. The program, Design and Technology Futures, was a collaboration between the Estonian Academy of Arts and IT University TalTech. The program focused on innovation: how innovative ideas are born, how user-centered thinking informs the work, and how businesses succeed or fail and why. By bringing together designers and engineers of various backgrounds and providing some tools and frameworks, the program encouraged ways to create value in the world. 

During the program, I developed a deep interest in digital products as well as a newfound understanding of user experience, user interface design, and computer model design. My first job was with a not-for-profit innovation organization called Satellite Applications Catapult, which was growing the UK space industry. The organization focused on both upstream and downstream applications. On the upstream side, we supported NewSpace businesses which built and sent products into space, like satellites, sensors, and instruments. Downstream applications included taking the data from satellites and considering ways it might be used for good. How do we use data and develop software applications that make a difference? Exploring the use of data was what I really loved. 

From there, I found my way to the development sector because we had many opportunities to apply these innovative space-based data and applications to sustainable development goals. Projects included anything from looking at climate resilience in small islands to helping improve the quality and productivity of cacao farmers in Colombia, to monitoring oil spills with space-based radar data.

In your current role with the World Resources Institute (WRI), what kind of problems are you trying to solve through digital product development?

I serve as the Head of Product Design for the WRI’s Data Lab. The Data Lab offers product design and development resources to program teams across WRI and our partners. We work with subject matter experts in different sectors and we design, test, and scale new solutions made possible by software, digital technologies, and data.

We do work a lot with space-based and satellite data because that enables us to view issues on a global scale. Our best-known products include Global Forest Watch which monitors deforestation, Aqueduct which looks at water risk assessment, and Climate Watch, an open data platform that helps track insights and contributions that different countries have made through national climate plans. 

We create these data applications to help policymakers around the world make better decisions and these data applications are useful to practitioners tackling these issues today. For example, the deforestation applications are used by forest rangers and local communities who receive near real-time deforestation alerts. These alerts help them learn where deforestation may be occurring and investigate.

Have there been any unexpected blockers to your career entry or progression? 

There have been some challenges along the way. Any new role I’ve entered has felt like it’s always been a massive jump. It’s always been terrifying at first, but I really like taking those big risks and jumping into projects, companies, and initiatives that offer learning opportunities. Being a woman in this industry has sometimes posed a challenge, as has adapting to the cultural norms of living in the UK. 

The most significant challenge has been the transition to leadership. In my early days, I prized my creativity and liked to develop and design applications myself using custom software. So the transition from being the creator to leading creators has been an adjustment. I’ve learned that I no longer have the time and space to take that hands-on approach to creating tools and products anymore, but instead have found satisfaction in building a team of amazing user researchers, designers, and product strategists who do the job with incredible results. 

What community of people or resources bolsters your work? 

I’ve always looked for and felt at home in organizations that are very collaborative and don’t work in silos. WRI is a great example of that. We really collaborate with our peers, whether it be other environmental organizations, intergovernmental organizations, environmental agencies and governments, the private sector, etc. The work environment and the culture in our organization make me feel like I don’t need to seek out special communities —  I have them around me on a daily basis. 

I find that other communities of people who inspire me and really help move my work forward are those with opposing perspectives, or who do completely different things that I don’t get to witness daily.

For example, when we work on big global agriculture data projects, we need to travel in-country and engage with farmers. I recently had a project where we collaborated with the Indian cotton and rice sectors. During our trip, we visited rice farmers, cotton gin facilities, and a broad community of associations who did work on the ground. We listened and learned from their experience, and focused on helping these local organizations build more climate resilience solutions and adaptation thinking in agricultural supply chains. Being able to spend time with communities whose day-to-day is so different from mine is refreshing, and informs our work.

Finally, I’ve participated as a community member in Women in Product, Women in Geospatial. 

Which skills beyond data and digital products have been your superpower?

In one word: empathy. It’s a real strength to be able to be curious, to truly listen to people, and to try to understand where people’s underlying needs and motivations lie. At the end of the day, the people who are using our products and technologies need to find them valuable. It’s been beneficial to listen to people and really take in what they’re saying. 

When I was at Satellite Applications Catapult, I was once working on a project that was developing this new technology to improve cacao production and quality in Colombia. We had a brilliant team of technologists and data scientists, and I brought in an emphasis on user research. This user research involved meeting with the people in the supply chain who worked locally in Colombia through structured visits to cacao farms, chocolate factories, and storage facilities. This was a valuable experience for the data scientists and developers who were planning to implement the technology. By the end of the trip, our team and all our partners were amazed by how much they learned from the process and how much better they could tailor solutions to these real-world needs and contexts. This user research mentality, driven by and focused on empathy, ensures that the technology meets the actual needs of people who use it. It can be a superpower if we allow it to be. 

Our WRI teams practice this, as well, devoting the time to engage with people in person and to really observe how people behave in their natural environment. We take interdisciplinary teams to project locations around the world and schedule time for that in-person collaboration. We find that engagement is essential to facilitate new ideas and more creative, effective solutions. 

What advice do you have for someone new to the field who’s interested in doing this work?

Try it! If you feel curious and there’s something about this sector that interests you, put yourself out there and contribute. When I think about when I first entered this sector and the way I felt, how out of my comfort zone I felt, I questioned my ability to add value. I was lucky to have great mentors who helped me overcome this blocker. 

I do think that it’s worth emphasizing for anyone entering a new sector that even when there are established norms and practices in place, your fresh perspectives can add value through the knowledge you bring from a different sector with a different skill set. Recognize your unique value add, and don’t be afraid to put it out there. 

What’s the next big thing in data or digital product development for social impact that you see and why? 

I guess I have to say AI and large language models (LLMs). LLMs will probably continue to be the center of attention and the center of many new solutions that we’re seeing. But I’m hoping that there might be a positive shift in terms of user-centric product creation. Today, we’re still seeing a big gap between technology and humans, which limits the way we interact with technology. There’s a barrier. I do think that large language models and explainable AI can bridge that gap and help make technology more human if we choose to design it that way. 

I think there’s a lot of opportunity for AI to democratize access to data analysis, even for those who don’t currently understand or know how to interact with complex data. I am hopeful that we can lower that barrier regardless of the user’s technical expertise. 

What’s your don’t miss daily or weekly read? 

Several pop into my mind — but one that stands out is the Climate Action Tech newsletter and Slack. They have a lot of accessible explainers for climate-related issues with a clear focus on the opportunity of technology within the climate space. 

I also listen to cool podcasts on design and product management, more focused on environmentally sustainable digital design, web sustainability, etc. Examples: Igniting Change by 3 SIDED CUBE, SUX by the Sustainable UX Network.

I also rely on LinkedIn and the network I’ve built there as a good source of new things happening in our space.

The post Pathways to Impact: Tea Tüür appeared first on data.org.

What led you to do this work? What did that career path look like for you?

I grew up in very small towns in the state of Maharashtra India, and we moved about every two to three years. My childhood exposed me to some of the social challenges these semi-rural areas face, including people who were victims of floods and earthquakes or struggling as subsistence farmers. I didn’t fully process it at that time, but looking back I see the effect that my upbringing had on me. My father came from a low-income community in North India and worked his way up to become the most educated person in his family. He joined the government, where he has been serving now for almost 40 years. My mother has also been working at a large private corporation in India with their Corporate Social Responsibility wing for almost 20 years. As a result, dinner table conversations addressed India’s development and raised questions of why such stark inequalities persist.

During high school, I lived in Mumbai, a city where these inequalities are very evident. Near my high school, there were urban slum communities where people lacked access to toilets and many other necessities for a basic, dignified life. In these communities, it was common to see women being harassed on the long way to use a public toilet. Combined with child marriage and domestic abuse — there was widespread resignation to this treatment of women as the norm. I was about 14 or 15 years old and I was angry to see other girls and women around my age in these communities who had such a different life.

I was volunteering with a women’s self-help group called Mahila Milan when a woman came in crying. Her daughter had stopped eating, and she wanted me to convince her to eat. It turned out that she had told her daughter that she must choose between going to school or having food on the table. The daughter’s stance was that she would give up eating because she so desperately wanted to go to school.

It was an awful situation, but it was also inspiring. It made me think: there are people here who truly can create a change in their own lives, but they need systems to support them. I’ve seen countless women get discouraged after having stood up for themselves only to realize they have no support — and then they just give up.

I became engaged in activism through a range of projects — including building safer public toilets. Eventually, I came to see all these problems as systemic and requiring broader support. Before starting my undergraduate studies at Duke University, I began talking to these women a lot more to understand what kind of support would help.

Duke was the first place I’d ever been where there were such unbelievable resources and a mindset of abundance rather than scarcity. Considering what you could do with all the resources in the world was very different from thinking about how you survive and run something daily. Duke really shifted my mindset toward how to influence systems, and that eventually led to the founding of Myna Mahila.

Where did you start to see that data and bigger trends that you could observe? 

My first community projects were focused on research before shifting to implementation. Data is critical to understanding what’s happening systemically at the community level. Stories are compelling and inspiring, but the data helps us dimension and address the problems.

Looking at data we collected at the household level alongside national statistics showed us that most women in India have used sanitary napkins, for example. The problem was not the supply of sanitary products but that usage and education about these products were lacking. Basic statistics showed that women were using multiple products at the same time, which told us that these women were probably not aware of the benefits of using a single product and how they should actually use it. We needed more data! We created a vertical in our organization called Myna Research. Myna Research helps us to collect data and build public data sets that can then be used by other researchers. 

A lot of our focus has been on primary data collection, and over the years we’ve even run randomized control trials. That’s been very important. Now we’re exploring training data sets for AI models, and trying to create these public goods that are beneficial for communities while localizing them to specific use cases using their own data. 

What problem are you trying to solve with data? 

Our overall mission is to increase women’s agency so that they have more decision-making power by giving them financial independence and keeping them healthy. We recognize that health and employment really go hand in hand with increasing a woman’s overall agency. So today, we have three verticals: Myna Health, Myna Employ, and Myna Research. 

Determining how to measure the change in women’s lives is where data is extremely important. How do you measure women’s agency? How do you measure if a woman is actually healthy and how do you measure women’s financial independence, especially when much of the self-reported data may not be accurate? Those are some of the key metrics that we track, and we’ve been creating our measurement and evaluation strategies to capture those in ways traditional surveys may miss. 

Measuring women’s agency is not like measuring household income or labor force participation. It is much more subjective; it can be tailored for different communities differently and the standards may differ. To achieve this, we’ve been piloting different tools and developing our own tools. 

We use data to measure this in different ways. Within Myna Health, we’re trying to measure knowledge shifts around the sexual and reproductive health of women. We’re trying to shift women’s behavior and practices around sexual reproductive health including shifting their product use to be more effective. We need to measure changes in attitude shifts and behavior change around these issues. We have this new AI LLM bot that we’ve been working on where we are trying to shift women’s family planning practice, particularly to be able to use more contraceptive methods as needed, as well as to be able to space their births and understand the advantages and disadvantages of getting pregnant at different times. At the same time, we need to help women clear up misconceptions about women’s sexual and reproductive health.

It’s extremely important for us to create primary data sets to understand the basic misconceptions in these communities. Often these are oral traditions that have never been recorded anywhere. We have the ability to bring together a very diverse data set of voices, perceptions, and misconceptions that people have about their sexual reproductive health. 

This work links to the Challenge project with Microsoft and data.org: many of the people getting employed in roles providing these query and generation databases come from our initiative called RANI. Rani means queen in Hindi; we’re trying to develop these women to really build their own kingdom, earn money, and be financially independent.

With Myna Employ, we are now creating a database of workers, cataloging skill sets of women to potentially match with available local jobs. This aligns with work in Myna Research, where we are creating survey data sets where we’ve listed households to understand the basic demographic. This work acts as a census of the slum and slum resettlement communities and particularly focuses on women above the age of 18 who are employable, and we track these women over time. This research is especially helpful where secondary data sets are incomplete or problematic. 

What have been some of the blockers to your career progression? 

Gender and age matter. Early on, most of the people that I was dealing with were middle-aged men and they would not take me seriously. I started at 19. Now I am 29, but look younger and still constantly need to prove my credibility. 

One of the motivating factors to get two degrees at Stanford was to build credibility beyond the topic of gender and women. In India, if you’re working on a women’s topic, people just dismiss you immediately as a feminist who is focused on a charity cause. We need to point out that women’s issues are serious and affect the entire economy.

For example: Do you know we could increase our GDP by $700 billion by bringing 30% more women into the workforce? This has a real impact, and yet people are dismissive of women working on women’s topics. Closer to home, I was constantly told I’d never ‘find a guy’ if I got a PhD because then you have to find somebody who is more qualified than that. Luckily, my parents have been very supportive of my education and work. My mother is a complete rebel, and she has protected me from many of those attitudes. As for others, I’ve found ways to use them as motivation for my work!

Which community of people or resources bolsters your work? 

I have a great board of advisors at Myna Mahila. Right now we’re trying to tackle some structural questions about the organization: Should we branch off a vertical separately? Should we keep them under the same structure? 

For my own personal decisions, I consult what I refer to as my board of personal advisors; my parents play a big role there, as do other trusted friends and mentors.

I’ve been fortunate to have been a part of several leadership groups like Glamour or the Queen Young Leaders Program, Asia 21 fellows, and Knight Hennessy Scholars. They’ve all been really helpful in some ways, particularly the Baldwin Scholars at Duke and the Knight Hennessy Scholars at Stanford. The MIT Solve cohort also had a big impact. We won one of their challenges and they assigned us an executive coach, which was very helpful. 

For inspiration, I always turn to my girls and women — the women who taught me and welcomed me into their lives. If I’m questioning whether I should do something, I think about these women and ask if it could change their lives, and usually, my heart knows instinctively whether I should do it.

Are there any other specific skills that have enabled your contribution to be able to lead a data-focused organization? 

Relationship management has been an important skill. For example, technology development at Myna Mahila has required skills in vendor management and building relationships because we hire product managers and work with separate teams of software developers, UX designers, and now ML engineers.

Getting into this more technical space within the organization helped me strengthen relationship management and know how to get products built within a timeline. My time at Stanford has also helped me develop this. But most importantly, our team in Mumbai is learning by doing every day. We have an excellent team who weren’t data experts to begin with but have slowly gained an incredible amount of knowledge in this space. This ability to upskill quickly, adapt to new technologies, and continue to learn has helped us get to where we are.

A lot of success comes down to people management skills, relationship-building skills, and being able to think about the big picture while managing the everyday day to day. We are able to ensure that the fires every day aren’t overshadowing what’s really important to accomplish by certain milestones.

If you met someone new who’s interested in doing data for social impact work, how would you get them started?

I think the key is finding the right fit to get them started. Which problems do you want to solve and how do your skills realistically match up with the need?

At Myna Mahila, we get a lot of outreach from people interested in the work we do, and many of them are remote. We try to identify opportunities to match skills: for example, we have a list of technical challenges, and then we try to assign them based on their interests and their skills. 

Some of that support has been mentorship and coaching. We’ve had good experience with coaching that upskills the team on the field so we can take it forward even when the volunteer is not involved anymore. 

What do you see as the next big thing?

The key right now is figuring out how technology is actually used by first-time online users, by people who currently don’t know how to interact with it. 

GPT-4, for instance, is well utilized by only a very small fraction of the world, and there is huge value in unlocking the benefits of the existing technology to the broader community. I don’t see this as a technical challenge alone; it’s more a challenge of understanding how people think and behave with technology. It’s more of a human-computer interaction (HCI) problem to really resolve. We need to determine what the technology brings that can be beneficial and how we then create further applications that can really help people engage. I can’t predict new technology over the next two years, but what’s going to be exponentially beneficial is figuring out ways of disseminating this to people in a way that maximizes its benefit.

What’s your don’t miss daily or weekly read? What keeps you current on data for social impact? 

There’s no single source! I draw from many WhatsApp groups: We have a lot of these Stanford GSB entrepreneurs groups where people are constantly asking a lot of good questions. That keeps me up to date as to what types of things people are still concerned about or if is everyone having struggles with hiring right now. 

LinkedIn is my place to go for grant applications — because we are constantly fundraising — and to see new projects that people have launched or come up with. There’s a lot of activity in this sector — and always a lot to learn.

The post Pathways to Impact: Suhani Jalota appeared first on data.org.

How did you find your way to this work? How did you come to combine the discipline of African-American Studies with data for social impact?

I can pinpoint the moment: my husband is in the military, and during COVID we were traveling and watching the news in a hotel. It reported that AI had been used to determine the placement of testing centers for COVID-19–and they were largely placed outside of Black communities. And I wondered: how does AI do that? Who agreed with that decision? Clearly, it was relying on census data or other datasets in which African Americans are underrepresented. I realized we have these computer algorithms that are making decisions for people, but that lack quality data, and people’s lives are being negatively impacted. As a result, we were seeing African American communities facing disproportionately higher rates of exposure and death from Covid, while lacking access to testing.

I was sitting there as a humanist, someone who’s passionate about African-American studies, and I was feeling like history might repeat itself — particularly with everyone investing in AI.

I began to think about how we might mitigate this risk to our communities, and not re-create a situation where African-Americans were impacted negatively. This interest eventually led to Mellon awarding me a new direction grant, which I used to go back to school and learn data science. I wanted to learn about these algorithms and about big data–and how these things are impacting many vulnerable communities. I had some statistics in grad school, but I’ve learned Python literally in the past two years. I’ve learned some AI. I began this learning journey very, very recently; I passed most of my classes, and I failed a few. But it was a journey to be able to understand what is happening and what we need to fix to make a significant and needed impact.

Today, I’m the director of the data science program at Howard University. Howard, with the support of Mastercard, has launched an online master’s degree program in applied data science analytics, with an emphasis on social justice. In this program, we’re focusing on engaging non-STEM learners. Part of the conversation we’ve been having about impact involves diversifying the pipeline. How do we get more non-STEM individuals into the pipeline of bringing their humanist mindset into data science and into tech? How can these new entrants to the field create more robust conversations about data and AI and their impact?

In your role as the director of data science at Howard, which problems are you focused on solving with data for social impact?

In our program, we focus on three main areas of economic empowerment: environmental justice, social justice in general, and health equity. We are bringing students together to propose data-driven approaches to these issues. We have students coming from political science, from nutrition, from history, coming from all these non-STEM spaces to say, ‘I have an interest and it’s non-STEM — but I’m looking to dive into data science’. For example, I have a student who worked in banking and now wants to address the large disparity Black women face regarding retirement – she’s working on a machine-learning model to create a solution. These learners, these problem solvers, are seeking to use data. We are benefiting from the perspectives of these non-STEM learners to tackle big questions that have yet to be explored thoroughly by the field.

What were some unexpected blockers to your career entry or progression?

I think the biggest issue is the language barrier. Computer science coding is a language, and just like any other language, it’s best to learn at a young age. There’s an inherent benefit to introducing kids to Python and coding very early. I realized that being 30-something and learning a new language is difficult – you need to learn the language, and also adopt a shift in mindset and thought process that takes a while to develop. Going from not knowing Python, not knowing R not knowing any coding–transitioning to this computational thinking is different.

Also, it was a transition for me to see how we deal with data. In many cases, when you’re learning how to code cleanly, they’ll say, if something is missing, just delete it. So if I have a variable that has a lot of missing cases, they may say, just get rid of it, or kind of take two averages and give a midpoint for everything that’s missing. But from my perspective, we have to think about the missing data.

For our communities, the question I have students address is why it’s missing. Can we fill in the gaps with more data instead of throwing it away? That’s a paradigm shift, thinking about what’s missing, and why it’s missing. How do we deal with underreporting? How do we allow women to be more represented in data? How do we get women more involved in the data work? All that just takes more time, and more sensitivity. We need to not continue to overlook what’s hidden or what’s not there.

I said one barrier is learning the language, but another is unlearning the culture. The culture has to stop jumping over the data that’s missing and focusing only on the data that’s available. Instead, we have to consider what’s unrepresented. If we’re creating a new culture of data for social impact, that requires a shift. Those are two things I would say are barriers both to my own learning and to the field in general.

Whether you’re thinking of yourself as a humanist or as an executive leader within a major university, what community of people bolsters your work?

Right now there are networks of Black women, Dr. Latanya Sweeney at Harvard, Dr. Safiya Noble at UCLA, and Dr. Ruha Benjamin at Princeton. I follow that community online and also through their published work. Their scholarship keeps me grounded in what’s possible. In big data and social impact, the temptation to just do data science is always there. To do data science with a focus on social justice, you do have to be surrounded by people who ask what’s missing and drive toward what’s possible. These scholars continue to shift your paradigm because the rest of the world is thinking of big data lakes, getting more numbers. But the missing part is the hard part: is this limited focus on more data healthy for us?

For example, we should consider: Do we need more data points? Do we need more satellites? Do we need to have more GIS locations, and surveillance? Being able to pull away from that impetus toward more and consider what is beneficial has been very healthy for me. Those scholars have fueled my work. Also, I’ve learned from those who are in library studies and communications. Even scholars who are in English; at Howard, the English department is very passionate about dealing with generative AI, and teaching writing. That’s also a space I like to stay connected to. There are many people who are not in data science or tech that are doing that work that keeps it grounded.

Which specific skills enable your contributions? You chose to retrain on data science, but is there a non-data science skillset that has strengthened your work?

I learned early on that leaders don’t run from problems. And there are many problems within the computer science, data science, and AI world.

I am not proficient in LLMs, nor am I a coder, but I do offer value in the questions that I’m asking because many times these algorithms are impacting my life. It affects everyone. For the average person, whether you’re scrolling through social media or paying your bills online, these algorithms are impacting daily lives. Sometimes it takes those of us who are confident to walk into a room that isn’t made for us and say, ‘I have a question,’ or ‘I don’t understand how this is working,’ or ‘How did you come to make this decision?’

If given the opportunity, more of us who are not in that space would find the confidence to enter these spaces, and we could see changes. But there is a real issue of opportunity. Many of these spaces are like algorithms: they’re black boxes. You don’t know where to ask the question.

And there are many times you need confidence–and persistence. For example, I was helping someone online and they wanted to go to customer service and there was no customer service. If the website or app was built to keep me out, and there’s nowhere to ask for my money or fraud issue, how do I complain about this?

In some cases, people who are coming from a certain zip code and buy a certain product are flagged for fraud. No one knows how to complain about receiving so many fraud alerts and that takes a toll on confidence. We need to ask companies to see what’s under the hood and push companies to be more transparent.

This kind of invisible exclusion does make you think of all the places people can be invisibly denied: a purchase flagged as fraud, an insurance claim, a plagiarism accusation… 

And getting a job! At this point, there’s no job that’s going to take a paper application. It’s going to be electronic. If the right jobs are not displayed to you based on your demographic or location, the result is unemployment.

What advice do you have for someone new to the field but interested in doing this work? Where do you suggest they start?

If you’re new to the field and not in the space of STEM, I would say still come!  We need more non-STEM humanists. We need you to come and to be confident about what you’re bringing to the space versus what you’re getting from the space. It won’t be easy, but your own domain expertise and experience can make data collection, analysis, and application better.

So that’s one. And then two, consider the changing nature of expertise. We all know that technology is changing very fast. Literally in the two years I’ve been learning, there have been four iterations of Open AI. The constant version updates are so consistent that the best thing you can do is bring your subject matter expertise to the technical conversation.

Third, I wouldn’t join this field thinking that you have to learn everything by yourself. The best thing to do is to partner with technical people who code, and with innovative people who can work with you to identify new approaches.

Finally, you should have an ear for policy. At the end of the day, policy is going to be the thing that dictates how things work. Even though there are market-driven approaches, a lot of technology will be regulated.

What’s the next big thing that you see in data AI for social impact?

See the policy point above – I’m really looking at the US Presidential election. I think the election will determine the policy impacting the field in this country. After the election will determine whether people have the access, the infrastructure, the accessibility to be able to be a part of the conversation, or whether it will be a closed-door field.

If we do have access, then we need to engage young people in coding and data. That will help us reduce the digital and data divide: kids who can code, kids who can understand data, kids who can protect themselves and create their own digital footprints. CS for all, even from K to 12, will be very impactful.

What’s your don’t miss daily or weekly read?

I keep up with my field reading about the human implications of technology from the academics named. I check in with the New York Times Tech section just to kind of see what’s happening.

Most of all, I listen to my students and their views on technology. Honestly, that’s my daily check-in. I start the day by saying, okay, what’s happening? And they tell me. This generation is facing challenges: loans, jobs, savings, and buying a home. I learn a lot by listening to young people and what they’re struggling with, which is sometimes depressing because some of what they’re struggling with is unseen. They’re dealing with likes, followers, and this whole different sense of digital self, pressures that I didn’t have. And the threat of them losing themselves to AI is very real. We need to work together to address that.

The post Pathways to Impact: Dr. Amy Yeboah Quarkume appeared first on data.org.

What brought you into a career engaged in data for social impact?

It’s been a long and winding road, starting from a love for scientific inquiry as a child. What began with early science experiments has evolved over time to fuse my passion for scientific inquiry with my passion for addressing systemic social injustices. These injustices were evident to me early on, having watched many of my family and friends struggle to break out of poverty.

From early on, I had this vision of becoming a leader in society to enact change in the world. At first, it was through human rights and advocacy, but eventually made my way into public health research and then program evaluation. My early career focused on public health epidemiology where I applied data-driven insights to inform infectious disease control strategies, but I realized that many health conditions were preventable if people had access to resources and environments that met their basic needs. So, I pivoted my epidemiology career towards upstream interventions that ultimately would prevent poor health outcomes. This practice of applying research, systematic inquiry, and collective learning towards social impact was called program evaluation. This career move showed me the pivotal role data can have in transforming the ways in which we solve societal problems. Without research and data, we would lose the ability to constantly learn, improve, and build upon our work.

The skills, perspectives, and empowerment that I’ve acquired through this journey have become the stepping stones to give me that privilege to enact the change I have wanted to see in the world since I was young. While my career has been a bit of a winding path of varying experiences, there has been a common thread of applying data-driven insight to community health solutions. As data became more prevalent, the opportunities to apply research became greater, with the potential for data-driven insights to play a bigger role in a society and influence its direction.

What were some of the signs that the time was right to apply data for social impact?

Working in the field on areas like homelessness, international development, and public health, I saw a gap between the great work being done in public health research and the work being done on the ground. The people who were doing the research and the systems development work were not working in tandem with the people who were doing the building, like the frontline workers providing services.

I observed this gap and also a cycle where we were repeating the same thing over and over again, hoping that the problems would vanish. We were doing the same exact intervention in different places. The intervention would fail in a couple of years and then we’d do the same thing in another place or in the same village or community a year later with a different funder. We were falling victim to short-term thinking, seeing ourselves as individual units competing in a resource-scarce world, even though we were all actually working on the same thing in the big picture.

My mission since then has been to fill that gap. The signals showed there was an opportunity for a new data and learning-informed approach that could interrupt that cycle and create long-term solutions.

A challenge was that the funding was primarily available for research and directed mostly toward academia. At that time, I worked with local health departments and saw how the infectious disease sector was actively using data to inform immediate action. Based on this experience, I translated those same tools and concepts over to my work in the community and with Engineers Without Borders. We built systems to collect the data, and then gradually changed the culture of how we think about how we do things, using the research actively and translating that data into action.

It’s important to acknowledge the role of community here. We recognize the need to work directly with the community as we decide how to translate research into action. Evaluation is not new in the international development field, but how it’s done and how the data is collected has changed. It’s less often about an outside person coming in to assess how well your project is doing, which will then decide how much funding you’re going to get in the next round. There has now been a realization that evaluation is actually a really important tool that belongs to the people doing the work. It belongs to the community it impacts.

What part of this problem are you seeking to solve with data for social impact? How does that translate into your role today?

During the past twelve years in either public health epidemiology or program evaluation, I’ve been trying to bridge the gap between research and practice. What I now know is that the bigger picture requirement is developing systems change across sectors. As a society, I think we now realize how interrelated all these issues are that we’re all working on, how complex things are getting, and how people and populations are growing and changing. My goal is to work with key change agents in the community to build the tools and processes to help them actively play a role in using data to inform their day-to-day work, as well as the bigger picture. And at the next level, we need to understand that the ways that we work with each other and the ways our work mutually reinforce each other contribute to a collective impact. It’s time we accepted and leaned into our interdependencies.

What were some unexpected blockers to your career entry or career progression?

The first blocker was a mental shift out of the poverty and scarcity mindset. There was a moment when I realized I could play a bigger role in society if I believed in myself and others believed in me, and I worked towards it. This was very different from what I saw many others doing, which was just really focused on individual careers—make enough money, buy a home, survive. It was just really hard to imagine a career, especially earlier on and there wasn’t a lot of money. I needed to broaden my thinking to imagine a path where I could still do what I really wanted to do while surviving and meeting my personal and family obligations. I understand why my family members were so set on survival because it’s really hard in the United States, as well as globally. At the end of the day, for many, life is about survival. I had to think of a way to do both—I was very creative, resourceful, and persistent in my path to get to where I am, with the help of many people who believed in me and supported me.

What helped me to make the shift was talking to people who had that similar realization who were further along in their career. I’ve had many mentors over the years who have given me great insight and advice to break through and change that mindset and perspective over time.

There was another mindset shift: breaking through silos. It broadened my thinking beyond defining my career as working in public health. The problems and solutions expand beyond neat boxes. The real world is messy, complex, and interconnected. I made this shift alongside a community of people—change agents—who were starting to realize that same thing.

What community of people bolsters your work?

There are so many. I’d even go back to the times when I was in middle school; my teachers in middle school and high school were really influential. When a mentor believed in me, that made me believe in myself. Now I mentor others to pay it forward.

There have been times in my journey where I felt alone. Even now sometimes it can feel really isolating and disempowering because change is so slow, and setbacks feel harsh when you’re dedicating your life to this mission. Having peers who are just as passionate helps me; I draw strength from surrounding myself with people who really care about the future and about each other.

Are there specific organizations you belong to that you would like to mention?

I’ve volunteered with organizations like Engineers Without Borders and Get-Us-PPE where I applied my data and evaluation skills to help meet community needs. I’ve also found it helpful to join professional organizations, like the American Public Health Association, and, when I was considering becoming a doctor, the American Medical Student Association. This was helpful in my earlier years because I was surrounded by all these people who were early in their career, too, and truly passionate about the work; we were all just trying to figure out our way.

Now that I’ve narrowed down my professional focus into program evaluation, I’m a part of the American Evaluation Association, where I’m similarly surrounding myself with a mission-focused community, with people doing it in different ways. The broad community at AEA has been affirming, helping me figure out what is different and what is the same with others’ approaches.

I’m currently serving as the Senior Director of Data and Evaluation at United Way Bay Area where I get to work with amazing change leaders and amplify their impact through data-driven insight and community-informed learning processes    

What non-data skillset do you consider to be your superpower?

Overall, a systems thinking approach has been really helpful. It’s helped me see things and design in a bigger picture, and then that marries well with my technical skills around analysis and evaluation because then I can more easily translate that research into action. Being able to bridge those gaps through my systems thinking perspective has been valuable.

Of course, relationship-building has been critical. There’s value in being able to work with people across multiple sectors: government, academia, nonprofit, frontline workers, and foundations. I’ve worked in many sectors and have been able to interact with and learn to understand a lot of the nuances of the different sectors; that has been my superpower. Building relationships with people and forging connections with people who are different from me has also been a superpower because in this work we have to work with people who are different from us to advance our vision of a better future. We can’t continue working in silos. We can’t insist on our own principles or mindsets as being the right and only way. We have to learn together to work together. It’s useful to remember that I’m a member of a community. I’m a part of this bigger system and so are you, so let’s work together to build a better future in the best ways that we know how. That collaborative way of thinking has helped me.

What kind of advice would you offer to someone who’s interested in data for social impact work?

One of the biggest pieces of advice is to get involved. Plug into circles and different local groups that are doing similar things that you’re doing or interested in doing. That’s the first step.

The second is recognizing your strengths, learning what your strengths and weaknesses are and embracing them. That approach is critical, especially for people who have a big vision: we’re going to change the future. If you aspire to make change that will last across generations, you will need to build solutions that can withstand changing times and challenges you may not anticipate. It’s really easy to get into a trap of thinking that you have to do it all, that you have to learn how to be good at everything in order to realize this vision. Recognizing and leaning into strengths and weaknesses is important because that’s the first step in recognizing you’re not alone and you can’t do it alone. You’re going to need other people that you’ll depend on. And that’s okay.

What’s the next big thing that you see in data for social impact? How do you see data being used for evaluation one, or two years down the line?

I see an encouraging movement of evaluation professionals shifting their focus away from just program-level work, and elevating that to systems change and collective impact evaluation. That’s going to be a big shift in the field. Alongside that, I see growth in incorporating culturally responsive and equitable evaluation principles. I think artificial intelligence could be a powerful tool for elevating community voices to inform community solutions. Quantitative data tells only part of the story, and qualitative data helps fill the gaps around community perspectives, context, and experiences that are often lost in research and evaluation. To date, one of the greatest barriers to scaling qualitative analysis has been how costly and resource-intensive it was to analyze data. AI is going to be a huge game changer. I’m excited for the ways AI will allow public sector leaders to quickly gather and integrate community feedback to inform real-time, responsive strategies. I’m very excited to see tools out there already to help interpret the amounts of qualitative data that we need to collect alongside quantitative data to make meaningful data-driven insights. We will need to be mindful of potential biases that AI could unintentionally introduce if we are not careful in how we build and use data tools.

Is data literacy improvement needed for communities to be strong partners in these conversations?

It’s a two-way street. I think the term itself is a little loaded because it assumes people are not literate, but the way that we deliver data is not always accessible. Certainly, we need to build up the capacity of community leaders and local decision-makers to ask the right questions and interpret data meaningfully so they can apply it to their work. However, there is also a responsibility to increase the accessibility of data to its audiences. We need to move beyond presenting a dense research report and involve communities directly in the use of data. In most of my work, I incorporate participatory evaluation approaches where community leaders and stakeholders are a part of the data collection, interpretation, and ultimately the use of the data. Interactive dashboards can also be a useful tool for communities.           

Data includes qualitative data — including people’s experiences, perspectives, and opinions. It includes the voices of the community. And so, we also, as evaluators and researchers and data scientists, need to recognize the value of community voices and perspectives alongside quantitative data.

Finally, sharing and changing the power dynamics is also an underlying necessity to improve data literacy.      

Can you give an example of a concrete way you could change a power dynamic around an evaluation project in the community?

First, we must treat the community leader as a partner in the evaluation and also an expert in their own community. I come to the table as an expert in evaluation and methods. They come to the table as an expert in their own community. It’s vital to treat each other as equal partners rather than acting like the assessment can be delivered absent community insight.

What’s your ‘don’t miss’ daily or weekly read?

A book I often go back to is “Thinking, Fast and Slow” by Daniel Kahneman — one of my favorites. 

And on a regular basis — this sounds really boring! — I am very plugged into my LinkedIn network. I think it harkens back to that idea of surrounding myself with people who are working on this broader mission, but all in different ways. I find the curated LinkedIn feed is my daily read that I review. It’s so hard to keep up with everything, and seeing the work of others is exciting and motivating. Through my networks, I get to learn about new ways of thinking about things or just the latest research on which I can build. The community and their knowledge keep me moving forward despite how hard this work can be sometimes.

The post Pathways to Impact: Jacqueline Chan appeared first on data.org.

You’re currently the executive director of AkiraChix, helping women enter and advance in careers in tech. How did you come into this work?

My background definitely informed my path: I’m a software engineer by profession. And when you work as a software engineer in different companies, it becomes obvious that there is a pressing problem when it comes to hiring and retaining female tech talent. Though this didn’t come as a surprise as I graduated as one of two women in my class, I was saddened by it and wanted to change it.

Beyond your personal lens as one of two female software engineers in your class, what else informed your belief that it would benefit society to broaden access to data and technology?

My belief that tech can empower came not only from my personal background but also from seeing how the field was picking up and changing the Kenyan landscape at the time. There were very many opportunities for people with technology skills, particularly for women. But women were being excluded from an industry that needed the very skills they had.

A good example is the launch event for the iHub, which is the first innovation hub in Nairobi. There were 200 attendees and only about 10 of them were women. Attending events and seeing very few women was a clear indication that something needed to change.

I come from a very humble background. Being able to get a job that paid me well allowed me to support my family, and to support their move from one socioeconomic status to the next. This change was an eye-opener and made me realize what could be achieved if women were included systemically.  The jobs were out there, so why couldn’t we get more women in technology to fill these roles and transform their lives? 

It’s no secret that when women are empowered, an entire community is transformed. I felt strongly about ensuring that we have more women taking up these opportunities. With women in these roles, there would be a shift in the intergenerational cycle of poverty within families and a significant change in the software industry. 

What part of this problem are you trying to solve? What opportunity do you see?

We have a multi-pronged approach. It’s about building the talent and working to make sure that the industry is set up to absorb the talent we are providing, while simultaneously shaping the industry. There are several key aspects that we need to put in place to support young women to thrive. 

First, there is an urgent need to develop the right kind of tools and resources to ensure successful hiring and retaining of female tech talent. Once they are in the industry, women need to have opportunities to upskill to allow them to compete equally.

AkiraChix builds talent based on the needs of the market. We spend a lot of time with companies to understand what they’re looking for, then train people in areas that are aligned with this. On the other hand, we work closely with these companies and offer support on how to hire junior tech talent and set them up for success.

Junior talent needs more support to thrive; they often need both scaffolding support and clear career pathways. We ensure that these companies have established proper processes that ensure employees’ career trajectory is clearly outlined. For example, one can move from an intern to junior to senior—or however far they can, and want to, rise.

Secondly, we are active contributors to the building of the tech industry. As an organization, we are more than a training institute; we also act as a think tank that helps shape the tech industry. We create very concrete standards that companies can follow when it comes to creating roles and expectations for junior talent.

This need is particularly pronounced in Kenya, where — as in most of Africa — there are many startups. Most startups don’t have the time or maturity to set up these processes. AkiraChix is bridging this gap and ensuring that startups understand how to structure roles and pay grades. From a data-informed approach, we advise startups on the minimum amount an intern can be paid. Currently, based on our data, we cap it at $250. 

After the internship, the intermediate level is an apprenticeship. During apprenticeship the pay increases to $400 as the role requires advanced skills: We focus on both transitioning from school to the world of work for our students and also help architect a way for employers themselves to ensure that they know how to work with new hires. This allows them to support and retain talent over time but also prevents the exploitation of young people who may be new to the workforce. 

What were any unexpected blockers to your career entry or your career progression as you moved ahead from software engineer to executive?

One blocker was my unconventional educational background. My mother was not able to afford university. Ideally, I expected to be in a university for four years and then become a software engineer, but that was not possible so I embraced unconventional learning. 

Having gone through an unconventional learning path, and seen its potential to set one up for success, I believe it is the best way forward when other traditional paths are inaccessible. 

Another blocker, as I mentioned above, is the lack of representation of women and its resulting assumptions. There were several moments when people assumed I worked in a non-technical role because I am a woman. 

How do we counter them? I step up. I  always ensure that I have a seat at the table at any given point. I’m sure, in situations where I’m vocal, it’s interpreted as the “angry black woman” but I recognize that there is a need to change the narrative and change the status quo.

Is there a community of people — either virtual or in-person — that supports you on your journey?

Interestingly, I have had men who were mentors and sponsors of my work, which was particularly valuable early on. These are allies who reminded me I was good at what I do, encouraged me not to doubt myself, and identified ways for me to advance. Many of the people who’ve supported me and the organization have been there along the journey, and many come from the tech ecosystem in Kenya.

Now we’re building our alumni community, and it’s become the best support system for AkiraChix — which ultimately extends to me. We’ve now built a community that’s not only supporting AkiraChix, but also each other. It’s great to see how someone just jumps in with their problem and others are able to help out.

Other communities include AnitaB.org, which has been a tremendous supporter of our work. Now we are also creating great partnerships in the more traditional development/SDG space and getting more guidance along with fundraising. We’re working with people as we elevate the status of women in society. My community has now evolved beyond just women in tech to building careers for women in nonprofit and social impact work.

I know you’re a software engineer: are there other nontechnical skill sets that have strengthened your work? Are there unexpected skills that have enabled you to become an executive or a senior leader in your organization?

My ability to move and adapt quickly stems from my childhood. My mom was very ill; for almost a year, she was an invalid. I had to grow up too fast, and I think that shaped who I became. At that time, I had to do it for myself and for my brother. We grew up taking care of ourselves and each other as well. This definitely helped me understand who I am and my capabilities. 

I’m also a very strategic thinker. That combines well with my background as a software engineer: problem-solving is what I enjoy the most. I’m comfortable asking the critical questions; how do we move? Which variables need to change? How do we do this differently?

Lastly, I believe I have a great ability to bring together and function well in any community setting. Now, communities are a very big piece of everything that I do. This whole movement that we are building for women is based around community. I know how to contribute to and mobilize communities — and everybody knows it! That ability to just give of yourself alongside others has helped me build communities that in turn support me.

What advice do you have for someone who’s new to the field but interested in greater representation in technology and data for the SDGs or in social impact more broadly?

I was asked this earlier this year by a young person at a university, on a long ride from Switzerland to Milan! My response is always the same: Don’t box yourself in. Develop a skill, be good at something: if it’s software, if it’s hardware, be excellent at it. But don’t box yourself in. Instead, ask yourself what contribution you can make to the rest of the world with your skills. What problems can you solve? It doesn’t matter whether it’s in your exact lane; consider how you might apply that skill in another way. Can you actually use that skill to create something?

Expose yourself to broader problems to solve, because that will unlock opportunities. I didn’t think I would ever one day be running a not-for-profit. Earlier, I was focused on running my own startup, a tech company that was going to make millions of dollars. Instead, I run a nonprofit developing tech talent that relies heavily on external funding. The exposure I had early on to the startup community gave me the skills to do this; I’m glad I got the exposure, and I think more people should do the same. 

My advice is always not to box yourself in; the world is open for a reason because we can always find problems and solutions that need the skillset and the tools that we have if we look broadly enough.

So what’s the next big thing in data that you see? What do you see emerging in data for social impact in terms of the work you do or just more broadly in the sector?

We are more of a data-driven organization. Everything we do—particularly the decisions we make—is based on data from our work. For example, we’ve just launched a new program for our alumni, codeHiveX, with a focus on growing their incomes and career paths. This was informed by data, the data we’ve seen in the last few years that helped us identify an opportunity. 

As I work in the sector and spend a lot of time with the funder community, there’s a lot of growing interest in matrix-driven impact. How do we ensure that we are just not talking about the qualitative side of things? Can we actually talk about the quantitative side? Especially when it comes to livelihoods which fits really well into the SDGs focused on decent work and economic development. How do we ensure that people are getting decent jobs, with meaningful pay, and that they can sustain those jobs over time? The only way to do that is to ensure that you’ve built yourself a data-led organization. You have to think, how are you collecting, analyzing, and synthesizing that data to be able to predict the outcome of our students in the next 10 years? We need more predictive use of data and I see that coming.

What we are starting to see in the not-for-profit world is many funders are keen on the metrics that can translate and lead to future impact. Often, funders ask how we measure our impact and how the data can inform sustainability. 

Are there new data collection techniques or technologies that make you better able to measure that sustained impact?

From a technology perspective, we are seeing more no-code engineering tools coming up. Not all nonprofits have engineers; these no-code engineering tools mean more people are able to see and analyze their data. In the next few years, I see these tools being very useful, especially for social impact organizations.

What’s your don’t miss daily or weekly read? What keeps you informed and sane in this world?

I’m a fan of Formula One. So I read a lot of Formula One blogs and podcasts like Silver Arrows. For general news, I spend a lot of time on Twitter; I collect a lot of data from Twitter and spend time each week reading through articles I have bookmarked.

Specifically on the development and data side, I am a big follower of the World Economic Forum, and I enjoy reading through the data and AI topics that they write about. I find myself constantly reading those. I see so many opportunities to bring women into data and AI, and we’re focused on getting the tech ecosystem ready for that. I draw inspiration from these data and AI sources to think about ways AkiraChix can help make all that happen.

The post Pathways to Impact: Linda Kamau appeared first on data.org.

How did you begin working with data? How did you begin to think about the social applications of data? 

When I left university after my undergraduate degree, I started working with iHub Nairobi, a local innovation hub affiliated with Ushahidi. Part of my work there was to support research analysis, particularly data that was being collected on the ground. At that point, our mission was to better understand different approaches to technology in Africa and to support entrepreneurs innovating with tech to solve local challenges. Through working at iHub and meeting all these entrepreneurs, I stumbled upon this field of data science and data in general. I was fascinated by the many ways you can utilize one dataset and the various applications. 

That initial exposure piqued my curiosity, and the timing was great—it was around then that Kenya launched an open data initiative. This was among the first open government portals sharing different public data sets on the continent and was inspired by the open government movement championed by former U.S. President Barack Obama; and there was momentum, as around the world, governments and civil society were starting to explore how open data could accelerate development and support transparency and accountability initiatives. 

Being involved in that momentum quickly expanded my interest in data science and how open data and ways to access public data sets could be used to promote social change. 

Can you share the problems you are trying to solve with data today? We’d love to hear about both your day job as CEO as well as your board role. 

After I started working with entrepreneurs within the iHub as well as international organizations such as the World Bank and IDRC, I realized that the impact I was having at iHub was limited to certain kinds of funders and engagements. Meanwhile, there was a growing conversation that there were too few people who were utilizing data effectively. There was a lot of hype around that time: open data, big data, blockchain. AI was not as hyped back then, but there was an understanding that data and AI were the direction the world was moving in. It was understood that this data revolution would affect not only banking, pharma, and other for-profit industries but also data-driven policy-making and social impact work. 

But the biggest challenge we faced at that moment was the lack of access to talent. There was a dearth of people who were able to sit at this intersection of data and an understanding of the potential societal or business impact. I felt I had the capacity to meet this need, so I set up my own company. I started primarily working with the for-profit sector because that is where the most available and most granular data is—but even that data was really underutilized. My aim was to plant foundational seeds of data use for these companies, first in Kenya, then in Africa and across mainly developing regions. I wanted them to see that the data that they had in their organizations was valuable and could help them improve service delivery to their customers. 

That’s why I launched Intelipro, which became my day job. Through Intelipro, I supported the creation of those foundational teams and projects within these companies to become more data-driven in their approach to financial sustainability and serving their customers. But at the same time, the fire that had been ignited in me working in open government at the iHub was still burning. I wanted to work with policymakers, international organizations, and with entrepreneurs engaged in civil society. At that point, a friend of mine invited me to join the board of the Local Development Research Institute (LDRI). My contribution would be both my networks with funders in this research space and helping them set up their research arm. 

So that’s how I got involved with LDRI, and supported their mission of working with governments and public institutions to use data to achieve sustainable development goals. More specifically, we support efforts to alleviate poverty, extreme inequality, and hunger. I appreciated LDRI’s bold mission statement, and the fact that data technology sat at the center of it aligned with my personal passion. Over time, my role has evolved. I still provide research strategy and advisory, but now I also lead their AI practice, which is about two years old. This practice focuses on how we can use AI to improve accountable and inclusive decision-making for citizens, while simultaneously using these innovations to support the broader mission of ending poverty, extreme inequality, and hunger. 

You’ve described some of the pivots you made, but what were the unexpected blockers to your career entry or progression? What’s blocked you at different points and how have you moved past it? 

I always say I’ve been quite privileged. My path has been to follow smart people working on smart ideas and join them. There’s been a lot of serendipity around my career, and I’ve been fortunate to never have to look for a job. That being said, there was one challenging transition when I left my role at iHub to set up my consulting company. We were working on the issues of the moment, and the expectation was that because everyone was excited about these buzzwords of big data, data science, and AI I thought, “I’ll just have customers coming knocking on my door to work with me.” And that, of course, was not the case! I think it took us about a year and a half before we could find solid footing. That required perseverance and an effort to hone our messaging on how we wanted to support these institutions. 

There were a couple of reasons for that challenge. One was that it was still too early in the technology adoption/knowledge curve for people investing in these projects. At that time, investing in data science and data initiatives was just not a priority, especially before there were many initial proofs of concepts or pilots. There were examples of how Shopify and Amazon and other large companies were advancing with data, but nothing tangible to show what it meant for local companies. Also, even if you roll out a data initiative, the impact will not be visible and measurable immediately. 

These challenges were much the same in open data and nonprofits. We were pushing governments to open up data and there were not yet any tangible results. There are many challenges to using government data, like the need for privacy protection. We’re grateful to pioneering funders like IDRC who have been committed to increasing research in developing regions around data. Over time, we’ve been able to get more funders aligned to support our work. 

Part of turning around this blocker was finding a Master’s program in business analytics and big data based in Spain. The program spoke to everything I wanted to do in the private and social sectors: skills to communicate better about the potential of data and increase my technical capacity. That program really propelled my career forward. 

I hear you on the challenges around early adoption of data. Was there a specific instance you can point to where you had an early win? 

In the beginning, financial services was a huge, huge conversation, in parallel with the opportunity for inclusive financial services. That data was almost readily available, and it was easy to show companies early benefits from using this intelligence to benefit both their customers and themselves. It saved companies’ costs and ultimately drove more revenue, while at the same time, improving access to financial services people. Across the world, that’s been one of the most successful use cases of data. 

Similarly, some of the early data work we did with the government was in the contracting space. We supported efforts to make contracts and government tenders open in an effort to reduce corruption, and the benefit was clear. We could see an immediate effect for the government through better quotations—more transparent avenues of procurement with citizens getting better value for money. Contributing to open data initiatives such as open contracts was helpful in cementing our work in the government and social sector; and it laid the groundwork for our later work in food and nutrition security because that foundation, with more support and grant funding, allowed us to better collect data on the ground from smallholder farmers, from agro-vets, dealers of agricultural inputs, and local governments, to promote climate smarter agriculture. In Kenya, it’s not commercial farms but smallholder farmers who feed this country. And when smallholder farmers are food secure, then the rest of the country is food secure as well. Data helped us achieve that. 

What community of people or resources bolsters your work? What makes you stronger as a contributor and a leader in data for social impact? 

In all the work I do, we find ourselves in a community, and each community differs depending on the work. For instance, in policy work, it’s helpful to be in a community with other civil society organizations working towards the same goal: supporting evidence-based policymaking. We meet up at conferences, or through the open government partnership and co-creation meetings. Through those encounters, we have formed lifelong friendships and I am grateful for that support and shared commitment to impact. 

I also actively create space and community for the small businesses that I serve, finding ways for us to share the knowledge we are learning from all the implementation projects we’re doing. I believe these insights are beneficial beyond the one or two companies we actively work with; they can support all businesses to remain sustainable on our continent. At the core of these, I would say are fantastic women—as well as what we refer to as “male champions for women.”  Sometimes the word feminist is controversial in our context, and there are men who really support the development of women’s careers. 

In both entrepreneurship and social sector work, we form WhatsApp groups to run these communities. Even though they can be big, I find them more personal than participating in open online platforms where sometimes the message gets lost. Not everyone in your community gets to see what you’re saying on those larger platforms. So intimate groups like WhatsApp and Telegram have been critical for my career and growth. 

Which specific skills enable your contribution? Beyond your Master’s in business analytics, I’m curious which non-data science skillset surprised you as a superpower. 

Research! That includes everything from just Google to rigorous social and qualitative research. I bring an ability to understand context, understanding nuance, and understanding why things are the way they are. This has been helpful for me to interpret my data in settings that are not typical for your standard data scientist. 

Secondly, being an entrepreneur, I’ve had to teach myself how to market, how to do sales, and how to speak to a non-technical person to sell products. And that entrepreneurial bent is also useful in my policy work because at the end of the day, we are trying to market our research and its outputs. It forced me to not speak in numbers, degrees, or margins of error, but to speak in a way that matters to my different audiences. 

What advice do you have for someone new to the field who’s interested in doing data for social impact work? 

While this comes from a privileged perspective and might not be applicable advice for everyone, I say, explore your passions. Do as many things as possible and see the one that sticks, the one that you can wake up every morning eager to work on. I tend to tell people this because most of the time, when they come to me, they have a vague idea of “Oh, I want to get into the data space; what Master’s degree should I do?” My response is that if you want this degree or this certificate just to jump careers, I think you need to understand where you want to commit for the next few years of your life. Because if you’re just doing it because of the hype, you’ll probably drop out and flounder in your career. 

I also tell people to be a jack of all trades. Try everything. If you’re working in a company, work in all the departments as much as possible, or with as many team members as possible. That experience will help you identify strengths and where you want to put your energy. 

What’s the next big thing in data for social impact that you see? Is there a trend you’re seeing that you think will be soon realized? 

Based on what we see the industry pushing for, the first thing is chat and chatbots. I don’t love them, but I think ChatGPT has just made it possible for everyone to dream about how chatbots could be used in different ways to engage with citizens, customers,  and communities. We’re thinking about interactive voice channels primarily because not everyone is literate. Not everyone in their local language will be able to read—so, we’re thinking about how to translate policies to communities for purposes of public participation. I see governments thinking that way. 

The second thing is digital identification and using that to deliver services, in both private sector and government / social sector contexts. I think there’ll be a push to do more unified delivery of services using digital IDs. There’s a huge question around data protection, discrimination, and leaving out marginalized groups because the rollout of digital IDs is not automatic. In many cases it’s voluntary and there are no clear regulations for collecting data around people to form their digital identifications. Are the practices benevolent or malicious? What protections are around that data? I expect to see more countries adopting digital IDs with more private sector activity riding on that, but also potentially more harm for communities if it’s not well regulated as part of service delivery. 

There’s a real risk with developing and running automated systems without humans in the loop who bring diverse perspectives and evaluate potential harms. We need to be thoughtful about the process, to avoid systems some companies and influential parties could push to be adopted without the necessary scaffolding. We need that scaffolding in place to protect human dignity and human rights.

One final question: What’s your don’t-miss daily or weekly read? It could be a data topic or just an addictive app 

I’d say as much as I hate it: Twitter. I use Twitter to catch myself up on news around the world, but it’s also useful for local news. It’s just a nice aggregator once you skip through all the toxicity. I don’t tweet as much as I read. 

And before the writer’s strike, I loved seeing updates on global news and issues from YouTube, especially the late-night shows. I think they provide an interesting perspective about global politics, and what’s going on around the world. 

The post Pathways to Impact: Leonida (Leo) Mutuku appeared first on data.org.

Please tell us about how your current work involves data for social impact.

Absolutely. Currently, my work touches on social impact and addressing health disparities through my affiliation with the National Alliance Against Disparities in Patient Health (NADPH). NADPH has partnered with collaborators like data.org to engage community-based participatory researchers as persons with lived experience. We’ve engaged them through in-depth one-to-one or group conversations to understand their personal journeys in their scope of practice or their field of investigation, and what their lived experiences have been with data. We seek to understand what role data has played along that journey, whether it’s demographic data, data gathered from digital behaviors, or more traditional forms collected through surveys.

We want to understand why data has been important to people and organizations in their professional and institutional level journeys. Oftentimes, as we think about data and how it has evolved over several decades, we can see that it’s become plentiful, yet the data itself is not always complete or accurate and can lack context. By talking to persons with lived experience, we can begin to learn that context and gain a deeper understanding of what stories data can and cannot tell us.

And how did you come to do this work? What drew you to it?

Honestly, I didn’t start off my career pursuing data. I finished my undergraduate degree in 2007, just before the Great Recession. At that time, I was a bench scientist working in x-ray crystallography and starting my pharmaceutical industry career journey. We were collecting data all the time, scientific data for our analysis, but collecting broader societal-level data was just not something that I learned to do. I wasn’t a social science major, nor did I study social sciences to an extent where I could apply a quantitative-level analysis to the human experience. I was trained to look squarely at natural science and the data that would interplay within that discipline and that’s it.

But one of the things that the Great Recession showed us all is that the practice and the dissemination of science cannot be relevant without understanding the social support needed to allow people like me to be scientists or scientific investigators within the natural sciences. What are the politics involved? How are they being informed? And really what’s the human impact? I hate to make it all about the human experience because science is of course focused on understanding the natural world through evidence. But really, in most instances, if you can’t tie the importance of science to the importance of the human experience, our role as part of the natural world, and the scientific world we’re investigating, then oftentimes you can’t communicate the need for science to policymakers, funders, or stakeholders.

Understanding and communicating that critical connection was an art form that I needed to learn. My own lived experience has allowed me to understand or at least have some level of perspective on the limitations of data that has been collected (or not collected) today, and to consider how we are making decisions based on data that’s probably incomplete or not telling the real or full story. I think we’re in a moment of generational renaissance, where we’re learning more about who we are and where we’ve come from, and what communities need based on the new ways of collecting data and analyzing data, as well as developing tools that we can leverage to collect specific data. That’s something that has been fairly novel, both generally within the scope or field of science, and also novel in my own professional journey. There are important questions that I couldn’t ask before that I can ask now.

Is there a specific problem within data for social impact that you are seeking to solve?

As a hard scientist, I’m looking to calculate how social drivers impact the outcomes that we see in ourselves and in our communities. Obviously, as we think about race and ethnicity as social constructs, now with the data that we’re able to collect and thanks to other scientific advances, we’re able to dig deeper into the human experience to understand what really predisposes us to disease, what really predisposes us to react in certain environments in a way that may or may not be in our best interests. I’m also exploring who has access to data, and who has access to novel ways of collecting data. Arguably, the most powerful stakeholders in the room have that access, but how can we transfer that power or share that power with communities who are perhaps less fortunate in terms of access so that they have a voice and so that they’re not just trampled over in the process of technological advancement?

We’re doing some work in gender now, where we discuss the need for women to be represented not only in the data but also as actors deciding what data is collected, analyzing the data, and making decisions based on the data. Does this resonate with you?

Exactly; that’s actually the crux of our work. How do we embed persons with lived experience more frequently and at deeper levels of the data life cycle? We’re looking at data collection, data analysis, and data dissemination: all those phases are critical because it’s important that people who are data subjects have opportunities to tell their story behind the data. Over the last several decades we’ve been doing a very poor job of that if we’ve been doing it at all. There have been people and organizations who have been able to make progress, meet people where they are, and meet their needs in whatever way they could without as much data. But now that we have all this data, how can we share it better and more effectively so that, again, persons with lived experience are involved in all aspects of the data life cycle moving forward?

Were there any unexpected blockers to your early career, or to your career progression as you advanced?

Honestly, there have been many. One struggle as a scientist is the inherent tension of the choice between pursuing science in the name of profit or in the name of passion. Especially today, as the income divide becomes more pronounced, scientists have to reckon with that question as they learn how to sustain their livelihoods and sustain their work. Working in a nonprofit or academia, the opportunities are rare. There’s always the question of, “Do I stay in the social impact sector and not earn the living that I want and run the risk of not having stable housing or affordable housing by living in a large metropolitan area where a university is based? Or do I go work for industry and earn the living that I want, but essentially abandon the side of myself that yearns for exploratory science for generalizable knowledge?”

I think that was something that I had to reconcile with very early in my career, particularly as I transitioned out of academia and government and into the private sector. I was able later to reconcile it, but it wasn’t something that came easily. I’ve had to challenge myself and make a lot of sacrifices along the way.

A second career challenge: I had to be patient with myself as I learned about the real world and take the time to work in jobs or industries that gave me a broader perspective outside of hard science. No scientist goes into science to do administrative work, but we end up having to do it anyway; we need to be open to learning how to do it well. I wouldn’t call it a roadblock, but it was another career choice I had to make and to which I needed to adapt.

Finally, there’s a moment when you might start a family and need to figure out where to live to be able to raise a family. For those who choose this path, it’s a moment where career decisions are no longer about just what you want but about what’s in the best interest of your family. As a woman, with a lot expected of us within and outside of the home, it’s easy to get burned out. I’d say this is the third challenge, realizing when and how to set boundaries to protect and preserve your own sense of self and your mental health.

What community of people or resources bolsters your work? What keeps you both professionally connected and personally supported?

Absolutely. I am an avid supporter and member of the Association for Women in Science; I’ve published in their magazine a few times, and I’m currently a virtual visiting scholar for one of their programs where I focus on the role of gender intersectionality in industry and academia collaborations and partnerships.

I also engage in local-level initiatives for women; I attend and support local women-sponsored events around the community. It’s a priority for me to support people of color, events that they host and promote, and groups that we put together to be a community. It’s easy in the 21st century to lose our sense of community because we have, again, a lot of digital means of connecting, but we don’t really connect. So, I try to make sure that I keep my boots on the ground to be a part of the community and show up for mothers and friends and family, and other folks in my community that I can support. I, myself, am from a village outside of Chicago; it’s literally incorporated as a village. There I watched my mother be a community leader as she demonstrated how you need to be the change you want to see in your community and the arbiter of your success. It’s not going to happen unless you get up and make it happen. You must also be able to inspire people around you to help you make that happen — to find your team. Community is where I draw my confidence to do the work that I do. Understanding the value of community, that we’re only as strong as we allow ourselves to rely and lean on each other. I bring a lot of that perspective and experience into the work that I do. I have a strong passion for what people and persons with lived experience have to say about that work. I believe that’s the ultimate foundation of our society: it’s in its people and their ability to be there for one another regardless of where they come from. If we can show up for each other, that trumps everything.

So, we can’t do the data divorced from these other communal activities, is what I think I’m hearing. We can’t bifurcate into, “Okay, now I’m a hard scientist and I’m collecting data and now I’m a mother in a community.” Those two streams of work and life need to be closer together, which was part of the takeaways from RECoDE report we worked on together.

Absolutely. I think what data does, or at least what quantitative data helps us accomplish, is give us the LEGO pieces and the colors and shades and sizes of all the LEGO pieces that we need to build the LEGOLAND. And I think when you’re able to have conversations with people in the community and know how to talk to them, as a person with a strong sense of community, then you’re able to better tell that story using those LEGO pieces to build out that LEGOLAND in a way that people can get behind.

Because ultimately what was great about RECoDE is that someone in the group that we engaged in the community, said, “Wow, I really feel seen.” And so, if you’re able to accomplish that, that means that you’ve effectively taken whatever data you’ve collected to actually tell a story and to therefore drive home an actionable point.

That’s where you make the most impact. A lot of people don’t want to do that work because it takes time: you have to slow down. You can’t think that you’re about to show up to a community and save the day; you’re going to have to do away with that savior complex and slow down and appreciate the journey. A lot of people want to speed to the result or speed up to the outcome without fully appreciating the journey and the process and doing the work, doing the trust-building work.

How do you combine that important trust-building work and the data work? How do you get the balance right — whether you’re accountable to funders or to the government or to even your own time — to do both as effectively as possible? 

As part of my journey as a professional, I’ve had to learn when to push back, when to say, “I know you’re the funder, I know you’re the person leading this project, but I need to push back to say you’re over-administering this project. You’re moving at a pace that the communities are not comfortable with. You’re moving at a pace that’s ultimately going to ruin our relationship with communities once we’re done with this project.” Ultimately, that approach will make us less effective with the data and otherwise.

I’ve had to learn how to have the strength to say that and how to have the tact to say it in a way that people can digest. If they’re good at taking feedback or constructive criticism, they’ll be able to hear it. But if not, knowing when to walk away is very important, too, because the last thing you want to do is sacrifice your relationship with communities for the sake of a single organization that might not be as connected to the community as you are.

Obviously, you have hard science skills, but when you think about the work you do with NAPDH or others, what other kinds of skills inform your contribution? Which skills in your career have offered the greatest return in your work?

One of the biggest skills I’ve been able to lean on — that I didn’t know I had, in fact, until later on — is my ability to engage people. This wasn’t something I learned in school; I learned that from my community. There’s great value in learning how to engage people, how to build consensus, and how to move at the pace of trust. That’s something that I exercised in school through activities, like in my role on the executive board of the Minority Association of Pre-Health Students as an undergraduate.

While science and writing abilities have been hugely beneficial, I didn’t realize that the true superpower that I held within myself was being able to talk to people and engage with them no matter who they are, where they come from, or what type of person they are. Channeling that skill contributed to being successful in my career.

What advice do you have for someone new to the field who is interested in doing this work? It could be a student you’re mentoring; it could be a mid-career professional who says, “I want to do more with my education and training.”

We all come to the table with our strengths and our weaknesses. I believe that if you’re able to leverage your strengths along or align them with your interests and make it a point to be a part of groups that have people that can fill in your weaknesses, that’s where you’ll do your best work.

I’d also offer: to be patient with learning, be patient with people. And I think it’s probably just human nature for us to indulge our biases and indulge our wishes and whims and impose our own will on people when we’re young. But I think the sooner you can realize that imposition of will is pointless, the more successful you can be.

What’s the next big thing in data for social impact that you see? What do you see coming that might help you in your work or help society get better through data?

I engage with a lot of people around that question! Given that we are currently faced with an oversupply of data in some cases and an undersupply of data in others, I think we can address that issue in the near term. And I think that undersupply is largely the social determinant of health data. We’re lacking the qualitative data that can help us tell the stories.

Social determinants of health data identify the level and quantity of barriers people need to overcome within a social context. For example, consider a child who has younger siblings and who also has a single parent for whatever reason: death, divorce, etc. That child would have caregiving duties for their siblings because the working parent is gone, the other parent is otherwise unable to co-parent, and that child probably must wake up early in the morning to make breakfast to take care of their parents or take care of whatever the other parent can’t take care of for whatever reason. They might live in an area that lacks transportation to school or reliable transportation to school. They may have little access to healthy food. Those are barriers that are just day-to-day barriers that that child has to overcome just to get to school. Compare that to a child who doesn’t have to overcome those obstacles: those two children might have the same academic or other types of performative potential, but one of those kids must overcome a whole mountain of obstacles just to actually demonstrate that potential.

Getting better social determinants of health data is attainable and would be hugely beneficial.

What’s your don’t miss daily or weekly read? What gets you through the day and keeps you informed and sane, if both things can be possible?

That’s a really good question. For one aspect of my job, I rely on particular sources like STAT News — that’s where I go to get all my health technology and innovation news. I’m also part of the Association of Healthcare Journalists where we get a daily newsletter and I can understand or track what journalists are looking at. These journalists are often gathering that anecdotal information and combining it with data, which is pretty cool. Finally, I get a lot of my information from LinkedIn where I am connected to my colleagues in the same or similar fields, so I get to stay up to date on whatever work they disseminate there about themselves. It’s a useful combination!

The post Pathways to Impact: Rachele Hendricks-Sturrup appeared first on data.org.

Would you please share a little about what role or sector you came from before moving to data for social impact? Please give us a sense of your career trajectory to date.

One of the compelling things about this new data for social impact space is that career paths are not straight, and mine is no exception. In college, I really liked math and biology, and essentially, I couldn’t decide. So, I found a major where I could do both.

I had a roommate who worked at a computer lab, and I didn’t know anything about computers. I offered to volunteer in the lab, which then turned into a job helping to administer the computers in the biology computer lab. This progressed through grad school where I studied both microbiology and metagenomics. Computers were vital in the early days of us getting back a lot of genomic data and using that data to analyze microbiology and microbiological communities.

By the time I was in my postdoc, it became clear that the ability to work with data was an important asset in the field of biology. As a result, some people who had data and open-source software skills ended up collaborating with those who could not — and that system did not scale. People without data skills were disempowered in their ability to conduct their own research. For example, they would send out samples for sequencing, and then not know what to do with the data they got back. Similarly — the people analyzing the data — like me! — did not always possess the subject matter expertise to direct the research questions or conduct the right analyses. The answer was clear: We needed people with that biological insight to become more data-capable.

I became interested in how we could train people to be able to analyze their own data. A good analogy is a car: I don’t know how to fix my car. When I take it to the automatic mechanic, I just trust it works out. And it’s never a good feeling. You’re thinking, “What’d they do? Is it fine? Is that the right amount of money?” You don’t want biologists feeling like that about their own research. You want to empower them to own the car and be the auto mechanic.

At the time, I worked with an organization called Software Carpentry which was teaching researchers how to write better code. A few colleagues and I began to envision a similar Data Carpentry, helping people learn how to analyze their data through a domain-specific lens. We realized that people were entering the data space via their own disciplines — as biologists, economists, or historians — not through some abstract interest in data and data science.

We co-founded Data Carpentry and then were awarded a grant from the Gordon and Betty Moore Foundation that let us turn it into an organization. I became the executive director of that nonprofit, and our team, together with Software Carpentry, developed workshops and instructors, and soon were training thousands of people all over the world. In terms of my personal trajectory, it felt fast to go from data training as an important part of what I do to being everything that I do!

Eventually, we merged with Software Carpentry to become The Carpentries where I served as executive director for five years. As I said earlier, these career paths are not straight — for me, it was a big shift from researcher/educator to running an organization, which requires a different skill set. That’s one career transition I’m still pretty passionate about trying to support people through. I always say that I tried to get my MBA via Google. My thought process was: “We need to do this thing. I don’t know how to do that thing. But other people have done that thing. Let me Google it and find the templates?” And as I learned from my time in the nonprofit space and the open-source community, there are different considerations.

Today, I’m at Posit PBC (formerly R Studio) as the open-source program director. My current role combines aspects of my experience in community engagement, open source, and tools. I spend a lot of time thinking about how best to support the Posit team here that works in open source and also thinking about how that team supports the broader R community and open source users.

What part of data for social impact does Posit (formerly RStudio) solve? What are you trying to do?

Posit is a B corporation, with a mission of creating free and open-source software for data science, scientific research, and technical communication. We’re focused not only on creating the software but also on empowering users to be able to use that software.

We create R packages: something like 30 of the top 50 most downloaded R packages on CRAN are ones that are developed and maintained at Posit. And to empower the users we invest in education and outreach, including good documentation on how to use these tools. We make an effort to understand how people use the tools in order to continue to adapt them for real use cases.

Ultimately, we want to ensure that more people are able to use data science to answer the questions that are important to them.

In one sense, your career journey sounds like smooth sailing. You went from college to grad school, and then, you’re an executive director. Were there any blockers along the way?

That’s a great question because the narrative does make it sound smooth, but that was definitely not the case! Let me just speak to that one transition from postdoc to executive director. As a postdoc in an academic environment, the only example of success is a professor. Even when you know there are other options, that model of success is still so deeply ingrained in you! Part of the decision around that transition was that I was an assistant professor, but not on a tenure track. My partner was at the same institution in a tenure-track position. And I didn’t want to be a non-tenure track professor forever, where getting funding and building a lab would be challenging. I knew it wasn’t what I wanted to do forever.

At the time I was considering my next steps, this grant from the Gordon and Betty Moore Foundation came through, and I made the leap. While I was passionate about the work, I was also pretty devastated to be leaving an academic path. That’s something I say about career transitions: while you can be truly excited about the path you’ve chosen, there’s an element of mourning a career and a life you thought you would have. In an academic context, your life is so tied up with your work, that it’s not just changing jobs: it’s really forming a whole new idea about who you are.

I was lucky that the challenges presented by running an organization were exciting to me in similar ways that research challenges were exciting to me. While that may not be true for everybody, I was lucky that it worked out for me.

You’ve mentioned discipline-specific social impact is possible when you enable more people to use data effectively. Any other outcomes you have observed?

How we saw it back at Data Carpentry, and how I still see it in my role now, is that with data science, we were and still are in danger of only certain types of people being able to analyze data. So, that means we’re only going to ask certain questions. We’re going to have certain biases when we analyze that data. We need to have more people who are capable and feel like they are capable of analyzing data or we are going to get a very biased perspective of the world when we’re taking data-driven approaches to decision-making. There’s still a really big danger of that; there’s just a certain set of people from primarily privileged backgrounds who have these skills, and they’re analyzing all our data. And where that gets us is not a good place — I think we’ve all seen the results of those biases playing out in code. 

Which community of people or resources bolsters your work? If you’re looking for advice on how to lead a program or how to solve a thorny problem, whether that’s a technical problem or a messy human problem, where do you go?

There are definitely communities of people who bolster our work. Carpentries or Posit PBC have the privilege of working with user communities who are really passionate about their work, and essentially volunteer their time to make cool apps, write documentation, or teach workshops. There are huge communities of people supporting our shared mission.

I have not really found a formal network. I do have an invaluable network of people I’ve met along the way. These tend to be people who have been in the trenches of leading communities and leading products because there’s so much you really can learn only by doing.

Here’s an example: my friend Lou Woodley, who runs the Center for Scientific Collaboration and Community Engagement (CSCCE). She is definitely a really important part of the set of people that I talk to about some of these challenges. Also, her organization has created a community of community managers. With community management being a part of my role, CSCCE has been really important both as a place I connect with people and a place to find great resources about community management. That’s definitely one of my go-to’s.

I also rely on the Software Sustainability Institute, which brings together a global network of instructors, and provides a space for us all to talk about how to teach these skills.

What non-data science skill set has offered the greatest return on your work?

I think of two things. One is comfort with uncertainty. I don’t know if that’s a skill, but it has seemed to be important.

And the other I would say actually is facilitation. I think that goes with empathy a little bit, but it’s being able to be in a room, help create a space where people can have conversations, listen to that conversation, and be able to synthesize and help figure out the next steps. Without the facilitation piece, you don’t have the power of the group, which is really what’s going to advance things forward. This not only draws out better ideas but also ensures all the people in that room are invested in the outcome.

It’s definitely something I’ve taken courses on and done a lot of reading on to try to be a better facilitator. I still have a long way to go.

What advice do you have for someone new to the field who’s interested in doing this work? 

I read a Science or Nature article about alternative career paths for academics. And there was something in there that reminded me that there are many ways to contribute to science. We have this idea that the only way to contribute to science is to be a researcher, and that’s not true at all. If you want to contribute to climate or to health, you don’t need to leave your strengths or your skills behind to achieve that. It’s more about aligning your skills and your interest with that topic and finding the ways that you uniquely can contribute in a way that feels authentic to you.

It could be leading a citizen science project where you’re helping high school kids collect data. It could be teaching others how to analyze data, or interpret a graph. There’s more to do than sit at the computer analyzing the data. If you are in finance and you know how to manage a budget, volunteer on the board of a data organization that you care about. Many nonprofits need those kinds of skills on their boards, and you get to be connected to that mission. There are many different ways to be in this community and contribute what you bring without wholesale retraining.

Finally, I would add that humility is important. If you are an amazing data scientist somewhere, that’s awesome. But then, when you go to the city government, they’re the experts. While you bring a certain set of expertise, they’re the experts on their community and what questions they have. To be successful in entering this space, set aside the view that “I will solve these people’s problems for them.” Remember: you’re all the heroes tackling these challenges. There’s not one hero in that scenario.

What do you see as an emerging trend or growing behavior in data for social impact over, say, the next three to five years?

I would say machine learning. That’s definitely already on the rise and will continue to be a driving force in data for social impact. You have more applications, more thoughtful applications of machine learning in social impact spaces.

What’s your don’t miss daily or weekly read? It could be related to data science or education for data social impact, or it could just be a guilty pleasure. What keeps you informed and sane in a busy world?

My local newspaper. It does really help me just know what’s going on in my city, to hear about things that I don’t do. I think otherwise, I do rely on Twitter and Mastodon to surface articles of interest. I also listen to podcasts, often management leadership podcasts rather than data podcasts.

I do read a lot of books on leadership, but especially on inclusive leadership. There’s a lot to learn about access and minoritized groups in data. And I loved Dr. Brandeis Marshall’s book, Data Conscience. She’s one of a few people whom I think, “Everything they write, I read.”

The post Pathways to Impact: Tracy Teal appeared first on data.org.

Would you share with us a bit about your start in data for social impact? What was the initial impetus? 

Oddly enough, I come to this work from a background in education – I’m not an engineer or a computer scientist by training. Right after high school, I started to study biology because I wanted to get involved in environmental studies and education, but I soon learned that the biology program was more oriented to research than to applied work. That spurred my pivot to education, and I ended up with a bachelor’s degree in general education and a second degree in social work education. Throughout, I had great teachers who emphasized both critical thinking and social justice. 

One of my early roles was offering gender-based violence workshops for teens and children in a school system in the Buenos Aires slums. I was fortunate to receive several scholarships for related research; for example, I conducted a study of the transgender community in Argentina in 2015. By participating in these research projects, I learned I was more passionate about direct service: using technology to help people in the underserved communities where I worked. 

And here’s how my education efforts connected me to technology. I was working in a food bank, a place where people in the neighborhood could come for an evening meal when they did not have food during the day. This food bank had received a huge donation of computers, which were all locked in a room gathering dust. No one was using them because they were afraid to break them! I asked my boss for access and ended up building out a computer room for everyone. All the women who were working there, preparing food and cleaning, had limited education because of their domestic and caregiving responsibilities. They were part of a program to teach them how to read and write and basically finish elementary school. Most of them were grandmothers, mostly women in their 60s. I started to teach them how to use computers to manage photos of their grandchildren, assemble a curriculum vitae, use email, and be on the internet in a safe way. This work showed me how powerful technology literacy could be, with the potential to be life-changing for many people. 

From there, things escalated: I started showing teachers how to include technology in their classrooms, and then I started to learn programming and robotics to teach in the classrooms. Today, I am the executive director of DataGénero, a gender data observatory. Based in Argentina, we are building a broader network to monitor practices and policies regarding data and gender in Latin America. 

What particular problem are you trying to solve today?  

At DataGénero, we focus on women and gender data issues – but our work is truly intersectional, looking also at racial and social justice. We want to have better data about really difficult situations for everyone, not just women, but gender is our entry point to this work. 

Overall, we have three big goals.  

One is to address the missing data in Latin America regarding women and the LGBTQ+ community. Today, we know that we don’t have the data required to understand the issues and to make good public policy. We’re doing awareness campaigns, and we are working with governments to collect and provide better data. In particular, we want to stop having gender data in a solely binary way. This can pose a challenge in some contexts, but luckily, we have some laws here in Argentina that are really progressive regarding gender identity. 

The second goal is to capture and understand the existing use of artificial intelligence in the region for social policy and practice. We find much of this work to be problematic, so we are surveying what’s happening, and to understand what companies and governments are doing with artificial intelligence (AI) in the region. We are even applying our own technology to understand this problem — we want to create an algorithmic register to see where and how AI algorithms are being used. 

Finally, we are creating datasets using AI and natural language processing (NLP) techniques to extract data from criminal court rulings in cases of gender-based violence. We are examining those rulings to dimension the problem as well as to see patterns in the judges’ decisions. Obviously, the data sets are anonymized, but there is so much we can learn about how gender-based violence occurs in our society. Was the victim dating the aggressor? Was it a family member or a stranger? What time of day, and what kind of violence? We are working on an AI to do that—of course, with the supervision of humans. We want to understand the whole picture, all the details that don’t get reported in the news. Only then, can we understand how to solve gender-based violence.  

There’s a clear cost to society if these goals are not realized. There is the obvious cost to women and oppressed populations, but also significant economic impact. We do some reporting on this impact, based on data services that are made open three times a year. We analyze these, and share the results publicly. I should add that Ecofeminita in Argentina is a great group that looks more broadly at how gender data links to economic impact. 

What were some of the unexpected blockers to your career progression? Are there challenges associated with being an executive director as a woman? How about your background in education?  

More the latter: I’m always paying the price of not being an engineer or a computer scientist, and I find that really frustrating. To me, that’s not the most important thing when we are talking about social policy and practice, but I do feel judged for having a bachelor’s degree in education. I also have a postgraduate certificate in data science but I don’t have a Master’s or a PhD. I am self-taught, and I code a lot in my free time, but I am not an engineer. Without that credential, some people do have the perception, “Oh, what is she doing here?” 

How have you overcome that perception for DataGénero? 

We have built a strong, multidisciplinary organization, made up of people that are excellent at their field. We have engineers, mathematicians, and physicists who work with us when we’re building tools or when we are interpreting new information, and we also have people from sociology and public policy and lawyers. We have a broad spectrum of people and advisors that come from different fields; in this intersectional way of thinking and seeing the world, we can’t just rely on a single way of seeing things. As a leader, I bring both technical and non-technical perspectives to that work. 

What community of people or resources bolsters your work? Is there an online community or a group of people you meet with in person?  

We have been able to connect with a lot of people because we started to tackle an issue that wasn’t really discussed in our region. Now, when people think about gender data in Latin America, they think about us. We have been fortunate to build great alliances with other networks of women in data science and also with people in the global north thinking about these issues. We don’t want to import solutions, but we want to see what everyone is thinking. We are grateful for an online community of people that are supporting us and talking about us in different fields, classrooms, and events. And then we have some in-person events that we are starting to do again. But we were born during the pandemic, so at first, everything was online. 

Personally, I am a part of several large communities I love. One is Open Heroines: they are great. They are global and work with data, open data, and open government. That community is a place where I can ask any kind of question and people will respond.  

Another is a strong network of women in the tech field in Argentina called Las de Sistemas and Mujeres en Tecnología Córdoba.  

Finally, there is the Latin American, Women in Bioinformatics & Data Science LATAM. This is the other network that is really helpful for me. 

What non-technical skill set has really helped you in your work, whether that’s program delivery or as a leader?  

Working in schools and with groups of teachers made me understand a healthier way to manage people and to build knowledge. This experience also enhanced my creativity to think outside the box when it comes to coming up with solutions or dealing with emergencies or things that are urgent. It also developed my sense of empathy: in teaching, you have to understand what the person in front of you is going through. If you work with children, with teenagers, and with people that are in really difficult situations, you always have to do that exercise to understand how they are feeling, and how you can help. That skill has translated well in my role as executive director and community builder. 

If you meet someone new who wants to work in data for social impact, what advice would you offer them? 

I always say that it’s easier to teach someone from a social sector background to learn coding or how technology works rather than the other way around. If you already have this critical lens of how society works, and the systemic nature of these issues it’s easier to translate it to technology. I’m always encouraging people to develop more technology in an interdisciplinary way to see what is happening, with a thoughtful and skeptical point of view. I am not telling people the only path is to become a developer and work for a company—that’s not my advice. There are many ways to contribute, such as surveilling, auditing, and registering what is happening with technology, data, and AI nowadays. We are really concerned about these issues, and we need more allies to get involved in different ways to advance gender data. 

What’s the next big thing in data for social impact that you see?  

I am seeing a building tension between open data and privacy, at least in our region. We all want to have more and better-quality data, but we also want better laws protecting our data, our privacy, and our rights as citizens. This is a growing and constant tension that we will have to learn to surf in between. 

On the practical side, I see a rise in data talent coming from giving better technical tools to people that are working nowadays with data in an Excel spreadsheet. We will increase data capacity by better tools and better analytic capacity, but also need to teach people to ask the right questions and perform well on an interdisciplinary team. I don’t want anyone else working alone in this. It’s really important to communicate and to do it in a team that is diverse and has this critical training. For example, we are now working with some governments and training people to work together. The hard part is that people are speaking different languages and we need to ensure they develop the skills to communicate. 

What’s your don’t-miss daily or weekly read? Are there specific books, blogs, or podcasts you recommend? Any beyond the realm of data for social impact? 

For books, I often recommend “Data Feminism” (which we are translating into Spanish!), “Algorithms of Oppression,” and “Weapons of Math Destruction.” 

On a daily basis, I read Twitter and LinkedIn to see what’s happening on everything from data to rock climbing, which is a personal passion. I also usually read the local news, then the national, and then the global — going from small to big, depending on the time I have. 

I enjoy two podcasts in particular: Algo que no sabías, by Tomás Balmaceda, a philosopher who in 15 minutes tells you a fun fact that you didn’t know. The second is Nuestro Día, a daily Spotify podcast about music, movies, and news. It’s easy to listen to, which is sometimes just what you need. 

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