We still have an urgent need to cultivate and retain a larger global workforce of purpose-driven data talent.  

In 2022, we collaborated with Dalberg and the Patrick J. McGovern Foundation to understand and dimension those needs. Together, we created our Workforce Wanted report, which outlined ways to create purpose-driven data practitioners. More recently, we articulated the emergence of a new role, “data ecosystem designer,” charged with creating the data ecosystem that allows digital public goods to thrive and scale in the social sector. We know the need for DSI talent is significant, and that there are currently no established “on-ramps” to these careers. 

Without these on-ramps, people come to data for social impact in many ways. To learn more about these career choices, we launched Pathways to Impact, a series of conversations with data for social impact leaders exploring their motivation and career progression – and what they’ve learned along the way. From Bangalore to Buenos Aires, people in varied roles are shaping this new field. Our Pathways to Impact series explores this growing community and asks data scientists, CEOs, and program officers how they came to this work. After more than a dozen of these conversations, we observed some common themes on these varied paths. 

1. This career can be a meandering journey  

Most of the leaders stumbled upon data for social impact from another field. There was no structured path or career progression. It was not data for social impact from day one and you don’t have to be a data expert to find your way. What matters is that they had the passion and curiosity to seek solutions beyond business as usual. 

2. Start with the idea you want to pursue 

Memorable journeys are characterized by stops and alternative routes more than the final destination. This one is no different. Everyone we spoke to first identified a social problem they wished to solve and then applied a data lens to it. Think of the challenge you wish to eliminate or a roadblock you want to overcome; data can be a critical tool to build the evidence for effective decision-making and turn the tide. 

3. Data and … ? Lean into non-data superpowers 

Leveling up with data skills does not mean one has to bury other skill sets. The magic happens when you bring them together. Most of the leaders we interviewed strongly emphasized the value of their prior training in communications, partnerships, and community management, or their ability to be empathetic, curious, and agile, among other skills.

4. Multidisciplinary approaches are needed and valued  

Don’t stress about having limited data science experience or being a “non-data voice” in the room. The reality on the ground often comes from these voices. To design effective data-driven solutions, we have learned that multidisciplinary approaches are indispensable in understanding the interconnected social systems and their problems. 

5. The pack survives 

While there might not be an exact match for the advice or peer support you need in existing communities of practice, reach out to the ones that do exist, and do not hesitate to build your own. The adage of if you want to go far, go together certainly applies. Finding supportive allies in unchartered territories helps build confidence and seek solutions together. Most leaders identified multiple communities and groups they are a part of and how that has helped them brainstorm, implement effective strategies, and get a sense of belonging. DataKind, Women Who Code, Women in Data Science, NASSCOM, and AnitaB.org are just a few communities that were mentioned and have a presence around the world.  

Read the full series here to delve deeper into their stories — and to start your own. 

The post Five Things DSI Leaders Wish You Knew About Working in the Sector appeared first on data.org.

As the social sector has become increasingly digitally mature, new digital public goods for creating social impact have started to appear. Ushahidi, the Infectious Diseases Data Observatory, Code for America’s GetCalFresh, and data.org’s Epiverse collaborative are just a few examples of digital products that use data to serve the impact needs of many organizations. However, these social impact products demand a more complex design process than commercial products do because of the complicated ecosystem of data providers, partnerships, and revenue streams required to bring them to life. Moreover, because social impact is at their core, these tools must be built in a way that centers equity and reducing harms, which requires us to co-design such tools with the communities they will affect at each stage of the development process. Despite the unique design skills needed for these tools, there is no formal role for the people creating and evangelizing them. As a result, digital public goods are often built without the skills and resources they need, making them less effective and harder to maintain.

We believe that a new role is needed for the social sector called the “data ecosystem designer” that is charged with creating the data ecosystem that allows digital public goods to thrive and scale in the social sector. These data ecosystem designers are akin to city planners:  Just as a city planner must weave together various transportation, housing, and commercial systems to create a city that meets the needs of a large population, so too must the data ecosystem designer weave together various data flows, stakeholder needs, and political processes to create a digital public good that meets the needs of a large population. Until we better define this set of skills needed and the career paths for this role, organizations will not plan for them, individuals will not train in them, and funders will not recognize the critical importance of funding them. The quality and quantity of digital public goods will suffer as a result. At data.org, we have been studying this role through interviews and workshops with leaders in the field. This paper summarizes some of our findings about how this role is defined, trained, developed, and sustained. We believe that better supporting this role will lead to better-designed digital public goods overall.

The Challenges of Ecosystem Design

There are some key differences between digital public goods and commercial products that exemplify the need for this role:

• Impact is key, but it is often hard to measure: In a commercial context, success is defined, crudely, by profits, which is reasonably easy to measure.  Digital public goods, however, have an increased onus to deliver a specific societal impact to be considered successful, but such impact is often only seen over a longer time horizon, a horizon that doesn’t often fit to demands for quick measures of success. This societal impact must also ensure that the data and models used don’t perpetuate systemic biases.
• Revenues don’t scale with use: In a commercial context, revenues almost always increase with an increase in customers. In our context, the users of the digital public goods will not always be the ones paying for the service. Unlike private sector models, monetizing digital public goods cannot include the reselling of user data.
• Customers are not uniform: In a commercial context, customers are often very homogeneous. Digital public goods often require the collaboration of users with very different digital maturities and operating models to succeed.
• The definition of “scale” may vary: In a commercial context, while there are various definitions of scale, success is often measured by “units sold”. With digital public goods and their focus on social impact, scale could equate to an increase in a number of users, but it could also represent a move from a pilot to a single major implementer (e.g., a government), a scale to a new geography, or even scaling a tool from one use case to another.

The uniqueness of digital public goods requires a unique skillset to build them and maintain them.

Responsibilities and Skills of the Data Ecosystem Designer

Data ecosystem designers that we worked with cited two main prongs of responsibilities in their role:

Product Ownership and Evangelism: Data ecosystem designers must successfully convey the value of their product and how using it will meaningfully impact the world. They must also recognize what classes of problems the product can address and identify customers with those problems, then convey to partners how to use the tool and what ROI they will get from using it. In interviews, one participant mused that “a product must be usable before it can be useful”, implying the data ecosystem designer needs to understand the needs of all their different users.

Partnership Development: Data ecosystem designers must identify and foster the relationships with external parties that are needed to fill out the data ecosystem as described above. In our interviews, people described having to build technical partnerships, data sharing and governance partnerships, stakeholder partnerships, and funding partnerships – and in each case the partner has to derive a clear benefit from participation. As one participant said, “This person has to be able to bring people on board to their vision, and they have to be able to build trust.” This all requires high EQ, political savvy, great communication skills, and an ability to innovate.

To fulfill these responsibilities, data ecosystem designers need the following skills:

• Data Governance Experience: Data ecosystem designers are building digital products that have data at their core. As a result, they must have a deep understanding of how that data will be used – which data, by whom, at what times, under what conditions. One interviewee also emphasized that data governance also implied an ethical lens. “Because I work with data about human subjects, it’s key to be able to manage the use, security, and privacy of that data ethically.”
• Technology Experience: In our interviews with data ecosystem designers, they all listed technical know-how as requisite, though they felt the data ecosystem designer simply needed to be well-versed in how the technology works. One interviewee pointed out “I think [the data ecosystem designer] has to have the skill to work with technologists, but they don’t necessarily have to have all the tech [skills] themselves.” Designers do not have to come with a STEM Ph.D. or be very hands-on technologically in this role.
• Product Development Experience: Data ecosystem designers will need experience overseeing digital product development, particularly as it pertains to hearing user needs and shaping tools to meet those needs.
• Partnership Mindset: More than any other skill related to technology or product development, we found data ecosystem designers exhibited a mindset for aligning networks of people and creating partnerships that provide value to all parties. It was clear in all instances we studied that digital public goods needed partnerships with other people and institutions to succeed.
• Local Context: On the non-technical front, the data ecosystem designer also must understand the local context within which they are working. They must have some connection to the problems of the people in the environment they’re serving, lest they design ineffective or harmful solutions based solely on their own, non-local context. As one workshop participant point out, it is unlikely that “an app you build for 20 people in San Francisco will work for 5000 people in Kenya”
• Appetite for Risk: In our interviews with data ecosystem designers, many cited needing “bravery” or “resolve” to push new ideas forward in this trailblazing role. Until the role is more standardized, this resilient attitude will play an important role in determining the data ecosystem designer’s success.
• Ability to Code Switch: Data ecosystem designers need to have a range of different product and technical conversations that go beyond just a surface-level understanding with multiple audiences. Code-switching between the language and norms of each group is critical.

What is perhaps most striking about this list is how many skills have nothing to do with technology explicitly. Beyond understanding data governance and how their technology works, data ecosystem designers are primarily tasked with advocacy for the work, building partnerships, communicating a vision, and understanding the human needs of the constituents they serve.

The Data Ecosystem Design Team

From the list of skills above, it’s clear that no one person can do all the work needed for Data Ecosystem Design. Just as the lead city planner works with their city planning team to execute their vision, so too the data ecosystem designer needs a team of technologists, partnership managers, and other skills to create a successful digital public good.

In our research, we found three ways in which data ecosystem designers balance their skillsets through their teams and partnerships:

• Balancing M-shaped skills: No one data ecosystem designer will have all of the skills listed above, but they often are quite deep in more than just one of the skills, giving them an “M-shaped” skill distribution. For example, a data ecosystem designer who came from a law and policy background may be very strong in data governance skills, partnership skills, and code switching. While they may be aware of technology and product design, they may want to bolster their skills there. Data Ecosystem Design teams therefore start by balancing the strengths of the lead designer.
• Balancing organizational constraints: Another dimension data ecosystem designers balance in building their teams is based on the organizational resources available to them. An interviewee pointed out that “[how you supplement your team] will depends on the size and flexibility of your organization, what field you’re in, and what your core product is.” If a data ecosystem designer is working in a tech company to build a digital public good for the social sector, it is a safe assumption that they have technologists and product designers in-house to work with. They may have to build bridges to partnership managers and social sector experts to succeed though. Inversely, a data ecosystem designer that hails from a nonprofit that is trying to scale one of their successful digital products may find the opposite situation. They may have strong social sector relationships and partnerships but need to supplement their technology team to bring the tool to scale.
• Balancing perspectives: Digital public goods must be built for social impact, which means they must speak to all the needs of direct and indirect stakeholders. They must elevate the people they serve in the ways they need without inadvertently inhibiting them in other ways. We found that successful data ecosystem designers made sure to build strong local context and perspectives on their teams. Either through their own experiences, hiring folks with local context, or bringing in people from the field with local context to advise, these designers put the voice and experience of their constituents center stage.

A Path Forward for Data Ecosystem Designers

We feel quite strongly that formalizing this role is important to move toward better digital public goods. Doing so allows practitioners to have a community to share best practices and lessons learned, and to advance the pace of innovation and efficiency in developing digital public goods. We also feel that well-trained data ecosystem designers play a critical role in ensuring that all digital public goods are built responsibly and with the highest standards of data and AI ethics in mind. Indeed, as a new wave of AI funding enters the social sector, we’ll need people trained in these skills to be planning for the long-term sustainability of the products that will be funded, advocating for representative data to be used, understandable models to be built, and products to be overseen so they meet the needs of all stakeholders. 

The lack of this role in the social sector currently leads to too much fragmentation and the curse of pilotitis: over-focus on short-lived demonstration projects and pilots that have no sustainability because there was no role dedicated to think ahead to what the ecosystem requires for the product to be useful and used in the long term, beyond the initial project that birthed the pilot.

In data.org we have learned to recruit for this role before we even knew what to call it. For example, data.org deployed dedicated staff to support and manage the ecosystem of epidemic and pandemic modeling in our Epiverse collaborative. This program replaces the perils of a fragmented community, where top teams routinely compete with each other for limited funding and produce duplicative ill-supported tools, with an opportunity for teams that used to be rivals in the space to come together around agreed standards and community guidelines to create free-to-use open source epidemiological tools for shared use by the whole community. As a result, the community unites around the best existing software, regardless of who created it, resurrects tools that had been orphaned, and creates new software only where there is a true need, all the while sharing best practices. Our data ecosystem designers’ roles are to bring the different parties together and broker new partnerships, at the same time as also building a network of funders, tech partners and government and intergovernmental agencies to support this growing global community effort. Without the data ecosystem designer role, we would not have been able to pull it off.

Learning from this example and from others, we have three recommendations for the social sector on engendering this role:

1. Organizations should acknowledge this role: Almost all folks who have played the role of data ecosystem designer have not had that title. They have been in a role called “Programs Director” or “CTO”, but then asked to carry out these additional, unclear functions. One interviewee in our research exclaimed “When I read this description, I finally thought ‘oh, THIS is what my role is called!’” By raising awareness of this role and the skills needed to carry it out successfully, organizations building digital public goods can begin planning for it and hiring for it.
2. Funders should fund this role: Many philanthropic funders have recognized the need for data scientists and AI researchers in nonprofit digital technology creation and have funded organizations to hire them. A natural next step would be to fund the role of the data ecosystem designer. This funding could come in the form of direct funding for a person to serve in the role or through supporting fellowship programs, like Schmidt Futures’ Technologists for Global Transformation program, that place people with these skills in nonprofits.
3. Programs should train this role: Just as data science programs arose over the last decade to round out the skillsets of computer scientists and statisticians holding the title, universities should launch programs to train data ecosystem designers. These programs could live at the intersection of technology, policy, and product design programs.

With these three shifts, we can start building capacity in the social sector to bring digital products from the pilot stage through to the level of scale we need for digital public goods. Data.org is committed to championing this role and, in addition to recruiting data ecosystem designers for our own programs, we are developing a charterhood process for other such practitioners in the social sector.

Conclusions

The social sector has made great strides in expanding its data and technology capacity in the last 10 years. To continue that trend to the point that the social sector is solving meaningful problems with digital public goods, we need to go beyond just training more technologists and also define the roles needed at higher levels of strategy. The data ecosystem designer is one of these roles that can bring the sector from pilots to products. The sooner we start prioritizing this role, and developing the career pathways that feed into it, the sooner we’ll be able to see data and AI conceived, developed, and deployed for the greater good.

The post The Data Ecosystem Designer: Designing the Future of Responsible Digital Public Goods appeared first on data.org.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

While such revelations spur public discussion of the climate crisis, significant gaps remain in our understanding of the potential impact of climate change on human health — and in our ability to provide concrete evidence of this impact. To drive sustained action by policy- and decision-makers, journalists, and citizens, we need to make a clear and evidence-based causal connection between climate change and human health. Being able to prove this connection requires active and intentional interplay among data, tools, talent, and policy.

The Interplay of Data, Tools, Talent and Policy

Accurate, diverse, and comprehensive data assets including climate, health, infrastructure, socioeconomic, and other data sources captured over extended periods are the bedrock upon which credible attributions of climate change impact rest. Coupled with tools that are fit for purpose and co-developed with relevant stakeholders, these datasets can provide us with profound and useful insights. However, data and tools on their own have little value;  they depend on scientific expertise, interdisciplinary collaboration, and innovative thinking to parse the intricacies of the field and transform raw information into actionable knowledge. Additionally, policy plays a pivotal role in this endeavor by enabling evidence-based decision-making and fostering cooperation among different sectors. However, four key challenges currently impede progress in the field and warrant careful consideration. Below, we lay out these challenges and suggest ways to tackle them.

1. Move Beyond Siloed Approaches

First, deciphering the complex attribution puzzle requires the concerted efforts of climate scientists, health researchers, data modelers, analysts, policymakers, and many other experts. However, a persistent challenge hampers our progress; the prevalence of siloed working environments. Siloed approaches can result in wasted resources, time, and effort, and may lead to biased, inconsistent, or conflicting findings. A 2022 Lancet Planetary Health paper reports that fewer than half of the studies on climate change’s impact on mental health were designed or conducted in collaboration with mental health researchers or published in mental health journals, implying gaps in interdisciplinary collaborations. This compartmentalized approach compromises our ability to address the multifaceted challenges posed by climate change.

2. Build a New Breed of Data Science Practitioners

Today, there is limited global technical capacity to conduct detection and attribution studies on climate and health. This limitation curtails our ability to generate robust evidence on the impact of climate change on health on a global scale. Addressing this challenge is crucial and demands a concerted effort to bolster capacity. We need a new breed of data science practitioners at the intersection of climate and health to unlock this potential through a thorough understanding of these and other issues in the climate and health field. This interdisciplinary approach is one of the core tenets of the Capacity Accelerator Network, where, with the support of Wellcome, data.org is building local data science talent across Africa and India. These cohorts of interdisciplinary data science practitioners are being trained to analyze complex data from multiple sources (e.g., climate, environmental, health, demographic, etc.) to generate a robust evidence base to support policy and decision-making processes in government, social impact organizations, and community-based organizations.

India’s National Programme on Climate Change and Human Health (NPCCHH) provides a clear and replicable example of how policymakers could boost the creation of this new kind of practitioner to support national and international efforts to make the connection between climate and health. Launched in 2019 by the Ministry of Health and Family Welfare’s National Health Mission, this initiative derives insights from complex, seemingly disparate datasets. Such initiatives mobilize action by providing directionality for matters of global emergency. Since then, the Ministry has called for state-specific action plans to which several states have responded with strategies and operational frameworks to engage climate and health experts and analyze data from both sectors to drive action. More recently, the Indian government is in the process of hiring data analysts and data scientists to enable various ministries to unlock value from their own datasets and make them accessible to others to generate novel insights.

3. Leverage Strategic Communication and Data Storytelling

Communicating insights in an easy-to-understand and actionable manner can ensure swift action by relevant stakeholders. This requires results from research to be available in accessible formats to laypersons, journalists, government leaders, and other relevant stakeholders. Examples of accessible research might include charts and visualizations, policy briefs, media articles, and short videos. These kinds of communications assets can clarify complex concepts enabling stakeholders to take action, and can also broaden and improve the public conversation about issues that affect everyone, like the impact of climate change on human health.

4. Activate Evidence-driven Policymaking

Finally, climate policies should consider both the scientific evidence and the real-world constraints of policy implementation. A policy-enabling environment demands a strong evidence base. This evidence-driven approach lends weight to policy discussions and might have far-reaching implications. For example, it can significantly bolster cases related to loss and damage, as countries affected by climate-induced health impact can substantiate their claims with clear attribution studies. Moreover, robust evidence on the health repercussions of climate change strengthens the foundation for countries to secure funds for adaptation and mitigation efforts. Such evidence provides a compelling narrative, demonstrating the urgency of action and the real-world consequences that demand financial support. Ultimately, this evidence-driven approach advances the cause of safeguarding public health in the face of a changing climate while also promoting equitable access to resources for adaptation.

As we gear up for COP28 later this year, a historic event that will dedicate a full day to the critical topic of health for the first time, we have an opportunity to underscore the impact climate change has on health. Amid the discourse on the global stage, we must address the current challenges to attribute climate change’s impact on health with a sense of urgency. By addressing these challenges across data, tools, talent, and policy in a collaborative and transdisciplinary manner we will fortify our evidence base, setting the stage for a clear and compelling mandate to demand resolute action from world leaders.

This post has been written with research support by Young India Fellows, Aadya Vatsa, Manan Batra, and Tanmay Sarkar from Ashoka University.

The post The Attribution Challenge Between Climate Change and Health appeared first on data.org.

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. 

The post 5 Minutes with Andi Suraidah appeared first on data.org.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

What kind of world would you design? 

Now, let’s zoom into our current global reality. According to the World Economic Forum (WEF), the world generated about 44 zettabytes of data in 2020. World leaders and experts are already grappling with understanding the impact of Artificial Intelligence (AI) on human life and even free will. Whether we like it or not, we are entering a data-driven world. This breakneck pace begs one question – who is making the rules in this new world? 

It seems the answer is, a relatively homogenous group of people: Only 15% of data scientists are female with a skew towards early career roles rather than managerial roles. Similarly, Black employees represent less than 5% of Google, Facebook and Microsoft’s workforce, significantly less when compared to their representation across the global population. These numbers are not likely to  improve across regions or sectors.

When a workforce that is not diverse creates the rules, especially as we forge our path in this data-driven world, we move away from the idea of fair and equal – as seen through biases in hiring decisions, access to housing, policing practices, prison convictions, among other areas. For reference, revisit the infamous bias reported in Amazon’s AI-based recruitment which penalized applications with words such as “women”. These data-related biases can arise even before data is collected – while framing the problem – and can find place anywhere across from data collection, preparation, and beyond. These biases are hard to overcome because a non-diverse workforce often lacks the social contexts to properly identify and frame a problem. There is a business, moral, and social impact case to be made to reduce inequities. And the solution lies in diversity – of all kinds and at all levels. 

A diverse workforce allows for different vantage points and closes the social context gaps that hinder unbiased decision-making. If we are to negotiate fairness and equity in a data-driven world, then it is critical to hire a diverse team. 

Here are three points to keep in mind as you build your new, more diverse team: 

1. Measure and report: We must be better at measuring and reporting on the diversity of the global data-driven workforce. Presently, information about the lack of diversity in the data workforce is mostly US-centric. Limited information, if any, is available for other geographies, such as caste representation in India. Further, there is insufficient data in US reporting for people who do not identify as either a man or a woman, people from various sexual orientations, people of different religions, etc. Building a more diverse workforce will be difficult to advocate for without quantifying the problems and regularly measuring our global progress.

2. Commit to dismantling the system: The lack of diversity is systemic and starts long before we enter the workforce. Unless we commit to change, it will continue. The data we measure and report must be leveraged to identify systemic barriers and develop solutions. For example, with data in hand, leaders and mentors can encourage others to aspire to a career in data, close the pay gaps, and build incentives for organizations to hire diverse talent and ensure representation at all levels – including in leadership. In addition to bolstering human resources, we must support policies that create a welcoming, inclusive infrastructure, such as breastfeeding rooms for new moms and health insurance for same-sex partners, to name a few.
   
3. (Most importantly) Invite diverse voices and perspectives to build and create change:  You will get the best results if you involve people with rich lived experiences and people who have been historically marginalized and excluded. Do not make the mistake of solving the problem for them. Solve the problem with them. Mere representation does not allow people from historically marginalized communities to exercise their agency. Question the intersection of power structures at play around you and if others feel confident to articulate their experiences. It is up to you to educate yourself and to be truly open to listening to diverse perspectives without dismissing or silencing them. 

According to WEF, data scientist and data analyst jobs will have the highest growth in demand by 2025. There is no time to waste. Let’s seize this opportunity to harness our superpower and build an inclusive and equitable data-driven world. 

This post has been written with research support by Young India Fellows, Ishi Shandilya and Khushi Baldota from Ashoka University.

The post Our Opportunity to Make the Rules in a New Data-Driven World appeared first on data.org.

Being physically present helped us hear firsthand about the specific challenges, cultural nuances, and social dynamics at play locally. Not to mention the opportunity to share meals, hear insights, and engage in casual conversation — all critical as we build trust and collaboration toward a common purpose. 

The reason for our trip was to launch the India Data Capacity Accelerator, part of data.org’s Capacity Accelerator program supported by Wellcome. The program aims to create curriculum, resources, and interdisciplinary and experiential learning programs to train data practitioners at the intersection of climate and health. 

We can’t act fast enough on climate. 

The World Health Organization estimates that climate change is expected to cause 250,000 additional deaths per year between 2030 and 2050. And we learned that doing this work in India — where there is a broad understanding of the urgency and a cross-sectoral commitment to action — poses a tremendous opportunity for developing effective solutions.

Climate justice, gender justice, social justice, and social protection are all interrelated. We need to really recognize that this whole question of justice and how data can unblock the system and understand real needs becomes very important.

Professor N. Vinod Chandra Menon, International Coordinator, G20 C20 Working Group on Resilient Communities: Climate, Environment, and Net Zero Targets

Under the auspices of the G20-C20 and hosted by the University of Chicago Center in Delhi with our partner J-PAL South Asia, the convening brought together nearly 100 passionate leaders to discuss how we can together build a workforce using data to combat climate change and its impact on human health. 

With the announcement of our new partnership with three prestigious universities (Ashoka University, Birla Institute of Technology and Science, Pilani (BITS Pilani), and Indraprastha Institute of Information Technology (IIIT Delhi) we underscored data.org’s belief in and commitment to working with in-country expertise — expertise that can that inform efforts globally.   

In addition to being reminded of the importance of in-person connection, three insights emerged:

1. Data can make sense of multiple, inextricable challenges 

Our event speaker, Vinod Menon, observed that climate justice, gender justice, social justice, and social protection are all interrelated — a critical piece that is often missed when developing strategies for social impact. Gone are the days when we could view climate change as a discrete problem to be solved. The impact of the climate crisis on human health is now undeniable. Climate change also has far-reaching consequences for social justice and gender equality. Our time in India shed even more light on the urgent need to harness the power of data to understand these complex interactions and develop effective, intersectional solutions.

2. Smart solutions have the potential to cross continents

The energy and focus on technology we saw in India mirrored our visit to Nigeria earlier this year. Both countries have young median ages and high levels of investment in technology and both are also developing data-driven solutions and training on-the-ground interdisciplinary teams focused on local impact. This is exactly the type of ecosystem work we are investing in at data.org. Recently, Inclusive Growth & Recovery Challenge awardee BASE adapted their cold chain storage solutions from India to Africa. We see that it is possible to have technology and capacity-building advances to cross continents.  

3. Don’t underestimate the power of partnering with the government to drive change

It was inspiring to hear the Indian government’s Capacity Building Commission (CBC) share its vision for upskilling government employees in various data-related fields. They shared their comprehensive plans to equip over a million government employees with the necessary skills and knowledge to work with data — effectively and responsibly. This initiative has the potential to create a profound ripple effect of data literacy and expertise across sectors. It also recognizes that data-driven governance can lead to more efficient and effective public services, informed policy decisions, and ultimately, improved outcomes for citizens.

Sure zoom makes work efficient — but without a doubt our in-person time in India (and Nigeria) was our most important takeaway. There is no substitute for collaborating and problem-solving together. 

Our conversations in India reminded us of the power of data and technology — including new, transformative technologies like generative AI — to tackle social sector challenges like the impact of climate on human health. Being together also allowed us the time and space to ask hard questions, and to share insights on the responsible use of data and technology — a topic that is in the news daily. 

We are so excited to scale our impact and build our partnerships through the India Data Capacity Accelerator, and can’t wait to share our next update. 

The post Insights from India: Face-to-face Connection and Climate Action 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.

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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.

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