This is the second post of a four-part series showcasing how WRI’s Data Lab is addressing global challenges through innovative technology strategies and products. In this series, we focus on the strategies, specific products and underlying technologies shaping our approach. Our goal is to inspire collaboration across sectors and highlight how data-driven solutions can drive meaningful, scalable impact.
Over years of building impactful technology products, we’ve identified several approaches for creating products and teams that work in the non-profit setting. These approaches are shaped by real-world challenges, the potential of new technologies and reflect how data and technology can evolve to become more useful, usable and grounded in local context. Each one is drawn from our ongoing work in the Data Lab, and we’re sharing them here to inspire other organizations to explore and adopt similar approaches. We’re always open to discussing their potential and sharing examples with others who share our commitment to innovation.
1. “Headless” impact data tools and products
Historically, impact-focused data applications use custom-built visual interfaces (“front ends”) for their data. While these visualizations can be useful and intuitive, they often have limitations. For example, many are difficult to customize or localize for different user needs and contexts. They can also be costly to maintain and update, especially for teams with limited resources. Additionally, these interfaces may not adapt well to the shifting ways people consume information today, with increasing use of smartphones and AI-powered chat tools, making traditional desktop-focused visuals less relevant or accessible.
We see a significant opportunity for more impact data products to build with a “headless” approach — in software development, this means products built to be accessed in a variety of customizable and flexible implementations or via existing tools. This could mean urban planners embedding a product’s data in their own dashboards or rural NGOs accessing datasets via WhatsApp (“chatting with the dataset”). It could also mean a policymaker discovering and downloading high-quality datasets through platforms like WRI’s Data Explorer, a CKAN-based tool that centralizes open data from across the organization. This improves WRI’s shared data management practices, benefiting users with improved documentation, consistent formats and easier access to trusted data.
WRI already offers API access for several datasets and applications such as Global Forest Watch, and we’re actively experimenting with more API access options, including existing tools like messaging apps to access data.
Developing data products in a “headless” way gives users greater flexibility and control while also enabling product teams to release data products faster and at lower cost. This approach makes it easier to work with users to understand their needs and eventually design data interfaces with less risk.
2. AI-enabled onboarding and education for complex data products
Creating intuitive onboarding flows is a common challenge at the Data Lab and for many teams building similar products. We often translate research from subject matter experts into products, interfaces and insights that will be used by a wide range of users with varying levels of expertise. Furthermore, our users aren’t always native speakers of the languages used in our products, despite our best efforts to translate our tools into other languages.
As artificial intelligence models increasingly show potential to act as autonomous agents, we see potential in training models/agents to help onboard and train users of complex data products by:
- Translating copy
- Manipulating interfaces to create scenarios that show the value of the tool
- Walking users through the process of using the tool to accomplish their goal, all in their preferred language and technical level.
In practice, this could look like a forest restoration worker in Brazil receiving onboarding help in Portuguese (when using an English-based app), while an urban heat mapping team in India could be guided step-by-step through a scenario using translated walkthroughs on their phones.
Already today, many people are using more generalized models and tools like ChatGPT to quickly learn how to use complicated software, such as Photoshop or Blender. Even simply providing these models with context such as documentation of a data product, and/or embedding a chatbot powered by these models into a tool’s interface, could make it easier for new users to learn how to make the product work for them.
3. Locally focused impact data products and tooling
Historically, the production of datasets on people, nature and climate has followed a pattern — the data providers (e.g., researchers collecting field data) are not the data analyzers or dataset creators, and they often don’t see the direct impact of their work. Rather, global organizations build datasets and generate recommendations to decision-makers, with stakeholders on the ground left out of many of the later stages of the process.
We want to see (and help create) more datasets and data products with the stakeholders for whom the data and insights are most relevant. This means aligning datasets with cultural and language norms, giving local stakeholders more ownership in the data analysis and decision-making processes, and platforming both local and global datasets to highlight any differences (while giving local stakeholders the ability to make their own judgments on the data).
A great example to follow is WRI’s Energy Access Explorer, an interactive online platform mapping the state of energy access in underserved areas across Africa and Asia. WRI has worked with stakeholders on the ground via working groups and local training programs to build a tool where the users are a critical part of creating, maintaining and updating datasets.
Through our experiences with products like Energy Access Explorer, we’ve seen firsthand how a locally-focused approach can create products that are more useful and impactful.
This is a strategy we’re particularly excited about at the Data Lab, and it reflects what we’ve learned building and scaling many impact data products.
In the next part of our series, we showcase specific products Data Lab is actively investigating and building. Read it here: Opportunities to Build Data Tools That Deliver
Across this series, WRI’s Data Lab outlines the strategies, products and technologies we believe hold real promise for accelerating action across people, nature and climate. These approaches reflect where we see the biggest opportunities to close critical gaps.
While many of these concepts or strategies are already in development at the WRI Data Lab, others need collaboration, investment and technical expertise to move forward and realize their potential. By sharing these opportunities, we aim to spark dialogue and build partnerships with those equally committed to technology-driven solutions that address today’s urgent challenges.
If you’re working on any of these exciting areas — as a researcher, funder, policymaker or technologist — let’s talk. Reach out to us at datalab@wri.org.
Other posts in this series:
