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. 

The post Pathways to Impact: Ivana Feldfeber appeared first on data.org.

How did you first get into social impact? What was your educational or professional background that led you to this work?

Like a lot of data scientists, I came to work in social impact through a lot of twists and turns and experiential learning. For me, that involved bouncing in and out of fields, figuring out what I liked to do technically, and then deciding to use those skills and experience for good.

As an undergrad, I studied political science and psychology. I was intrigued by what we can learn from psychology, and how it might apply to political science. For example, I was interested in cognitive dissonance and how people with strong political views integrate new information that might counter their beliefs. This was back in the MySpace era when social media had not yet had an outsized effect on our political discourse, so I didn’t have all the experimental and empirical options available to explore those questions.

When I graduated, having spent a lot of time working on theory and small-scale experiments, I realized I didn’t actually know much about how politics worked in the real world. In a stroke of luck, I found out my college had an exchange program with a university in Turkey. I spent my last semester in that program and then just ended up staying in Turkey and working a range of odd jobs from English teacher to news wire copyeditor. This put my career on an entirely different path than I’d ever planned.

Over the next five years, I bounced around the Middle East, Caucasus, and DC working on human rights and media freedom as a researcher and program manager. This was during and after the Arab Spring and it was an incredibly heady time where massive change—both a positive and negative sense—felt quite possible. On a personal level, I valued the opportunity to do this work and credit this period in my career with giving me solid project management skills and confidence operating in extreme ambiguity.

Across these jobs, I started to naturally gravitate toward quantitative and technical work. I had some knowledge of statistics from undergrad and wasn’t afraid of computers, which meant I was often the person who was assigned to tackle technical projects. This led to some unique opportunities to pair data and technology with social good efforts. In Iraq, I had the opportunity to manage national surveys on media use and public opinion. I helped build a platform that gave citizens journalists a way to publish securely from inside repressive countries. And I did advising on how to run analytics-driven social media campaigns to support social and political rights.

A few years in, I took a step back and realized that it was time to commit to fill in some of the gaps in what I’d learned experientially. Data science was a natural choice for me. As a field, it’s methodologically omnivorous and deeply concerned with practical impact—be it on a business’ bottom line or social impact. I went back to get a master’s in quantitative methods and since then, I’ve been lucky to build and lead some groundbreaking data and technology teams at organizations like Media Development Investment Fund, The Rockefeller Foundation, and now the World Resources Institute.

None of that would have been possible without the time I spent not doing data science in my early career.

At WRI, which part of the data for social impact puzzle are you working on? As you consider how to structure data teams, are there particular social challenges you’re applying those new data approaches to?

WRI has hundreds of amazing researchers and domain experts working to protect and restore forests, improve access to clean energy, and improve resilience for people living in cities around the world. Our job in the Data Lab is to help these experts translate their research into products that inform decisions that matter, enable accountability, and drive the conversation.  

WRI has been a data science leader for years and has produced marquee products like Global Forest Watch and Aqueduct. I joined about six months ago and it’s been a real honor to work with our team to build on this strong foundation to take our technical work to the next level. Right now, we’re focused on building a new product studio, expanding our data science capacity with our international offices, and strengthening our data infrastructure and APIs to support the next generation of products that help improve lives, protect nature, and ensure just climate transitions.

What were some unexpected blockers to your career? Which leaps were harder than expected like going back to university or then choosing where to re-enter the workforce?

As someone who loves learning new things and working in new domains, one consistent struggle: it’s very easy to take too big of a bite. It can be overwhelming keeping up with very fast-moving methods at the same time as you’re learning new scientific or business domains. Trying to do it all, know it all, be it all is—ironically—a huge blocker for actual growth and learning.

If you don’t take the time, acknowledge your limitations, and give yourself grace you’re going to burn out. On the flip side, if you admit what you don’t know and give yourself grace, you’ll find you’ll actually learn more and learn the information deeper.

You mentioned you’ve learned how you admit when you don’t know things. Has that changed the culture of the teams you work on, and perhaps made it safer for other people on the team to acknowledge areas for growth?

Absolutely—I think creating safety within a data science team is extremely important because there’s no way everyone can know everything and our job is often to learn as we go. In some ways to be a data scientist at this moment is to always be slightly behind the curve on something. There are so many new frameworks, languages, approaches, and papers to keep up on that if you don’t operate from the perspective of “you’re welcome here, you’re valid here, your work is important here,” people will get exhausted. Something I’ve always aspired to—and try to follow through on—is creating space for people to learn new technologies with the expectation they’re going to need time and energy to learn them, integrate them, and apply them.

Modeling these areas for growth and practice of intentional learning for the team seems very powerful.

I agree. One of the biggest shifts I’m seeing in “data for good’ is the shift from lone data scientists working in ‘hero mode,’ to diverse teams building and learning together. Once you start working in teams it becomes quickly apparent just how much you can learn from your peers and how that allows you to scale up your work.

What community of people or resources bolsters your work? When you’re stumped, where do you go, and who helps?

I’ve always learned a great deal from two groups. First, I’ve learned so much from the data journalism community. I’m not a journalist myself, but the way they use data science to collect data from really messy sources and communicate those data clearly through visualization has consistently motivated me and informed my approach. The presentations at conferences like NICAR have helped me to stay up-to-date on new techniques and see imaginative ways to use data science to understand the world around us.

Second, coming from a quantitative methods background, I have always really appreciated the R and specifically the Tidyverse community. The people around R gave me a leg up in learning statistics and data visualization. I’ve always appreciated the way they’ve approached community building and knowledge sharing. It feels very open, warm, encouraging, and practical.

How much of that community interaction is in person these days?

I’m living in a largely remote world right now. A majority of my team is remote as are many of our collaborators. Like any community, we have to sustain and support it and that doesn’t happen without intent. But when you get it right, I think it’s very much possible—and even preferable—to build teams that aren’t all working next door to each other all the time.

What non-data science skillset has offered the greatest return in your work? What’s your superpower?

Comfort with ambiguity. I’ve always really enjoyed the part of projects where things are the most fluid and dynamic. In a fast-moving field like data science and in a world where new crises emerge and overlap this inclination is really helpful.

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

Find a topic you’re passionate about and then apply data science to that topic. That will drive your technical development more than trying to learn Python or R in abstract. If you’re really passionate about the environment and health, grab some air quality data from Resource Watch and build a map. If you’re passionate about politics, build a topic modeler for political speeches. Solving a concrete problem that you’re interested in is the fastest way to learn.

Another piece of advice: data scientists, myself included, can have a real perfectionist streak. This often prevents us from finishing projects or sharing what we’re working on. As much as possible, I’d encourage you to put your ideas, code, and work out there. Be open to feedback and you’ll learn and grow.

What do you see as a big development in data for good in the next decade?

There are so many exciting things happening right now. A big one for us at World Resources Institute is all the new sensors available to help us understand the world. In the sky, we have new satellites feeding us optical, radar, and hyperspectral data that help us see how environmental, agricultural, and societal systems are changing. On the ground, we have tools like low-cost air quality sensors that allow us to understand hyperlocal environmental impacts.

The second thing I’m really excited about is that the toolkit for doing data science is getting so much more user-friendly and accessible. Previously, there were all these frustrating, under-the-hood steps when it came to setting up a data science environment. Now there is a range of amazing tools and platforms that abstract a lot of the annoyance away. On the geospatial side, platforms like Google Earth Engine, and Microsoft Planetary Computer really make life easier for analysts. I’m really excited about doing data science in the browser through the combination of traditional data science tools like Python and R combined with WebAssembly. Replit is another great example of this trend. The idea of doing data science in your browser is the dream for me. This will really expand who can be a data scientist and who can benefit from these approaches.

What’s your don’t miss daily or weekly read? It could be a data science topic, or something entirely different.

I must confess that I sink too much time into Twitter but at the end of the day it’s probably the place where I learn the most. I also subscribe to way too many newsletters. Two I’m enjoying right now are Bob Rudis’ Daily Finds and Jeffrey Ding’s ChinAI newsletter.

At WRI, I’m lucky to get to work with a lot of smart people who are extremely well-read, so I get to draft off their knowledge and expertise. We have an internal Data Lab newsletter that I edit every month that has a “must-read” section and that’s probably where I see the highest density of great papers, great articles, and great insights.

The post Pathways to Impact: Evan Tachovsky appeared first on data.org.

How did you start out doing this work? How did this work become on your radar as a possibility for the kind of work a person could do in the world?

I first started doing things with computers as a teenager. And for me, getting access to a computer was actually a pretty big deal. My parents had moved to the United States from Guatemala before I was born, and my father had a career as a night shift mechanic in a factory. In the mid-90s when my parents got the sense that computers might be important, they scraped together the money for an Intel 386 desktop computer that we could hopefully do something with as kids.

But we didn’t have the money to license software. I ended up with a computer, and a manual for the basic programming language, and a challenge from the computer shop owner to learn how to do things with it. That was my baptism by fire into computing, perhaps like many people who got into it as hobbyists — but in my case it was borne of necessity.

With a fair amount of computer experience as a teen, I actually studied literature as an undergraduate. This was in part because of some great advice from a local college professor, who said that while computing is great, and an important area to study, ultimately, computers are a lens on the world. If I wanted to do interesting and meaningful things with computing, I should first learn about the world. As a result my major was literature with a minor in computer science, and it was this combination that launched my career as an academic and in the startup world.

That’s wonderful. You had an early perspective on technology as a lens on human behavior and experience. But when did you get a sense that technology could be in service of good or bad acts, and that there was a problem that technology or data might improve or worsen?

I started thinking clearly about technology and society while studying post-colonial literature at Cambridge University, on a scholarship for people underrepresented in their fields. At Cambridge I learned about people like Solomon Plaatje in South Africa who used the printing press and created dictionaries as a way to reshape knowledge and power in the colonial era. Back then only certain people had access to printing presses, the communication technologies of the era, and they used this control over information to reinforce the power of colonization and empire. I read stories of people who grew up in the empire and saw these communication technologies as ways to remake power for democracy. That was the moment when I realized that I can — and should — apply a similar lens to the technologies of our time. I saw that we all need to be asking where these technologies are (or are not) serving the common good, where they are serving freedom and democracy, and what we might need to do differently to make sure that happens in the future.

And now you’re at Cornell — what part of this problem are you seeking to solve?

At Cornell, I lead the citizens and technology lab (CAT Lab), which works for a world where digital power is guided by evidence and accountable to the public. We work toward that goal by collaborating with the public in citizen science or community science. We talk to and organize members of the public who face issues like online harassment, or who worry about inclusion in knowledge on sites like Wikipedia, or who are excited about creating a flourishing digital society. We collaboratively design research studies that answer questions exploring the impact of a technology on people’s lives or some idea for creating change, whether that’s social change or change in how technologies and technology firms operate.

How often in your work in citizen science or community science does the word data come up?  Is it perceived as an opportunity or as an issue? 

I often find that people who are most affected by technology issues have a profound and nuanced understanding of how data impacts their lives. They might not know all the technical details, but they’re the experts in their own lives and situations. And more often than not, when our team has collaborated with communities, we’ve learned things about the data that we didn’t know, that people intimately know because they have to live with it.

One example is a project we did a few years ago to support a group of women who were documenting how tech platforms did or did not handle cases of online harassment. We were trying to support them both to report the issues they were facing, and also to document those issues in a way that could be written up for policy makers and platforms. We learned so much from the level of understanding and detail that these women had about how online harassment happens, how it is or isn’t recorded, the kinds of evidence that law enforcement needed, or didn’t need, or didn’t pay attention to, and the mismatches between the data and interactions the platforms recorded and what survivors needed. It’s fair to say we learned more from them than they learned from us in that process.

What were some unexpected blockers to your career? Your path went from being from the child of immigrants pulling together money to pay for that first computer to working on entirely new problems at an Ivy League institution. What fueled your progression and what were some of the things, expected and unexpected, that posed obstacles?

You’re right that one of the basic challenges was that there was no defined category for what I do today. It also took me time to gain a sense that I could do things that mattered in the world. I remember showing up at Cambridge and being surrounded by people who expected from an early age that they would go on into careers that would have consequence and power. And it shocked me because I had not grown up in similar circumstances, or with the expectation that my views or actions could really change society. I needed the right support to see those pathways and follow them, to learn how to participate in conversations of consequence with the values I brought as someone under-represented in academia.

One thing I’d add: if you want to do something innovative that contributes to the common good, sometimes there’s an existing pathway, and sometimes you have to create that pathway. This is particularly true if your lived experiences and perspectives lead you to do things differently from the norm. I think that in higher education and powerful institutions people who come from traditionally underrepresented groups are often innovators in that way. But forging those pathways often requires even more creativity and support from institutions to become a reality.

One challenge that trailblazers working on technology and social good have faced is the mismatch between the goals of tech company leaders and the public purpose that social good innovators have. Often, there’s a lot of overlap and opportunity for collaboration. And sometimes there’s real conflict. As someone who’s been part of the startup world and the tech industry, I eventually realized that my kind of public interest and citizen science work needs to be independent of the tech industry. There are just certain kinds of public service that are easier to do well in a trustworthy way if there’s no question about the influence of industry on your work, especially where your work holds companies and industries accountable.

Whatever path people choose, everyone has to navigate a tech world that is largely driven by profit motive. There will absolutely be moments where you can align the public interest with those profit goals. There are moments where you find a way for them to travel side-by-side. And then there are moments where the people need to say “stop” — we need to use our power as a democracy to force companies to steer in a different direction. Navigating these tensions and decisions was one of the most challenging aspects of my time as a PhD student and now as faculty.

We still struggle to bring together conversations about values and social impact in the same spaces as technical details, mathematics, and statistics. Scientists often call something pure science or if it avoids ethics conversations and prioritizes white, Western ideals. Values conversations continue to be split off into new fields, without giving students in what we might call “purely” technical fields as much of an introduction to the important ethical and social issues that they’re going to confront in their work. Those of us who are responsible for higher education need to bring those fields and topics closer together. Students face really difficult choices about which lane to go into, especially because the lanes that are in front of them were created at a time when those social and ethical implications were less recognized.

What community of people or resources bolsters your work? I’m curious where do you go when you need a question answered, or to exchange ideas around the work your lab does today?

I love that question, because I strongly believe there is no success without some set of communities supporting and uplifting that person — and that’s totally been true for me. In my early career, communities at the Berkman Klein Center at Harvard, the Center for Civic Media at MIT, and also the community of bloggers and activists at this network called Global Voices profoundly shaped my understanding of what was possible, and have provided so many sounding boards for ideas and ethics. As a professor who cares about both our lab and supporting a wider ecosystem of more people to do this kind of work, I’m finding myself more and more involved in creating and sustaining communities that can offer that kind of support.

Some of those communities are within academia, within the Association for Computer Machinery. Some of them are new organizations. For example, I’m a co-convener of something called the Coalition for Independent Tech Research, which has just launched this summer. The Coalition is trying to create both a supportive community for people who want to do industry-independent research in the public interest, and also to advocate for and make the case for the rights and freedoms that we need to be able to inform the public and provide evidence to guide democracies on tech and society.

Specifically, there’s a constellation of fellow travelers that I find myself sharing queries and ideas with. Over the years, this has included Public Lab, the team at the Algorithmic Justice League, the Distributed AI Research Group, and the Digital Labs at Consumer Reports. I’m often in conversation with the Center for Information Technology at Princeton, Data &  Society, and the Knight First Amendment Institute at Columbia.

The computing ecosystem often moves too fast to look back or forward. I have ultimately decided in my career that I want to do work that is valuable both now and in the long term. That’s one of the unique privileges that scholars have in academia: we can be thinking about that long term. It’s one of the reasons I’m so excited to be here at Cornell in the Communication department, where we have technologists and social scientists who care about public engagement while also doing work that stands the test of time.

You have a degree in literature, and you’ve done postgraduate work. Beyond the more technical parts of your job, what are the skills you feel like really help you be successful? What non data science or technical skillset has proven valuable?

There are a few skills that we might not think of as data related that have transformed how I work. One comes from the humanities, and it’s a willingness to research the history of the values and politics of technologies and systems.

Many of my computer science papers, we go deep into that history. In our most recent paper, which re-imagines citizen science tests of technology concerns, we briefly review 1930s debates over the relationship between statistics and eugenics. Many of today’s statistical tests were first imagined by eugenicists. By revisiting eugenics debates among statisticians at the time, we can re-imagine the assumptions behind data science today.

Reading that history gave us insight into how to rethink the design of research that would benefit people at the margins. It drove us to look beyond the average outcome variables, which privilege the majority, to examine other parts of the distribution where marginalized groups might be facing harms you wouldn’t see otherwise. That’s just one example of the contribution the humanities can make to fundamental advances in computer science. When I get to write about technology and society for a public audience, I often weave poetry, history, and original reporting into articles about our digital future.

I’m also grateful for my graduate school training in community organizing. When I was a PhD student at the MIT Center for Civic Media with Ethan Zuckerman, he insisted that we get trained in organizing and facilitation, because Ethan believed, rightly, that the most powerful outcomes are created when people come together for a common cause, and when you’re genuinely engaging with and listening to the people you’re collaborating with. I would love to see more people receive organizing training. Whether you are negotiating with a client, or collaborating with policy makers, or working with communities, there’s a give and take. This collaborative negotiation of understanding is central to the success of any data project.

What advice do you have to someone new to the field who’s interested in doing this work? If someone wants to get started with a background in CS, or just a strong interest in how data is affecting their lives, what might you recommend?

We’re still creating those pathways, which is one reason I’m so glad we’re having this conversation. My top suggestion at this stage is to follow the people whose work you admire, connect with other people who care about the same things. You can reach out to those people whose work you admire to ask for suggestions and introductions. You can also have a conversation with people who share your values and goals about the best pathways for you.

The world of data and social good is so varied, and it encompasses so many fields that it would probably be a mistake to say, “here is the one true pathway”. But what is really important is to find those people whom you trust, and who share your values, and who you feel can help at least light the next few steps. I know when I was a student, you were one of the people I reached out to in that regard, Perry, and I appreciate having that conversation many years ago. 

The day I learned I could contact the people whose work I admired was a game changer for me. I remember being a college student in the early days of blogging, and one day I emailed a blogger who I admired, and I said, “Can I interview you for this project that I’m doing?” And they said yes, which blew my mind. And if those people are busy or overwhelmed— show up at events, join local meetups, participate in forums— finding people who share your passions is an important step to finding your path.

One piece of advice for students right now is to consider the advice that I was given by a professor: if you focus on the technical skills alone, it’s going to be harder for you to chart a course through the values and issues you care about. Take advantage of a course in the humanities or social sciences, or some other field that you’re curious about. Learn about your own identity and history, or maybe something about the world that you’ve never encountered before. Those are the courses that will help you chart the path, even as your technical courses help you build the skills you need to follow the path.

There are, of course, fields and disciplines where we’re trying to assemble the pieces for you. Communication is one where we bring together the social sciences, computer science, humanities, and history. Information science is another great example. But even if you’re at an institution that doesn’t have those interdisciplinary programs, taking those electives, going to public events with speakers, or getting on Zoom to hear live streams with people whose work you admire can make a big difference.

One final piece of advice: it’s important for young people, especially, to overcome the fear of not having enough technical capability. There will be endless paths you can take to further refine and improve your technical skill, and that can be an ongoing journey. But the technical skills take you nowhere unless you have a vision for where to take them.

What the next big thing in tech or data for social impact that you see? Do you see people creating and using these technologies becoming more aware of data?

I think and hope that we’re going to move towards the expectation that new technologies and existing technologies be supported by evidence — evidence that the risks and harms are being managed effectively. We’ve spent so many years in the digital technology world where the approach is “put it out in the world and see what happens.” Democratic societies and our representatives have gone through enough crises as a result of that approach. We are going to see more expectation that the benefits be proven and the risks are provably managed. That will create an amazing opportunity for people working with data to help us navigate those big decisions so that when we are debating topics like mental health and social media, we’ll have the evidence we need, and we’ll be able to see that companies or public interest organizations are using their power in a way that’s helping and not hurting.

Achieving evidence-based tech policy will be an ongoing journey and I would love to see it fulfilled within my lifetime. I feel like we’re reaching a turning point in the US, in Europe, and elsewhere where that’s starting to become the default expectation, that you have the data to back it up, not that you bring in data as part of a crisis management approach when things go wrong.

Remember, there was a period of time where we put cars on the road without knowing if they were safe. Large numbers of people died before the United States government finally realized that we might want safety standards and evidence practices. Someone had to invent the crash test dummy. We were driving cars and people were dying before we even had a way to tell if a given car model was safe. Similarly with food safety and drug safety: there was an era where anyone could make and sell anything. We may or may not borrow different parts of regulation from those pasts, and digital technology definitely has its differences. My hope for our lifetime will be that we figure out what that model is and create a robust, ongoing evidence base for making those decisions as a society.

What’s your don’t miss daily or weekly read? What are the kinds of media you consume to stay informed or encouraged?

Almost every day I listen to PRI’s The World. I hop on my bike, read the book of nature, and hear stories from all around the world about the issues and challenges that people face, as well as fun and interesting stories about culture and the arts. Even when the news is dire, The World gives me hope as I hear about the challenges, aspirations, and work of so many people in other parts of the world. I also stay heartened by listening to poetry, fiction, and nonfiction in audio form, most recently Robin Wall Kimmerer’s luminous book Braiding Sweetgrass, about science, the environment, and indigenous knowledge.

The post Pathways to Impact: Nate Matias appeared first on data.org.

What was your first role in data for social impact and how did you get into it?

I entered the social impact field with Ushahidi at a fairly young age. My very first interaction with the Ushahidi platform was back in 2010. I started off by volunteering with them during the Constitutional referendum in Kenya.

At that time, I was in the third year of my undergraduate studies at Strathmore University in Nairobi. When I graduated a year later, I reached back out to the team and let them know I was eager to find a way to apply my technical skills. Having been exposed to that community as a volunteer, I was able to see the power of technology for social good firsthand.

That early experience of being able to make use of my technical skills to do something good in the world opened my eyes. It was a new possibility to see this use of technology and data applied to social challenges. To me, this was a new and different approach to helping other people using your skills, rather than your typical 9-5 job. I ended up joining the Ushahidi team as a software development intern in 2011 and then signing on as a junior software developer a couple of months later.

Your degree was in computer science?

Yes. I did my bachelor’s degree in business information technology, which is a mix of both computer science and also some business courses. That particular program at Strathmore was focused on building tech leaders in Kenya.

Was there anything particular about your educational experience that influenced your decision to do social impact or was it more just seeing the world around you?

Honestly, I think it was more about seeing the world around me than any particular academic course. We did have coursework in ethics and philosophy, but the greatest influence was seeing the challenges that we as a society were facing.

When I think about Ushahidi and some of the other companies that have emerged in the tech scene in Kenya, it’s been a result of scratching our own itch: having a lived experience with a particular problem, and then trying to figure out how best to address it. For example, Ushahidi was founded when the nation was mired in post-election violence, and nobody was documenting or sharing anything around that crisis.

This has also been true for some of the other innovative projects we’ve come up with. For example, BRCK was founded in 2013 to focus on bringing internet access to East Africa, and then eventually spun out. The reality we faced included frequent blackouts and inconsistent internet connectivity, yet we had to use infrastructure designed for places like the UK and other high-income countries. The team formed to build something rugged that would accommodate some of these lived realities. The bulk of our tech for social impact innovation has been inspired by our lived reality and context.

So today what are you working on with Ushahidi? What particular social impact problem are you trying to solve?

Our main focus is empowering ordinary citizens to make use of data to solve problems in their communities. Our goal with the Ushahidi platform is to raise the voices of marginalized groups and underrepresented people. Our hypothesis is that typically decisions are getting made in high places without knowledge of or consideration for what ordinary people might be experiencing. As a result, we’ve built the platform to surface the opinions of those ordinary people as a source of data. This will enable data-driven decision-making, using data that is largely representative of the needs of the people on the ground.

We do this work in three main areas of social impact: humanitarian and disaster relief, human rights protection, and good governance. We’re not limited to those three, but these are our primary lenses.

We also use data to make sure that underrepresented groups’ voices are heard, and that decisions being made about them are representative of their expressed needs.

When you say you’re trying to empower the ordinary citizen through data, is that by building platforms they can use? What shape does that work take?

It starts off with our open-source Ushahidi platform that enables massive data collection. We seek to reduce the barriers to entry and interaction by providing various mechanisms through which people can send in their opinions. You can use a text message. You can use email. You can use Twitter. You can use smartphone applications. The platform widens the scope for people to be able to communicate their opinions, and for those opinions to be captured and analyzed.

Beyond the building of the technology itself, we consider how the data gets accessed and used. There is an element of advocacy to this work. It’s powerful to be able to highlight how many people are on this platform saying X around this particular issue and using that knowledge to lobby for change or a particular decision.

Typically, the part that we are responsible for at Ushahidi is building the technology tool itself, and then providing the support and expertise for the grassroots organizations or local NGOs that are using the technology to collect data. And then those organizations will use the data to engage with the ordinary people on the ground and advocate on their behalf. Our contribution based on our own theory of change is creating a platform that can surface and raise people’s voices. As a result, these grassroots organizations are empowered, and data then leads to the change that we want to see in the world.

What were some unexpected blockers? Were there blockers around gender or access to the tools you needed or prioritization of the work? I’m sort of curious what were some early challenges you overcame.

At the point I joined the team, there was already a shift going on in the tech industry around trying to get more women in the space. I am one of the lucky ones: the kind of culture that the Ushahidi founders and the rest of the leadership created was very cognizant of the need for diversity and inclusion. There has been such a deliberate effort to make sure that our organization is representative of the kinds of people that we serve. We are intentional in our approach to increase a pipeline of women in leadership positions — I mean, look at me now! — and to women working in the tech industry overall.

I’m grateful that the challenges have not come from within the company itself. I would say that as an organization, we face challenges around sustaining funding. It’s a reality that a woman in leadership, coming from the Global South, can face more challenges in fundraising.

Women in leadership are held to in some ways an impossibly high bar. You need to be very careful about how you conduct yourself and what kind of failures you can permit yourself to accept. Because the world might not be as forgiving as it would be of a man in that same position. You also have to measure your reactions in the workplace. There’s the specter of being called emotional for a strong reaction — because you are a woman.

What community of people or resources bolsters your work?

Throughout my career, I’ve been privileged to have wonderful support, starting with the Ushahidi founders. I am also a founder of women in tech organization called AkiraChix in Kenya, and being involved there expanded my network quite a bit. Through that network, I have access to people whom I look to for advice and mentorship. Day-to-day, I have the support of our board, as well as a leadership coach.

I’m grateful for the engagement and support of my peers, nonprofit leaders in the tech space-based all over the world, in part because Ushahidi is a 501c(3) registered in the US.

Your computer science and business information background are obviously directly applicable to the rise of your career. But is there a non-data science or non-technological skill set that has helped your work?

I’m naturally a very social person. And because of that, one of my core values is helping people. While I didn’t quite realize this until later in my career, this emphasis on helping others has been beneficial. I started at Ushahidi on the software development team but found that I would lean into supporting the users.

That emphasis on helping users helped me transition into being a community manager — trying to act as a bridge between the people building the technology and the ones using it. From there, I became the Director of Community Engagement, building strategic partnerships with different organizations, doing outreach to get community members into Ushahidi, and advocating for open source as important and key to our strategy.

That emphasis on user needs and outreach is perhaps one of the reasons why when the board re-evaluated the future of the organization and focused on taking it back to its roots, I was the one that they came to. As someone who had been such a passionate advocate for focusing on the users, focusing on their needs, and building appropriate tools, I was a stronger candidate for the leadership role. What began as a personal quality ended up being a muscle I built while working at Ushahidi, and helped me rise up the ranks.

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

First and foremost: have an open and very curious mind about learning. Place yourself in spaces where you’re able to learn. I know there are quite a lot of meetups and events where people are talking about data for social impact. There are a lot of organizations that are willing to provide entryways to volunteer. Use those opportunities as a testing ground to see what works and what doesn’t, and where you might fit in. And ask a lot of questions. Please do not be shy about asking questions!

One of the patterns I have noticed in people who are making a transition to social impact is that sometimes there’s this feeling that they need to throw away the skill set that they had before to fit into this new field. And I don’t think that that’s necessarily the case.

Instead, I would look at that skill set as a potential way to bring in a level of diversity of thinking around how to do things with social impact. So come in, obviously, with a curious mind to learn. But come in with confidence, as well, about all the experience that you’ve built up to try and open up opportunities moving forward.

What’s the next big thing that you see in data for social impact? Are there big developments on the horizon, whether it’s around open source or new kinds of data sets?

Right now, I see interesting developments in the realm of public and private data exchanges for social good. There’s an opportunity in transforming the ways the nonprofit sector works with businesses and works with the government for the greater good of the world. I know that there are significant challenges around how these data exchanges happen, and a bit of friction around what different motivations might be.

However, that is still the biggest thing in my mind. A clear impetus for this collaboration is the crisis the world is facing around climate change and our environment. That’s a very, very big one. This shared crisis is really forcing us to think about how we collaborate as all these different organizations to do good in the world. Governance will of course be important.

What’s your go-to daily or weekly read? What keeps you informed and sane in today’s world — which sometimes can feel contradictory? 

Informed and sane — I think I’ve probably been relying more on the sane side of things, which then kind of requires me to take a step away from the usual. To stay informed, I definitely look at my Twitter feed, which is very well curated. I follow a lot of different nonprofit leaders in data and tech, whether working in open source or some other focus areas that we work in. I also keep in touch with articles and research from the World Economic Forum.

But when I’m looking to unwind and stay sane I go for fictional books. I confess that I am a huge fantasy nerd. So right now, I am digging into a 14-book series by Robert Jordan called The Wheel of Time. My goal is to try and finish all 14 books this year.

One of the things I’ve really been trying to do as well is deepened my understanding of global economics. It plays such a huge role in so many aspects of social impact — whether we’re looking at food insecurity, humanitarian crises, or challenges around human rights. I’m learning more about the ways economics correlates with, triggers, or accentuates some of the issues that we face in the world. It’s of course helpful to see how these trends influence funding for nonprofit organizations like Ushahidi, and how they inform how we support the organizations we work with more broadly.

The post Pathways to Impact: Angela Oduor Lungati   appeared first on data.org.

How did you find yourself in this field, and your current role?  

I grew up in Asturias in the north of Spain and then followed my academic interests to engineering degrees in both Spain and France. My initial focus was on math and signal processing. My curiosity then branched out to humans — I pursued a Master’s degree in cognitive science, which was essentially psychology and neuroscience. Around that time, I was fascinated by exploring artificial intelligence (AI) and creativity and created an AI that could write poetry in 2003. After that, my interests shifted again — I became intrigued by biology and the elements of life itself.

It was an interesting trajectory: science to humans and then to what’s going on inside humans! My Ph.D. work was on mathematical models applied to biology and genetics. Essentially, we did something like Google Earth of the first 1,000 cells of a vertebrate embryo.

In addition to my academic background, I spent some time in Silicon Valley. It was there that I realized that I wanted to have an impact beyond research.

I have always admired the United Nations (UN) as a place to have that kind of impact. Every country has a seat so the organization represents, as much as possible, all of humanity. In 2011, I applied to a position they were opening in a new unit called the United Nations Global Pulse (UNGP), and I became the first data scientist working for any international organization.

2011 was a big year for the awareness and practice of data science. These were early days for this work, particularly at the United Nations. A precipitating factor for the UN position was the aftermath of the global financial crisis: in mere weeks or months, many countries lost the development progress that had taken them years to build. And the UN — and often the governments of those countries — had limited visibility into these losses. The UN was looking for new ways to measure, in real-time, the well-being of people worldwide. This led to the creation of UNGP allowing the broader organization to innovate and experiment to benefit from the use of data and AI. The organization has now evolved to maximize the impact of digital innovation to address emerging global challenges and crises, minimize the risk of harm, and create pathways to scale and sustainability.

Having worked as a researcher in several areas, I have benefited from multidisciplinarity; for me, it’s always about trying to see the connections. In my career, that’s meant drawing analogies like, “Look, the same way we can see how cells move in an embryo might help us understand where population moves after an earthquake.” I know there are many analogies you could draw with other fields of science that would apply to social impact.

What part of the impact challenge does your work solve? What are you working on right now?  

At UNGP, I’ve been working on so many challenges: poverty, food security, refugees and migrants, conflict prevention, human rights, gender issues, and climate. Our team always works closely with other UN agencies that have a specific mandate and issue expertise. For instance, during the peak of the COVID pandemic, our team created the epidemic models for the biggest refugee camp in the world: Cox’s Bazar in Bangladesh. This was a joint project with the UN Refugee Agency (UNHCR), the World Health Organization (WHO), academic institutions, and private sector partners. Together, we created a digital twin for this camp, to aid in decision-making when crafting health policies for the camp. More broadly, we mobilized a community around challenges and opportunities for epidemic modeling for people that have been forced to flee their homes.

With the Ukrainian crisis, we are supporting UN colleagues from multiple agencies to estimate challenges like numbers of affected people, either refugees or internally displaced populations, using AI to speed up satellite image analysis or understanding future network effects of broader socioeconomic impacts.

Another interesting area we work in is not focused on what we can do or how we can do it — but on what we shouldn’t do. We work on guidelines and instruments to assess the potential risks and harms of data science projects themselves. When the risks are too great and cannot be mitigated, there are things we must not do.

We also work on the governance of new and emerging technologies. Consider neurotechnology, for example. Imagine something can read and write the neurons of your brain directly, what are the mechanisms for consent? We will need to tackle issues like mental privacy. Therefore, it is important to identify early pathways for the governance of emerging technologies.

Were there any blockers bringing this data science competency into the UN, creating this team? What were some of the early challenges for you?

We’ve been fortunate in a number of ways. First of all, the nature of work at the United Nations is already aligned with social impact. Secondly, since we launched, we have had the support of leadership, including the UN Secretary-General.           

There was a lot of work that needed to be done creating those first data science teams: the terms of reference simply didn’t exist. We had to create all those terms of reference and job descriptions: data scientist, data engineer, data visualization specialist, etc. And when we first started to open those positions, we did not get a high volume or a diverse set of candidates.

Over a decade later, we’re in a more mature market: data scientist is a regular job in the United Nations and many other international and humanitarian organizations. Now it’s possible to get hundreds of applicants representing a broader base of diversity and backgrounds. For example, we saw positive change recently when our most recent call for Fellows drew 50% female candidates. Back when we started, it was a lot of work to create and fill these roles; we also had to lay the groundwork for their success which required navigating strategy, politics, and many other things beyond data science. Still, bureaucracy is not always easy and there is much more to do.

You’ve built an innovation network of excellence that can partner across the UN with different agencies, that bring in issues like refugee resettlement, poverty, or epidemiology. Was there any resistance to bringing along area specialists into the practice of using data and data science?  

One of the ways we addressed this challenge is by acting as a partner that can provide a safe space for innovation. At first, the requests were about showing our UN colleagues the potential of data science through proofs-of-concept, then we saw an increase in the appetite for more projects but also an understanding of the risks and limitations. Then we were asked to support during emerging crises. These days, requests are more holistic including anticipating possible futures, building digital innovations that are more inclusive, and looking to mobilize communities to address global challenges.

Our team worked to launch innovation labs around the world, in New York at the UN Headquarters, in Indonesia, Uganda, and Finland. Creating and nurturing those local ecosystems was important, and equally important was using these spaces to bring the data, tools, and expertise together with the different agencies. Like introducing any type of innovation, progress is not always straightforward or linear: you need to look for local champions, communities, and the enthusiastic early adopters, while also ensuring you have buy-in from leadership and the critical stakeholders.

And of course, it has not always worked. Failure is also part of the process and the journey of organizational change. In any context, it’s hard to change, even if you create the safest place for innovation. Not every organization is going to go there, and some may be reluctant to change. Sometimes it’s been slower than expected. More work needs to be done to scale the impact of the innovations, which is something the UN and many organizations like data.org are looking to accelerate.

But I think now we are in a better position, and the digital revolution is here. Data roles are everywhere, especially in the private sector. The next step will be when we have a new generation of senior managers and leaders that also have data backgrounds framed by strong ethics and values. We’ll pave the way for more effective leadership and change when we are routinely promoting people who bring a background in data science, on-the-ground fieldwork, and a deep commitment and understanding of policy-making, human rights, and humanitarian aid.

What other community of people or resources supports your work? Are there specific groups you engage with?  

When I look at my career trajectory, I’ve tried to have an impact on research and methodology, and then to have an impact on operations and operational impact. Last but not least is to have an ecosystem impact on communities.

I believe that science has a lot to offer, and I want to bring academics closer to social impact. Data science or AI — however you refer to it — is an enormous field. The strategy for me has been to engage with different sub-communities. For example, you have communities of practice around complex systems, deep learning, human-computer interaction, or computational social sciences, each with its own groups and conferences. I try to send signals to all those communities that they have something to offer, and maybe they don’t know it yet. We are also approaching communities that a priori might even look further away, like behavioral sciences or futures and foresight communities. There is great potential there.

My other communities are made up of development, humanitarian and policy experts working on key issues for the UN: people, this planet, its peace, and its prosperity. I can draw from communities of colleagues working for children, refugees, food security, gender equality, human rights, and health. And I’ve been really lucky to be exposed to the ins and outs of all these topics through these amazing experts.

What is clear is that to build an equitable future, across all these communities and ecosystems, we need to foster diversity and inclusion, and make sure that no voice is left behind.  

Your background in mathematics, cognitive science, biology, and data science all enable your contribution. But is there any non-data science skill set that has been beneficial? What are some of the skills that you’ve developed in the job, or as a leader, that has helped you? 

Empathy and learning to listen. Understanding where people are coming from, listening, and then translating between domains. I think it’s being able to translate between data science and human rights, between data science and food security, and between data science and policy making or diplomatic issues. These translational skills have been critical, because context matters.

When we work on a project, of course, part of building out the project is about the technology, but most of the time it is even more about people and cultures. Here’s an example of how context matters: when you do AI for satellite image analysis, imagine you are counting shelters. This typically is done by someone by hand. And it’s done by someone by hand because the count has to be perfect. Based on this count, someone will then assign a number, like X tons of food or whatever we are delivering. So essentially, when you do an AI algorithm, you have to focus on the most important thing. It’s not important to optimize the performance: we don’t care how good the algorithm is across all dimensions. We care that the algorithm will give a timely response when it is used by the human expert embedded in existing processes for emergency response.

Another example, we worked on a water delivery system project in a refugee camp in Jordan during the Syrian war, where we had to align stakeholders in the camp. There were many stakeholders, including organizations providing clean water services, truck drivers delivering the water, and of course the citizens in the camp. Here the first proposed solution did not work. The technology was not the most difficult part, so we focused on alignment. Once stakeholders were aligned, we made an alternative technical solution, and the project succeeded.

So really, context matters, not only the technology on its own. Being able to have empathy, translate, and understand what everybody is “really” saying, is critical for success. We always have to consider the human first, and then the technology. And then, you have to make sure that the tool works as it should!

What advice do you have for someone new to the field who’s interested in doing data for social impact work? That could be a student entering the workforce, or someone mid-career with great skills looking to apply them.  

The most important message is that we need all these people, the early stage and the mid-career and senior entrants!

We need people with hard science skills and social science skills; the future is at their intersection. We must convey to people that data for social impact is a real job. I think that is important to convey because sometimes that job and career have not been so obvious. You can build a career, but the impact you’ll make won’t be as easy to measure by financial metrics.

For someone just getting started, or wanting to begin, I would suggest seeking out a role that gives them a lot of exposure and lets them understand the context, to start to learn to translate. For someone mid-career, I would urge them to keep an open mind. I’ve seen many people enter the sector: astrophysicists, health care professionals, bankers, and intelligence analysts. Keeping an open mind and staying adaptable is key to success — along with being great at your work!

For many mid-career people, it’s also about learning to see the big picture. For example, if you come from a bank where the focus is on optimizing time series forecasting, the metrics for success are very clear and relatively narrow. In the social sector, there are more layers: human rights, environmental impact, and advocacy, Looking back, I think the people who have made the transition best are the ones that can adapt and adjust to seeing the multiple layers — and scales — in the big picture context.

What’s coming next? I know you don’t have a crystal ball, but what are your predictions for data science for social impact?

Beyond the obvious challenge, the climate emergency, I see big-picture public and private opportunities.

The first is a movement for broader creation of and support for digital public goods. I am hopeful that these public goods will continue to grow and be shared as a kind of digital infrastructure for humanity.

On the private sector side, I believe we will benefit from having more social entrepreneurs, like those in one network I support, Ashoka. If more social entrepreneurs manage to sustain successful businesses around social impact, that might change many things. For that, we need more investors in the intersection of technology — and deep tech — and social impact. This intersection does not yet exist, but I believe there is opportunity there. 

Beyond digital public goods and social entrepreneurship, I hope to see mainstream concepts around the responsible design of technology. We need to start asking upfront what we won’t do. Not just that, but also anticipate potential governance and regulatory pathways, especially for newer technologies. Where are the limits? Where are the red lines in the digital world? How do we design for good and also ensure that what we are putting out in the world does not have negative or unforeseen consequences after it has been released?

Finally, the last point on the future is about trust. Working with the WHO on infodemics, we have witnessed how mis- and disinformation can have a real impact on people’s health. Not just that, in politics, conflict, and climate — it is clear that we’re at an inflection point with objectivity and we need to find a way to restore trust in this post-factual world.

What’s your don’t miss daily or weekly read? What do you read to stay sane and happy and informed on a daily or weekly basis?

I listen to a lot of podcasts. Every week, I listen to Nature and Science magazine podcasts to understand what humanity is doing around science. One other I like is called Exponential View.

I also go down deep dives into specific topics. One week I might decide to go deep into the latest IPCC climate report and explore a bunch of different podcasts and articles. Another topic might be public speaking, so I listen to experts on how to make a persuasive and compelling talk. I enjoy deep dives into very different things and authors.

Beyond podcasts — and doing sport while I listen to them — I also experiment with concepts I learn about, like quantified self activities. For example, conducting ten antibody measurements in my blood over the last year has allowed me to model my immune response and show that every ~40 days, my antibodies for COVID divide by two. Just an “n” of 1. Small data gives me useful information and I find it fascinating!

The post Pathways to Impact: Miguel Luengo-Oroz   appeared first on data.org.

By founding Dasra, you’ve played a huge role in the social sector from awareness to partnerships to funding. How did you personally make that first foray into data for social impact?

Mine was not a straight path — and I’m so inspired by folks that do have straight paths! I think you might see more direct paths to social impact roles these days than in my time. I was a child of Indian immigrants and grew up in Canada. My parents were engineers, and my sister and I were supposed to do science. Frankly, I thought I was going to be an actuary; I studied math, and I didn’t know of any professions other than actuarial science. My aspiration was to just have a job with a salary, which I know sounds quite silly. But my whole life was based on, ‘Will I get hired?’

It was during my undergraduate studies in Canada that I was first exposed to something called business. I did a dual degree in math/statistics, and in business. At that point, I realized that there were interesting applications of math and statistics in the real world. Business was a place where I could leverage both the stats skills and the softer skills that I was developing.

From college, I went into investment banking: mergers and acquisitions at Morgan Stanley in New York. This was in the late ’90s, when markets were hot and analyst programs had just started. Looking back now, at every stage in life one gets more and more exposed to possibilities and opportunities. I began to see opportunities — but at that stage I wasn’t able to find the courage to take a risk, even though I was seeing everything around me begin to change, especially in the social sector.

Around the same time, my mother moved back to India and started a boarding school for Adivasi children. Adivasis are indigenous peoples who don’t have access or privilege; many don’t even have access to school. She started this school at IIT Kharagpur (Indian Institute of Technology), where she and my dad are both alumni. This school was my first exposure to rural India, where there wasn’t this kind of basic access to education.

At the same time, I read The Price of a Dream by David Bornstein about microfinance, Grameen Bank, and Muhammad Yunus. That’s where I started to see a possibility at the intersection of finance and development. Microfinance was an amazing way to empower women to give them not just financial skills, but the power to save and be in control of finances. I realized how all this can converge and be intersectional, and considered where I might play a role in this intersection of corporate and financial empowerment.

I came up with the idea of Dasra while still at Morgan Stanley. That’s where I met my husband, Deval, who was also an analyst. We were working 100-120 hours a week at that point, and that pace and the kind of work we were doing as junior analysts started to make us question our purpose. Why were we focusing so much energy on PowerPoints and Excel spreadsheets?

We decided we needed to do more and make a change. We wanted to find a way to support organizations through our funding and management skills; we believed there was a valuable intermediary role to be played, and that was the genesis of Dasra.

Deval, my husband, actually started working first at Dasra; I had been accepted into Harvard Business School, something I never imagined would be a possibility. At HBS, I learned and gained courage from the School’s focus on whole leaders that make a difference in the world. I joke that I was brainwashed there! All this privilege of education and exposure gave me a sense of responsibility. After graduation and a brief stint in banking, I joined Dasra full-time.

When you think about your work at Dasra, what specific problem are you seeking to solve? And how does data play a part?

What we’re trying to drive in India is: how do you invest in systems change? It was relatively simple in the beginning when we focused on how to support a nonprofit. But soon we pushed ourselves to achieve scale, and we realized you needed to have a systems approach.

Over the last 22 years, we’ve seen value in a shift to focus on collaboration, to focus on being an aggregator, and on working closely with the government. Because in India this collaboration is essential for scale. Government is the biggest funder, the biggest player in education, health, and other development areas: one has to have them as a key partner.

During that time, we’ve landed what’s really core to our mission: driving collaborative action to accelerate social change. Our vision is to transform India and help a billion thrive with dignity and equity.

Data plays a vital role in our work, but it’s been a journey for us and the sector. It can be a bit of a buzzword: everybody feels we’ve got to say data, but what do we actually mean? We’re continuing on this journey but we’re still very nascent in India with not only how data is collected, but how we are using it for learning, decision making, and improving our work.

Lately, we’ve been focused on what we call data empowerment, which is bringing the collection of and the utility of data directly back to the communities so that those feedback loops are stronger. We want to ensure the data is in the hands of those who can demand what services they need from the government or from the NGO sector. We believe that for using data in the service of society, it is important to reframe boundaries by involving communities and building trust.

A challenge we face is the real underinvestment in the infrastructure that is needed to collect the data which continues to remain siloed, unavailable, and lacks credibility. Infrastructure is not an obvious place for funders. And even if you put in piles of cash for this kind of work, the capacity of organizations and the sector to absorb it is very weak. That brings us to the work that we’re doing in data for good, with Bloomberg and other players, which acknowledges that data capacity needs to be built to empower communities. The opportunity is enormous when you see how advanced the private sector is in the use of data, data science, and AI. It’s clearly not the same in the social sector. Building that bridge between these sectors and finding a way to accelerate that talent and capacity for the social sector is a real opportunity here in India, given that data has often been considered the new oil.

Did you encounter any obstacles to pursuing social impact work?

I think the biggest challenge is that it’s actually not about ideas. And I may even venture to say it’s not so much about innovation, although we keep saying that. The challenge is actually taking an idea or innovation and delivering and executing it.

We as a sector underplay the importance of core skills like project management, data management, and people management. Building teams is critical. All of these things are often taken for granted in the social sector when you have a big idea and passion and inspiration to effect change. You need to invest in the innovation side, but also in the nuts and bolts of what it takes to execute.

Another obstacle is figuring out the right balance between what to build yourself, and where and how much do you partner. We don’t do enough partnership in the social sector, even though we’re far more resource-constrained. 

Finally, the pace of change can be frustrating. You’re always trying to shape outcomes. For example, in the work that we do with adolescent girls, we seek a common set of outcomes: delaying the age of marriage, delaying the age of first pregnancy, and keeping girls in school past grade 10. And then looking at increased agency and employability.

Clearly, it takes generations for these kinds of outcomes to move. So, there’s a lot of work to be done — with data, and otherwise — on proximate indicators, for people to actually have a sense if something is moving in the short term, and is ultimately going to lead to that outcome. Social change takes time.

Here in India, this work is affected by patriarchy, cultures, and traditions, and none of that changes overnight. That has been a challenge for myself and for the sector broadly: staying motivated and on track for the outcome despite a slow pace of change.

Has being a woman leader posed a challenge?

In certain ways, it’s been a challenge. But I have a double challenge of not just being a woman but also one with an accent. Is she really Indian? Is she a foreigner or not?

It works both ways: that double challenge can also be a form of privilege because I don’t have to fit in. My approach has been to figure out where to play and where my husband, Deval, might be more effective. Sometimes it’s not about making the point about gender or pursuing that level of equality. It’s ultimately pursuing, well, what’s going to get us there?

Times have changed, and it is better. But I think what’s incredible about India, not in a good way, is that the patriarchy is strong everywhere. It doesn’t matter what caste, what class, what status you come from. There is still a stigma about women working.

Do you have communities of people or resources that bolster your work? Are there communities or networks that not only strengthen you, but also you can go to when wrestling with a challenge?

There aren’t many networks one can easily tap into for the social sector. I recognize that I’m speaking from a tremendous amount of privilege that I am able to have a broad network of access to bankers or to Harvard alumni. It’s been great to see over the past 20 years, the openness with which these kinds of networks now want to engage in the social sector. People see an opportunity for purpose, to use their networks to make a difference in some way but are lost on where to start. That’s a role we’ve played, in making those connections.

But I’ve learned to be patient, because along with all that desire for purpose comes a tremendous amount of arrogance. I think it’s humbling for a lot of these networks and folks who start to engage with the social sector to see some of these challenges and come in and not be able to effect change. Maybe you can move fast in an M&A deal but if you’re trying to advance adolescent girls’ education, there’s a lot more complexity.

Other networks for us include corporate networks, like NASSCOM (National Association of Software and Service Companies), which brings technology companies together, or Family Business Network because we work a lot with families and family offices. There’s demand for purpose when they have liquidity events or when the next generation wants to get involved in philanthropy.

We’ve spoken about your math and STEM background. But are there other non-data science or math skills that have benefited more than you expected?

It’s amazing, and it’s quite sad how the more you grow older or grow in a sector in your leadership, the further you are from some of these harder skills you used every day when your career started. For a while, I didn’t fully appreciate the importance of the data and statistics foundation I had. And now it’s come full circle: as I think about and lead collaboratives for Dasra, whether it’s our work with adolescents or in urban sanitation, I can understand the value of data aggregation and statistical analysis and the value those skills bring to the sector.

You cannot underestimate skills that enable leadership. I was fortunate to attend HBS, where I experienced the rigor of leadership thinking and training, and in retrospect, it has been hugely valuable. I learned from going through all those cases, seeing how as leaders you have to make decisions with a very limited amount of information. Because of my experience at HBS, I started the Dasra Social Impact Program about 12 years ago to bring this training to others.

Ultimately, both hard and soft skills propel people and organizations. We must also think about what it takes to build a culture geared toward social impact. One idea is using voice and influence to bring people into a sector where not everyone sees opportunities for themselves. It’s to take on that responsibility.

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

I would say, do it. You won’t know till you get your hands dirty. It’s really important to speak to people because sometimes you go into this with a set of assumptions. Meet as many people as you can to get a broader perspective on the social sector.

And remember: it’s not all or nothing: Entering the social sector does not have to be like jumping off a cliff. You can start by doing small things, and there are different avenues to getting involved. Everyone can start with a different level of involvement with a different sense of purpose.

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

There are probably two things. One is definitely increasing the data talent and capacity for the social sector. Our ability to accelerate social change and systems change will come from an investment in accelerating data talent and capacity, without a doubt. But I believe it will have to come from partnerships with both the private sector and the government.

If we tackle data on our own in the social sector it will limit the change we can see, and where the government can invest. We need to ensure that we as a sector are open enough and that the government is open enough to build that partnership. We need to collect the data and use what we learn where it’s needed without worrying about looking bad. We need to agree to be transparent about the data to make very visible what’s not working.

The second big thing is using data for storytelling. So many critical drivers of our sector come from the heart, and rely on inspiration — and it should be that way. Data will help us tell stories that cause us to question where resources are going and spur us to move resources to where that impact can be. We need to make better use of data to tell these stories that resonate and inspire action.

What’s your daily or weekly read? It could be a guilty pleasure, or it could be pure social impact, all business?

I read HBR faithfully – I’m a devotee of the feeds and am always looking at the research into culture and teams, as I try to keep and grow leadership in our organization.

And right now, I’m reading The Power of Regret by Daniel Pink. In part, he suggests framing regret as an opportunity for growth and learning. My career is defined by finding ways to continually grow and learn – so I found that an interesting approach!

The post Pathways to Impact: Neera Nundy appeared first on data.org.

What role or sector did you move from — and how?

I come to DSSI from the theoretical end of the spectrum. I studied mathematics for my bachelor’s degree, and I loved it. But I realized quite early in my career that I wanted to do something more applied. Rather than doing abstract math and creating new theorems, I was eager to apply math to real-world problems. This tied in with a longstanding interest of mine in how people make decisions, and how this decision-making connects to biology.

I moved first from math into the field of computational neuroscience, which basically sits at the intersection of math, computer science, and neuroscience. My master’s and Ph.D. were in computational neuroscience, and very specifically research into memory processes. We were studying how we can remember episodes of our lives, even for a very long time.

Wait, what’s the answer to that!

Really, it’s all about good sleep! That’s really important to consolidate past memories. All these processes actually happen during sleep — not in the dreaming part, but in the non-dreaming part.

Neuroscience is a fascinating field, but it’s also not so applied in the sense that the research takes a long time to be proven or unproven. To do the science well, you need to wait a very long time to find out if the models that you created are right or wrong. All this waiting didn’t appeal to me. At the same time, I developed an interest in applying data science to all sorts of topics. What I was doing in computational neuroscience was already data science because I worked with large quantities of data. I had to analyze the data and create models out of that, and I found it very fascinating that you can use practically the same models for very different topics.

From neuroscience, I moved into a private-sector education role, working as a data scientist for a couple of years here in Zurich. During that time, I volunteered with DataKind for about a year, which gave me my first experience in the development sector – and I absolutely loved it. And then I took this position at BASE – my route to data science in the social sector has not been a very straight line!

How did you hear about the BASE job? What are you working on today?

I first learned about the BASE role on a mailing list about social impact jobs here in Switzerland. At that time, I was working as a volunteer with DataKind, and heard that other volunteers were reviewing the technical proposals for the Inclusive Growth and Recovery Challenge – which included BASE.

For those who don’t know, BASE is an not-for-profit organization which for more than 20 years has been developing business models and financial mechanisms to drive investment in climate change solutions. Thanks to the data.org Challenge, we have now moved more into data and data science, bringing together agriculture and climate change solutions. That pivot toward innovative use of data is why I joined BASE. 

I work as a data scientist and technical lead of a project called Your Virtual Cold Chain Assistant (VCCA). Your VCCA helps smallholder farmers to get access to sustainable cooling. We partner with local organizations who own, maintain, and operate cold storage, that is, refrigerated containers powered by solar.

We’re trying to drive change with Your VCCA in two ways:

• One is a pay-per-use business model, that enables farmers to bring their produce in cold rooms instead of storing it outside, without an upfront investment of buying the fridge or the container. They can just bring whatever they have harvested and store it there for the number of days they want, and for a fixed price per day and per crate.

• The other piece of the equation is a mobile application, which helps service providers offering cold storage manage the room digitally. Previously, they used a register where they manually entered all the information about the farmers who came in, what they brought, etc. As you can imagine, it’s very hard to monitor, especially if you’re not physically at the cold room, and if the cold room is in a place that it’s not very accessible.

Data science plays an important role here. First, there are machine learning models that help predict how many days the produce is going to be good if it were outside, versus inside the cold room. This helps people understand the benefit of cold storage. And the second component is informing cold room users of market prices around the area. Knowing both the remaining life of the produce and the forecast market prices, smallholder farmers can make informed decisions about when and where to sell.

This work addresses two important social problems. The first one is that a huge amount of food that is produced and harvested gets spoiled, and one of the main reasons is the lack of cold storage. I didn’t know about this before starting this project, but the figures are something like 30% to 40% of food is spoiled post-harvest, with peaks of up to 70%. The number sounds crazy to me. But apparently for some crops, and in some seasons, it can be true.

The second social problem is that farmers are often forced to sell at a very low price because they have no alternatives. This project really tries to tackle both aspects — reducing food loss by providing cold storage, and also increasing the income of the farmers by providing them with new information about when and where to sell.

You’ve described a path from math to a neuroscience Ph.D. to a private-sector job to volunteering with DataKind to BASE. Was there anything that you feel blocked your career entry or your progression in this field?

It sounds more complicated than it was. I felt it was kind of natural to go in the direction that I liked at the moment, moving on as I found out about something that was more interesting or more useful. I’ve been fortunate, but a blocker for those wanting to go into data science for social impact is definitely the lack of jobs in this sector.

Today there is more and more available data, even in parts of the world where, historically, there hasn’t been a very strong open data policy. However, there are not as many projects that leverage this data, especially in the social sector. There might be a few positions where you are a data analyst or a data scientist, but sometimes there isn’t a clear data science-able problem or defined project.

While working in the private sector, I learned that I really like to work on a defined project with a product, with something where you can engage with users, and hear their feedback to build a strong solution. I wouldn’t like to just do analysis of some data for the sake of it, or maybe in the hope that one day we can use the knowledge for something. And so the job that I found at BASE was very special in that there was a defined project, and there was a clear product, the mobile application for cold storage. I know many data scientists who would like to go into the DSSI sector. But one of the hardest things is to actually find this kind of position. It’s getting better, but I think there is still a long way to go.

What community of people or resources would you say bolsters your work? What community do you engage with or rely on?

Two groups stand out:  the first is a community of data scientists who come from an academic background, as I do. We did a bootcamp in London together a few years back. It’s called Science to Data Science, and it’s a really amazing environment because you get to know a lot of data scientists who come from academia.

The group shares perspectives on why they ended up in academia, and why they now want to enter a new field — the motivations are usually quite similar. It’s a very supportive network. We have regular meetings, and we have a chat where, if you have a technical problem, someone can always help. It’s the first place I would write to with an issue.

Also, it’s nice that we all started basically the data science journey in a similar way. It’s inspiring to see how people’s careers are evolving over the years. People in data science change jobs quickly. In this group, on average, I would say that we all have changed jobs at least once. It’s very interesting to see how career pathways emerge.

The second community that I rely a lot on is DataKind. It has changed the way I think about data science problems. Working in the development sector, you get exposed to complexities that are linked to data science which normal data scientists working in a private sector might not notice. For example, when designing our app we have to consider issues related to accessibility like internet connectivity, road connectivity, literacy levels — as well as how we can build models when little data is available.

And these considerations are rare in the private sector big data world. A data scientist coming from the private sector would tend to overlook these. But having worked with DataKind people and projects, you develop an eye for this, and you learn to pay close attention to those topics. That’s definitely important.

Women are still underrepresented in data science. Are there any gender-based groups you belong to?

There are two. One is Women Who Code, which is not for data scientists, but it’s more for women in any sort of tech job. There have been only virtual gatherings over COVID time, including very useful seminar series, which has been nice, but I get a bit annoyed by that right now.

And the other community is the Women in Data Science group, which is a bit academic as many people come from, or are currently working in, academia. But there is also a growing community of professionals from the industry side, which I think can trigger interesting exchanges.

Here’s what I am missing: a group of professionals working in the development sector who are data scientists. Not data analysts, but focused exclusively on machine learning techniques for the development sector. And this is something I haven’t found, virtually or in person.

It’s clear that your specialized degree, your trajectory from math to neuroscience to data science, represents your primary contribution to the sector. But is there a non-data science skill set that offers the greatest return on your work?

What helped me a lot is the fact that I’ve been educated from very early on, already when I was transitioning to neuroscience, that the world is very interdisciplinary. What this means in practical terms is that you really have to learn to communicate with people who have a very different background from yours, and also with people who might be maybe technically very strong, but on another side of the technical world.

To succeed in this environment, you have to be open to asking stupid questions. Maybe they are experts in computer science, but you’re not. In turn, you also learn to expect stupid questions. This means that you cannot really take anything for granted. You have to make sure that whenever you’re explaining something, you are providing the relevant background and context. This teaches you to boil down a complex problem, or a complex model that you have designed, in simple steps so that people who have not worked on this model can understand.

This is an important skill, it’s especially relevant now because we are working right at the intersection of different fields. There are people who are experts in the modeling of, say, fruits and vegetables, some who are experts in the climate part, and others who are experts in the engineering of how the cold room functions — with the sensors and everything. And we have to bring everybody together so that we can design the app in the right way. The ability to work effectively with people from different backgrounds is a skill set that provides huge benefits.

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

I think it’s important to build a solid foundation as a data science practitioner. My advice would be to first look for a mid-sized organization, where there are already other data scientists to learn from. It’s really essential that you exchange ideas with other data scientists so that you can build up your expertise before you try to go into an NGO or a smaller organization where you might be the only data scientist.

This might mean you maybe spend a couple of years not in the most exciting field, but you develop a lot of skills which you know will be useful later on. It can be tempting to jump straight into cool DSSI applications, but I think this strategy pays off in the long run. You build a better data science foundation to make a bigger impact when you come to DSSI.

And the other advice I would offer is to be aware of the fact that data science is a fast-moving field, especially the link to the data engineering part, and everything that concerns the deployment and maintenance of the models into production. It’s a field where the technologies evolve very quickly, so you have to be open and willing, to always stay trained, which might mean volunteering, doing online courses, going to meetups.

What’s the next big thing in DSSI that you see?

I’m personally very excited by the potential for geospatial data and satellite imagery. I think that’s really something that has gained a lot of attention in recent years, because a lot of satellite images have been made publicly available for the first time. And satellite imagery has become more and more precise both in the spatial and temporal dimensions.

What we can do with this data has also grown incredibly. While it has not fully developed yet, I see a lot of potential in applications like monitoring deforestation, or anything that’s related to index-based insurance for agriculture. How can you monitor conditions, especially in regions that are hard to access, and how can you make the most out of this data? How can you apply machine learning to this data?

Related to that, I see so much potential in visualizations that rely on spatial data, especially for a non-technical audience. My experience so far is that stakeholders get more engaged if you can present data on a map, instead of showing them graphs or tabular data.

We recently developed an interactive map of India, where we’re displaying different types of data coming together from various sources such as roads, connectivity data, market and census data. The feedback that we’ve gotten is that it’s a great benefit to visualize in one place data sources that otherwise would have stayed very compartmentalized.

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

On a daily basis, I follow LinkedIn. It took me a while to get into the right spaces and the right connections, but now there is a lot of relevant content that I look at there.

More on a weekly or bi-weekly basis, I like to listen to the 80,000 Hours podcast. I find it very inspiring because it mixes different perspectives related to how to make a positive impact in the world. And some of these conversations can get quite technical, covering topics in data science, ethics in AI, and current research in DSSI.

I very much appreciate their openness in discussing the reality that working with data can have a huge positive impact, but that we must also watch out for certain pitfalls. For example, we need to ensure we are creating applications that are inclusive, bringing in the voices and perspectives of the communities we serve. We also have to check the completeness and the reliability of the data sources we are using. It’s essential that we reflect on these potential problems, to ensure that we hold ourselves accountable for using the best data science approaches to drive meaningful social impact.

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Tell us a little bit about yourself. What brought you to work in data science for social impact (DSSI)?

I’ve always been driven by a passion for economic development. I came into this field after completing my master’s in international economic policy at Sciences Po in Paris. My studies focused on economic policy in Africa, with an emphasis on public-private sector collaboration in infrastructure and service delivery. I believe that to really make progress against the challenges that we face as a world and to meet the sustainable development goals, there really needs to be more collaboration between the public and the private sectors. This needs to be underpinned by companies seeking to go beyond mere short-term value-creation for shareholders, and a desire to contribute to solving the long-term challenges the communities they work in face.

One challenge that stood out to me across a number of different experiences in developmental economics is the issue of data scarcity.  I vividly remember researching different economic trajectories of various African countries and noticing that certain data sets were out of date. There were certain analyses that I couldn’t rely on because I just wasn’t sure whether the data was up-to-date. This frustration set me out on a path to DSSI.

Are you working on the data scarcity challenge today at GSMA? What else keeps you busy?

I am! Recently, my team and I published a report titled Innovative Data for Urban Planning, The Opportunities and Challenges Associated with Public-Private Data Partnerships. In that report, we highlight how data scarcity affects urban planning and inclusive urban service provision, and how innovative data sources such as mobile network operator data, remote sensing data, utility services data and other digital services data, could help meet the challenge. Accessing this data could provide huge public benefit. There’s so much we could do with more and better data, but this requires tackling and overcoming barriers to public-private data collaboration.

Unfortunately, today a lot of innovative data held by the private sector isn’t used to inform public policymaking. If we look at some of the challenges that transport authorities in many cities face, for instance, rolling out a new bus rapid transit system, which requires understanding where to place the optimal bus stop locations, oftentimes these kinds of decisions are being made in a context of data scarcity. So by partnering with private sector data providers, such as mobile operators and ride-sharing companies, and also by engaging in innovative survey methods that use mobile tools, public sector transport authorities can leverage much more information, and make better forward-looking decisions.

Personally, I gravitated toward the topic of data in urbanization because 90% of urban growth from now until 2050 will be concentrated in Africa and in Asia. Africa hosts many of the fastest-growing cities in the world. And for me, that rapid transformation presents not only an important challenge, but also an exciting opportunity: 2/3 of infrastructure investments from now until 2050 in African cities are yet to be made. So, there’s a huge opportunity for urban policymakers across the continent to make evidence-based planning decisions. These decisions would  ensure that cities across the continent become engines of economic prosperity and social mobility – and that could yield returns over the coming decades. To do this work well, cities need to invest in their public sector data capabilities and build enduring public-private partnerships around data.

We hear a lot about the value of and potential for partnerships in our Inclusive Growth and Recovery Challenge conversations. It’s not always easy, is it?

Partnerships require a lot of effort and communication. There are a range of barriers to successful partnerships: the need to take local contexts into account, the digital capacity of the public sector, aligning different stakeholders with competing priorities, identification of economic models financing those partnerships. Launching partnerships correctly and stewarding them is never easy, but the impact can be enormous.

Our work at GSMA AI for Impact program and the GSMA Digital Utilities program is driving public-private partnerships that can allow the public sector to make use of different kinds of data sets. Obviously, what we’re highlighting throughout that work, though, is the complexity of creating and maintaining those partnerships. Saying, “build a partnership” is not enough.

What in particular has fueled your career progression? Any blockers you’ve overcome?

I’ve embraced multidisciplinarity throughout my career, and it’s proven to be a true advantage. I’ve always thought that in order to solve complex challenges, you need to be open to different schools of thought, and different fields as well. As a result, I’ve been passionate about not just immersing myself in developmental economics, econometrics, and data analysis, but also in political economy, sociology, economic history, understanding postcolonial societies, and so on.

That multidisciplinarity has really helped me to understand why certain projects fail, and to have a sense for the kind of implementation challenges you might face when something looks very good on paper from a data scientist perspective.

I’ve also benefited from studying and then working with a lot of inspiring people solving different kinds of problems. And that breadth has helped me gain the perspective that a lot of paths towards progress aren’t that linear – there are lessons to be learned from setbacks and course changes along the way.

Finally, I’ve always been quite passionate about elevating the representation of people of color within the international development sector. I’ve been lucky enough to have older mentors that are also people of color working in the sector. By engaging with some of those mentors, I’ve really gained a lot of knowledge and was better positioned to navigate some of the challenges one faces as a person of color in the sector.

I understand that you have strong mentors, and benefit from learning from those who have come before you. But is there a broader community that you rely on that bolsters your work?

That’s a great question. My work currently sits at the intersection of a lot of different themes. There isn’t necessarily an existing digital-utilities-mobile-big-data-in-cities community!

Instead, I try to learn from different communities that do exist. When working in big data — and any field that’s related to digital innovation — it’s really important to pay attention to the frontier of research. What’s going on at the cutting edge? I make sure to follow a wide range of researchers because it allows me to also understand where the field is moving, and what opportunities there might be in the future.

On the flip side, what gives me a lot of inspiration is to not only be informed of the latest theory, but also to develop a deep understanding of the problems we seek to solve. Our team at GSMA focuses on supporting urban resilience in low- and middle-income countries. And we’re specifically focused on improving the livelihoods of lower income urban populations. When you look at algorithms being created, and you look at the challenges that these low-income populations and cities are facing, there’s sometimes a stark disconnect: people can’t eat an algorithm, right? As practitioners we need to make the work very relevant to the problems that communities are facing. Staying close to those realities drives my work, and ensures we are together developing effective programs and interventions that can actually be sustainable, and accurately respond to challenges.

What non-data science skillset has offered the greatest return in your work? 

I’d have to say that being trained in political economy of development has really helped me because understanding the way decision-making in governments actually operates is critical for success. It’s critical also from a data scientist’s perspective, because obviously if you’re in partnership with a government institution, you want to understand the incentive structure that impacts that public sector stakeholder to gain adoption. But more significantly, you also want to ensure that the outputs of your data analysis will actually be implemented and are able to support the decision-maker in their own context.

The need to understand the context of the public sector stakeholder is something we flagged in our report on Innovative Data for Urban Planning. We highlighted a range of different product outputs, ranging from reports to apps, to visualization, to a decision support system. Getting those product outputs right requires co-creation with the user of the output, which often is the public sector, from the outset of the project. Of course, throughout the product development there might be things that evolve, and interests that emerge, so it’s important to remain adaptable and responsive – but early alignment is critical.

Something else I’d like to point out – and to be more disciplined about myself —  is to make use of the amazing array of opportunities in online courses and additional resources that are now available for people that want to learn more about data science in the context of specific subfields and sectors. Last year I completed a fellowship on climate tech with OnDeck. and it really opened my eyes to the opportunity of big data use cases in the context of climate mitigation resilience and adaptation. There’s a lot out there!

What advice do you have for someone who’s new to the field, but interested in getting started in this kind of work?

My advice would be to not be discouraged by the fact that one is new to the field – consider that newness to be a feature, not a bug. Data science for social impact always needs new energy, because it benefits so greatly from people who bring experiences in other industries or different backgrounds of study. Combining one’s own unique background with a certain data science skillset can open the door to new use cases, and new ways of applying data. As a field, it’s something we encourage. It’s exciting that there’s a growing community of people with diverse skill sets and backgrounds that is entering the field; it doesn’t feel as lonely anymore!

Another important thing for people considering doing work in this area is that the data for good space is quite broad. You can work in big corporations. You can work for NGOs. You can work for enabling organizations. You can work in philanthropy. As a new entrant, it’s really important to find something that aligns with your values and sense of impact because there is a range of different avenues to impact in the field.

What do you see as the next big thing in data for science for social impact?

Within the GSMA digital utilities team, we’re quite excited about the opportunities associated with the Internet of Things and are planning to write a report demystifying some of the use cases on IoT. For example, the use of sensors in a water utility to detect leakages is becoming more common and relevant as hardware costs decline and the technological sophistication of the different suppliers’ increases. We see IoT and the data it generates as a powerful tool for social impact.

But then, more generally speaking, beyond the field of IoT, I think what we’re also going to see is more public-private collaboration on data sharing, and more institutionalized partnerships that go beyond pilots. Our goal is to help drive the creation of long-term partnerships that can deliver value to the public sector, while also creating benefits and clarity to the private sector stakeholders that are engaged in the partnership.

Just for fun: what’s your don’t-miss daily or weekly read?

I really enjoy the MIT Tech Review. They often write interesting articles about data science, but take a very critical perspective and look closely at themes related to diversity, bias, and societal implications of data and algorithms.

There’s also a publication that was recently launched called Rest of World. I really like it because it shares tech stories from the perspective of people living in emerging markets, and takes into account the opportunities and risks associated with digital innovation. This is so important, given that we’ve seen that even in the richest countries of the world, digital innovation and data can have a lot of negative externalities. If we look at topics related to AI applications and policing in the context of institutionalized racism, there are important moral questions that we have to think about as a society. I appreciate the publications that open our minds to those important perspectives and considerations.

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