ICTworks

Vu Le writes the amazing Nonprofit With Balls blog, and while he is focused on the USA domestic NGO community, the points he makes transcend borders and are applicable to all of development – domestic or international.  For example, this excerpt from his post, Weaponized data: How the obsession with data has been hurting marginalized communities, should make us all rethink how we use data in development:

Register now for MERL Tech 2016 to debate how we can be more responsible with development data.

How We Weaponized Data

A very serious danger regarding data is when it is deployed without consideration for cultural and other competencies. Poorly thought-out data, unfortunately, is rampant in our sector. Again, I don’t think anyone has bad intentions when using data, but that does not prevent data from being used to cause harm.

It has been done so numerous times in history, such as “scientific data” being used to perpetuate things like phrenology, eugenics, and Apartheid. Here are ways that I’ve seen data being weaponized in our sector:

1. Data is used to hoard resources, perpetuating Trickle-Down Community Engagement

If an organization does not have resources to collect data, then it does not have the data to collect resources. I call this the Data-Resource Paradox, and it mirrors the Capacity Paradox, when smaller organizations cannot get resources because it does not have capacity, so it cannot build capacity to get resources. Unfortunately, marginalized communities—communities of color, disabled communities, rural, LGBTQ, etc.—are left in the dust because they simply cannot compete with more established organizations to gather and deploy data.

I have seen this repeatedly, recently with a City levy grant that forces nonprofits to have not just one, but two years of strong data before they’re even eligible to apply for funds that are, ironically, supposed to be going to low-income communities. Larger, more mainstream, and usually well-meaning, organizations get the resources, and unfortunately many do not have connections to the diverse communities targeted, so they “trickle-down” some of the funding to smaller organizations, perpetuating a vicious cycle.

2. Data is used as a gatekeeping strategy

Similarly, I’ve data being used to prevent strategies from being deployed. In Seattle, for example, we have been talking about this education and opportunity gap for ages. It seems all these data-backed strategies have not been working to close this achievement gap for the past three decades. Yet, when community leaders advocate for trying something new, the response is often, “Yeah, that sounds good, but where’s the data proving that will work?”

When I pushed for closer collaborations between schools and nonprofits, for example, with funding going equitably to nonprofits to do family engagement, another committee member smirked and said, “Show me the studies that prove funding nonprofit partners directly will lead to results in school.” Dude, what I’m proposing MAY not work, but what you have been supporting HAS not worked. Yet, because he was able to pull up studies faster than I could, he was able to sway the rest of the group.

3. Imperfect data is used as a convenient way to make tough decisions.

For example, a local university decided they would discontinue a staff position focused on recruiting Asian students, stating that the data shows that there are enough Asian students, so there’s no need to focus on recruiting them. However, when the data is disaggregated, it shows Southeast Asian kids were underrepresented. The data, flawed as it was, was a convenient way for the school to make a decision and cut down on costs. (Luckily, the community banded together and pushed back, using the disaggregated data, and the position has been reinstated to focus specifically on Southeast Asian students).

4. Data is used to pathologize whole communities:

As Dr. Jondou puts it: “There is a dangerous pattern of behavior that emerges for researchers and consumers of research looking at data comparing groups. The first time we look at the data, we see a difference between groups. The next we look, we see a problem. Then we look again and we assign responsibility. Finally, we pathologize entire groups of people.”

Look at this study published in the Washington Post: “Researchers visited the children’s homes twice: when they were nine months old and again when they were 2 to 3 years old. On the first visit, the researchers assessed babies’ ability to manipulate simple objects, such as a rattle, and use and comprehend words; on the second visit, they assessed the toddlers’ memory, vocabulary and basic problem-solving skills.” From this, they concluded: “The research suggests that prekindergarten may be too late to start trying to close persistent academic achievement gaps between Latino and white students.”

Rochelle Gutierrez of the University of Illinois at Urbana-Champaign talks about this “Gap-Gazing” as a fetish many researchers have, and a harmful one that offer “little more than a static picture of inequity, supporting deficit thinking and negative narratives about students of color and working-class students […] and promoting a narrow definition of learning and equity” and proposes “a new focus for research on advancement (excellence and gains) and interventions for specific groups.” Why exactly are White students the gold standards for all kids to aspire to, when they all have different strengths and needs?

Really, testing kids at 9 months old and then when they are 2 or 3 and concluding that one group is definitely deficient and that it’s hopeless for them even before kindergarten? No one disputes the fact that there are differences between different groups of kids. But the conclusions place the blame and responsibility on 2-year-olds and their parents instead of looking holistically at all the systems that affect families, including poverty, education funding, curriculum, different learning styles across different cultures, etc.

If you’ve read this far, be sure to read Vu Le’s full post: Weaponized data: How the obsession with data has been hurting marginalized communities.