Ep. 70 - With Michelle Dunivan, PhD (Best Friends Animal Society)

Speaker A: Mhm. Welcome back everyone to the Data Ideas podcast. Podcast episode 70 underway here. Um, appreciate those that are joining live for those listening to the recording. This podcast will be out tomorrow. Uh, that is August 22nd. But um, if you are joining joining live, let us know in the comments. It's always good to see who's joining live, where you're joining from. Go ahead and drop a note and we would love to hear from you. Um, so this is Dustin, of course, joined today by Michelle Donovan from the Best Friends Animal Society. How's it going, Michelle?

Speaker B: Great. Thanks so much for having me, Dustin.

Speaker A: Yeah, absolutely excited to have this conversation. Um, Michelle, you've had a progressive role, um, with lots of kind of vertical movement and many years in the field. Um, but I always like to ask folks how did they first get into data and analytics? Uh, what, what got you into the field?

Speaker B: Yeah, well, when I was in academia. Right. A lot of what you're learning in those courses and uh, along the way is how to bring in, you know, how to quantify things that are fuzzy and messy and things like that. And that really just got me excited about like, you know, just hypothesis generation overload. Like I wonder if you could measure this thing in the world and I wonder if there could be an answer if you could, you know, how do you collect that data to figure that out? So it there where, you know, as soon as there was a pathway for being able to answer those really messy questions, especially about relationships and, you know, communities and things like that. And so, um, when I finished, yeah, that was the type of work I was looking for. And that was back in 2012 where I think the term data scientist had like just been coined. And you know, there wasn't a whole lot of information there about how to learn Python or you know, any of that kind of stuff. And so I had done a little bit of coding for my dissertation work and for my coursework and so I just really try to capitalize on that.

Speaker A: Awesome. Very cool. And we'll talk a little bit more about, uh, having a PhD. You mentioned your dissertation work. Um, I think that's an interesting part of your background. So looking forward to learning more about how that fits into your work in data and data leadership today. But maybe we'll take a step back and um, you work for an awesome organization, a mission driven organization, Best Friends Animal Society. Um, can you talk a little bit about what the mission is of that organization?

Speaker B: Absolutely. So the mission for Best Friends Animal Society is to bring about a time when there are no more homeless Pets. And so especially for the last about 10 years, our focus has really been on helping shelters and communities to reduce the number of animals being killed in shelters. So that it's, it's not, it's not a zero death goal, but it is really to really focus on only, um, only euthanizing the animals that really need that and really finding other pathways for all of the other adoptable animals that are out there. And so that's been, that's been the focus. Um, and it's great for a data person to have that really specifically measurable outcome that we're, that we're working towards.

Speaker A: That's awesome. Very cool. And curious too, to learn later in the episode here how this mission is supported specifically by data and analytics. But I want to ask first, you know, it's interesting because the last guest on the podcast, Brian Julius, um, was on and we were talking about a little bit different topic. We were talking about model context protocol. But, uh, Brian actually reached out when he saw that we were doing this episode this week and said, hey, I'm looking forward to this. Uh, because Best Friend Animal Society has been a big part of my life. I've been a contributor. You know, he's even traveled to volunteer. Do you hear stories like that often?

Speaker B: All the time. I couldn't believe it. You know, when I started working at Best Friends, I hadn't heard of them, to be honest. Right. I didn't even know that they existed. And as soon as I started telling people, oh, yeah, I'm starting a new job, uh, all these people came out of the woodwork that really knew about it and loved it. And that's one thing about Best Friends is that, uh, you know, we have a lower brand recognition. Another data point that we, that we follow a lot. So we aren't as recognizable as some of the others that are out there, but we have the highest favorability ratings once you do know us because of the incredible work that we do do across, uh, you know, every type of community geographically across the US and uh, so yeah, we hear, we hear a lot of things like that. And what's really incredible about Brian is I, he reached out to me when I had first started to talk about how he could help us. And we get that from a lot of volunteers that are interested. They say, I have this skill set. I have data visualization skills. I am, ah, a data scientist and I would love to help you out. I do program evaluation. Is there something that I can do for you? I have a PhD and I'd really Love to, you know, apply my skills to something that's, you know, really impactful. And so can I, can I help you with, you know, survey design or something like that? So we do get a lot, a lot of support, um, from the data community in the work that we're doing.

Speaker A: That's cool. That's awesome. Yeah. I mean, someone like yourself and someone like Brian, I mean, combined forces. I mean, that's scary in terms of what you would able to produce and support Best Friends from a data. Yeah, that's awesome. That's really cool. Uh, so you mentioned, you know, you're involved with communities across the United States. How broad is the reach of best friends?

Speaker B: So we, I mean, you can measure it a few different ways because, you know, we're in data, so we got to measure all the, all the meaningful ways.

Speaker A: So you're the perfect person to ask, ah, that question of.

Speaker B: Exactly, exactly. I've got all the numbers for you. Um, so we do have over 5,500 network partners. And for us that ah, is different organizations across the US that are supporting and helping pets. And so that's mostly shelters and rescues, but it's also, you know, spay, neuter operations and uh, you know, just national organizations that are helping with, with other work and how they can support in different ways, um, intake, diversion programs so that the pet never ends up in the shelter in the first place. We can find it a home outside of the sheltering system. So we have 5,500 organizations and there's only. There's a little bit less than 4,000 shelters. So that's a lot of organizations, considering the size of the, uh, the um, that are, that are committed and connected to us. So that's from the organization side, but we also have supporters. We have, uh, literally hundreds of thousands of supporters. And one of the reasons that we have so much is that there are so many, uh, there are so many paths to help us. So of course donations are always helpful and sometimes that's the best way that somebody can support us. But then there's also volunteering opportunities. I see in the chat, Adriana has, uh, you know, been in volunteer for quite a long time. And so that's another way that we really can get help. So the other thing that Best Friends does that we're mostly known for is actually not our national programs. It's our sanctuary. We have the largest companion animal sanctuary in the US and so that's the sanctuary you were talking about. Brian came to and volunteered. And so there's a lot of really Good volunteering programs, but also, like I mentioned, a lot of volunteering opportunities, uh, in the community from your home. You know, we have a really robust volunteer community that helps us collect data from all of the shelters. And then we also have an advocacy team, a grassroots advocacy team. So when we're going to go in and help a shelter, we really try and figure out in the community who's there that wants to help and that wants to be part of the solution. And so when we find these folks, we can also figure out a way to keep in contact with them and let them know how they can help these shelters that are in need. So uh, even if, you know, we've got a huge, huge network of, like I said, maybe even millions of supporters, but then over, you know, over 5,500 organizations that we're also helping in every single state across the U.S.

Speaker A: very cool. And um, if you're interested, if folks are interested, um, if you're watching the live or you see this after the fact on LinkedIn, if you click down into the comments, uh, I believe it was right, posted right onto this live Brian actually, um, or maybe the post for it that I did, but Brian actually shared a picture of him. So he was in the sanctuary I think.

Speaker B: Yep. Yeah, there in Canal, Utah. Yeah, it's rural Utah. It's gorgeous. It's right next to Zion. It's incredible. It's a really great opportunity to go in and really work directly with the animals because that's something that you know, not everybody gets a chance to do.

Speaker A: That's cool. That's awesome. And so before I get into the next question, just um, wanted to encourage folks as you mentioned, Adriana from Los Angeles mentioned that uh, she's here, she's volunteered in animal rescue for 20 plus years. So she's heard of the society. Um, we have LinkedIn user apologize for not being able to see uh, your name. I think some, something security related there. But um, they're mentioning they're currently post adoption behavior support volunteer. Um, if you are joining live, uh, we'd always love to hear from you. Let us know uh, who you are, where you're coming from and if you've had an interest or you know, been involved with best friends in the past, let us know that too. It's good to hear uh, those stories but um, you mentioned a little bit kind of facts and figures on the reach um, of the organization and you've obviously got a lot of data points behind what you do with how you support um, through your mission. But what are some ways that you specifically support the mission of your organization with data analytics and data science?

Speaker B: Yeah, it's a wonderful organization to be at because you don't typically see uh, a mission driven organization, especially in animal welfare, that can connect to the heartstrings of the people who care so much about the animals, but then can also be so objective about what is the reality out there and how can we measurably move that needle. And so there's a lot of support across the organization and expectation across the organization that everybody is using data to help move the needle. And so we have uh, like I mentioned briefly, we have uh, a really big part of our program is collecting data and making sure that we know what's happening. Animal welfare is one of the most decentralized, distributed, inconsistent industries you've ever seen. It does not. There's no federal regulation. Uh, every community can mostly do what they want. There's a few states that have oversight. But getting some consistent data from 4,000 shelters is a huge undertaking because uh, well, for obvious reasons but uh, they can. So we collect that data to find out where the challenges are and where we can help the most. And then when we go into these communities we look deeper at their data, we look at the animal level data, not just the aggregate. And so every single shelter gets that deep dive into where's the gap, where are they, where are they struggling? And so there's a lot of data literacy across our organization and working directly with the shelters to figure that out and then to measure, okay, here are the places we see gaps and we're going to continue to collect data more regularly so we can really see the effectiveness of our interventions and our support that we're providing. So uh, that's two main ways where we are really like, we're really pushing what is out there, where can we help and are we being effective in the help that we're providing. And so it really helps us also to measure the movement in general. Um, are things getting better or are they not? And what is the best friend's impact? Are we seeing a, ah, differential impact when we go in and help a shelter compared to when we don't help a shelter? And we see life saving at about two times as high in any shelter that we are working with compared to similar shelters that um, aren't engaged with best friends? So it's really helped, it helps us and it's part of just our DNA at this point to make sure that we are collecting the data that is going to help us and that we are actually using it to make decisions moving forward.

Speaker A: That's fantastic. Ah, there's, I mean the first thing that I think of when I, when I hear what you just said was I think a lot of mission driven organizations could benefit um, from having this type of support from a data standpoint. Um, so it's fantastic that this is embedded in your DNA as you said, um, at Best Friends Animal Society and look at the results that you're getting from it. That's really cool. Hopefully inspiring to others to maybe perhaps consider making that investment um, in a program similar to what, what you have if they haven't already. Um, what do you think is. If you think about uh, the. Actually you know what, I'm going to take a pause here and share. There's some good comments in the, in the chat. So Brian actually. So Brian Julius, who is uh, who we were mentioning earlier, he's here live, he said. One of the things I think Michelle and her team do an amazing job with is data storytelling and communicating data findings and insights to non data people.

Speaker B: Thank you so much. That's the best compliment I could get. I really appreciate that.

Speaker A: Very cool. And then Adriana says Clover Linwood is so important to ensure a good fit and low boring return rate. Cool.

Speaker B: Um, yeah, I think what Adriana's referring to there is there are a lot of instruments to. And it looks like she's a behavior support volunteer. And so I ah, would imagine it's maybe something along those lines where it's you know, we have to track what the actual, you know, experience is and then track it over time to see, to really be objective and clear about what the challenges are. And that's such a difficult thing to do a lot of times. But you have to, yeah. You have to know what you're working with. To start with that, that baseline data. That can sometimes be difficult in any project that you're pursuing. It doesn't have to be animal welfare. Right. You always need to know what happened before so that you can see um, what you're. Yeah. What you're. Whether you're, whether you're making progress or not. And be really honest with yourselves if you're not making progress because sometimes you're not going to make progress and you need to be really aware of that.

Speaker A: Sure. What do you think is when you think about, you know, so there's a ton of investment obviously in data analytics and data science in the private sector and in various industries. You know, I, I think there's not as much in, you know, Nonprofit and mission driven organizations, you know, which is unfortunate because it's obviously very needed and can benefit significantly from it. What do you think are some of the biggest differences between working in data for maybe a mission driven nonprofit organization versus in you know, private industry, as an example?

Speaker B: Yeah, I think I've already touched on some of the, some of the challenges and one of them is that, that uh, head versus heart approach, uh, you know, you have people who are so passionate and so experienced and we've got a volunteer for 20 plus years and I love that it appears that, you know, that one volunteer has already um, bought into the data that that is useful in making these really difficult decisions. But a lot of times it's really about uh, it feels like it's a luxury. It feels like it's uh, you know, it's cutting edge. It doesn't feel like it's foundational to uh, doing what needs to be done. So that can really be a challenge is getting leadership on board because oftentimes leadership has also been in the industry for so long, pre data science being a thing that you had in general organizations. And so you do have to have that, that ah, leadership buy in. I think we all know if you are, you know, if, if you don't have like CEO level support for data initiatives, they, you have a ceiling and you can't go past it. And um, and so I think that's a totally big barrier. Another, another difference that I see is that I, I don't see, you know, there's, there's ah, you know, shareholder and annual report reporting that happens. But kind of to Ryan's point, we do a lot of work to share our data very openly and transparently with the public. And that's not usually something that you see in that I've seen anyway from uh, from more private sector work. Right. We want the people, we want to get the word out there about what's happening and where there are opportunities. And that can be really complicated because animal welfare is complex. It's not as simple as just saying, oh, this many came in and this many left. There's so much nuance and like I said that decentralization makes like, well if it, if it works in one community, it doesn't mean it's going to work in another community. Or if this is the trend in most of the country, it doesn't mean that it's the trend in these, you know, really impacted um, organizations. And so uh, being able to tell that story in a way that is authentic to what really is Happening across the movement, but then, you know, having some, some questions or criticisms or, uh, you know, like, like that. Picking apart of like. But that doesn't make sense. How can you say that? And how can you say this? And it's like really bringing it all together in a way that, uh, is understandable and meaningful, but doesn't get too far in the weeds to alienate people who are like, what do you mean? The error bars are, you know, whatever. That's not, that's not a good use of the time either. So we really do invest a lot of time in working with our communications teams to say, these are our findings. How do we communicate it out to a different group? And they come back to us and we say, actually no, that's not right. You can't say it quite like that. You can say it like this, you know, and so really a lot of tweaking to make sure that that public narrative is authentic and uh, inspiring, you know, and balancing those two things is really difficult. It's not something that you think of in a data person. Right. You want the truth, the whole truth and nothing but the truth? Um, it's not always that simple. It's not always that straightforward.

Speaker A: Absolutely. And in that same vein, you know, there's a lot of talking about public, um, perception, uh, there's a lot of facts and figures thrown out around pets and adoption and things like that, you know, the news and in the media. Uh, but we know that data doesn't always support some of the anecdotal perspectives that are thrown out there, whether it's in the media or whether it's in meetings in our organizations or whatever. You know, um, data doesn't always support that perception that's out there, um, or it doesn't support what the loudest voices are saying. Are there some examples of that are kind of glaring to you where, you know, you see some things in the news and media that don't necessarily align with some of your findings and research.

Speaker B: You have no idea. The, uh, just a couple of weeks ago, we rolled out our mid year, uh, data report and it showed that There was a 19% improvement in life saving in over 2024. The same period in 2024 that is in direct conflict with the narrative going out there with the news right now where shelters are overwhelmed. They've never seen so many surrenders, they've never been more overburdened. And our numbers don't bear that out. Right. Our numbers are showing that by and large, across the board, not maybe for that one individual shelter. But overall we're not seeing an increase in surrenders. We're not seeing, you know, we're not seeing an increase in uh, you know, we're actually seeing an increase in adoption. So there's, there's a lot of things that are contributing to this, this overall life saving improvement. But that doesn't mean that that one shelter that is being profiled, it didn't have something. In fact we went back, we look through, we look, we pay attention to what the media is saying and it is challenging sometimes. And so you go back and you say, well, is that accurate? Yeah, actually that news story is accurate for that one shelter, but it's not accurate um, for the entire landscape. And uh, that conflict is really uh, challenging for our comms teams to figure out how do we get that narrative out. And also, just because we're improving doesn't mean that it's not still hard for all of the shelters like they are making improvements. But that doesn't mean that their lives are easier or that their jobs are easier. It might mean that they've taken on more responsibility in order to improve life saving. And so like really uh, understanding, even though we're the data team, understanding that human impact and the human story and those, those n of ones that we try to avoid in ah, you know, in a, in a data context, recognizing that those are real and they are going to have an impact on um, you know, the trust that people have in, in our messaging and the numbers that we have especially because when we throw out those numbers, if we try and explain, oh, we have a 95% confidence interval that tells us that you know, we have a sufficient sample like that is not compelling to the general public. And so how do we, how do we weave those different elements together?

Speaker A: Makes sense. That's really interesting. Um, and I did want to just pause for a moment. I see more folks joining. If you are joining. Um, it's always good to hear from you, where you're coming from. Let us know in the chat, um, and if uh, you do have any questions for Michelle, let us know in the chat as well. Maybe we'll have time near the end to take one or two. But um, good to see everyone join in. Let us know where you're coming from. Appreciate uh, those that are able to join the live today. Um, so switching gears a little bit, you've moved as we mentioned in the beginning into you know, different roles in the field. You've had kind of this progressive career journey. Can you talk a little bit about your journey into a senior leadership role in data specifically, as I know, you know, hands on, you know, kind of. And you're probably still doing data practitioner work, but like when you start out as a, as an entry level analyst, that can be very different work than actually leading a data team and leading the vision and strategy. Can you talk a little bit about that, what that that journey has looked like?

Speaker B: Yeah, so when I started out, I was very much individual contributor practitioner in the weeds. In the, you know, report development, it was just reports. There was no visualization software available to me. Uh, occasionally I would doctor up some Excel to make my point that I needed to make, but generally it was very SQL based. And um, but I think at the time that I started my career, like I said, you know, data scientist was just becoming a job description or a job title that people had. And so it was like there wasn't a whole team that I was competing with. I was like the one person that people would come to. And so you have a lot of exposure to leadership then because leadership is who is asking those questions and who wants to understand it. And so, and also my, you know, my, my studies are in communication. So it helped me to be able to apply some of those, um, understandings of, you know, organizational communication and interpersonal communication and leadership communication and understand, understanding where they're coming from and what they truly need out of it. So really helped me to understand, okay, this is where I should be allocating my time. These are, this is what they really want. They didn't know to ask for this thing, but this is what they're really getting at and building those relationships so that you can answer the questions that they didn't know they had. Um, and that it really helps them to make the point that they're trying to make. Then that, that was really helpful to me. And um, you know, moving into my first management role, I had a very small team, but the same thing it was, my team is I'm asking my small team to do things that they had just, you know, been doing because they're the one Excel expert in the organization for many, many years. So having that understanding of like, okay, you're used to a certain thing and you're, you're used to certain tools and it's meeting most of the needs right now. But I also, you know, because I'm curious and a constant learner, like, ah, there's a potential here to do something bigger and bolder and more exciting and so pushing the envelope and working with uh, leadership to continue to make Those improvements that are possible, um, it puts you in a position where you know, you are uh, thought of when other opportunities come available. So that's really. I had a manager that uh, really supported me and gave me another opportunity which was more, it focused, less just, you know, pure data analytics. And that has really, really served me well to be part of more of the software development and the um, you know, the, the project management side of, of Iot, uh, because especially in today's, I mean it just ended up being lucky that I had that opportunity. But in today's world with building AI products, you have to have that, you know, both sides of that to, to do good work. Um, and so yeah, just understanding that and, and being able to make those connections, you know, that that team just got bigger and bigger and uh, even here at best friends, just understanding like where we started and knowing that I see something better and if I can just like push the envelope a little bit and a little bit and a little bit, you know, I can m really help them to understand how valuable this is to the whole organization and why, you know, why this is its own discipline. Um, so I would say there's, there's no one big thing besides having supportive managers. And I know we can't all uh, predict that, but it's a huge, huge piece of why I've been able to do. What I've been able to do is, is having that relationship with multiple managers along the way is being able to really help understand for me to understand them and what they need and um, also to be able to translate that back to my team and then translate my team's needs, um, to my manager and build those relationships across the organization so that you aren't seen as just a ticket taker. Like this is your one span that you really could help and at least consult in a lot of different areas of the organization.

Speaker A: That's really fascinating. And I think, you know, there's so many perspectives around data and AI and what can be done with it. And you uh, know, I think there's even, you know, there's a lot of emotion around that as well. And so I think the manager that you have is really, really important, um, just in terms of the impact that you can make as an individual, the impact you could potentially make as a leader and the of ultimately the ROI that your organization gets out of the work that you and your group are doing. That alignment with your manager is really important. So I'm really glad you mentioned that. It's the same for, you know, I I wouldn't have been able to make the progress in my career had I not, you know, at the right time, been aligned with, um, a leader that um, we just, they, as you mentioned, you know, they kind of believed in what I was doing and was willing to push for it and advocate it. And um, I, I actually always uh, try to thank that leader in hindsight for all of that support because that was a big part of my, my own development. Do you have any advice for folks on like, I know you mentioned, and I agree with you completely, it's, you know, it can be hard to find uh, a leader to align with. Any advice on how to, to, to seek someone like that out or to, you know, try to find your way under someone like that in your organization. What's your perspective on that?

Speaker B: Well, I think the interview process is important and I don't know that people feel comfortable asking the questions that they really ought to be asking in that interview to find out is this actually a supportive environment? Uh, if it's not, you are not going to get very far. If, if leveling up is your goal and it's not for everybody. I mean, I work with the most incredible people that are like, absolutely not. I never want to manage people. And that's great. Don't force them to. Right. Like they don't, they don't need to do that work. But if you want to, you have to. You know, it's also, I would say even a little bit of a precursor to whether or not you can have those hard conversations as a manager because they will come up. You'll need to be able to have, especially in data and analytics. Right. So yeah, the interview is a, ah, piece of it. And um, yeah, ah, this feels extreme. It's like the exact opposite. But really being able to recognize when you have hit your ceiling, when the organization is just not interested in more change than what you've already delivered. If they're not interested in more transformation, they've stopped investing in your program, they've stopped promoting the work that you're doing. You have to be really honest with yourself and see that like uh, you may have hit your ceiling here and if you want to keep growing and maybe you don't, but if you do, it might not be here.

Speaker A: That's really insightful and that can be a hard realization I think, for folks to reach. Um, and we could probably do another episode on that topic in particular. I think that's really, really insightful. Um, a couple changing gears here a little bit. And again thanks to Everyone that said hello so far in the chat, if you are joining live, um, again, I'll give one last call. Let us know where you're coming from. Let us know who you are. It's always good to see, see, uh, the faces and names of folks that are joining live, um, but also, uh, hello to those listening to the recording, which is, uh, going to be available tomorrow, August 22nd. Um, so you're also a member, um, of a group, Women Leaders in Data and AI. Can you talk a little bit about, uh, this group?

Speaker B: Yeah, this is one of the groups that I've connected with. Um, as I mentioned, you know, when, when I started my career, I'm like a single data person, and as I moved up, I was like the most senior data person in a small group. And then you move up again and you're, you're the most senior person again. And I'm like, but where's my ment. For like, where's the person that can help me understand, well, what if I want to work for an even bigger organization or I want to work for, uh, you know, like a, uh, bigger team? I want to grow my team. How do I, how do I do that? How do I get that work? How do I, how do I navigate that? And so, yeah, that was a really great organization for me to get connected with. And I met some incredible, incredible women in that group that, uh, yeah, they are, you know, just. I don't even know how to describe the amazingness of, of all of the work that they've done. I've, you know, I've worked with some of them as contractors now. I've, you know, I follow them and I, you meet up at conferences and those relationships, um, have been, have been monumental. I, I, um, yeah, I think it's, it's really important to, to have that kind of leveling up opportunity to see people who are doing more than you are. And um, yeah, I, I hope that I can be that for somebody at some point who's, who's interested in moving into leadership. I've, I've been in smaller, and I think that's been almost like a, like an inferiority complex for me a little bit when I'm working with those people who are working for like, Fortune 5 companies. Um, and it's like, but you hear what they're dealing with, and it's so much of the same. It's just, they've got more people working on the problem, and the problem is bigger, but they've got more people working on it. And so but it's the same challenges that we're dealing with. And that's been very comforting also to have that connection with people, you know, just in very different environments.

Speaker A: Yeah, and I think you hit on some really important benefits of being part of a community. One you mentioned that you're inspired by some of the work that you see others doing, and that kind of, you know, motivates you to strive to that level, which I can totally relate to. That's definitely a benefit I've gotten out of being a part of data communities. Um, you also are able to kind of commiserate, you know, and get that encouragement from others and maybe some tips on how to, to get over obstacles, because there are a lot of similar obstacles that everyone is, is facing. But then also I thought an interesting thing you said was, um, you've worked with some of the folks as well that you met there, and that's an interesting point. You know, I get dozens, um, of messages per week on folks asking like, hey, should I go after this skill or that skill? Or, you know, what should I do to try to, you know, get my first job and things like that. And um, you know, sometimes when with the way that they ask the questions, I'm like, you know, looking at their experience and what they've done and things like that. Like, I'm not always sure that I would go after like, another skill or certification. Like, I would probably just like get really intentional about meeting people, getting plugged in, like making that kind of part of your, your job. You, um, know. And, um, I'm just curious, like, what, what are your thoughts on the value of kind of plugging into groups? In terms of the value you would get to like, potentially have opportunities open to you that you wouldn't, you know, have, um, if you weren't plugged into groups like that?

Speaker B: Yeah, it's, it's. I love that kind of stuff. I love going to community building events and going to conferences. I, I love meeting new people. And even if I, I don't know how much it's ever like, helped me with a job opportunity. Exactly right. But it's always helped me to think about things just a tiny bit differently or be aware of something that's going on. You know, this, um, ambient listening idea that's going on in a lot of healthcare fields right now. I heard about it at a random dinner that I was invited to that. I don't really know why I was invited to it, but I was invited. So I'm gonna go and I'm gonna meet Some cool people in the Phoenix area that are doing really great things. When I heard about that, I thought, what are the applications for veterinary medicine? What are like what are the challenges or what are the drawbacks? And so like I, I'm now hearing about, but at the time I hadn't heard about it from any. And this, this person had been leading this effort in a, in a physical uh, therapy group that I was like that's so incredible. And just being able to ask uh, similar things about like, well, how many people do you have working on that? Like, is that a huge project or is that something that, you know, like just some of those things where I think especially data and AI are moving so fast you couldn't possibly keep up with everything and there's so many things being thrown around. Even Brian last week with his uh, mcp, I'm like, I don't really know what that means. How is that different from, you know, these, like, from an agent? How is that different from like I'm trying to figure that stuff out and just being in person with somebody and not sending them an email that just adds to their to do list. But like just being there and in the moment asking a question or two, uh, is just really valuable for me and it gives me ideas about going back and so maybe that puts some things on my resume for uh, you know, the next, next job opportunity that comes along. But um, yeah, I think, I think just if I had to say anything it's like just be curious and um, not m. Risk taking. That's not the word that I'm trying to use. But just experimental, right? Like just go ahead and try some stuff and see if it works. And I think that's going to be probably the best thing to um, give you those opportunities to move into a data career is if you're really interested in something and you're not trying to find the one best, best thing. Because there's just so many paths, there's so many things to just try something and see where it takes you, see if it gets you somewhere, see if, and if you can attach it to the work that you're already doing and you can show that you've made an improvement. Like having that data point on your resume that you use data to have an impact in your organization. You don't have to have a data title to do that. And I think you've said that quite a lot, Dustin. Like you don't, you don't have to have a data title to do the work. And I just, especially now when data literacy is expected across, across organizations.

Speaker A: True.

Speaker B: You absolutely like, you would likely be encouraged to do that kind of work, um, or expected even to do that kind of work. So go ahead and just do it. Right?

Speaker A: Absolutely. Yeah. And I think that in whatever role you're in, you know, if it's a non technical role in title, you know, uh, formally not a technical role, I think it only increases your personal value proposition in that role. You know, I think it, it increases the likelihood that you're going to be seen as a very valu valuable contributor in that role and also potentially be considered for other technical roles as they come up in the organization, even if you're not getting that exact title that you want right now. So I love that advice.

Speaker B: Um, I want to add one more thing too. The data community is. This might be hyperbole, but I feel like the data community is universally friendly and welcoming. And so if you are not in your data team but you do something cool with data and you get stuck, it's a great networking opportunity also to reach out to the data team and say, I want to try this cool new thing I got here. I'm a little stuck. Could you help me? Uh, even if you don't have a data team, but you've got an IT team, you can, you can, you know, network a little bit. You'll learn something, you'll have an ally, you'll be making a little bit more of a connection. They'll remember you if they need help maybe testing something or if your comes up in the next video vacancy. So I would, yeah, that's something that has surprised me since I really started getting involved in the LinkedIn data community is just there's so much openness to help each other out as well.

Speaker A: Definitely. For sure. And you mentioned even. And I was in the same boat where um, when I first started hearing about M mcp which by the way was like two months ago, I mean this was not long ago, you know, I was like, um, yeah, I need to learn more about this. Like I'm starting at square one. And but the cool thing is, is that there's low barrier to entry to learning these things. Like you can start out at square one and with all of the information being put out in the community. I know Brian just posted something um, yesterday or this morning on you know, just like how to even get started. Like what's the first starting point if you want to use model context protocol? Like there's so much content out there, folks that you can reach out to directly to Ask questions to your point that are, are, you know, very open to helping others. Like um, and that's the cool thing about being able to be an analyst in your role, even if you're not in an analyst role. Like you know, you don't have to go out and get an advanced degree to necessarily do a project. You know, you, the education is largely there to be able to do it on the side and produce something of immense value without any formal education per se.

Speaker B: Yep, absolutely. I 100 agree.

Speaker A: Um, very cool. Awesome. Uh, so speaking though, I am a big believer though informal education and I think it fits in really well um, for certain roles, um, and leadership roles. You have a Ph.D. and I'm curious, um, how do you take, you know, how has that helped you in your, I guess ascent to your current role in the data field and kind of in what you do maybe as a translator of information, you know, today in insights like uh, what, what, how does it help you today in your current role and how has it helped in your journey as well?

Speaker B: Yeah, so when I first started I would joke all the time, I would say that was a huge waste of years and tens of dollars of my time because the job that I had I absolutely did not need a PhD for. Right. Um, and so that was, that was kind ah, of a self deprecating moment. Right. But then as I moved up in the organization and as we did a lot more, um, you know we're getting like the AI stuff though and the machine learning, all of that really, really benefits from an understanding of statistics and an understanding of research design and the scientific method. Like having all of that really helps you to be confident that the results that you're finding are not a fluke. And so that's where I see that kind of uh, that flip of you know, okay, I'm pulling numbers that are important for somebody to measure, like the time to disposition. It's important, uh, it does not require the scientific method, it just doesn't. Right. It's pulling numbers from a database and formatting them in a way that's going to be useful and aggregating them in a way that is going to fill a need. Um, but then yeah, like I said as you know, trying to expand my team and get them to understand more and more about inferential statistics and the things that you can say and the things that you can't say. Um, and can we be confident in this? And in fact just this last data release that we had for our mid year, we put together uh, a list of all of the kind of decisions that we've made along the way and like, why having less than 900 shelters represented is adequate. Right. And if you talk to somebody with, uh, you know, a research background, a sample of 900 is a lot, a lot of times that you do not often need that much. But then you have the AI side and you're like, actually, we would like thousands and thousands, hundreds of thousands of observations if they're available. And so just being able to see both sides of that has been really helpful in elevating the team and the organizations that I've been in and what they're capable of doing and how they see the data program and how much more we can, could be doing. So really helping to scale, um, all of those organizations has probably been the biggest benefit of that doctoral program.

Speaker A: Makes sense. Very cool. And for someone that's maybe considering going down the path of pursuing a PhD, um, particularly to do work in data science or in the data industry or in AI, you know, what, what kind of thought process would you have today in terms of evaluating, you know, whether you, you or they should do that if you were to go back and put yourselves in, in their shoes?

Speaker B: Yeah. I did not take the most direct route to where I am today. And so, you know, if, if speed is your. Or like, efficiency is your goal, I don't, I don't know that a doctorate will help you. You get a lot of, uh, the, the research methods and the statistics. You can get that with a master's, and it's not as rigorous, it's not as time consuming. It's, you know, you're not as likely to want to pull out your hair every single night. And so it's something that, yeah, you really want to think about, like, what are you trying to get out of it? I don't know. It's kind of applicable to the whole, you know, every, every data question that we are trying to get out of this. Right. And so from a doctorate perspective, are you trying to work in tech and build something new that's never been built before and understand something new that's never. That a question that's never been asked before, then a PhD is probably right for you. If you're trying to, uh, you know, optimize or maximize what you're already doing, or you're working in industry and not in, not in tech, and you're not, you know, you're not building the next generative AI product, then it might be overkill. It might not really help you to get any further than you would have gotten with uh, with a master's degree or boot camp or something that is more focused on the technical skills and applying them. I don't know. I've always wondered, should I go back for an mba? Because applying those technical skills to a business context is that other thing that I think I bridge that gap through my communication expertise. But I think a ah, business expertise would also be really valuable, um, especially for somebody who's already been in the workforce. That's another thing that I think about is you're just going straight through, ah, get a job after a master's probably. It'll help you refine what it is that you really want to do. And if it's uh, if, if that's the right path for you getting even more education, if that's, if that's really helpful or just a waste of time and money. Education is never a waste of time and money, but depending on your goal it might be a waste of time and money.

Speaker A: Awesome. Thank you for, for that uh, perspective and what's a piece of advice as we close down here? Last couple questions. Um, this has been really, really insightful and wanted to thank you for, for taking the time to share. Yeah. Your insights and perspective. Um, this has been great. What's, what's one piece of advice you would have for if you think about folks that are trying to move up vertically, you know, speaking specifically to folks that are already in the field or, or they're doing, you know, a lot of data work in their job and they'd like to move up potentially into a data leadership role like you have. What's a piece of advice or two that you would have um, for them to, to start that journey.

Speaker B: Yeah, I think the, the difference is how much time do you, you know, you have to be prepared to spend a lot less time doing the analysis and a lot more time uh, bridging that you know, data to outcome gap and understanding what people really need and working with stakeholders that don't understand the data world as much as you do, as they shouldn't because that's your, that's your niche. Right. That's your expertise. And so if they are interested in mo up definitely um, you know, pulling up like looking at, you know, taking more of a 35,000 foot view and even looking ahead. What is it that we're going to need? Don't just you know like really be more of a co creator of the future as opposed to, you know, doing what your, your manager is telling you to do. If you want to move up. Right. Uh, if you, if you'd like to take on that leadership role, that's going to really serve you well to be thinking ahead to, to. Okay, they want to be able to do this in a year. So if I work backwards, what am. What, what can I do or what should I do or how can I, how can I make that happen? Um, I think those are, those are two things. Like really like taking a broader view of the organization and what its goals truly are and building those relationships to uh, fully understand the, the business and, and what the individuals need in the business because you're going to need those allies. Um, I've never seen great management that didn't have great relationships with their own team and teams across the organization.

Speaker A: Love it. Uh, it's great advice and just wanted to give a shout out before the last question. Um, we're going to learn more about how folks can follow you as well as Best Friends Animal Society and learn more. But, um, just a shout out to uh, uh, the individual in the comments joining from Austin, Texas. Can't see uh, who they are, but, um, thanks for joining. Looks like we had folks from variety of different time zones. Um, that makes me feel good about the time that we selected to do the.

Speaker B: For everybody who joined us today. Thank you.

Speaker A: Yeah, absolutely. Brian was saying he agreed with you on the LinkedIn data community, being generous and helpful. Um, totally agree. Jake wanted to give a shout out to Best Friends and shout out to Jake too. I used to work with, uh, Jake here in our area. But Jake says shout out to Best Friends. We got both of our cats from the Sugar House location.

Speaker B: Love it, love it.

Speaker A: Very, very cool. Thanks uh, to everyone that joined live. If you're listening to the recording, which I know is where most folks listen to the podcast, um, and you would like to join the live and have an opportunity to uh, to weigh in or ask questions live. Um, we do try to go live once a week, um, and record the broadcast live so folks have the opportunity to duck in and um, interact with, with the guests if, if they so choose. Um, so something to keep in mind, it's broadcast live on LinkedIn as well as on the Data Ideas YouTube channel channel. Um, as we wind down here, how can individuals learn more from you, follow you and your content? Um, and also the Best Friends Animal Society.

Speaker B: Absolutely. So I'm on LinkedIn. Michelle Donovan, uh, is the best place to get a hold of me and uh, best friends.org is a great place to. If you're not familiar with Best Friends to take a look. Uh, yeah, come visit the sanctuary if you're ever uh, so inclined to take a trip out to, to Zion. It's right up the road. And uh, yeah, your local, we have lots of, you know, commit uh, commitments with the uh, the shelters in every or in every neck of the woods. So you can definitely find somewhere to, to help out. Uh, and you can find all of that on bestfriends.org that's awesome.

Speaker A: Very cool. And I will put links, um, to everything mentioned in the show notes so you can go back later if you're listening and find those um, in the show notes and uh, click on them and you can follow Michelle on LinkedIn as well as check uh, out Best Friends, Animal Society and all the resources that they have. Thanks so much Michelle for taking the time to join today. This was awesome. Yeah, super insightful. Yeah, super insightful. Tons of uh, knowledge shared here that I think folks would be very, uh, wise to, to listen to from you. You've obviously, um, become a leader in our field, you know, in, and you're a leader at Best Friends, but also in the industry and um, just appreciate your willingness to share your perspective and journey, um, with everyone.

Speaker B: Thank you so much. It was great to be here.

Speaker A: Thanks so. Yeah, absolutely. So thanks so much for joining everyone and we will see you again next week, um, for episode 71 of the podcast. Thanks everyone and have uh, a good rest of your week.