Data is Information. Period. It's that simple

No need to be intimidated. Don't over-complicate it. Data is just information, y'all!

T'Pring and Allison break down how to make your relationship with data less stressful. 

Transcript

SPEAKER_01 0:00

Hate Spring. Are you ready to data?

SPEAKER_00 0:02

Yeah. Let's data.

SPEAKER_01 0:05

Awesome. So I am Allison Holmes, developmental psychologist by training, philanthropic funder by day, researcher and evaluator at heart, author by passion, and data enthusiast in my soul.

SPEAKER_00 0:20

And I am Tipring Westbrook, and I need to apologize because we already recorded this, and we're now recording it again because I completely lost the first one. Don't know where it is. So I am a researcher and an educator and an author and a podcaster and a lover of all things data, but I also am uh absent-minded and lose things a lot.

SPEAKER_01 0:49

It's real. This is the realness of what life looks like. And also, you know, it's a nice little connection into some of our data stuff because who has not had a data experience where you're either entering information, trying to like find information, and it's just not there. It's just not working for you. Like this, this is very real. This is this is part of what life looks like. And uh, I think it's also pretty familiar to any person who has been on this planet for more than five minutes because we are all in technology and data. So to bring, I think you're in good company is the moral of that story.

SPEAKER_00 1:39

And I think it immediately lets anyone who's listening in know that we have no uh what's the word? We have no illusions or delusions about about our own perception and expertise and perspective because it means nothing. We're just people, we're just people peopling.

SPEAKER_01 2:04

Just doing doing all the people stuff like that. That is that is what we do. Um, but in in the spirit of peopleing, this this is the true invitation for everyone to come and people with us as we as we dive into the data. Apparently we're talking about data. What what does this mean? And we're we're surrounded by data party, but what what is that actually about? Like why why are we really here?

SPEAKER_00 2:38

Let's explain a little bit until people get used to us, they'll have to understand why we are doing this. So if you haven't gotten the book, you will see that we first put ourselves into the context of why we're doing this. So this isn't a data lecture, this isn't a data test, this isn't data authoritarianism, this is a party, this is data party. And the reason I think it comes off that way, and the reason that I think that we really um grasp that kind of concept, and a data party, to be sure, is not a phrase we made up, but it's what we're endorsing. Right. The thing about a data party is it's collaborative. There is no like hierarchical structure or anything crazy like that. It tries to be as fun as possible, it can break the rules, it can go outside the box. Like a data party can evolve to fit its need. It can be small, it can be big, it can be rowdy, it can be quiet. Like, that's what we're trying to get. Like, this is this is something that's uh both event and also commonplace, right? Yeah, like a birthday party, right?

SPEAKER_01 3:57

Right, right. And it's like, and like in addition to whatever party might trigger for you in a good way, and and maybe in a bad way. I mean, like it can do all of the things. Uh, but like you were saying, Tiffany, like the that idea of a data party, which is a full event in and of itself, um, documented in literature, it's meaningful, right? This has meaning for people. It is not, it is not going through motions randomly and without purpose. It is really meaningful to everyone that is there. And that I think really strikes a chord with some of our goals.

SPEAKER_00 4:42

So, why are we the host of data parties? So let's start. We are applied scientists, we're applied researchers, which means that we have our content, which uh where our content overlaps is early childhood and um developmental study. So those are the two big academic slots in the most broad definition. I mean, we can go we can go through the weeds, but I think broadly speaking, we are both early childhood developmentalists. We were interested not just in the theories and what happens in the lab, which is absolutely fascinating, and love to read about it, love to hear about it, go to presentations about it, and everything on that level. But we wanted to take what we were learning and take, and by we, not just the two of us, but what the field was learning in the research literature, we wanted to take that and hand it to the people who are actually doing the work. That crossover is a bridge. That is what translation is. When they talk about translating research into practice, that is exactly how we saw it. We're like, okay, this research tells us this is a good path to follow. How does that look on the ground? How does it how does it withstand the messiness and how can it work into the already existing funding structures, uh licensing structures, whatever? There's there's an existing infrastructure that this new idea or variation of an idea needs to fit into. And that's where we come in. That's what we really like. Going the opposite direction, we like to hear what practitioners, programmers, policymakers, all the non-researchers, what they want research to teach them. What questions do they have that they're wondering how to get at the answer? And then we like to, we in in different phases of our careers, take that information and translate it to the researchers and say, okay, people are looking for, you know, they're seeing that their evidence-based programs aren't working, their quote unquote off-the-shelf programs don't work off the shelf. What can we teach? What can we learn about adaptations? What can we learn about um minimum requirements uh for impact? All these kinds of things.

SPEAKER_01 7:22

Yeah. And I think like the kind of the underlying theme that folks may be picking up on is we are in the mess of all of this. There is nothing like super clean and like rigidly steps one, two, and three. No, no, no. We are sitting in the messy mess of research, evaluation, learning by doing that translation and connection. There's a lot of squishiness and ambiguity, and and like we swim in that. Like that is such a big thing. And as we were swimming in all of the ambiguity, like we have clear questions. And we had such a great question that came up, and it was an echo of previous questions before. But how am I supposed to be using this data? Like, where does this sit? And when we had that question, which is so clear, and like, what a great question! Like, love that, there was not a clear answer, right? And because we are researchers, we put our researcher hat on. We're like, let's go to all of the potential sources. And there wasn't an answer. So when we go to like data science, and when we go to um vendors who are selling data systems, right? Like the application and how we're using this, um, the answer wasn't there. When we go to, again, we're trained as researchers, researchers and evaluators, we're familiar with our training, but we checked other places too, just to make sure we covered that base. And the answer wasn't fully there either. And and then we we've sat in a funder's seat and we're like, oh, well, we'll just, we'll just fill this gap, we'll we'll pay for it. But that that actually isn't the full answer because there's not enough dollars to go to all of the places. And we know that this is like a common question that's coming up. And and then later we even look to see, oh, well, wait a second, wait a second. There's an entire professional um training related to nonprofits. So like it's gotta, it's gotta be there. Also, no. Also, not there. Spoiler alerts. Not there. So, really, we had kind of mapped out a lot of this ecosystem. And when we have you know, these amazing partners coming forward and saying, hey, I am really interested in, you know, doing this kind of evaluation or learning about this, but I don't know how to make some of this happen. Or maybe that I don't know how to make some of this happen comes as a result of trying to engage in research and evaluation. Cause again, that's where we're the hat that we wear and where we come in a lot. And we're like, well, who has this? There's clearly, you know, this gap of information. And at the end of the day, what we ended up doing was started to compile a little bit of what are those, you know, step, what we started to refer to as like step zero before others in the ecosystem were getting to a step one and starting to compile all of that together in engaging with partners in the nonprofit world. But the moral of the story is this is a continuation of how we were trained of the ambiguity and the messiness, and that it's not straightforward because it intersects with so many different pieces across that. But we just swim in it.

SPEAKER_00 11:14

And so we are and let let us let us say this at the at the near start. Would have been the start, but again, apologies. I lost the episode. So we're doing it. Uh we, Alison and I, um, we're we're kind of sarcastic. We can we can wallow in um gallows humor for a good while, very comfortable in that space. And we also use hyperbole sometimes for effect. I say all that to say when we're thinking in general, there is actually an assumption that everyone's trying to do the right thing. We're so we're gonna hear us on this podcast, trash researchers, we're gonna trash funders, we're gonna trash.

SPEAKER_01 12:06

It's a little extreme. I maybe I don't know. Look, I'm gonna be honest.

SPEAKER_00 12:11

No, because I I mean I do. For effect, if I'm trying to make a point and I'm just calling myself out on this now so that don't get lots of the questions about later, I will say, like, ah, like decisions I just made, they're trash. I obviously don't believe that. I am a researcher. I've I've been a researcher, I've been a funder, I've been a practitioner, I've been a programmer, I've been these things, and I know that in my most earnest, uh pure motivation, I have made mistakes, I have made false assumptions, I have somebody somewhere has called who's has loved me in the the bucket of trash. So I just want to make that clear. Like a lot of these disconnects are about miscommunications and not understanding. And so, yeah, in a 30-minute podcast to make effect, we might make some like really big slashes with no nuance. But just understand that uh we are we are talking about particular issues. So if we say uh fun uh if we say policymakers don't like to read policy briefs. That's a general concept. It's a general uh what do I want to call it? It is a general belief within the sector. You have to write a brief in a certain way if you're writing it for a policy person versus writing it for a researcher versus writing it for your for um like a practitioner who's gonna put it into practice. By saying that, that doesn't mean that any one person can't do what the other person is doing. It is really about the situation. So just understand that if we're saying this is where someone is falling short or some area is falling short, it's not because we think the area like like we just mentioned data scientists. We don't think data scientists, and actually this is a good place to start, we don't think data scientists don't care about nonprofits. We don't think that they are just doing cash grabs. We don't think any of those things. We think they come from one world and we and nonprofits are in a different world and they're just talking past each other. And so that's why we're really in the same way that we try to help help with the translation and the making that bridge, we also try to help uh each side by helping them communicate. So, like when we're working with practitioners, for example, we might say, What does your program need? And we'll say, Don't tell me, don't try to put it in research words. Just tell me what you actually need. And then we can do that translation. And so part of what we're always trying to do is to get people to they feel very confident that they know what they need and that language and jargon is the only barrier. And it's an easy barrier, it takes a little extra work, um, takes a couple of iterations sometimes. But no one, we assume that people are doing what they think is right and that they're doing whatever community spoken using that word as broadly as you can think about it, whatever community, whatever audience they are approaching, we assume they're doing that in good faith. So I I just wanted to say that because we're gonna get I'm a little I'm not gonna speak for Allison. Although I've read the book. And there's some there's a little there's a little spice in the book. But this is also a good time to plug that we're also watching a Patreon. The Patreon is where we might get a little looser with our tongues. Uh we might we I don't think we don't know what's gonna happen. That's just that way. We're gonna put, we're gonna We we we just we're gonna we're we're gonna say we're gonna say things, but we'll also be, we'll have the time to be more nuanced, more of a deep dive, more in the weeds, because the assumption is if you're supporting the Patreon, you're already invested in the conversation, and you want to really hear beyond what's on the pages of the book or what you might hear if we did a presentation at a conference. You want to hear the conversation we have in the car after the conference when we're all driving to the airport. Like that's what you're that's what you want when you're a Patreon.

SPEAKER_01 17:00

Yeah. And and those uh, I mean, are always amazing conversations. Like the conversation, like when you when you wrap up the site visit, when you finish that presentation, like all of those things, no matter what role you play, though Yeah, the car the car debrief, that's that's where it's at. Yeah. After hours.

SPEAKER_00 17:28

After the party, it's the hotel lobby, and after the sorry.

SPEAKER_01 17:39

Because the topic is vast, it is incredibly um pervasive, and there is so much um that we can't cover all of it um in this podcast. So like that's that's part of that purpose. And it's just really underscoring um the like how um foundational and critical data is uh in the nonprofit space. Um, because if it was not um that critical, if this was not such a core um aspect of work, we we wouldn't need all of that additional space. We could just, we could, you could just read the book, you could just come and listen to these, you know, little party bites of additional things in the podcast. But that's not that's not what it is. That's not where we are. Like data is so core and fundamental. Um, the the that paradigm and thinking that people haul the like, oh, this is this is just a luxury. When I have extra time and extra money, then we're gonna really dig into this. That ship has sailed. Like we are so long past that. And and so that that is that's part of why we really want to have this space. And again, motivation for why we put the book together. But it's just acknowledging we're we're not in, we're not in luxury good aisle conversation. Like we are at basic necessities meeting fundamental needs. And that that is how data and capacity and strategy work within nonprofits, which is like every other sector. But we're gonna acknowledge those some of those differences that you know are different versus like a public or a corporate sector that we see in the nonprofit space.

SPEAKER_00 19:42

And we might want to ease this up because clearly we're upsetting egg.

SPEAKER_01 19:48

Because we can oh yeah, well, that that time I was at, yes. So as everyone will learn, I have I have three cats and they are always very excited when uh when the ring light comes out um for various meetings. They're like, oh, oh, thank you for that spotlight. It is my time to shine. And so you will you will see. Uh, you will get to meet Egg because she usually likes to be right in front of the camera. And uh and now you get to hear a little bit of Nog. Nog is uh, she's she's one of the hunters, and so she wants to make sure she's like, oh mama, you're sitting down. Let me bring you, let me bring you a little treat. Let me go get the one of my toys and bring that to you because I am such a good hunter. So yes, always, always something.

SPEAKER_00 20:43

Just for continuity, you said you had three cats.

SPEAKER_01 20:46

Yes. Egg, nog, and glacier nutmeg, um, whose nickname is now Bobcat because she eats everything and she's big, like a bobcat. So yes, you will you will you will get to meet all the kitties, egg, nog, and glacier. So wonderful. Well, back to the data. That's the data. Here we go.

SPEAKER_00 21:11

Here we go. So absolutely, the the main I want to I want to make two points. Um, and I'm actually gonna write them down. The idea of um hold on. See, that's what I'm saying. See what oh, did I forget the second one already? I know I don't know, I think I might forgot the second. I'm so laughing because Maintenance Phase is a wonderful podcast that I listen to. It's a non scientific podcast that uses data really well. Okay, but that's not the point. The point is both of the hosts are over 40, and I recently listened to one of their podcasts. Where they're like after 40, you never say first of all, because you will forget second of all. So I was gonna try to write down my notes, but I only remember one. Hopefully the second one will come to me. I was like, oh, I got it. I got second one. All right, I'm gonna write it down. Okay, okay. Okay. So my first thing First of all, data is definitely not a luxury. I mean, it's it's it's information. And that's something we we we go over so much in anytime we're talking about data. Data is just a fancy word for information. And if you have information, then you have data. We say all the time, if you are like a Head Start teacher and you're like, oh, I don't know the data, but you know that you have 10 kids in your class, seven of them are boys, three of them are girls, two of them come from homes that are led by fathers, fathers, uh single fathers, um, and two of them are having their parents are having trouble finding a place to live. That's a lot of data.

SPEAKER_01 23:09

That's so much data.

SPEAKER_00 23:11

Yes, so much data.

SPEAKER_01 23:12

And no and it's useful data, it's not random data. Like that is useful information, like for that teacher, for that center administrator. So many things. Yes, yes.

SPEAKER_00 23:28

If you have information and if you value that information, then you are ready to talk about data and quality data. Like you're ready to do that, right? And yes, if if if knowing that, that little scenario I just threw out there, if that helps you determine, all right, so for today, the way I'm gonna run the class is XYZ. Today, the book that I'm gonna read for them is I'm gonna read a book about single fathers because I know that some of my kids are. This is now you're into data decision making, data-driven decision making. And we're we Allison and I honor that, we appreciate that, we value that. We think the fact that when researchers come in, the first thing they should be doing is sitting down with you and saying, What do you know already? There's a problem sometimes, and it's like, well, what are your variables? Or that somebody comes in with the wrong language. But you are the expert. You know everything. You're gonna tell me which questions are really relevant to your work, which ones, what is the stuff that you think makes it work? What is the stuff that you think is critical? Like all of that is coming from a practitioner. And I'm using teachers specifically instead of saying um a board member or a director or manager, uh, whatever, because another one of our points is everyone who touches your organization is part of your data team. Like from the person who they fill out their own, let's go, let's go all the way from furthest away we can get from uh a high high-earning CEO, high school volunteer, fills out paperwork and tells you where you're getting your volunteers, tells you which activities are drawing volunteers, helps you make budgeting decisions. Hey, you know what? This one seems to really work well with such and such high school. There's a class there that lines up what we're doing, we can create a relationship. I do you see how this can one piece of information drives so much, and we really want people to understand and and to understand and value themselves in the same way that we understand and value organizational data.

SPEAKER_01 25:58

Yeah, yeah. No, I love that you're saying that because it's it's starting to bring up the piece, you know, when because we're gonna keep repeating it, like that data is information, so 100%. And that data has a deeply human element. And that there, and this is something we were we will continue to talk about. And then and folks don't even get like, oh my gosh, we got it. We got it, but no, no, no, like we are gonna keep saying it, uh, because it is something that gets lost. And I think that example that you gave to bring is a really nice way to illustrate that of connecting people who are like both providing information and and like and that's that's exactly how this exists. Like the information is there, but people are providing it. There is a deeply human element to this and how that information is used, right? Where connecting those dots, that is that is still all very, very human. And we have to acknowledge the humanity of all of that, like who that information is, all of those pieces, as well as how it moves about within an organization, that at the core of all of this are actually people, right? Like sometimes we might call to mind data in a way that feels really dehumanizing, right? And that it's very disconnected. So because of that, like we're just acknowledging the world that we live in and how everything happens. But data is an information, has a core human and person-centered element. And I think that's an opportunity, just like you were saying, for people to feel confident in what they're doing, because you are connected to this, you are part of this. For you to feel empowered for what you're able to use with that, because we know that there is in the use of information and even in gathering some of that information, um, there are ways that you can exercise agency and power. So to name that, to see that, but that's that's part of that human experience. It doesn't matter what you what you paint, right? You can get into dashboard mode, you can use whatever technology tools you want. I mean, you can create an infographic. Like what, but uh underneath all of that are all of the people who are circling around this information. And like that's that's nothing that we can get away from. Now, at the same time, though, we are also living in this world where just saying again that like data is so critical, it is a form of currency, it is a language of communicating across, and just like we said at the beginning of all of this, seeing ourselves as part of some of this translation, but data is part of that language that is there, which is why it just feels even more important for everyone to be connected to it in order to use to use that power, to use that language because of what it is going to be doing out in the world, whether you like it or not, like that's just how it is. The request that you're gonna get, like what you're trying to share with other people, that is all a form of communication. And and that's and right now that data is a part of that. Information is a part of that. And so what would it mean to to recognize that and to sharpen how you feel your practices individually as well as an organization, recognizing that context.

SPEAKER_00 29:47

Yeah. I wrote down second of all. So I'm gonna go to second of all. Awesome. Second of all, what you're going to what we're doing with the products, the data party book, the data party podcast, and the data party Patreon, is we're essentially taking about 10 years of presentations, um, technical assistance, study, research, development, and we're putting it all in one place. So as Allison and I were doing our our jobs, and this again, I don't care if you have a data contract or not, you have a data contract. You know, like so we were going around doing this, and um the same questions were coming up and the same concerns over and over again. And then Allison and I found ourselves kind of staying, giving the same responses, and not like as a kind of pat response, but more as a, yeah, we've seen this problem, and this is how we've seen people work through it, right? Um so, in a way, this is, you know, Allison was talking, we're talking about like the Patreon is in the car. So basically, it's kind of like the book is the paper, the podcast is the presentation, and Patreon is the hundreds of conversations we have in hallways after the meeting, after meeting, after we're done talking, and people pull us aside. Like that's the all the whatever. So it's actually in Allison and I our our intention, our motivation when we when we did this, when we just started with the idea of writing the book, was we're uh this is seems to be helpful information. We want as many people to have it as possible. How do we do this? And by that time, we had learned a lot about the language we use. Um because we're saying we were doing all this, but again, it was being informed by what we were receiving and being iterated upon and revised and all of that. So we feel confident about the things we're saying only because they have been checked and rechecked and rechecked and rechecked. And one of the reasons why our comments, we really are gonna ask people all the time, hey, step into the comments, tell us things, and in Patreon, hey, let's have a conversation, because again, that's where we start. What do the the organizations who are quote unquote doing the work, quote unquote, on the ground, pick your phrase, what do they need? And what can we in our position as researchers with our networks, with everything, how can we inform that question? So we really want this to be as much as um possible, we want this to be a a conversation.

SPEAKER_01 32:58

Yeah, uh so much of a conversation. And and that the conversation, just to underscore part of what you're saying, they're tipping, the conversation isn't necessarily between us or between us and and folks doing the work. Like we keep that, like that's just part of you know how we're continuing to process the world. But so what do we see? It's the conversation amongst each other, like all of the folks who are out there and being able to say, like, yes, this is the space for you to come together and have these data conversations. And because again, we're just we're just translating, we're just connecting a couple dots. Everything is really held by you out there doing doing the work. And uh yeah, we're we're we're just connecting. That's we are just your party planners. We are translating, we are connecting, but it is just to bring folks together around these things, like you said, that we kept seeing. So to just sort it's one thing for us to say, like, oh my gosh, I've totally seen that you're not alone in that. Here's what this other organization did, may or may not be the solution for you. Let's like work through what a solution is for you. But to have the space for everyone to be able to share that, like you you don't need that translator for that conversation. There are other things that we can support in translating and and doing the party planning.

SPEAKER_00 34:36

We are we are definitely trying to work ourselves out of a job. Like we definitely would love we didn't we didn't have to do this because everyone knew it already and everyone had a grasp on it. That's that's our goal. That's our that's our goal.

SPEAKER_01 34:53

All right, Alison. Can we okay? Yeah, no, great. Go ahead. Well, I was gonna I was gonna wrap this up. Yeah, no, I think I think we did, I think we did cover it. Yeah, we've got all of our main points. Um folks know who know who we are. Hello. Hello, little bit of why we're here and where we're trying to go with all of this. Um, and I think those, and also what their role is, right? Yes, yes, yes, yes. Yeah, just in the passive role. This is not passive, no, no, no, no. I mean, like, listen, but then like, yes, come do, explore, connect, like all of those. Like, we can't do that. And that's not our job. That's not as good.

SPEAKER_00 35:39

Yes. All right, so this has been great. I hope that this kind of gives you a good idea of what our vibe is and also what we're trying to accomplish. Um, so that you when you step in the conversation, you're uh you're stepping in, like Alison said, knowing your role. And also don't don't show up regretting begrudgingly. Like, if you imagine Allison and I have done this over and over and over and over and over again. So we are really uh open to amusing ourselves. So and being amused by others.

SPEAKER_01 36:24

So yes, we're and we're having fun, actually. Like, this is fun for us, and so that's why it's an invit invitation to to come enjoy and come have fun with us.

SPEAKER_00 36:35

And and we're very self-aware people. I was I was listening on um audiobook, I was listening to the book uh The Body by Mike Bill Bryson by Bill Bryson. And I have no recommendations for the book, I'm just enjoying it as a book, but whatever. But I was so aware after a while of how statistically how much how data dense the book was, and how much I was enjoying that, and how but I think the growth that Allison and I had over the part uh over the years from starting in grad school together to writing this book is that yeah, not everyone finds that as fascinating as we do. So we know that we we we we we know that it's okay. Yeah, yeah.

SPEAKER_01 37:26

If you're into data at the same time, like we fully like hold the data nerd cards, like swipe it routinely, like show it proudly. Absolutely. So like both of those are true. Both of those are true. Both of those are true at the same time.

SPEAKER_00 37:42

So yeah, just wanted to make sure we were transparent about our nerdiness. Uh yeah. I hope you see us for who we are. All right.

SPEAKER_01 37:52

We'll get little tags. This is what a nerd looks like. So I'm doing this.

SPEAKER_00 37:57

I love it. All right. So, peoples out there, I hope you are being getting excited about the journey we're gonna go on. We really, like I said, we really want to have some fun, but we also really want to, as the social sector is kind of reshaping itself and restructuring, we really want to make sure that we we as a sector gain control over our data, that we um own our data, and that we use our data as currency to leverage, to get with our families, our communities, our businesses, whomever, to give them what they need. Yep.

SPEAKER_01 38:35

Yep, absolutely.