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Unbiased Narratives: Celebrating Black Excellence through Conversations About Career, Business, and Life!
With your host Kenneth Edwards, we'll have in-depth conversations with various guests to discuss Black excellence, including their personal journeys (such as, grit, learning from failure, etc.). Stay tuned!
Unbiased Narratives: Celebrating Black Excellence through Conversations About Career, Business, and Life!
Results Oriented: Driving Social Change through Research and Data
Discover how data can be more than just numbers and charts—it's a catalyst for social change. Join us as we chat with Dr. M. William Bill Sermons, Vice President of Research at Volunteers of America, whose journey from a civically-engaged upbringing in Washington, D.C to pioneering data-driven social change is nothing short of inspiring. Bill shares how his mother's involvement in the March on Washington ignited a lifelong pursuit of equity and impact, emphasizing the importance of being results-oriented in data science to truly understand and reach the populations that matter.
Explore a fascinating case of how a health outcomes management project in D.C. used data to transform community health. By targeting individuals previously unengaged with the healthcare system through strategies like risk-tiering and neighborhood outreach, Bill discusses how data can illuminate paths to better health outcomes. He also delves into the challenges that come with aligning independent data with government standards and underscores the critical role open-government data plays in shaping policy decisions that affect communities.
AI is revolutionizing how we process data, but with great power comes great responsibility. Bill offers an enlightening perspective on teaching data analysis using R at Carnegie Mellon University and navigating the complexities brought on by AI advancements. While AI offers the potential for deeper analysis, Bill stresses the necessity for careful implementation and awareness of data's limitations. As we wrap up, he imparts the essential value of lifelong learning in the field of social services and advocacy, equipping us with insights to harness data for meaningful social change.
Welcome to Unbiased Narratives, the podcast where we celebrate Black excellence through conversations about career, business and life. My name is Kenneth Edwards and I'm your host, and in this episode, we'll be talking about being results-oriented and driving social change through research and data. In this episode will be my very special guest, Dr M William Bill Sermons, who's the Vice President of Research with the Volunteers of America, located in Washington DC. I'm really excited about this episode's conversation, so let's get started. So, my brother Bill Sermons, thanks for connecting with me. How are you doing today, sir?
Speaker 2:I'm doing great. Great to be on the podcast with you.
Speaker 1:Well, it's really great to have you on, so let's dive right into our discussion. Bill, you're one of the leading researchers and data scientists who's out there doing it on the nonprofit level. But it all had to start from somewhere, right? So tell me about what sparked your passion for both data science and social change, and how did you bring those two worlds together in the work that you do.
Speaker 2:Yeah, good question. My interest in social issues started from a young age. I grew up, I moved to the Washington DC area when I was, you know, nine years old and sort of started exploring the city by metro and transit and sort of understanding oh well, different people live in different parts of the city, have different kinds of access. So even when I went into college and studied engineering the part of it that I was most interested in as it related to transportation some of my colleagues were more interested in the trains themselves and how efficient they were. I was always interested in like well, how do our decisions around technology impact people, particularly people of color, particularly vulnerable populations, that sort of thing. So that's always been something I've been interested in. My dad was always heavily involved my mom as well in civic pursuits. One of the stories that is often told of the background and my family is around my father staying home with the kids while my mom boarded a bus to go to the March on Washington.
Speaker 1:Yeah, that's what's up. I love it, man. It sounds like your upbringing really laid the foundation for your later professional and career pursuits. But, bill, I want to take a moment. Let's pivot back to the title of this podcast. Tell me, how do you define being results oriented in the field of data science and research? What does that mean to you?
Speaker 2:So, for me, the focus there on being a results-oriented, is doing the work of really understanding, you know, the populations that you're concerned about and the outcomes that you want for them, because I think one of the challenges, you know, we're in a sort of we're drowning in data these days. There's so much information that's available, so many public data sources, there's so much consumer data that's available, and so it's not difficult to get out there and find information, to find data, to find content, if you will, but making sense of that data is really, you know, really a challenge. There's so many things you could do, so what are you going to be driven by? And so for me, in order to be efficient in our use of resources, I always say let's start with well, what's the outcome that we want to impact, what's the population that we want to impact, and what do we want the world to look like for them, and why does the world look the way it does for them now?
Speaker 2:And oftentimes, that analysis is really what I see as making it results-oriented, because the alternative is that sometimes you see analysis that's done because it's interesting, or you see analysis that's done because it's interesting, or you see analysis that's done because that data were available. But you know, I always say, well, let's stay focused on our populations and the outcomes we want. And you know, if that means initially that we have, you know, some sort of introductory findings, until we get better data, let's do that. But let's not be distracted by richer data that maybe doesn't really give us insights into the problems we're concerned about.
Speaker 1:Bill, I think you might've just said something there. What's the outcome we want to impact? If I got that right. What's the population we want to help? What does the world look like for them? What's the population we want to help? What does the world look like for them and why does the world look the way it does for them? Those are very laudable approaches to take before you begin your research. Can you share specific examples where you went about researching that way?
Speaker 2:And tell us whether or not it led to a breakthrough or success in your work.
Speaker 2:So I would say that the best example I can think of and it's actually one of the projects that really, you know, in my work life first oriented me around that outcome question and the need to adhere to that, and it had to do when I was working as outcomes manager at an organization, a federally qualified health center in the Adams Morgan section of DC, and you know I had been, you know, bought on board as outcomes manager and I was focused a lot on, you know, our quality improvement work and we, you know, were in the position where we had our sort of first contract where we were getting reimbursed not just on the services that we provided but based on the outcomes, for sort of first contract where we were getting reimbursed not just on the services that we provided but based on the outcomes for sort of a full list of people that we're responsible for.
Speaker 2:And one of the things that we learned in that work when we started to look at our outcomes was when we looked at people who we were serving regularly, that were coming in the door, that were showing up for appointments, our health outcomes were outstanding, but when we looked at the full list of people that we were responsible for, what we saw was that there was this huge group of people that were just like unengaged and disconnected from care.
Speaker 2:And so, for me, disconnected from care, and so for me, that focus became like, no matter how good we do with individuals that come in the door, we're never going to be able to meet our thresholds or we're never going to be able to actually serve the population we're responsible for unless we do outreach. And so one of the things that we did there is, you know, I worked with a team it was summertime, we had some interns and we worked on a strategy for basically sort of like risk tiering, you know, sort of risk segmenting the folks, and so who are the people on this list, based on you know diagnoses and other characteristics, where they live, who are the people that we should probably most target first?
Speaker 1:You started with those folks.
Speaker 2:We started with those folks and we also had sort of these levels of outreach. So we started with phone calls and then we went on to neighborhood visits and we would in those neighborhood visits sometimes give tokens or transportation access or whatever we needed to do.
Speaker 1:But how did you stay clear and focused on the population you were actually trying to serve?
Speaker 2:One of the things that was clear was that, when I think about the results-oriented approach, we were focusing on this narrow segment of the population. We were concerned about those who were engaged in care, whereas the real population was this broader listing of people, and I'd even go even further. There were even people outside of that, probably, who were in the same communities but not necessarily on our list, and so I think that the importance of being able to focus on that full population that you're concerned about is critical. Without focusing on the full population that you're trying to impact, you might end up in a circumstance where what you're doing is that you're focusing on this really narrow piece. You're creating a nice boutique service for them and improving their lives, but the big problem that you said you wanted to solve, whether that be health access or health outcomes for this broader population, you're not moving the needle if you're not getting to them.
Speaker 1:Right and absolutely. And how does the data itself, you know, play a role in this bill? You know, listen, I've heard in working in space, all data, and good data and also data tells its own story and it's better you understand that before any inquiring minds want to know what the scoop is. So, Bill, with that being said, where do you go to get your data and what data do you find most powerful when working to drive social change?
Speaker 2:One of the sources I've started to rely on a lot in recent years are, you know, open sources. You know government sources of data. You know we often, when we think about the government, we often think about the census, and maybe some people are familiar with the American Community Survey, but there's so many other you know sources of information that are out there. Some of those sources are other surveys. You know there's a National Health Interview Survey. There's American Housing Survey. There's so many different ongoing surveys and even specialized data collections that happen because they're a rich source, they're an open source. Everybody can access them, everybody can repeat your results in them and oftentimes, if they're government sources, oftentimes know, understand, you know impacts on populations, impacts of policy decisions on populations.
Speaker 1:What sort of challenges do you run into when you're doing your own data collection?
Speaker 2:as it happens when you go out to do sort of your own data collection, is that the definitions that you use may not be precise or may not be, you know, aligned with other government definitions, and so, again, that's the nice thing about it also saves resources and allows you to maybe explore a broader set of issues.
Speaker 1:Got it and you know, bill. When looking at various data sources and trying to understand what would be impactful, what would be useful, I would imagine it's very easy to probably want to keep this stuff at a theoretical level. Right, but we live in a real world. So how do you stay grounded and practical when analyzing data, especially to answer questions that may relate to equity?
Speaker 2:Yeah, and I think that there's two thoughts that I have in response to that, and they both really relate to frameworks and I think about analytical frameworks.
Speaker 2:One of the things that I do is that I'll teach common statistical methods in a way that focuses on how to interpret those results, as it relates to issues like around disparity and disproportionality.
Speaker 2:So, for example, just this week in my class I was teaching a methodology that you know sort of popularized, I would probably say, in the child welfare world, where it was known that there were very different outcomes for children who were in the child welfare systems in various states by race and ethnicity.
Speaker 2:And so there was a methodology that came up about how to you know sort of compare the populations of children to the populations that are in care, to the populations that have certain outcomes, and actually calculate sort of disparity indices that showed who are the groups that are being disproportionately impacted by the policies or the decisions there. And I say that only to say I think that it's important to teach the methodologies that allow you to actually answer those kinds of questions If you don't have a point of view on it or if you don't have an analytical framework that allows you to actually calculate and say, oh OK, well, this group is overrepresented here. This group is underrepresented, and to be able to approach things that way, still using the exact same methods that they would use to analyze other kinds of data, but teaching it in such a way that it's specific to how to address questions of equity.
Speaker 1:Good deal Lots to consider. Hey, Bill, I know you mentioned that you're adjuncting at Carnegie Mellon University. I know you've been doing that for quite some time, but how's teaching going? What's happening in that world, my man?
Speaker 2:It's been great. I've taught in the public policy program as an adjunct at Carnegie Mellon for almost 15 years now. I originally taught a benefit cost analysis class that was sort of a class that already existed in the university, that I taught it in the DC program. But then now I'm teaching a class that I created and yeah, and that's really gratifying to sort of have a class that's, you know, got my fingerprints all over it.
Speaker 1:Bill, why don't you tell us the name of the course you're teaching?
Speaker 2:The class. It's the, you know using R for policy data analysis and R is an open source data analysis tool that a lot of researchers use and as an open source tool, it's free to download for students. You can have it on Mac, you can have it on PC, and so that sort of accessibility, lowering the barrier to entry, is a big part of it.
Speaker 1:Good stuff. So, Bill, you and I can talk, so let's take it there. My brother, the field of data science how shall we say? Lacks diversity, especially among African-Americans, Black folks, right, I would imagine. Oftentimes you're the only brother in the room. I'm sure there have been challenges. What's been your approach to dealing with and or navigating those challenges?
Speaker 2:Yeah, you know it is challenging in a lot of ways. I mean, I will say that you know, one of the challenges that exists, you know, relates to I'm often again thinking about, you know, vulnerable populations or disparities. Oftentimes those are involving, you know, those disparities disproportionately impacting people of color. So sometimes there's this oh okay, well, the black guy is worried about things affecting people, kind of approach.
Speaker 1:Right, right. But like you and I have discussed before on occasion, that perspective is sorely needed.
Speaker 2:One of the outgrowths of the pandemic early in the pandemic, the notion around health equity. I think it's something that people got to understand firsthand. So people started to understand that, oh okay, there's certain populations who are more likely to die from COVID whether that be age grouping. It did resonate with them during the pandemic that, oh okay, I've got a grandmother. I have one grandmother who's living at home on her own and has access to stuff. I have another grandmother who's in a nursing home and I know that it's moving quickly in those settings. So people recognize that there are things outside of their control that might impact how different groups of people with different characteristics or in different settings might be impacted. And I would say that that was helpful because a lot more people sort of started to understand notions around health equity, and so I think that that's been. I think that that's been important.
Speaker 1:Great, I know you told us, bill, at the top of our conversation that you moved to DC, but where are you originally from, my man?
Speaker 2:Yeah, so I was born in central New Jersey and I grew up in a town called Plainfield. I have siblings who are nine and 11 years older than me and you know, once my sister was off to college, you know, my mom and I moved from Jersey to first to DC and then to Prince George's County.
Speaker 1:And what about college? Where did you attend both for your undergraduate and graduate in education?
Speaker 2:Yeah, so undergraduate was at Duke University. I did my doctoral work at Northwestern. I also have a social work degree. So I have three degrees in engineering and I have a social work degree, and while I've met one other person with engineering and social work degrees, it's not that common.
Speaker 1:No, I'm sure it isn't all that common. But for a guy like you, your background, your credentialing, real talk what would you say that excites and scares you the most in your field? These?
Speaker 2:days in terms of emerging trends, I mean so you know I would say that you know the developments that both excite and scare me relate to AI. You know it's interesting to you know a sense a few years ago that we could limit how much people use it. But you know we've got to recognize that we're in a world where it's a useful tool and you know, one of the things when I think about the promise of it is that in this cohort, you know, I have students going further and further in terms of how deep the analysis is that they're doing than they were, you know, say, five years ago. Because they can debug. You know they can. If they, if their code isn't working right, they can feed that error into AI and it gets what's wrong with the code.
Speaker 2:Analysis like that, more accessible, creates this additional burden because now you've got to really understand the limitations in your data.
Speaker 2:Or maybe, to state it differently, you know, before you had to spend so much time getting access to data, figuring out how to analyze it that you got intimately familiar with it.
Speaker 2:You know I've had students in the past in my class try to work on issues like crime policy and there's a lot of research that indicates that there are places where there's more underreporting than others, and so if you don't understand those facts when you're analyzing data, you can make some inferences that you know, you think are about crime, crime behavior, that are really about, you know, local police policy or local criminal justice policy, the variations in it, and so you know that's the thing that worries me is that the ability to sort of take data and analyze it quickly and make a bunch of decisions without really understanding the implications of it.
Speaker 2:That both worries me, but it excites me the ability to be able to work with these students and get to much more complicated topics and more complex analyses with them. And so I think, as with all technologies, we get that sort of there's the good and the bad and trying to figure out how to both, you know, take advantage of these efficiencies but at the same time, figure out what guardrails could be put on so that we don't have, you know we don't have, you know, computers, you know, dictating our lives.
Speaker 1:I certainly take your point on that, sir. What you're describing, it's essentially the worst case scenario. It's called Skynet from the movie the Terminator.
Speaker 2:Right right, right right.
Speaker 1:Bill, as we bring this episode to a close, is there any advice you would give to professionals, emerging leaders, who aspire to make an impact in social services and advocacy? Like yourself, any words of wisdom to share?
Speaker 2:Yeah, I would say, you know, commit to being a lifelong learner and being curious, and you know whether that be, you know, I think these days there's so much content out there but being able to be a discerning consumer of you know the kinds of analysis that's, you know, really impactful and well done. I think there's a lot more. You know I get you know messages all the time about. You know you know short courses you could take that are a few hours or, but I think committing to like continuing to learn how to use new skills is, you know, is another piece of that.
Speaker 2:But I think that really you know that commitment to being a lifelong learner, whether it be, you know you're coming out of school right now like you're not done. You know you're going to have to continue to develop new skills and new tools and also put the time in I mentioned frameworks earlier put the time into learning the frameworks that govern certain policy domains or practice domains, so that you're not just doing, you know, sort of uninformed, unsophisticated analysis that you really are really like okay, in this field, I'm doing something on housing affordability, so really understanding what those measures are around, you know, whether it be housing cost burden or residual income or the other things that are part of the language, of how people talk about affordability. It's important to be able to be willing to put in that time to do that learning so that when you are able to jump into the analysis, it's informed and it's speaking to the people who have a lot of knowledge, decades of knowledge, on these topics.
Speaker 1:I love it. Brother Bill Sermons, you certainly dropped some knowledge on us in this episode. Thank you for the dialogue and your time. I certainly appreciate it.
Speaker 2:Absolutely Ken, Pleasure to talk with you I certainly appreciate it Absolutely, ken.
Speaker 1:Pleasure to talk with you, my listeners. You heard it from the man himself how to be results oriented and drive social change through research and data with the one and only Dr M William Bill Sermons, vice President of Research with the Volunteers of America. Thank you, my listeners, for tuning in Until the next episode. Stay blessed.