How we can help
We combine strategic thinking, scientific expertise, and practical data solutions to help impact-driven organizations turn evidence into action.
Diagnose
Data Strategy & Capabilities Diagnostic
Identify gaps between your data infrastructure, analytical capabilities, and organizational goals. Get clear priorities and actionable recommendations for improvement.
Focused Diagnostics
Build & Improve
Data Systems & Decision Intelligence
Design and improve data workflows, analytical tools, monitoring systems, and AI-enabled solutions that turn fragmented information into reliable insights.
Tailored Solutions
Advise & Lead
Strategic Data Advisory & Leadership
Bring senior expertise into your organization to shape data strategy, guide transformation, strengthen internal capabilities through mentoring and knowledge transfer, or provide fractional and interim leadership.
Strategic Partnerships · Capacity Building · · Interim Leadership
FEATURED SERVICE
Data Strategy & Capabilities Diagnostic
Understand where you stand. Identify what matters. Decide what comes next.
Many organizations invest in data collection, reporting tools and analytical capabilities, yet struggle to translate them into better decisions.
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Our focused diagnostic examines how data, people, systems and decision-making connect—identifying bottlenecks, capability gaps and practical opportunities to improve organizational impact.
What you'll get
A concise assessment, prioritized recommendations, quick wins and a practical roadmap.
Typically 3–5 days · Scope tailored to your organization
Sounds Familiar?
In a recent program, a comprehensive dashboard was in place:
• clean data
• multiple indicators
• comparisons across regions
And yet, it was about to be discontinued.
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Not because the data was wrong,
but because no one was using it.
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The core issue was not the dashboard itself,
but the lack of connection to actual decisions.
Examples of work
Examples of how data and evidence were used to support real decisions.
American Journal of Clinical Nutrition
Future-proof and sustainable healthy diets based on current eating patterns in the Netherlands
To keep global warming <1.5°C as recommended by the Intergovernmental Panel on Climate Change (IPCC), eating patterns must change.Within Dutch eating habits, satisfying optimization constraints required a shift away from beef, cheese, butter, and snacks toward plant-based foods and fish and shellfish, questioning acceptability. Satisfying 2050 food system GHGE targets will require research in consumer preferences and breakthrough innovations in food production and processing
Frontiers in Nutrition
To quantify the performance of food products in a sustainable diet based on the balance of their contribution to nutrient intake and environmental impact, within the context of the Dutch diet.
Lancet Global Health
The SWPER index for women's empowerment in Africa: development and validation of an index based on survey data
The Sustainable Development Goals strongly focus on equity. Goal 5 explicitly aims to empower all women and girls, reinforcing the need to have a reliable indicator to track progress. Our objective was to develop a novel women's empowerment indicator from widely available data sources, broadening opportunities for monitoring and research on women's empowerment.
White Paper
A Conceptual Model on Women and Girls' Empowerment
This is a citation of your published work. Make sure you list the precise publishing medium of your work, whether it be a book, an essay, an article, or a research manuscript. If your work appears only on certain pages, include those details and the date of publication to make it easier for your readers to identify and find your piece.
Experimental Economics
Information and strategic voting
We theoretically and experimentally study voter behavior in a setting characterized by plurality rule and mandatory voting. Voters choose from three options. We are interested in the occurrence of strategic voting in an environment where Condorcet cycles may occur and focus on how information about the preference distribution affects strategic behavior
Living Income
Guidance manual on calculating and visualizing the income gap to a Living Income Benchmark
A main objective of this guidance manual is to contribute to creating consistency of process and (visual) language in how income gaps are calculated and reported once income data is available. It intends to contribute to the discussion about standardized approaches
Q&A with Marcelo Tyszler
Marcelo Tyszler
25+ years at the intersection of data, economics, sustainability and international development.
Translating data and evidence into practical, decision-oriented solutions.
Bio
Who I Am
I help organizations in development and sustainability ensure that their data actually supports real decisions.
Across development programs, sustainability initiatives, and research projects, large amounts of data are collected every year. Yet surprisingly little of that data directly informs strategic choices about how resources should be allocated, which interventions should scale, or how programs should adapt.
The missing piece is often not more analysis — but better decision systems.
Through Impactful Data, I work with NGOs, research institutes, and mission-driven organizations to design the structures that connect data, evidence, and decision-making.
This includes:
• Making sure monitoring and data systems actually inform decisions
• Clarifying what evidence is needed before scaling or adapting programs
• Translating analysis into clear strategic choices
• Structuring data systems that are reliable and usable in practice
My background combines economics, impact evaluation, and data science, with over 20 years of experience across Europe, Africa, and Latin America. I hold a PhD in Economics from the University of Amsterdam and have worked with organizations operating at the intersection of development, sustainability, and public policy.
I am particularly interested in helping organizations answer questions such as:
• What evidence is needed before scaling an intervention?
• Which data truly matters for decision-making?
• How should programs design learning systems that improve over time?
