Webinar Resources

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Michelle HaynesBaratz: Evening. We’re

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Michelle HaynesBaratz: wherever you may be, joining us from, and be it live, or asynchronous welcome. My name is Michelle Haynes Barretts. I am a managing associate at community science. And I am so glad that you are here with me to talk about data by purpose and context matters.

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Michelle HaynesBaratz: So I know that the world is a busy and very swirling place. I don’t take lightly that you’ve chosen to spend some of your precious time with me, and my sincere hope is that it will feel like time well spent.

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Michelle HaynesBaratz: So this is the final session of our 3 part Webinar series on strengthening strategy and impact during uncertain times. Hopefully, you’ve had a chance to tune in previously and hear from my amazing colleagues. We had amber trout who kicked us off with a session on sustaining momentum when everything seems urgent. How do we keep going and working towards that long game? And then we heard from the wise Jasmine Williams Washington, who invited us to reimagine capacity building

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Michelle HaynesBaratz: as a holistic approach and means to achieve strategic alignment as we work towards lasting change.

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Michelle HaynesBaratz: So today, I’m going to round out the conversation by talking through the measurement process. That is, as we’re building as we’re moving through. How can we measure what matters? So that we know what’s working, what’s not and where we might need to pivot. So

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Michelle HaynesBaratz: some takeaways for today.

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Michelle HaynesBaratz: data is a tool. It is a means to an end. It is not the end, and of itself important to remember. Takeaway number 2. There is no one way. There is no right answer in terms of what one should measure or how to measure it. Rather, context matters deeply. And last, but not least, your data strategy needs to match the moment and your horizon of change. And this really has to do with thinking about change over time.

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Michelle HaynesBaratz: So before we dive into our conversation about data, let me introduce you to community science. I am but one of many voices of our collective, and I’m very grateful to have my colleague, Carlyn Morales, who is joining us here today who is also helping with making sure that this webinar runs smoothly so as questions come in and so forth. She’ll be in the background making sure that I’m paying attention to those. So

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Michelle HaynesBaratz: community science, we work with governments, we work with foundations, we work with nonprofit organizations on solutions to social problems. And we really do so with a systems change lens and always centering equity at the core of what we do. So this is actually a 3 part Webinar series, as I mentioned previously, and it is being brought to you by the organizational effectiveness practice area at community science.

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Michelle HaynesBaratz: where we really partner with mission driven organizations to strengthen their capacity, their responsiveness, power, and impact.

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Michelle HaynesBaratz: And we really see organizations as living organisms themselves, and also as part of a larger ecosystem, and we believe deeply that organizations are always stronger when they center equity in what they do.

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Michelle HaynesBaratz: All right. So let’s go ahead and dive in and talk about why it is that we are here. So the landscape is shifting. We’re in a moment right now across philanthropy and social change movements. Really, where so many of us are trying to work differently. We’re trying to be more trust, based, more collaborative, more grounded in relationships and lived experience. And this shift has been sort of in the works for some time

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Michelle HaynesBaratz: now, but in some ways the push has really been accelerated recently

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Michelle HaynesBaratz: and frankly, when everything is on fire as seems to be these times. It’s easy to get caught up in the overwhelm and the frenzy. But as a sector, as we mobilize away from transactional grant making, and from sort of the rigid logic of turn on investment, especially as philanthropy is eager to meet the moment. If we’re leaning into nuance, if we’re leaning into flexibility and complexity.

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Michelle HaynesBaratz: how do we still stay grounded right? How do we stay accountable to our purpose, to our communities and to ourselves? And how do we know that we’re on the right path? And that’s really what today’s conversation is about, how can we measure what really matters without getting lost in the weeds or being tethered to the wrong indicators.

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Michelle HaynesBaratz: So the key one key, I would say, is to remember that data

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Michelle HaynesBaratz: is a flashlight. The data is not the endpoint, it is a tool to guide you through the unknown.

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Michelle HaynesBaratz: So once upon a time in my former life I was an academic, and I taught research methods religiously for a hundred years, and I loved it, and no one ever wanted to teach the class. It was a required course. Students sort of loathed it, and frankly, it was because they were scared of it, you know, they’d say, Oh, but why do I need to learn this? And I’m not good at math. And I want to be a psychologist because I want to help people. You know, why do I need this?

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Michelle HaynesBaratz: But for me. I really, I love methods, and it’s not because I love math. It’s because it’s a powerful tool to help people better. Right? It’s about coming up with clever ways to understand, to inform. So how can you help if you don’t know what’s wrong, or you don’t know what’s needed, or you don’t know what matters? You don’t know what’s working? What’s not right. So

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Michelle HaynesBaratz: my love of teaching methods was really about sharing that insight. You know, I lived for those light bulb moments where you’d feel that resistance sort of melting into curiosity like there’s really there’s nothing to be scared of. It’s just a tool. It’s a flashlight that helps guide you through the dark.

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Michelle HaynesBaratz: So that said, we need to learn to use the tool wisely. Okay. So you may have heard this expression, what gets measured gets managed. It’s a quote that’s been attributed to various folks, Peter Drucker, typically. And then we’re wondering if that’s true. And often if you go back digging. It looks like it was likely the average way that that made the point. But the issue remains which is.

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Michelle HaynesBaratz: once you really start measuring something that sort of focused attention right? And so when people pay a lot of attention to that metric and de facto, not others.

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Michelle HaynesBaratz: you can miss really important stuff, right? And the lesson there is that you better make sure that if you are measuring, you’re measuring something that actually matters. And the problem is that we often can fall into some common traps. So let’s talk a little bit about what those traps are that can distract, that can freeze, or that can mislead us.

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Michelle HaynesBaratz: So the 1st one we measure what’s easy, right? We measure how many people went to a training or liked a training, but not whether that training actually mattered, or whether it changed behavior or made a difference.

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Michelle HaynesBaratz: Sometimes we measure too much right? So this kitchen sink approach. You know. One more. Question. One more, one more. And then eventually, we’re drowning in data, and we don’t know what to prioritize or, frankly, what it tells us. And people can only hold on to so much information. And if everything is important.

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Michelle HaynesBaratz: but nothing’s important, right? And we see this one a lot, actually, this sort of data overwhelm, and it freezes folks. And there’s so much data by the time that they make sense of what to do with the data, it’s no longer even relevant because the time has passed or the moment has moved on.

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Michelle HaynesBaratz: So that’s definitely one to to pay attention to.

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Michelle HaynesBaratz: We lock ourselves into rigid measures. And so we kind of miss real change that’s happening in front of us again. In fact, we stay so focused on this metric that we’re not open to paying attention to other indicators. That may be signaling, hey? Changes afoot right? Something’s evolving in a slightly different way than maybe you had anticipated.

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Michelle HaynesBaratz: Another one is that we spend too much time reporting or asking for metrics that it drains the work. I’m sure folks can relate to this one. Right? So you’re prepping for this meeting. You’re prepping for that meeting. You’re collecting data. You’re crunching data, and you’re doing it in such short periods of time you don’t actually have time to do the work right? So

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Michelle HaynesBaratz: question becomes, what do we do right? How do we avoid these sort of pitfalls?

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Michelle HaynesBaratz: so here are some considerations and some offerings. I’m afraid that there’s excuse me no right answer. There’s no sort of magic wand, but it’s important to keep a few things in mind. The 1st is that context matters

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Michelle HaynesBaratz: deeply. In the tundra, you know, a good winter coat is essential.

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Michelle HaynesBaratz: If you’re in the tropics. That is just not at the top of the shopping list, right? So, even though it would be great to always say X plus y equals, Z. That’s the way you do it. It’s not the way that works. Context really really matters right.

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Michelle HaynesBaratz: And then the second consideration is that sometimes we need to slow down to speed up. And this is, I think, really hard. I catch myself falling into this trap sometimes, where everything feels urgent and swirling, you know, and we can lose our focus in that frenzy. And then when I personally feel that swirling, I have come to train myself.

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Michelle HaynesBaratz: It’s a signal. It’s a data point, if you will, to to breathe, to slow down and to move with intention and purpose. Right? Because ultimately it’s what prevents those missteps in the long run.

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Michelle HaynesBaratz: So with those 3 considerations in mind.

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Michelle HaynesBaratz: here are 3 questions that hopefully get you a little bit closer to using data effectively.

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Michelle HaynesBaratz: So the 1st one is, are we what we’re really trying to understand? Right? Really thinking through what you need to know. So we worked with this one client that was clearly committed to using data.

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Michelle HaynesBaratz: But the data collection process had sort of taken on a life of its own over time. The process had just multiplied, it had cauliflowered, and there was this very long survey, and while it was interesting there was no clear why, so it wasn’t clear.

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Michelle HaynesBaratz: that is to say it wasn’t clear why, knowing X about a particular set of people would change anything in terms of how the clients were approaching the work, and so ultimately we ended up partnering with them

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Michelle HaynesBaratz: to refine that data collection strategy and to make sure that the data that they gathered was truly meaningful and could generate insights that would actually drive decision making that would change the way if needed. This client was approaching the issue.

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Michelle HaynesBaratz: Question. 2. What would progress actually look like, what would it feel like? So in another example, we partnered with a foundation that was working to transform the early childhood education, landscape at a systems level.

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Michelle HaynesBaratz: and from the beginning we worked with them to really co-create and operationalize their vision, and we did so with both quantitative and qualitative indicators of progress as well as cross-referencing and cross, checking that with the grantees, and so forth in the space.

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Michelle HaynesBaratz: But another key to their success was their openness and their curiosity to really understand what progress looked like, and also using that data that we were collecting in real time to make tweaks, to make decisions, for example, to provide technical assistance or capacity building to grantees as needed, so that the progress could continue to be realized.

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Michelle HaynesBaratz: Last, but certainly not least.

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Michelle HaynesBaratz: who defines what matters right? And are we actually listening? And so in our work with another foundation, we’re seeking to understand best practices for inclusive decision making. There’s definitely a literature that speaks to this.

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Michelle HaynesBaratz: And we’re working and learning from that literature. But we are also working and learning from those who are most proximate to the issues in their particular context and context is always important. But in this particular project it’s really nuanced and critical to make sure that the voices of those who are typically divested or marginalized from this decision-making process

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Michelle HaynesBaratz: get to define their version right? Of what meaningful inclusion looks like when we’re doing this measurement. So

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Michelle HaynesBaratz: those 3 questions are hopefully useful tools to think through how we can use data effectively. Now, something.

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Kerlin Morales | Community Science: To keep paying Michelle. We have actually a hand raised, Jeff. I’m gonna allow you to talk to ask her question.

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Michelle HaynesBaratz: Okay.

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Jeff Gilbert: I misclicked. I am so sorry I did not.

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Kerlin Morales | Community Science: Oh!

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Jeff Gilbert: My hand raised. Sorry.

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Kerlin Morales | Community Science: I’m sorry.

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Michelle HaynesBaratz: Yes, I overstand. I misclick things all the time, but thank you so much for for letting us know. All right.

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Michelle HaynesBaratz: So

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Michelle HaynesBaratz: all right the 3 horizons. So something to keep in mind. With those those the answers to those questions that we just reviewed is that it really kind of depends where you are in the process of change.

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Michelle HaynesBaratz: And so if you have been following this series, you’ve seen this slide before the 3 horizons of change model. So the idea is that you have the 1st horizon, which is immediate. It’s right. Now it’s what’s in front of you, and it’s sort of like what’s urgent. Right? And then you have the 3rd horizon, which is that long, long term vision. Right? It’s basically

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Michelle HaynesBaratz: it’s why you’re doing the work. It’s the impact you want to have, and it’s the why. And then you have the the second horizon, which

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Michelle HaynesBaratz: is often the hardest to sort of think through right. It’s the bridge. It’s how you get there. How do you get from where you are now to that long term goal and where you want to be. It’s the how

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Michelle HaynesBaratz: and from a measurement perspective, you want to make sure that you’re aligning the measurement with where you are in the horizon right? And oftentimes the best way to do that is to backwards map right? So you start with that 3rd horizon.

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Michelle HaynesBaratz: What do you want to build? What are you working towards, and then think backwards. What are the signals that would suggest that you’re on your way? What would help you? Course correct if you needed to. So there’s now what’s emerging? Really early science

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Michelle HaynesBaratz: horizon? 2 sort of soon it’s all relative. But in the middle, what are the patterns? What are the relationships. What are the culture shifts?

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Michelle HaynesBaratz: And then, last, you’re working towards that 3rd horizon which is deep change, which is typically policy, institutional change, systemic change as well.

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Michelle HaynesBaratz: so

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Michelle HaynesBaratz: when we’re thinking about those 3 horizons. So we’re thinking about sort of okay, these are the questions, the framework that I’m applying. And I’m thinking about, how do I apply that in this context of thinking through time?

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Michelle HaynesBaratz: Some other things really to keep in mind change is not linear, and it it doesn’t happen at a constant rate of acceleration. Right? So folks might typically think of this kind of line graph when people think okay, increase over time. But rarely is that how change happens right? So think of the notion of things like tipping points.

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Michelle HaynesBaratz: A lot has to happen oftentimes to tip the scale which frankly, looks a whole lot more, either like an S curve. Right? So slow, slow, slow, and then eventually you get a tip, and then you get a plateau, or sometimes we see exponential growth as well. Right? So the idea is that we have to be really nimble and and thoughtful in the process of figuring out what to measure, because we don’t know before it happens, what the shape’s going to look like.

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Michelle HaynesBaratz: And it really depends on place and time, what you’re going what you’re going to see. And if you go looking for linear though

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Michelle HaynesBaratz: chances are you’re not going to find it. It’s not, I mean, it’s not de facto, but chances are you won’t. And so tomorrow it’s different than 6 months. It’s different from 10 years from now, and I’d actually like to share what I think is a really nice, real world example. This was a data visualization put together by Tribu and Collins. I may have said that name incorrectly

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Michelle HaynesBaratz: case. I apologize if I did but from Bloomberg business. And they created these data visualizations that look at the rate of change

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Michelle HaynesBaratz: for social policy issues in the Us.

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Michelle HaynesBaratz: And what I love about this graph. So various issues, you have interracial marriage, prohibition, women’s suffrage, etc, etc. What I love about the graph is that you see a few things really clearly.

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Michelle HaynesBaratz: So first, st you see that depending on the issue.

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Michelle HaynesBaratz: that the rate of change looks very different. Right? So clearly, context matters change doesn’t always look exactly the same. You also see some nice examples of some S curves. You see, some nice examples of some exponential stuff. You don’t see too much linear going on. But I think it really is a nice underscoring of this idea. That change. It depends right? And so some things even go up.

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Michelle HaynesBaratz: or rather go down before they go up. Right. Things get worse before they improve in terms of thinking through your metrics. So again, all in the context of really being open to what the pattern of change is, how it’s going to unfold.

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Michelle HaynesBaratz: So something else to keep in mind.

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Michelle HaynesBaratz: there are subsystems that are also changing at different rates, right? So organizations are not monolithic. They’re sort of the rate of change of the whole organization. But then, within an organization.

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Michelle HaynesBaratz: there are these subunits, right, these subsystems. So, for example, you have individual departments. You have different organizational functions, and they also have their own rate of change, right? These nested systems. And so you may have one department that’s going 20 miles an hour. You have another that’s going 45 miles an hour, and you have one department that cannot turn on the engine right? So, keeping in mind that you have the whole unit. But even within the system that you’re studying, if it’s an organization or that you’re working with.

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Michelle HaynesBaratz: there’s other things going on below the surface that are potentially moving at different rates.

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Michelle HaynesBaratz: And last, but definitely, not least, is this we we like to use this expression, that the the runway is long.

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Michelle HaynesBaratz: It’s really a truism that we see when we’re working with a lot of clients. So we get in and we realize that that on ramp, those building blocks. They take a long time to set up to lay the groundwork.

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Michelle HaynesBaratz: And and for anyone on the front lines who’s actually doing this work. You all probably know this pretty well right, and the key is that your measurement strategy has to be attuned to this.

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Michelle HaynesBaratz: So another. Once upon a time story, I was part of this multi year organizational change, initiative, and for the 1st year we were laying groundwork. It took a year a year to meet with all the different stakeholders to. With all the different groups, to discuss issues from their perspective, to get buy-in, to do all the things right, and and some of it kind of felt invisible. Right? If you were measuring our our end.

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Michelle HaynesBaratz: our end goal, our end, Kpi, if you will, which was culture change, which is what we were working towards. There’s no way it would have showed up.

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Michelle HaynesBaratz: But that relationship and awareness building

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Michelle HaynesBaratz: was critical. We would have had a huge organizational initiative that would have never taken off, because it would have been tone deaf, or it just would have been launched in a vacuum, so that whole year

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Michelle HaynesBaratz: was really crucial to building the bridges that we needed to launch phase 2 and phase 3, which eventually did lead us to those kpis which was what we were working towards. But again, the the point of the story is to be realistic, that change takes time, and that the rate of change really is important in thinking through our various measurement strategies.

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Michelle HaynesBaratz: So

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Michelle HaynesBaratz: as we round out the conversation today, so this is sort of the last content slide, if you will. I want us to consider one more quandary, and I imagine this is a situation that will resonate with many of you. It’s what we in the Oe team have been calling the head heart decisions right? You want to do it all. You have limited resources. I think many of us can probably relate to that. And so then the question

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Michelle HaynesBaratz: from a measurement perspective becomes, how do you use data to make those really hard decisions?

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Michelle HaynesBaratz: And so we recently worked with a client where that was precisely the question, right? This organization was trying to figure out how to maximize this impact of a pretty limited resource for the most needy in their community. And historically, with this this particular context, this had translated over time into spreading the fund funding more broadly, so they wanted to make sure that everybody got something.

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Michelle HaynesBaratz: but as the number of requests grew, the amount that any one person might get, or 1 1 org or initiative might get

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Michelle HaynesBaratz: decreased.

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Michelle HaynesBaratz: So when we started to engage with multiple stakeholders in the ecosystem, we really started to look at data holistically. And what we found was that this very well intentioned approach was really diluting impact rather than driving change. And so in the end, what we did was we really focused on

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Michelle HaynesBaratz: defining what was essential in their particular context, right? And once we were able to figure out what was important for this group of folks, we really helped to reimagine what meaningful equity driven investment might look like. So instead of spreading those resources thinly.

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Michelle HaynesBaratz: they’re now considering some recommendations for making more more strategic, more high impact funding decisions that serve the most underserved communities.

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Michelle HaynesBaratz: And so as we wrap up right thinking through, that’s really the goal. Right? When when I started. I said, one of the key takeaways data is a tool.

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Michelle HaynesBaratz: That’s exactly right. So the question becomes, how can we use data in a thoughtful way, not just to collect data for data sake, but rather so that we can use it as a tool, so that when we are in the dark and we don’t know how to proceed. We can use our flashlight right and thinking through again that

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Michelle HaynesBaratz: how I use the tool is going to depend on where I am in the process, you know. What do I need to know? Now? What do I think is happening next? And what do we hope to see?

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Michelle HaynesBaratz: And so with that I am happy to take any questions that folks may have, and I thank you for your time.

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Kerlin Morales | Community Science: You know, as people are thinking of their question, I know that there were submitted. There were some submitted earlier. So if we want to get started with one of those

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Kerlin Morales | Community Science: as people thinking. The 1st one would be, what are some useful practices to account

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Kerlin Morales | Community Science: for personal bias when contextualizing data and the analysis and reporting section.

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Michelle HaynesBaratz: Yeah, it’s a really good question, because we all have them right. The personal bias is a very real thing. I think that being aware of them is really critical clearly, and then also to the extent that when you’re engaging in these measurement strategies that you are working very much in community with

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Michelle HaynesBaratz: other stakeholders, to make sure that you are collecting data in a way not only that is useful in terms of how you are seeing it, but also in the way that that they too are seeing it. So really using a participatory approach

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Michelle HaynesBaratz: and doing that from the beginning as much as possible. So that means involving folks from the beginning in data collection. So when you’re thinking about what to measure, when you’re thinking through how to collect that data. What are the best strategies for collecting that data, and then also for making sense of of the data as well. So I’ll quote

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Michelle HaynesBaratz: my colleague, Jasmine.

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Michelle HaynesBaratz: She likes to say we don’t.

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Michelle HaynesBaratz: We don’t like to be right. We want to get it right. And one of the ways that we can make sure that we are getting it right is to check our assumptions. And so oftentimes we go back to folks that we have worked with, and said, Hey, here’s what we heard

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Michelle HaynesBaratz: is this, resonating with you to make sure that in fact, we have gotten it right? So hopefully

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Michelle HaynesBaratz: that provides they’ve got ticks.

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Kerlin Morales | Community Science: Yes, and we actually have a hand up from one of our attendees sharing. If you would like to share your question.

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Sharon Duncan Jones-Eversley: Great. 1st of all, thank you so much. I really appreciate it. It was just very refreshing and definitely needed one of the things that I really love is working with communities, particularly as it relates to people’s lived experiences.

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Sharon Duncan Jones-Eversley: And I guess maybe about 20, if not 35 years ago Robert Wood Johnson did, or had a series of really helping community

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Sharon Duncan Jones-Eversley: investors what I call them, people who live in the community to really kind of understand research. And then, you know, because of funding interest, it kind of went away. But are there any suggested user friendly software that you have found that has that have that has been

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Sharon Duncan Jones-Eversley: works well with communities with diverse strengths as well as challenges.

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Sharon Duncan Jones-Eversley: because I really think along the way I feel like sometimes our research is like a foreign language, and once we leave, how are they continuing to interpret or build upon some of that information? So thank you so much. I’m gonna I guess you all will mute me so I can get the response.

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Michelle HaynesBaratz: Okay, let’s thank you for that question, Sharon. I really appreciate it. It’s a great question.

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Michelle HaynesBaratz: so I, I agree with you wholeheartedly right? So when we’re thinking through sort of centering the experience of of our communities, we want to make sure that it’s not just in an extractive way, right? That that we are providing resources in terms of folks taking ownership of their own sort of

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Michelle HaynesBaratz: data and knowledge and continuing to build upon that. You asked about some software. I don’t know that I have a software platform per se recommendation. But I do think that there are some, some tools, and actually we have one that when we

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Michelle HaynesBaratz: sort of that that talks through, you know what is a logic model and sort of talks a little bit about the basics. Of what research can look like and and and breaks it down in a it doesn’t have to be technical right? I think that

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Michelle HaynesBaratz: that it that the that when folks get beyond the the jargon, it’s something that everyone can resonate with right just collecting information to make better decisions. So we could certainly include that at the tail end of this sort of 3 part series we’ll send an FAQ. And we can send that link along. No problem. Of course. Of course.

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Sharon Duncan Jones-Eversley: That was very helpful. Because I think that is it. It is.

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Sharon Duncan Jones-Eversley: It’s just like when we have to become familiar with their language and how they operationalize terms that that exchange should be bidirectional and not from a hierarchical perspective. So thank you so much. I really appreciate it.

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Michelle HaynesBaratz: Of course, thanks for taking the time to join us today. I appreciate you.

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Kerlin Morales | Community Science: Next question could be drilling into the how, what approaches have you used that help? Teams develop indicators? Indicators that really matter.

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Michelle HaynesBaratz: Say, Oh, okay, I I have now just managed to find the question thing so I can read it again. Thank you. All right. Let me take Julian. What are the approaches? You

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Michelle HaynesBaratz: tell the team development indicators that matter? Yeah, it’s a great question. So drilling into the how the how is oftentimes we don’t do it alone. So we really do take the time to work with our clients. And you know we sometimes use Community Advisory board, so on, and so forth, to

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Michelle HaynesBaratz: yes, use what we know, but also to make sure that we are working with

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Michelle HaynesBaratz: folks with whom we are trying to serve. And so, having a conversation in terms of thinking about what not? What do I think change would look like in your community? What do you think change would look like in your community. What do you think it would feel like? So it’s really about that participatory co-creation process. And you can collect that data in multiple ways. It can be stage right in stages. It can be

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Michelle HaynesBaratz: through a survey. It can be through focus groups. One of the things. I think that’s important is that you know, data collection is not often a 1 and done right. But we take the time to collect some data, have it feed into our process in some sort of way, and then go back, see if we need a little bit more, maybe around 2, refine so on, and so forth, and so

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Michelle HaynesBaratz: I think that that is one possibility. And then also you don’t always have to reinvent the wheel right? So I do think that there is merit and value at looking at

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Michelle HaynesBaratz: previous approaches, what indicators have potentially worked well. And then building upon that right, using that sort of expansive mindset rather than always sort of

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Michelle HaynesBaratz: insular and and closing.

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Kerlin Morales | Community Science: And I don’t know if you would like to go to kind of that 3rd question that kind of drills more into.

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Kerlin Morales | Community Science: Do you have in mind any specific metrics for those who are supporting lending and technical assistance to both small businesses and real estate developers.

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Michelle HaynesBaratz: Listen. Oh, I’m not seeing the real estate developer question. Oh, recommended data metrics for this.

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Michelle HaynesBaratz: I don’t know that I have a commonly recommended metric for that specific context.

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Michelle HaynesBaratz: I would have to think about that. And then perhaps we can add that to the to the FAQ. After we have a moment to to percolate that, but nothing off the top of of my head comes to mind.

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Michelle HaynesBaratz: I see another one here. So now that I’ve actually found the box the one about. Can you talk a little bit about navigating between quantitative and qualitative data? We all know that Quant doesn’t capture the nuances of what all is actually happening. But qualitative data is hard to quantify and show overall impact. But when we do that it flattens out the nuances what to do.

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Michelle HaynesBaratz: Great question. I think it’s a both, and, to be quite honest. I I truly believe that there is

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Michelle HaynesBaratz: merit and value in both. And so if there is space and capacity to have those

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Michelle HaynesBaratz: quant indicators with the contextualization of the qualitative, I think that that’s the best of both worlds. I also think that depending on who you’re speaking to.

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Michelle HaynesBaratz: and

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Michelle HaynesBaratz: the data, certain data is more compelling to certain kinds of folks, right. And so to to the extent that that’s also true. I think that there’s value in capture, in, in

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Michelle HaynesBaratz: not capturing. I think that there is value in trying to get both and seeing

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Michelle HaynesBaratz: how they can play together. Right? It’s the both. And instead of the either or so that would be. That would be my recommendation, and that is typically our approach. I have. I have not done something solely quant or quantitative in quite a long time, and it’s and I was trained as

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Michelle HaynesBaratz: as very much as a quantitative person. But I see tremendous value in making sure that we have the context. Right? So

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Michelle HaynesBaratz: both.

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Michelle HaynesBaratz: Okay, as founder of an intermediary. What are the best approaches to balancing?

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Michelle HaynesBaratz: So I don’t know if other folks are able to see the question. So I don’t know if no, they’re not. Okay. So another question as a funder of Forgive me. Funder of intermediary funders and technical assistance providers, what are the best approaches to balancing. How to ask for the right data from the intermediary about outputs and outcomes from both

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Michelle HaynesBaratz: the intermediary and the end. Beneficiary.

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Michelle HaynesBaratz: Well, best approaches for balancing. I mean, I do think that there’s something to be said for really taking that.

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Michelle HaynesBaratz: What do you need? What? What do you need to know right? So I I do think that there is something to be said for

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Michelle HaynesBaratz: have to know versus nice to know. And again, I do think that that’s a a function of

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Michelle HaynesBaratz: I think that’s a function of what you’re trying to accomplish. And so if you are in a capacity building perspective. The question is, I think, oftentimes what we want to know is, is it working right? So what are the one or 2 or 3 things that you can ask to figure out whether or not your approach is working, and or if there needs to be tweaks, I do think that there really is something that can be that is beneficial to re imagining data

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Michelle HaynesBaratz: as something that’s feeding into a continuous decision making process rather than this. Just this static thing that we use. And so if you reframe in that light hopefully that can help narrow the problem space. Because what you also don’t want to do is, fall into that trap of asking for more, more and more, and then it creates an issue where we can’t even make space to get the work done in the 1st place. So

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Michelle HaynesBaratz: And then I think that there was another one that came in beforehand. I want to be able to spread

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Michelle HaynesBaratz: a little bit of attention. Here we had someone who had asked about

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Michelle HaynesBaratz: how to make competing entities play nicely in the sandbox when it comes to mapping data elements and sharing definitions. And I think that it’s a really excellent question. I think we are now in a moment of collective.

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Michelle HaynesBaratz: you know, collective movement, all trying to hopefully work towards the same thing. And so then the question becomes, how can we reframe our work rather than this sort of 0 sum kind of competitive game. How can we reframe it as working towards the the collective

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Michelle HaynesBaratz: the collective good, and there is certainly plenty of work to be done to to get us there. And so I think, really recasting and reframing that collective goal. What is it that we are all trying to to work towards is a useful 1st step. And I think that hoarding

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Michelle HaynesBaratz: data or hoarding information really doesn’t do anyone any good in the long in the long term. And so thinking through ways of

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Michelle HaynesBaratz: of of recasting, and also potentially carving out space, so that folks have their their pieces of the collective pie, if you will, is also another thing to think through.

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Michelle HaynesBaratz: So

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Michelle HaynesBaratz: there is another question about asking about how to track racial wealth gaps. So knowing it’s difficult to track how racial wealth gaps are measured, what are ways to track proxy data and what publicly available resources are out there to try to track them in Wealth Gap

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Michelle HaynesBaratz: to track wealth gaps in communities. I don’t have off the top of my head. I have some databases. That that I’ve worked with. But I don’t want to give incorrect information. That’s something we can also make sure that we

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Michelle HaynesBaratz: we attend to in the context of the the FAQ. When we do our follow up Post Webinar.

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Michelle HaynesBaratz: All right, folks, I will take this, then, as

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Michelle HaynesBaratz: as our opportunity to say, Thank you. Thank you so much for

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Michelle HaynesBaratz: joining today if you had an opportunity to join my colleagues. That’s wonderful. And if you haven’t. I know that there are recordings so you can listen to those wise folks

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Michelle HaynesBaratz: talk about their perspectives as well. And by all means, if you’re anything like me. You hang up or you get off a call, and you think oh, I had that one extra question. Please feel free to reach out happy to hear from you, Mhanes Barretts, at communityscience.com. We will follow up with a recording and some faqs as well post this webinar.

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Michelle HaynesBaratz: and with that I say, thank you very much.

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Michelle HaynesBaratz: Take good care, everyone. Thank you. Bye, bye.