Tell us about the role of data collection, analysis, and action in your work. What significant findings have emerged from it and what impact or outcomes have you observed? 

At Legal Dignity, we use data collection and analysis to address gender-based violence and support sexual and gender minorities in Malaysia. By gathering and studying relevant data, we identify areas of concern, monitor progress, and create evidence-based interventions. 

These insights guide the development of policies and laws promoting gender equality, protecting LGBTIQ+ individuals, improving access to justice, and tackling systemic discrimination. 

Data also helps prioritize awareness-raising, public dialogue, and advocacy efforts for positive change. With data-driven knowledge, we can target specific intervention areas and implement focused programming and projects to improve outcomes for the LGBTIQ+ community.

Our data revealed significant patterns of gender-based violence, discrimination, and harassment against LGBTIQ+ people in Malaysia. A 2022 survey showed disparities in their access to justice, influenced by other social identities like race, religion, and socioeconomic level. These systemic barriers must be addressed to improve justice system accessibility and equity.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

Data is the lifeblood of the work I do at Legal Dignity, and through my experience, I’ve become aware of a significant and worrying issue: the acute lack of gender-disaggregated data in government datasets. 

This not only means we can’t precisely identify the number of LGBTIQ+ individuals harmed by the inaccessibility to justice, but it also makes it difficult for our organization to analyze the effectiveness of existing programs. 

To bridge the gap in gender-disaggregated datasets, we are taking on the responsibility of developing and utilizing our own data. We launched a nationwide survey to determine the scope of injustice in relation to access to justice among Malaysian LGBTIQ+ people. 

Our data collection provides crucial insights into the LGBTIQ+ community’s experiences and needs and allows us to develop and tailor targeted programs, addressing the specific challenges that our data reveals in accessing justice and navigating the legal system. 

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

LGBTIQ+ individuals often face significant stigma and discrimination in various countries, including Malaysia, where same-sex sexual behavior is criminalized and freedom of expression and association for LGBTIQ+ individuals is restricted.

For these reasons and more, people hesitate to openly share their experiences, and the hidden nature of this population makes gathering comprehensive and inclusive data challenging. Additionally, the lack of sufficient government support or funding for data-gathering activities targeting this population further compounds the difficulties.

As a result, we approach data collection with the utmost care, recognizing that asking individuals about sensitive topics like sexual orientation and gender identity can put them at real risk. Researchers must navigate intricate ethical considerations, emphasizing confidentiality, privacy, and participant safety, which ultimately impacts the accuracy and reliability of the data collected.

To overcome these challenges, building trust and cooperation with the LGBTIQ+ community is crucial for successful data collection, and establishing partnerships with community leaders can help facilitate data collection efforts so that, ultimately, we can get larger, more representative datasets.

How did data.org’s Gender Data 101 course influence your perspective on your work, and what were the most significant insights you gained from the course?

The Gender Data 101 course begins with the basics — how does gender intersect with race, class, and sexuality? The training helped me fully comprehend the scale of gender complexity, which is essential for data analysis and interpretation.

Moreover, the course provides step-by-step processes and best practices for conducting gender-responsive data collection, ensuring data is appropriately disaggregated by gender and other relevant variables. By following this process, I have been able to identify and address gender biases in datasets effectively. The course also includes real-world case studies that have greatly aided my understanding of theory-to-practice implementation. 

Armed with this comprehensive framework, I am better equipped to recognize and rectify gender biases and disparities within datasets. As a result, my work fosters a more accurate and nuanced understanding of the issues at hand.

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data?

By incorporating the data lifecycle methodology that the Gender Data 101 course taught, we have been able to ensure that gender analysis is integrated into every stage of our research processes — from the planning and design phase to data collection, analysis, and interpretation. This methodology also helps us identify potential biases, gaps, and limitations in our research, allowing us to generate more reliable findings.

The gender analysis frameworks in the course also offer us a structured way to examine the differential impacts of our research on different gender groups and, in turn, will inform our advocacy as we work to better understand how gender intersects with other social identities and influences access to justice.

To ensure that these valuable insights and approaches are consistently applied across our organization, we are in the process of translating and summarizing the course content into a policy for research work. This policy will serve as a reference and guide for all researchers at Legal Dignity, ensuring that gender analysis is a fundamental component of their work.

What are your hopes for the future? What will more, better, and better-applied data change? 

Improved Decision-Making: With access to more comprehensive and accurate data, decision-makers across various fields can make more informed and evidence-based decisions leading to more effective strategies and policies to support marginalized groups.

Evidence-Based Social Interventions: Better data and its effective application can contribute to addressing pressing societal challenges, such as poverty, inequality, and climate change through evidence-based policies, targeted interventions, and sustainable development initiatives.

Enhanced Efficiency and Productivity: Better data and its application can streamline processes, automate repetitive tasks, and optimize resource allocation leading to increased efficiency, reduced costs, and improved productivity in organizations working to make social change. 

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Tell us about the role of data collection, analysis, and action in your work. What significant findings have emerged from it and what impact or outcomes have you observed? 

I work with both public and private organizations on how to use gender data to inform their work. Collecting and analyzing data is key from the beginning of any engagement — not only to provide facts to those who are still skeptical, but also to identify an organization’s specific needs and measure the results of any intervention. 

Just asking the question “Do you know how many women are part of your clientele or workforce or recent promotions?” moves the needle because more often than not, the answer is “no” or “yes” but only in absolute numbers without real analysis.

Recently, I worked with a financial institution that wanted to strengthen the gender perspective of its client portfolio, particularly in rural areas where they suggested that the local culture made it difficult to include women. Understanding the local dynamics was critical, as many of the potential clients were actually partners of the current male clients. 

The problem was that the institution had a credit limitation per household. In other words, the credit provided to the man limited the amount left to be offered to the women of that household. Data was key, but going beyond sex-disaggregated data and understanding social dynamics was also essential to identifying a solution for women´s financial inclusion.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

It became clear to me that gender was a critical component of solving complex challenges when I kept encountering the “whys” every time I would assert a gender lens. I’d be asked: Why are we talking about gender in water and sanitation? What does gender have to do with this technical issue? The more technical the person was, the more I realized I needed to show facts and numbers to get the point across. 

But not just any number. I came to realize just how important it is to consider biases and understand the context around numbers to provide accurate and insightful information. How we present, collect, and interrelate data can create entirely different stories.

Realizing the power of data analysis motivated me to learn as much as possible. This understanding gave me the tools to convince others that applying a gender lens makes our economy and community stronger. 

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

Anticipated: Official data is insufficient and changing these systems is slow and expensive. Governments and corporations are just starting to collect some key gender indicators and understand how to analyze them. Collection methodologies differ from one country to the other, or even between organizations, so comparison or additionality is difficult, if not impossible.

In smaller interventions, it is expected for participants to answer surveys to get data, and it is hard to get a high response rate. Usually, participants (for instance, women entrepreneurs) have little time for additional activities or do not check emails. There is often a lack of trust or a misunderstanding of what is being asked. The design of surveys is not as straightforward as it may seem.

Unexpected: There is, at times, a disconnection between who designs the indicator, who collects it, who has the information, and who is going to use the data. So, often we find forms incomplete or systems that don’t understand how to process the information received. 

How did data.org’s Gender 101 course influence your perspective on your work, and what were the most significant insights you gained from the course? 

I decided to take the Gender 101 course because I believe in continual learning and I wanted to obtain technical insights to improve my work. One of the learnings I have applied is the understanding that the people who provide the data are the owners of the data and we are asking them — for free — to provide their valuable time and share their information. It is important to make clear why we are collecting data. And it is important for data collectors to pause and ask themselves why. Is it just because I find it interesting, or is that data going to benefit those who provided it? I think it is very important to reflect on the use and need of data before designing and requesting nice-to-have indicators.

Additionally, I came to recognize that what we measure and what we publish is a political decision. There is a reason behind what we collect and the story we expect to tell with that data. Gender 101 helped me to become conscious of this — both when I make decisions and when I read research papers on gender statistics.

Finally, biases. In my job, I talk a lot about biases, but I had not related them to data and analysis before. Or at least not so clearly. This has been very eye-opening, and I have taken bias into account in the design of a survey just recently. 

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data?

I design surveys and questionnaires as part of the projects I manage and I am constantly reading research on gender topics in different sectors. After taking Gender 101 I now know how to identify and avoid potential bias in data collection and analysis

I am also eager to learn more about visualization techniques after the course as it was clarifying to understand that the way we present data can actually misrepresent the truth if we are not careful. 

I will also make sure that no private data is shared without the permission of the owners of that data. This part looks obvious, but it is so important. I intend to take a course on cybersecurity to continue learning about this issue.

What are your hopes for the future? What will more, better, and better-applied data change? 

On the one hand, I wish for a future in which more gender data is collected and taken into consideration intersectionally to create equity across policies, products, services, solutions, and more. The use of this data should benefit those who have provided it, and they should have ownership of the data. But for this to happen, there must be a real understanding of what the data is saying because the same data can be used to tell different stories. 

There is also already a lot of interesting data available, but it is not shared or known. Before starting whole new research, asking the same people, again and again, let’s search what data is already available and could be useful for the objectives we have. And let´s share lessons learned to make the process more efficient. 

The post 5 Minutes with Cristina López Mayher appeared first on data.org.

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed? 

My work at the Aspen Network of Development Entrepreneurs is focused on building a stronger ecosystem for entrepreneurs in India and the broader South Asian region through strategic collaborations with the members of the organization as well as convenings, research, and advocacy. Over the past few years in our work, the urgency and importance of incorporating a gender lens to understand what challenges women entrepreneurs face has become very apparent and we are working to help bridge these gaps at a systemic level. But to make real change, access to gender-disaggregated data (both academic and practitioner-friendly) becomes pivotal to ensure any interventions are evidence-driven to the greatest extent possible. 

The issue briefs that I’ve co-authored highlight the status quo of women entrepreneurship in India and the significant challenges that women entrepreneurs face, most notably the financial gap. According to the IFC, this gap is nearly $320 billion in developing countries, and what’s more, they estimate that 70 percent of women-owned small and medium enterprises have inadequate or no access to financial services. It is also widely documented that the female labor force participation rate in India is among the lowest in the world. 

As we grapple with these numbers and qualitative evidence that women entrepreneurs continue to face disproportionate obstacles to success, the role of data collection and analysis becomes all the more critical to paving the way for the evidence-based change we are seeking.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

Data plays a strong role in building a narrative toward driving change. More often than not, it is not just the mere numbers but how it is presented to the stakeholders involved in decision-making that makes the real difference. For example,  when the statistics on the female labor force participation rates were released, the numbers shocked me since in my silo as an educated, urban-dwelling woman in India, my friends and I were the exceptions and not the norm. Seeing this data was a major shift and inspired me to get my hands on as much sex-disaggregated data related to entrepreneurship as possible to begin understanding the extent of the gaps in the ecosystem.

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

There is not enough (and in some cases, none at all) data! To date, there is a lack of extensive detailed survey data on women’s entrepreneurship (covering different stages of entrepreneurs across all geographies) in the country to help provide a baseline for stakeholders to build relevant support programs and initiatives.

How did data.org’s Gender 101 course influence your perspective on your work, and what were the most significant insights you gained from the course? 

The Gender 101 course helped build a framework for how to understand gender and data, along with concrete action steps that one could incorporate into existing program interventions at their organizations.

Plus, the access to valuable resources — both through the course as well as those shared by the cohort participants — was truly helpful. 

One of the main insights for me was how to contextualize the data. More often than not we fall in the trap of presenting too much data in the belief that it helps in making our case stronger. Based on the frameworks from the Gender 101 course, I now know it’s more important to keep the stakeholder in mind and analyze and present data accordingly. I also better understand that data is not necessarily always objective — it depends on how it’s collected, who analyzes it, who presents it, and who it is presented to.

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data? 

My initial steps will be to simply engage with as much data as is available to comprehend the status quo of the ecosystem. What are existing support systems for women-led businesses looking like? What are the major systemic gaps? What are the opportunities that could catalyze the growth of existing businesses? Next, I would help bring to the forefront the role strategic collaboration can play in supporting women entrepreneurs, as no single organization can take this mandate on alone. 

What are your hopes for the future? What will more, better, and better-applied data change? 

More women joining the workforce. More women retained in the workforce. More women-led businesses being built and scaled towards greater impact and significantly more capital flowing into this space. 

The post 5 Minutes with Saipriya Salla appeared first on data.org.

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed? 

At CCHUB’s Tech & Society Practice, we firmly believe that safe and unrestricted access to the Internet is a fundamental right for everyone. My work involves helping people grasp the basic concepts of digital security and emphasizing the importance of online privacy.  

To accomplish this, I collaborate with local partners who assist us in implementing various training, intervention, and technical support projects for organizations across Africa. What’s been interesting is that as we’ve assessed the impact of our initiatives, we’ve been able to identify a correlation between women’s online security and their experiences within broader societal circumstances. 

For instance, Nigeria is experiencing extreme inflation right now, and a substantial number of women sell groceries in the markets — they’re the ones doing most of the trading, and they’re also the ones financially supporting their families.

But the current economic situation is creating new financial constraints, which can cause or exacerbate domestic abuse within these women’s households. At the same time, they are confronted with the need to open bank accounts or secure online loans — something they’ve never done before — which puts them (and their data) at great risk for cyber targeting.  

That’s where our cybersecurity training can help. We focus on empowering women to become technically savvy with the ultimate goal of becoming financially independent. Because honestly, that’s the easiest way to get them out of the situations they’re in at home. And that’s what I’m really passionate about — making sure women can improve their personal lives.

When did you first recognize a gender challenge that could be addressed through data? 

When I started this job, my primary goal was to ensure compliance with security policies. But, during the pandemic, I realized there was a deeper need for people on the ground to learn about online safety — and that this was something that I could do to really contribute to the broader culture.

The number of people using the internet or smartphones grew rapidly during the pandemic when face-to-face interaction was sometimes impossible. And with so many new, and often naive, technology users comes an increase in online security threats — particularly our most vulnerable populations, including women, children, the elderly, and the LGBTQ community.

For instance, organizations and individuals working with human rights defenders often seek our assistance. These people are tackling issues that are considered outside of the mainstream, such as writing articles or posting social media about LGBTQ people in Nigeria, despite the laws that criminalize unions or same-sex marriages. Not only are these individuals at risk, but the people working to amplify their voices are also a target of online attacks — whether on their assets or even on their physical safety. 

What are some of the challenges of doing this work? 

One of the challenges I face is the perception among some cybersecurity and technology professionals that people-facing roles are not as serious or valuable. As someone who transitioned from a law-related career to technology, I have obtained cybersecurity certifications and developed expertise in both technology and communication. Bridging the gap between understanding cyber security and people is crucial because they are often the weakest link in information security. I strive to break down advanced online security concepts in a way that non-technical individuals can understand to keep themselves and their data safe. 

In addition to these challenges, the technology industry remains predominantly male. Even when women enter the field, they are often confined to non-technical roles or face biases that question their competence. Overcoming these biases and encouraging more women to pursue technical positions in cybersecurity is essential for fostering diversity and expertise in the industry.

How do you see your work with gender and data evolving in the future? 

I want to expand my work with organizations that focus on individuals, particularly women, who may have limited access to technology or lack knowledge about online safety. In the long run, I aspire to help people use the internet,technology, and data to improve their lives while also educating them about basic security practices that can keep them safe online.

What are your hopes for the future? What will more, better, and better-applied data change?

I’d like to see the development of a culture where digital security is ingrained at the organizational and personal levels. Data security should be an easy and natural habit for people so they don’t have to exert extra effort to protect themselves online — whether it’s simply enabling two-factor authentication or using strong and unique passwords across different social media platforms — small steps will make a genuine impact to safeguard those most digitally vulnerable, particularly women.

The post 5 Minutes with Wuraola Taiwo appeared first on data.org.

As the Director of Research, Evaluation, and Learning at Equimundo: Center for Masculinities and Social Justice, Dr. Taveeshi Gupta is leading groundbreaking research on the role of gender norms in perpetuating violence against women and children, creating gender-unequal environments at home and work, and reinforcing harmful gender identities for both boys and girls. 

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed?

At Equimundo I lead the global portfolio of research and support the collection, analysis and reporting of data focused on three interconnected pillars: violence against women, equity of care, and gender socialization. Alongside stand-alone research products, we also use this evidence and data to drive our programs and advocacy efforts.

One of our flagship research projects is the International Men and Gender Equality Survey (IMAGES), which began over 15 years ago and measures not just violence perpetration, but explores men’s and women’s gender attitudes. Through quantitative and qualitative data, we dive into various aspects of participants’ development of gender attitudes, such as their childhood experiences of gender roles at home, their health and health-related practices, household division of labor, men’s participation in caregiving and as fathers, and men’s and women’s attitudes about gender and gender-related policies.

What we’ve uncovered are intriguing connections and important linkages between gender attitudes and behaviors. For instance, in nearly 32 countries where a recent global analysis was done, men who held more restrictive gender norms also had more negative health outcomes. Conversely, we found that not surprisingly one of the strongest factors associated with men’s use of intimate partner violence was witnessing their own fathers or another man use violence against their mothers. And as adults, these men were more likely to abuse alcohol, to be depressed, to have suicidal thoughts and to be generally unhappy. 

When did you first recognize there was a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research?

My research journey started with a genuine curiosity about how immigrant parents in the US teach their kids what it means to be an immigrant and, specifically, what it means to be an American. But as I dug deeper, I noticed something fascinating: these parents weren’t just passing on lessons about being immigrants; they were also shaping their children’s identity as immigrant boys or girls. It’s like this blend of fitting in with a new culture while also adhering to gender norms dominant in American culture. For instance, the universal message that “boys don’t cry” is complicated in the immigrant context because it’s coupled with the message of fitting in in a new culture. It hit me that gender is learned — and parents, media, teachers all over the world unintentionally play a part in gendering their children. 

Then, around the same time two significant events happened that affected me personally. In 2008, ten Pakistani men associated with the terror group Lashkar-e-Tayyiba stormed buildings in Mumbai, killing 164 people. My father had been in one of the hotels ambushed by the group but luckily remained safe. A few days later, a group of young boys attacked a close friend of mine on the subway in New York City as part of a gang initiation. He was hurt but luckily he escaped. 

These events — where essentially a group of boys and men were behaving in ways that are not core to who human beings are — were a turning point for me. I began questioning everything. What causes boys and men to be willing to inflict violence? How do they learn to become these people? How is masculinity weaponized to create people capable of causing this kind of harm?

I had a deep core recognition that if we’re not talking about masculinities and collecting data on the experiences of boys and men, then we’re just not having a holistic conversation.

What are some of the challenges doing this work? Which were anticipated, and which unexpected? 

Data continues to be the best way for us to understand what’s actually going on when digging into systemic problems. And yes, having more data is helpful. But often research stops short at just quantifying care. That is so important but it’s hard to understand what is happening in people’s day-to-day lived realities. 

For instance, global data shows that women unequivocally shoulder the largest responsibilities as caregivers. So the default assumption is that men don’t want to be caregivers. But when you start collecting qualitative data at even a very basic level, this assumption doesn’t play out.

Through our qualitative research as part of Equimundo’s Equity of Care initiative, it was discovered that many men do want to be involved caregivers, but often are entrenched in structural barriers that constrain their ability to do so, such as socialization and gender norms, workplace norms, the gender pay gap, economic vulnerability, and the lack of paternity leave. So the solution needs to be both at individual level as well as at the larger, system and structural level. 

Once you start uncovering the daily lived experiences that reveal why this problem exists, you can begin to work with governments and communities to create conditions that allow people of all genders to be good and present and equitable and involved caregivers.

How do you see your work with gender and data evolving in the future? 

When it comes to the future of my work, I see a lot of exciting possibilities. The field of boys and men is continuing to expand and becoming more mainstream than ever before — but it’s also facing tremendous backlash. So at a high level, I want to find ways to talk about our work from a feminist space so that we are not inadvertently causing more harm while also finding ways to talk about how to break down the gender binary and talk about our shared humanity. 

In terms of data, I want to continue to apply an even more comprehensive and nuanced approach to both the types of data we collect and the methods we use to collect that data. This more personal insight will allow us to tailor our interventions and policies to better address the specific needs and challenges faced by different communities.

What are your hopes for the future? What will more, better, and better-applied data change?

I am hopeful that there will be an increased global focus on supporting and calling in men when it comes to challenging damaging gender norms and expectations. Gender equality is about creating equitable worlds for women and it’s about recognizing that men are also affected by societal pressures related to masculinity.

I also hope that in the future, we can put a stronger emphasis on qualitative data collection given the current donor trends. I value both quantitative and qualitative work but rarely do we get a chance to do truly mixed methods projects. Intentionally trying to inject qualitative data that gives us a more personal and in-depth understanding of what’s really happening on the ground is something I want to move the field towards. When we have the stories, struggles, and aspirations of people impacted by gender norms, it adds a whole new dimension to our understanding.

By gathering better-applied data, we can make a real difference. It can help us challenge stereotypes and misconceptions, shape targeted interventions, and drive conversations around gender equity.

The post 5 Minutes with Dr. Taveeshi Gupta 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.

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.

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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