Edition 58: Decolonizing Data Will Fail Unless…

Welcome to data uncollected, a newsletter designed to enable nonprofits to listen, think, reflect, and talk about data we missed and are yet to collect. In this newsletter, we will talk about everything the raw data is capable of – from simple strategies of building equity into research+analytics processes to how we can make a better community through purpose-driven analysis.

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Update: data uncollected will continue to be a monthly newsletter.

To make this work we do - of equal parts heart and mind - sustainable, joy-giving, and care-centering – rest has to be at the top of our to-do lists. Even if we are unsure what the rest could look like.

Because rest cannot be treated like a reward.?

Rest has to be sought actively for thinking, feeling, and engaging deeper with our work, especially when communities worldwide are in pain and trauma of their political and social conflicts.

To prioritize this rest as a continuous practice, data uncollected will continue to be a monthly newsletter until chosen otherwise.

I am deeply grateful for you to share this space with me. This newsletter allows me to co-create with you, edition by edition, an optimistic outlook that we can do better with our data.

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This newsletter and I are always in your corner, believing in your brilliance.

Keep shining, you.

With love.

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Imagine this:

A 200-year reputed institution publishes research that claims the first instance of ethnic and racial racism in the country happened in the past 350 years.

There are several ways one could react to this hypothetical research.

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It could be

1. The research comes from a well-established institution backed by big names, so we trust it.
2. Or we look for some additional materials this group offers as methodology, and then we trust it.
3. Or we look for evidence going back in the past beyond 350 years to evaluate if there was missing racism data that was never reported, or perhaps this institution chose not to factor it into this research.

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Chances are scenario one will be picked way more than scenario two (and way, way more than scenario three).

But in choosing to accept that research as-is, here is what you and I risk: the complete erasure of Indigenous history and reality. That research above instantly wipes off all engagements with Indigenous Peoples (read: traumatic experiences) post “discovery”.

Our commitment and interest in decolonizing data failed when choosing that research to be accurate.

Because from that moment, the data was weaponized to tell a different story - one where a lot of contexts, names, and history get sanitized.

The opposite of all of that above – that is what decolonizing data entails.

Decolonizing data involves recognizing and addressing the inherent biases and power imbalances in data collection, interpretation, and usage. This is especially true when, in many instances, data practices have been shaped by colonial legacies, leading to the underrepresentation or misrepresentation of marginalized communities.

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In this first edition of the year, you and I are talking about this. We will explore why efforts to decolonize data can fail unless we take active steps to address those challenges collectively.

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Let’s start with some fundamental reasons why our efforts to decolonize data fail:

• Lack of awareness: One of the primary reasons for the failure of decolonizing data is the need for more awareness about what it entails. Most often, we continue to collect and use data without acknowledging the biases that might be present or making efforts to recognize those biases.

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• Inadequate representation:?Data can misrepresent the diversity and actual context of the population accurately. This can happen anywhere – in the assumptions and exclusions of certain groups in data collection processes or the formulation of data interpretation models.

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• Over-reliance on quantitative data:?Historically, we are notorious for prioritizing quantitative data over qualitative insights. To fit data into neat boxes has been our desire for a long time. This often leads to a need for more attention to the rich, nuanced information that qualitative data provides, especially regarding cultural and contextual specifics.

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• Centralized data control:?When data and its narratives are controlled by a few powerful entities (for example, the reputed institution in the example above), the outcomes can be a lack of representation and voice for those from less privileged backgrounds.

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• Technological constraints:?This is perhaps becoming an increasingly popular challenge. With well-intentioned organizations wanting to work with their data differently, limited access to technology for some of them can hinder the collection and analysis of data, leading to gaps and inaccuracies.

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So, what can we do?

Let’s flip the script above?

We can

• Promote awareness about equity and inclusion: Educate your team and stakeholders about the importance of equity and inclusion, which leads to active steps toward decolonizing data. Understanding the issue is the first step towards addressing it.

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• Push for inclusive and diversified data collection teams: Ensure that our teams involved in data collection have an inclusive environment for (all forms of) diverse individuals. This is especially relevant when diversity is not designed artificially, only to mimic the population under research. We need those inclusive environments - so that this diversity can expand multiple perspectives and help recognize biases.

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• Incorporate qualitative data:?Balance quantitative data with qualitative insights. We can make this possible by encouraging the collection of stories, opinions, and experiences, which provide depth and context to numerical data.

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• Include communities from the start of data collection: Involve and include communities from day 1 in the data collection and analysis process. And I don’t mean in an artificial sense as some “honorary guest” but as a real, active participant. This not only ensures representation but also helps in building trust and understanding. This way, communities can have control over their data.

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• Adopt context-specific data collection approaches:?We must remember that one size does not fit all. Adapt data practices (starting with data collection) to suit the specific cultural, social, and economic contexts of the communities you are working with. These practices can then ensure that the data respects the dignity and rights of individuals and communities stored in those data points.

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• Invest in capacity building:?Our training and resources to staff and local partners to improve their data-related skills can be the long-term outcome of our commitment (of decolonizing data). Through this, we can set ourselves up to produce and engage with a more accurate analysis.

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• Design collaborative partnerships: We need a collective, sector-wide understanding that we are not in any form of competition with each other. Our bottom line – to make this world better – happens in collaboration. So, when it comes to doing things with data, our partnerships can be designed to share the power and the accountability of what happens to and through shared data practices.

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• Continuous evaluation and adaptation: This is a non-negotiable part of the deal - regularly evaluating your data practices and being open to adapting them. This involves being responsive to community feedback and staying updated on best practices with human-centered data practices.

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Decolonizing data is complex but essential, especially when we, as representatives of our missions, regularly have to work in diverse and often marginalized communities. It requires a commitment to continuous learning, adaptation, and collaboration.?

I refuse to believe that we are incapable of doing more, doing better when decolonizing data.

By taking intentional (and sure, sometimes painfully challenging) steps to understand and address the biases in data collection and interpretation, you and I can ensure that their work is inclusive, respectful, and truly representative of the communities we serve.?

The stories we pass on to the next generation can either be of convenience (where the data stays misrepresented in context) or be of choice (where we spend our energies, perhaps painstakingly, to collect as much relevant context in the data as possible).

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I believe in us.

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***?So, what do I want from you today (my readers)?

• What resonated with you in this edition? Do we risk decolonizing data through our unlearned behaviors?

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