Data Walks – From Research Subject to Research Partners


Part of the reason I chose to come to MIIS was because I wanted a school that was focused on outcomes and not solely research for research, like many graduate programs.  Academic research is helpful but largely inaccessible to the general public.  Try to think back to the first time your teacher had you read a scientific paper in school.  They can be overwhelming and confusing if you’re not used to the language being used.  Even in public policy and social sciences, where much of the research is aimed at helping low-income, BIPOC communities, the results of the research are seldom shared with the communities they are designed to help.

Most research ends up in formal reports, filled with technical jargon and confusing infographics.  Should community members even be exposed to the final product, it can seem intimidating or patronizing.  Unfortunately, hiding these findings is a missed opportunity to gather context for the collected data and also to promote self-determination and community-based decision-making.  Building these connections with affected communities is a win-win; researchers gather better context for their data and the community has access to information that can aid them in implementing programs.

Thankfully, in recent years, organizations have developed methods to bridge this divide and help make data more accessible to communities, which has important potential for low-income, BIPOC communities.  One such tool, Data Walks, was developed by the Urban Institute to help encourage dialogue by engaging community members to view data presentations in small groups and then jointly interpret the results.  This method allows participants to tap into their individual experiences to connect it with the data and discuss ways to improve policies and programs.

Some perks of the Data Walks method is that it can help researchers improve their analysis and understanding of the data, shape policies in order to address both the strengths and needs of specific communities, and to even inspire action among community members.  This method moves community members from research subjects to active research partners.

Sharing community data on employment, food security, mental health, and more combined with program-specific data, such as participation and engagement, can be very informative, especially when combined with individual experiences.  In some cases, community members that partake in the Data Walks method were able to give more informed answers in focus groups than was typical because they were able to pull from specific data points and a larger context.  It’s important to note that providing national or state benchmarks for context is often necessary to comprehensively interpret data.  Careful preparation must be taken when preparing the data and presentations for the data walk to ensure it’s accessible for the intended audience.

When used correctly, the Data Walks method has the potential to benefit a range of programs within BIPOC communities.  This could include designing and evaluating school programs, housing opportunity policies, adolescent sexual health and safety programs, and more.  Almost any community program would benefit from informed conversation and personal experiences from residents, and that’s what Data Walks aim to achieve.

Data analysis can help reveal problems and solutions across programs and communities, but it can also perpetuate the issues if not used correctly.  The ability of the Data Walks method to engage BIPOC communities can help address some of the structural racism embedded in data analysis and presentation, while also benefiting the communities directly.


Assessment: Just as Important as the Program


The problems with policing in America have, for centuries, been a long-unheard outcry from communities of color, dating back to the origins of police practices as slave-catching patrols[1]. In the summer of 2014, four Latino citizens killed by police in the Monterey County seat Salinas, CA brought the issue closer to MIIS than ever before. Although the officers involved were eventually cleared of all charges by the DA, the public unrest that was sparked following the shootings had already brought the topic of much-needed police reform to the city[2]. The Salinas Police Department’s (SPD) first act was the request of a review by the Community Oriented Policing Services department of the Department of Justice. Their 2016 report highlighted, among many concerning discoveries, a weak relationship between the SPD police and the communities they were meant to serve and protect[3].

Vox; Data from FBI’s 2012 Supplementary Homicide Report[7]

Police reform over the years can be characterized by two steps forward, one step back, sometimes achieving progressive victories such as Miranda v. Arizona (1966) (which instituted the required reciting of Miranda rights by law enforcement upon arrest)[4], and just as quickly reverting back with Terry v. Ohio (1988) (which led to the famously racist policy of Stop and Frisk)[5]. Although discussed, debated, and legislated upon for years, awareness of racism present in policing practices was not brought into mainstream American consciousness until the early 1990’s, when the 1992 Los Angeles riots made the issue unavoidable. Conscious or not, racism in policing didn’t become a topic of priority for mainstream American politics until the 2010s, thanks in large part to the emergence of the Black Lives Matter movement following the acquittal of Trayvon Martin’s killer[6]. They brought to attention the drastic disparity between the rate black Americans, often men, and white Americans are killed by police[7].

The recommendations made by the DOJ in their report brought about a host of new initiatives at the SPD to strengthen internal policies and practices, as well as several new programs for the much needed strengthening of community relations[8]. One such program was Why’d You Stop Me? (WYSM), which our very own META Lab was contracted to help assess (as required by the grant which funded WYSM in Salinas). The final report, published in 2018, is a prime example of what happens when qualified, well-intentioned data researchers are given unrealistic requirements to evaluate programmatic success or failure. The META Lab members who created this report would likely agree, as they state in the methodology section it was “… expected that the time allowed under the grant would be insufficient for substantial changes in perceptions to be formed, much less detected”[8] (p. 16). The researchers specified four long-term goals to evaluate which would have served as excellent indicators for program success[8] (p. 12-13), had their timeline allowed for a full assessment of them.

Authors of the report are very clear in the limitations of their findings, none of which result from any shortcomings of the researchers themselves. They can instead be traced to the inadequate timeline that the researchers were offered to produce a robust evaluation of the program. The final recommendation in the report, and perhaps the most important one, is “to continue to monitor and test whether or how the program is having an effect” and “that a follow-up evaluation be funded and conducted in Salinas to test whether general outreach has been achieving the goals that were behind the grant application that initiated this project and this process in Salinas”[8] (p. 49). Were Salinas decision makers not to heed this final recommendation, they would face great risks when basing future decisions on highly limited data.

Quantitative evaluation is a vital component of any program, but when done inadequately can cause more harm than good. Decision makers must understand that holistic assessment cannot be an afterthought to new programs and policies, but rather an integral part of any earnest initiative for positive social impact. Even if a program fails to meet its intended goals, a well-designed assessment plan applied from start to finish can inform future progress. Failure is a far better teacher than success, and without assessment we are rendered unable to even determine the difference. Unfortunately, the price of failure when it comes to police reform is the loss of lives, disproportionately black and brown, and continued failure (or lack to assess thereof) can not be tolerated.


Facts Only: Real life impacts of racism

By: Daniella Saint-Phard
Date: April 6, 2021

Racism persists all around us, whether in education, housing and infrastructure, or healthcare systems. Racial groups face problems on every front. Some of these problems include more police presence, less funding, social interventions, opportunity, and credibility. Data is so important to combatting the everyday issues BIPOC folx face. It is literally a driver of change and we must be responsible with it.

“The PRMR [pregnancy-related mortality rate] for black women with at least a college degree was 5.2 times that of their white counterparts.”

“Cardiomyopathy, thrombotic pulmonary embolism, and hypertensive disorders of pregnancy contributed more to pregnancy-related deaths among black women than among white women.”

Have you ever wondered why pregnant black women fatalities are higher than all other races? Or wondered why women of color in general have higher pregnancy-related mortality rates than white women? These terrifying statistical findings are provided by the CDC. These statistics serve as a tool to illuminate the lived experiences of the BIPOC (Black, Indigenous, People of color) community. How can data then impact BIPOC experiences? It reflects realities and provides insight into areas (variables) of possible change. Throughout data analysis, it is important to be mindful of implicit racism while navigating the method planning, data framing, and historical context.

Based on the CDC’s findings, the following recommendations were made to hospitals and healthcare providers: provide higher quality care, pay closer attention when diagnosing, and learn more about warning signs across different races. The implementation of these recommendations can prevent at least 60% of these deaths and lower the PRMR. 

A glaringly important aspect of data collection, analysis, and presentation is utilizing ethical, responsible, and unbiased language

These recommendations do not address the implicit racial bias faced by black women and other minority groups. Data science and analysis should be for the good of people. As demonstrated in the PRMP statistics, you can have all the numbers, but clear and accurate presentation is equally important. A glaringly important aspect of data collection, analysis, and presentation is utilizing ethical, responsible, and unbiased language. There are three important takeaways from this mini case study of real life data to consider when doing analytic work: methods, framing, and context



The provided PRMR statistics reflect one aspect of the population, but many other aspects (variables) of life that impact outcomes are not included. It is important to be mindful of reflecting the analyzed population in all stages: planning, methodology, implementation, analysis, and reporting. Tailor data collection methods to your population including, but not limited to, survey design, population sampling, administration/implementation, and monitoring/evaluation.


The wording of the above reported data can read as accusatory, placing the blame on black women and other minority groups, when in reality the most effective intervention should be implemented by healthcare providers. The wording also includes high-level understanding of medical conditions that the average reader may not immediately understand, which is why it’s important to know your audience. Language is a powerful tool for advocating and presenting data. Check your biases. Peer-review. Communicate openly with the data’s reflected population. Report clear and concise information. Make raw data and findings accessible to affected communities. These are simple habits to develop while dealing with data collection, analysis, and reporting. 

notebook with pen on messy desk


The historical and background context is so important to understanding figures and statistics and applying them in a beneficial and ethical manner. The goal should be to provide a full picture of the situation and the “why” behind data results, rather than reporting data for open interpretation. These reports impact expenditure, planning, and policy decisions that significantly impact life trajectory for many people. When context is not taken into consideration, racial biases and discrimination persists for the BIPOC community.  

In conclusion, data is a powerful undercurrent of lived experiences and gateway to change. Be an ethical and responsible data analysis change driver for not only BIPOC communities, but everyone. That’s it. That’s the message of this blog post.

For more in-depth information on data analysis, visit the META Lab bootcamp course. For more information on the insane reality for BIPOC pregnant women, visit the website.