Inclusive Data: Five Key Principles for Success

Krista Jones Baptista January 24, 2024

This blog post was created from talking points from Festival de Datos townhall, ‘Inclusive Data: The Foundations for Success,’ which you can view a recording of here.

Inclusive data is at the heart of creating a fair and equitable society—and it is more than numbers and statistics; it’s about ensuring that every person is seen, heard, and counted. It is also the key to building a society that values diversity and recognizes the unique experiences of all people, and something we need to strategically integrate into our work in 2024 and beyond to accomplish this goal.

I have worked in the data and digital development space, including on gender data, for nearly twenty years. In that time, the data landscape has changed immensely, and we’ve recently seen exponential change with new opportunities and challenges of citizen generated data, better understanding of intersectional approaches, new data models, and new technology, such as advanced cloud computing and artificial intelligence, to name a few.

These changes make it even more imperative that we embrace inclusive data approaches. At Data2X, we work, through advocacy and strategic partnerships, to improve the quality, availability, and use of gender data to make a practical difference in the lives of women, girls, and gender diverse people worldwide.

The principles of inclusive data revolve around five key principles, and at Data2X our work has endeavored to encapsulate these ideas, as illustrated below:

#1 – Representation

At Data2X, we understand that gender data plays a crucial role in understanding and addressing gender inequalities—and including data on gender is a foundational part of inclusive data.

It provides insights into the experiences and needs of women, girls, and gender diverse people, enabling us to design policies and interventions that promote gender equality and improve the lives of women, girls, and the broader community of which they are a part.

#2 – Disaggregation and Intersectionality of All Identities

Inclusive data is about disaggregation and intersectionality of the identities we all carry, and which can provide richer, more complete pictures of what people need. An example of this, illustrated through the collection and use of gender data, is our work within our Women’s Financial Inclusion Data partnership.

We conducted a three-year study, beginning in 2019 and culminating in 2022, within six countries: Bangladesh, Honduras, Kenya, Nigeria, Pakistan, and Türkiye. Through this work, we discovered if gender data were sufficiently used to create financial products and services tailored to the needs of women, the annual revenue opportunity of reaching unbanked or underserved women could be over one billion USD.

When we use data to reach typically underrepresented individuals in various sectors of our society—such as the financial systems—we can create better inclusivity and equality for all.

#3 – Inclusive Data Must Be Drawn from Multiple Sources

Inclusive data must be drawn from multiple sources and include traditional and non-traditional sources. An example of this work from Data2X includes the Gender Data Network, for which we have connected national statistics officers and gender data focal points in twenty-two countries in Africa to one another and to resources to improve their use of gender data from across sources and link that data to policy, is now expanding to include participants from the Asia Pacific region and we plan to expand to the ECLAC region soon.

#4 – Inclusivity Respects Privacy

Inclusivity goes hand in hand with respecting privacy and confidentiality. As we collect data, we must ensure that we do so with sensitivity, safeguarding personal information and upholding ethical standards.

We see this taking on a new urgency – particularly when it comes to data about women – as AI systems consume data in new ways. Without care, these systems will replicate the biases and discrimination that have consistently held communities and countries back.

#5 – Inclusive Data Requires Support

Lastly, inclusive data requires human and technical capacity, which can only be put in place if there is adequate financing for data and data systems. In our work with Open Data Watch, a technical partner of ours, we find that gender data financing still falls woefully short of what is needed – to the tune of 500 million USD a year.

And we are in the midst of further explanation of the shortfall and of how domestic resources may or may not be able to help.

Inclusive data is not an end in itself; it’s a means to informed decision-making. The goal is to use this data to shape policies, programs, and strategies that promote inclusivity and equality.

It’s our belief and experience at Data2X that inclusive data and inclusive data systems can lead to better data and better data systems. It is only with inclusive approaches that we can impact the data value chain, including the quality, availability, production, and use of data for real change, whether that change is focused on access to services, gender equality policies, or more inclusive climate strategies, more accurate understanding of time use, or any of the other domain topics that we all work in.

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