1 00:00:00,000 --> 00:00:05,040 All right, welcome to Making Data Matter, where we have conversations about data and leadership 2 00:00:05,040 --> 00:00:10,000 at mission-driven organizations with practical insights into that intersection between the 3 00:00:10,000 --> 00:00:14,960 mission, the strategy, and the data. And I'm your host, Sawyer Nyquist. 4 00:00:14,960 --> 00:00:18,320 And I'm your co-host, Troy Duhik. And today we're joined by guest, 5 00:00:18,320 --> 00:00:21,360 Emily Hicks-Fortella. Emily, welcome to the show. 6 00:00:21,360 --> 00:00:24,320 Thank you so much. And Emily, for people just meeting 7 00:00:24,320 --> 00:00:28,240 you for the first time, give us a little background, who you are and what you do. 8 00:00:28,240 --> 00:00:34,400 Sure. So I usually start my background way back saying that I was in college for music, 9 00:00:34,400 --> 00:00:41,040 theater, and literature and was actively avoiding data technology. But through a series of events, 10 00:00:41,040 --> 00:00:48,160 got introduced to it and was really intrigued by the capabilities of these things to move 11 00:00:48,960 --> 00:00:53,680 organizations more efficiently and effectively towards their very large, important missions. 12 00:00:53,680 --> 00:00:59,840 And I had to learn everything on the job and through Googling and late hours and redoing my 13 00:00:59,840 --> 00:01:05,920 work. So I really feel that it is possible for anyone to learn data and technology, especially 14 00:01:05,920 --> 00:01:11,840 for social justice work. And that's what I'm focused on now. So I am a consultant with many 15 00:01:11,840 --> 00:01:19,520 different organizations across all areas of the public sector to help develop data and technology 16 00:01:19,520 --> 00:01:24,480 culture at organizations, especially small to mid-sized ones where they don't have necessarily 17 00:01:24,480 --> 00:01:31,280 all the resources and funding to focus on that. But we're finding the ways to get it to them. 18 00:01:31,280 --> 00:01:36,560 And okay, I want to start this conversation a bit of an unconventional place because Emily, 19 00:01:36,560 --> 00:01:41,760 we'll get to data in a second, but Emily, I love emailing you because when I send you an email, 20 00:01:41,760 --> 00:01:45,680 I get an unconventional response. I get an out of office, not an out of office. I get an automated 21 00:01:45,680 --> 00:01:50,720 response that says, first of all, the subject line reminds me to take five deep breaths today, 22 00:01:50,720 --> 00:01:56,400 which usually I haven't, and it lets me slow down. But then your automatic reply says something else 23 00:01:57,040 --> 00:02:02,000 and it says you check email once a day. So before we get into data world, tell me just a little bit 24 00:02:02,000 --> 00:02:06,960 about how do you approach your inbox and the way you work and approach technology? 25 00:02:06,960 --> 00:02:14,480 Yeah, well, this is about data world actually. So I think that if you're a person that's 26 00:02:14,480 --> 00:02:20,640 working on the data team or the tech team, a lot of urgent messages come your way. There's a lot 27 00:02:20,640 --> 00:02:28,080 of urgency. There can be requests, fires to put out when, and it can feel for the person working 28 00:02:28,080 --> 00:02:33,280 in that role, like you're the person that's responsible, not that no one's necessarily 29 00:02:33,280 --> 00:02:38,960 holding the technology responsible or they just think you're the expert, you're the wizard behind 30 00:02:38,960 --> 00:02:43,840 the magic that makes all this happen. And when something doesn't quite happen towards expectations, 31 00:02:43,840 --> 00:02:49,280 that urgency really gets ratcheted up. And I felt that as a person working in this sector, 32 00:02:49,280 --> 00:02:53,680 and because I care, I really care, I want this to be right for the people who I serve. 33 00:02:54,640 --> 00:03:02,160 But that urgency was really leading to burnout and distraction from work and making my responses 34 00:03:02,160 --> 00:03:09,200 to those fires less quality than they would have been if I took a breath and really assessed the 35 00:03:09,200 --> 00:03:17,040 situation from a state of mindfulness and calm, to whatever degree is possible. So part of this email 36 00:03:17,040 --> 00:03:23,600 routine for me is to lower that urgency rate and to let people know that that's going to be my 37 00:03:23,600 --> 00:03:29,200 response. It's going to be a thoughtful quality response for them. But that has spilled out into 38 00:03:29,200 --> 00:03:36,880 other areas of life. And it has been to me a really great vehicle for concentration and lowering 39 00:03:36,880 --> 00:03:42,400 distraction and making sure that when I am focused on a task, I'm really putting as much of my mind 40 00:03:42,400 --> 00:03:47,440 into it as I can. We've all been there getting emails that just you can feel your anxiety or 41 00:03:47,440 --> 00:03:52,880 your blood pressure just kind of rise of like, oh, that's stressful. That's something to fix. 42 00:03:52,880 --> 00:03:58,000 That's happening right now. Somebody's frustrated with me or regardless of what was actually said 43 00:03:58,000 --> 00:04:02,480 in the message, all those stories start to play. And so I love that idea of like, hey, I'm going 44 00:04:02,480 --> 00:04:06,880 to slow down my pace in the way I interact with that and how that can shape not just how you're 45 00:04:06,880 --> 00:04:12,480 responding, Emily, but also how the other person receives the interaction from you. It kind of 46 00:04:12,480 --> 00:04:18,640 permeates a culture of an organization. I'm so glad that you've been able to take some small 47 00:04:18,640 --> 00:04:24,080 deep breathing moments from that. That makes my heart sing. Yeah. I don't email you that often. 48 00:04:24,080 --> 00:04:27,440 And I forget about it when I do email you. I'm like, oh, that's right. I get this beautiful 49 00:04:27,440 --> 00:04:32,960 out of office or automated response from you. It's great. I almost feel like this is going to be the 50 00:04:32,960 --> 00:04:38,960 episode titled yoga and data or something like that. We all better start doing some breathing 51 00:04:38,960 --> 00:04:46,960 exercises right now and just deescalate all the data problems we have. Data is stressful sometimes. 52 00:04:47,600 --> 00:04:52,080 I have a colleague who runs an organization. They just changed their name to be more aligned 53 00:04:52,080 --> 00:04:57,840 with what their philosophies are. It's called Data Plus Soul. They're a great organization. 54 00:04:57,840 --> 00:05:04,880 I feel that really deeply we need data plus soul and tech plus soul in this work to really make it 55 00:05:04,880 --> 00:05:10,080 mission driven and mission focused. Yeah, that's great. Emily, tell us about some of the work 56 00:05:10,080 --> 00:05:15,840 you've done with education and teaching around data. I've seen you've had some classes and some 57 00:05:15,840 --> 00:05:19,840 different training things that you've done over the past, but around data management. Give us an 58 00:05:19,840 --> 00:05:23,840 idea of what types of topics in data and specifically for nonprofits that you've found 59 00:05:24,400 --> 00:05:27,840 passion or interest in trying to teach and do educational initiatives around. 60 00:05:27,840 --> 00:05:34,240 Okay. This could be a long one. There's so many to pick from. I'm really excited. Down to just data 61 00:05:34,240 --> 00:05:45,280 organization. The difference between what is data analysis from just your data analysis just for 62 00:05:45,280 --> 00:05:49,840 data cleanliness and data governance versus predictive data analysis. There's so many, but 63 00:05:50,560 --> 00:05:56,160 I'll start again. Something you mentioned at the top was how I got started in a kind of education 64 00:05:56,160 --> 00:06:03,600 based role for this. Teach for America, that was my first nonprofit job and it was my first data 65 00:06:03,600 --> 00:06:08,640 and tech role. It was a lot of job embedded learning, which I highly, highly value and is an 66 00:06:08,640 --> 00:06:16,240 adult learning principle that I think is really useful to remember. I had to learn how to approach 67 00:06:16,800 --> 00:06:21,920 learning, how to use data, how to use the technology and how to use the data. At first, 68 00:06:21,920 --> 00:06:27,120 I was just learning from a technical viewpoint, how to press the buttons and how to straight 69 00:06:27,120 --> 00:06:32,960 answer questions. I thought maybe there was a straight answer to any question. That led to 70 00:06:32,960 --> 00:06:38,800 hours of redoing poor work and having to ask questions and Googling and not finding my way. 71 00:06:38,800 --> 00:06:46,320 What I was really fortunate to find was a shift in my framework from this technical mindset to a 72 00:06:46,320 --> 00:06:53,600 more mindset approach from the core values of the organization. When I started learning data from the 73 00:06:54,640 --> 00:07:00,960 framework of grit, resilience, relationship building, critical thinking, problem solving, 74 00:07:00,960 --> 00:07:07,920 innovation, it opened a whole new world of learning for me. The learning curve was so different. 75 00:07:07,920 --> 00:07:13,840 It was enjoyable. It was actually enjoyable to learn and use data. At that time at Teach for 76 00:07:13,840 --> 00:07:18,000 America, there was this initiative where national team members could develop programs that the 77 00:07:18,000 --> 00:07:24,080 regional teams across the country could buy into with their budgets and get those services that 78 00:07:24,080 --> 00:07:28,880 they thought they needed. I created one to help folks learn how to use their data and tech from 79 00:07:28,880 --> 00:07:35,200 this framework and was traveling about twice a month, maybe for eight months, to all over the 80 00:07:35,200 --> 00:07:44,000 country bringing this learning. It was so powerful, so powerful personally to see people have a shift 81 00:07:44,000 --> 00:07:50,400 to say, oh, I can work with this system. This system's not working against me. Oh, this data is 82 00:07:50,400 --> 00:07:56,800 my data to own, not just somebody sends it to me and I am unquestioning or frustrated with it. 83 00:07:56,800 --> 00:08:02,160 That shift made a huge impact on me and has made me want to continue in that education-based 84 00:08:02,160 --> 00:08:10,080 approach for using data. Now, is that for technical users or non-technical users or how do you even 85 00:08:10,080 --> 00:08:13,680 think about the distinction between the two? Because it sounds like you're kind of helping 86 00:08:13,680 --> 00:08:18,720 everybody or people from all sorts of that across that spectrum interact with data differently. 87 00:08:19,920 --> 00:08:25,440 It is all across the spectrum, but I would say it's more non-tech team users. 88 00:08:25,440 --> 00:08:32,000 Folks on the program staff, on the development staff, who have to use data for their jobs, 89 00:08:32,000 --> 00:08:37,120 there's no opting out of it, but that's not why they got into this work necessarily. They're not 90 00:08:37,680 --> 00:08:44,240 always supported in their professional development to continue to learn how to use it. On the 91 00:08:44,240 --> 00:08:51,520 technical folks' side, often it's a lot more of this coaching around how to interact with users 92 00:08:51,520 --> 00:08:57,920 and how to do storytelling with data, which is also really fun. That shift that I had from 93 00:08:57,920 --> 00:09:02,800 being very technical, I wasn't doing the being very technical very well at the beginning. 94 00:09:02,800 --> 00:09:08,480 Folks who are doing being very technical and doing it very well, sometimes there are folks who have 95 00:09:08,480 --> 00:09:14,160 a gap to bring it back to the human side. The classic role for this is the business analyst 96 00:09:14,160 --> 00:09:19,120 who can talk in the spoken language to users and can talk in technical language. 97 00:09:19,120 --> 00:09:23,920 To developers, it's a really sweet thought. I actually think that that's available to most folks. 98 00:09:23,920 --> 00:09:29,360 I'm doing more technical training to program users and then more of that relationship building, 99 00:09:29,360 --> 00:09:35,440 human side with more technical folks. I wanted to put more flesh on the bones around 100 00:09:35,440 --> 00:09:42,480 that learning journey, knowing we've all been there where we've done something and we look back on it 101 00:09:42,480 --> 00:09:47,680 and say, wow, that was poor work. That just didn't work out the way that we wanted it to. 102 00:09:47,680 --> 00:09:55,600 That just didn't work out the way I wanted it to. Or, wow, everything I've learned now is so much 103 00:09:55,600 --> 00:10:01,040 better than what I implemented over there. I'm wondering, Emily, if you could just give us one 104 00:10:01,040 --> 00:10:06,720 instance of that aha light bulb moment where you looked back on some of your prior work and get a 105 00:10:06,720 --> 00:10:12,160 little more specific with us in terms of what that learning was like and encourage the audience as 106 00:10:12,160 --> 00:10:18,240 it's okay to actually have work that you look back on and say, I've learned so much since I did that. 107 00:10:18,880 --> 00:10:23,920 Give us a little more flesh on what that learning experience is like and how you 108 00:10:23,920 --> 00:10:28,320 observed a moment like that where it's like, I can do that so much better now. 109 00:10:28,320 --> 00:10:33,600 Okay, yes. All right. I'll try to bring up two topics and maybe you can ask if we want to 110 00:10:33,600 --> 00:10:38,880 elaborate on one or the other. The thing that comes to my mind first is a kind of non-technical 111 00:10:38,880 --> 00:10:47,840 one, which is at one organization I was at, I was sort of told, analyze the data, but no matter what, 112 00:10:48,400 --> 00:10:54,640 we always report that we get like 30% in this area. So make sure it's that. We can't go below that. 113 00:10:55,280 --> 00:11:01,120 And that was an experience that I really had to contend with and understand like, what is my role? 114 00:11:01,760 --> 00:11:08,640 What's my voice? Is it that I should question myself? Because obviously the answer is the data 115 00:11:08,640 --> 00:11:16,240 should always point this way, or can I be less biased than that and bring more information in? 116 00:11:16,880 --> 00:11:23,920 And then similarly, I was in a position where a manager was making decisions about what tech 117 00:11:23,920 --> 00:11:28,960 platforms that we should use. And I really, really doubted it and thought we can't go forward with 118 00:11:28,960 --> 00:11:33,760 this, but at the end of the day had to go forward and then sort of sell the whole thing. Both of 119 00:11:33,760 --> 00:11:39,200 those, I think I've learned that I have more voice and everyone has more voice at any level of their 120 00:11:39,200 --> 00:11:43,840 career that if they want to question things like that, they can and should. And then on the side 121 00:11:43,840 --> 00:11:51,440 of like making data errors and being wrong and things like that. Oh boy. Yeah. So I think anyone 122 00:11:51,440 --> 00:11:57,040 who has used a spreadsheet can relate to the fact that it's a little better now, but if you don't 123 00:11:57,040 --> 00:12:03,600 put the filters across all of your column headers, then you're going to sort your data and one column 124 00:12:03,600 --> 00:12:09,920 not going to get sorted. And now a person's name who's like Emily Rotella, their email looks like 125 00:12:09,920 --> 00:12:15,760 Michael Fletcher and you're like, what happened? And you send a mail merge out to all those people 126 00:12:15,760 --> 00:12:24,720 to thank them for their donations. So I have specific Emily. Oh yeah. It's happened to me 127 00:12:24,720 --> 00:12:30,880 specifically. And it happened to people on my team and people adjacent to me in the work. And I've 128 00:12:30,880 --> 00:12:36,640 seen it happen. Yeah. Many times. And that can be from a database as well. Not just the spreadsheet. 129 00:12:36,640 --> 00:12:42,800 You can kind of misplace one filter in a database. I'm emailing all my non-participants to see if 130 00:12:42,800 --> 00:12:48,880 they want to participate, but actually you're emailing all the wrong people. And I don't know 131 00:12:48,880 --> 00:12:55,520 if we want to think about like, how do you come back from that? That's a whole podcast topic, 132 00:12:55,520 --> 00:13:01,200 I'm sure. But one thing that's important for me personally, having been in-house and on the consulting 133 00:13:01,200 --> 00:13:07,680 side is just to know that no, my definition of expert is someone who believes that they can learn 134 00:13:07,680 --> 00:13:15,840 something, not someone who knows everything. And I think there's a sort of toxic trait of thinking 135 00:13:15,840 --> 00:13:23,280 that consultants know everything about technology or about their specific data skills. Like sure, 136 00:13:23,280 --> 00:13:28,080 they know a lot. That's your focus. But everyone makes mistakes and there's new things coming out 137 00:13:28,080 --> 00:13:32,560 all the time. So really we can't expect someone to know everything and we're always learning. 138 00:13:32,560 --> 00:13:37,360 You've mentioned the phrase a couple of times earlier, data culture. And I'm wondering if 139 00:13:37,360 --> 00:13:42,240 that's even related to what you're talking about with learning and not knowing everything or being 140 00:13:42,240 --> 00:13:46,880 able to embrace that sort of perspective on an expert. And how does that relate to data culture 141 00:13:46,880 --> 00:13:51,600 or the way you think about data culture at an organization? Yeah. So there's two big ways for 142 00:13:51,600 --> 00:13:57,600 me that that relates. One is this kind of learning culture in general that organizations I hope would 143 00:13:57,600 --> 00:14:04,160 have and works really well when applied on the data technology side. So learning culture, first off, 144 00:14:04,160 --> 00:14:09,280 assumes that you always have to be learning. That should predicate the assumption that you have to 145 00:14:09,280 --> 00:14:15,520 support that learning through actual dollar and time investment for your employees and that you 146 00:14:15,520 --> 00:14:19,040 can make mistakes and learn from them. You're always learning. You're going to make, we learn 147 00:14:19,040 --> 00:14:24,560 from mistakes. So hopefully that kind of in the learning culture, this idea of support for it and 148 00:14:24,560 --> 00:14:31,760 the way that we deal with mistakes should be really central. And the second big pillar of this 149 00:14:31,760 --> 00:14:39,120 learning culture is that that part of evaluation for your organization. So we talk about a lot for 150 00:14:39,120 --> 00:14:47,520 program evaluation. We should not be working in the field if we're not evaluating how we're working. 151 00:14:47,520 --> 00:14:54,400 I mean, I don't want to be hyperbolic about it, but it can be dangerous. It can be harmful to 152 00:14:54,400 --> 00:14:59,920 communities that we're trying to help if we're not actually reviewing what impacts we're having. And 153 00:14:59,920 --> 00:15:07,680 data is such a huge tool for doing that. So I think both learning in the data tech realm, how we learn 154 00:15:07,680 --> 00:15:13,200 there, how we make mistakes there, and using data to learn about ourselves and learn about the work 155 00:15:13,200 --> 00:15:18,560 we're doing. Those two areas are really central to good learning culture at a nonprofit. 156 00:15:18,560 --> 00:15:25,520 Oh, you just opened up like two huge topics for us to explore here. So I want to, can we start with 157 00:15:25,520 --> 00:15:32,960 the learning culture within an organization and support for education and training and just like 158 00:15:32,960 --> 00:15:37,440 nurturing that sort of culture within an organization? What are some examples of how 159 00:15:37,440 --> 00:15:40,800 organizations have done that well? I think I'm going to get really specific 160 00:15:40,800 --> 00:15:45,760 because I have a bit of a go-to example here. So there's an organization in Connecticut called 161 00:15:45,760 --> 00:15:50,800 the Compass Youth Collaborative, youth serving organization in Connecticut. And they have 162 00:15:50,800 --> 00:15:56,320 partnered with another great Connecticut organization called the CT Data Collaborative. 163 00:15:56,320 --> 00:16:03,840 The CT Data Collaborative provides resources, training, education, and structural strategy 164 00:16:03,840 --> 00:16:11,440 work for nonprofits in their data culture. So the things that Compass Youth has put in place 165 00:16:11,440 --> 00:16:18,000 are so central. So they involve all of their stakeholders in their data decisions, and 166 00:16:18,000 --> 00:16:25,440 especially in things like building the stories around their data. They have sort of like this 167 00:16:25,440 --> 00:16:31,760 really good relationship that they've built between the data folks and everyone else on the team. 168 00:16:31,760 --> 00:16:37,600 So there's trust, there's open door policy. They built it into their, they have weekly data 169 00:16:37,600 --> 00:16:42,880 calls where it's part of that culture of the organization, part of check-ins and meetings, 170 00:16:42,880 --> 00:16:50,240 the overall, you know, like org-wide kind of communications. They have really good relationships 171 00:16:50,240 --> 00:16:55,440 with everyone that is a part of their data and they know that. So even the youth that they're 172 00:16:55,440 --> 00:17:01,280 serving can take part in some of that like data culture aspect of it. CT Data Collaborative, 173 00:17:01,280 --> 00:17:08,240 as that organization does like youth data walks in the Connecticut like areas that they serve. 174 00:17:08,880 --> 00:17:17,120 So they really make it, it's part of everyone's job. And I think that's a big shift that we're, 175 00:17:17,120 --> 00:17:25,600 we are seeing and will continue to see that the tech team integrate more into program teams. 176 00:17:25,600 --> 00:17:29,200 And that really helps with budgeting too, so that we don't have to have donors say, 177 00:17:29,200 --> 00:17:36,000 no, you can't send this to your tech team, your overhead, right? Anytime I work with organizations, 178 00:17:36,000 --> 00:17:40,720 I try to, the grant writers or anything, we try to say all the money that would be spent on the 179 00:17:40,720 --> 00:17:46,080 data tech side of this is programmatic money spent. So the two things kind of required, 180 00:17:46,080 --> 00:17:52,000 even in that example of how like, as an organization who wants to and has embraced like learning and 181 00:17:52,000 --> 00:17:57,840 how holistic and integrated data is to their success and them having organizations that they 182 00:17:57,840 --> 00:18:04,160 can partner with that have some of that more niche expertise around data and can invite them in and 183 00:18:04,160 --> 00:18:10,080 collaborate well. And there's some like mindset around, we want you to be successful, you want us 184 00:18:10,080 --> 00:18:14,880 to be successful. And so there's a, yeah, that's, that feels like a rare example. It's a beautiful 185 00:18:14,880 --> 00:18:19,920 one of two organizations kind of working alongside each other well, and not everybody has kind of 186 00:18:19,920 --> 00:18:26,880 that place. What are the ways that, so when you're developing data culture, so it's the more 187 00:18:26,880 --> 00:18:33,280 technical skills, maybe on the other side of it of like developing a data tech role or people who 188 00:18:33,280 --> 00:18:37,760 are niche and for smaller organizations or mid-level organizations, when do they staff 189 00:18:38,400 --> 00:18:44,160 somebody who is just focused on technology and data or when does that team need to be more than 190 00:18:44,160 --> 00:18:47,520 one person and multiple people? Or how do you think about that kind of, that kind of role in place? 191 00:18:49,040 --> 00:18:55,520 So many different thoughts, really pretty dependent on the organization and where their places and 192 00:18:55,520 --> 00:19:01,120 even the time period that we're in now and the like what the grant situation looks like. I mean, 193 00:19:01,120 --> 00:19:07,680 we're in an election year right now, so things are really up in the air. But I have a baseline 194 00:19:07,680 --> 00:19:12,400 that I try to stick with, like a line in the sand. Every organization should have this role 195 00:19:12,400 --> 00:19:17,840 that I, and I understand that not every organization is actually going to be able to fulfill that, but 196 00:19:17,840 --> 00:19:25,280 I think if that's the goal, then when new nonprofits are starting, having a data tech role becomes as 197 00:19:25,280 --> 00:19:31,440 central as having a fundraising, a chief of development role or a chief of staff. And I think 198 00:19:32,080 --> 00:19:36,240 to put that message out there is going to be something that drives us towards having that 199 00:19:36,240 --> 00:19:46,480 support for all organizations. If you are using a login database, something like a CRM, an LMS, 200 00:19:46,480 --> 00:19:52,640 something you log into and there's some pretty user interface over a database, I think at that 201 00:19:52,640 --> 00:19:58,800 point you should have someone on your team. It introduces this new level. All of those are going 202 00:19:58,800 --> 00:20:04,400 to be in some way a relational database. It introduces a level of data complexity and 203 00:20:04,400 --> 00:20:08,880 reporting complexity that I think is really well served to have someone on the team. 204 00:20:09,520 --> 00:20:16,560 And the more that you interact with your data, the more you ask from it. So once you understand 205 00:20:16,560 --> 00:20:21,040 that you can really define things well and get good answers, you start asking more questions. 206 00:20:21,040 --> 00:20:26,960 And then it just ratchets from there, which is a good thing. I think that we should go that way. 207 00:20:26,960 --> 00:20:32,640 And so once you have support in place, you can imagine that in your five to 10 year plan, 208 00:20:33,200 --> 00:20:38,480 depending on the goals of the organization and their growth plans, that you would be adding on 209 00:20:38,480 --> 00:20:44,160 more team members. Now I will couch that to say that there's a lot of different variations of 210 00:20:44,160 --> 00:20:50,960 how that can look. I serve as the tech team, part-time tech team for one of the organizations 211 00:20:50,960 --> 00:20:57,280 I work with. It's been five or six years now. So that's an example of having someone not in-house 212 00:20:57,280 --> 00:21:02,880 but in a long-term contracted role. That's what I want to see for organizations, that this is 213 00:21:02,880 --> 00:21:07,920 built into their budget, it's built into their culture. In-house I think is great because that's 214 00:21:07,920 --> 00:21:12,480 even more embedded. You've got a job description, you've got the responsibilities, people 215 00:21:12,480 --> 00:21:17,440 interacting with them. You can transfer that over when someone, it's easier to transfer knowledge 216 00:21:17,440 --> 00:21:22,480 and transfer files and ownership of records and things like that when there is a staff change. 217 00:21:23,280 --> 00:21:27,200 But it doesn't necessarily have to look like that. And for a lot of smaller orgs, 218 00:21:27,200 --> 00:21:29,840 they need to figure out a different configuration at first. 219 00:21:29,840 --> 00:21:34,560 And so as you're thinking about this data role, and I'm assuming you're saying once you get to 220 00:21:34,560 --> 00:21:43,360 that, you've got a CRM, LMS, ERP system, something like that, it's a full-time role. What would you 221 00:21:43,360 --> 00:21:50,560 say are those non-negotiable core functions of that particular data tech role? Are you thinking 222 00:21:50,560 --> 00:21:55,600 this is some kind of a, a lot of people might accuse you of saying, well, that's a unicorn in 223 00:21:55,600 --> 00:22:01,920 such a small organization because you're looking for someone who can manage the database, they can 224 00:22:01,920 --> 00:22:06,880 pull the data into some kind of a model so that it can be analyzed and they can build the 225 00:22:06,880 --> 00:22:10,800 visualizations. Next thing you know, you've got a long list of responsibilities that you're 226 00:22:10,800 --> 00:22:17,920 expecting of one person to do for an entire small organization. So how would you put some 227 00:22:17,920 --> 00:22:24,000 guidance around that, some scope, just to limit it to say, these are core functionalities that 228 00:22:24,000 --> 00:22:29,840 you kind of have to hit on and others are a little more negotiable for each organization. 229 00:22:29,840 --> 00:22:34,560 I don't know. I'm not trying to put you on the spot, but offer a little bit more around that. 230 00:22:34,560 --> 00:22:37,440 What are those core non-negotiable functions of the role? 231 00:22:37,440 --> 00:22:43,920 Yeah. I haven't written this out before, so I can give you some thoughts just having been posed 232 00:22:43,920 --> 00:22:52,320 the question. So they start for me again in the like mindset, non-technical area. So a core 233 00:22:52,320 --> 00:22:59,280 responsibility is going to be curiosity and learning. So knowing that there's always more 234 00:22:59,280 --> 00:23:08,160 to learn and both in the data analytics sense and in even the base of data governance, data 235 00:23:08,160 --> 00:23:18,720 organization, things like that. I do think that some experience and some learning in database 236 00:23:18,720 --> 00:23:28,320 administration modeling is really helpful. When Teach for America went from spreadsheets to 237 00:23:28,320 --> 00:23:34,080 Salesforce, that was my first introduction to it. And it was a total mindset change to understand 238 00:23:34,080 --> 00:23:39,680 what a relational database behind a UI was. So having been introduced to that is really helpful 239 00:23:39,680 --> 00:23:45,040 and being able to talk to other people about that, being able to translate technical stuff to 240 00:23:45,040 --> 00:23:49,600 non-technical speak, I think is a real... You know what? That's it. That's what I'm going to say is 241 00:23:49,600 --> 00:23:57,280 the most core value that there is. As long as you can bridge that gap and take technical knowledge 242 00:23:57,280 --> 00:24:01,760 that you have and be able to talk to non-technical people about it, I'd say that's the most important 243 00:24:02,400 --> 00:24:09,520 part of it. And there's an organization, a consulting firm called the Build Tank, and they 244 00:24:09,520 --> 00:24:15,680 have a whole curriculum around, I think how they call it is distributed ownership across the 245 00:24:15,680 --> 00:24:21,440 organization. They build different roles so that that job description, hopefully as big as it might 246 00:24:21,440 --> 00:24:29,200 be for that one data tech person is shared across other roles. So the responsibility for the numbers, 247 00:24:29,200 --> 00:24:34,880 for the actual data point, that can be distributed to folks, how to pull them from your database, 248 00:24:34,880 --> 00:24:41,840 how to analyze them, that might be on your data analyst or data tech person. But making sure that 249 00:24:41,840 --> 00:24:48,800 number of participants is 30 and that jives with the reality in the classroom or wherever it is, 250 00:24:48,800 --> 00:24:56,480 that's someone on the program team. And maybe the thinking up of what are the reports for grants 251 00:24:56,480 --> 00:25:00,720 that are needed, that's spread out across the organization. So I think that really helps take 252 00:25:00,720 --> 00:25:06,000 that kind of pressure off, even if the job description is big. I'd be curious to ask both 253 00:25:06,000 --> 00:25:11,440 of you if you've looked at some of these job descriptions recently for data tech roles at 254 00:25:11,440 --> 00:25:19,600 nonprofits from manager or associate level up to CIO. Sometimes they look exactly the same from 255 00:25:19,600 --> 00:25:27,600 the manager level to CIO. I am in shock at how much is being asked on job descriptions of any 256 00:25:27,600 --> 00:25:34,960 level data tech folks. And maybe the expectation is that people will see that and think that 257 00:25:34,960 --> 00:25:39,920 there's a growth opportunity for them. But I wonder if it's actually creating a barrier to entry 258 00:25:40,480 --> 00:25:44,400 because you're saying you should have this, should need this, where are you going to get that? 259 00:25:44,400 --> 00:25:49,520 That's partially why we don't have to go into this too deeply, but that's partially why I started my 260 00:25:49,520 --> 00:25:54,240 apprenticeship program, because I was given the experience to learn on the job. That was 261 00:25:54,240 --> 00:26:00,400 so valuable for me and made it possible for me to work and get better at this stuff. 262 00:26:00,400 --> 00:26:07,600 And I see a lot of experience being asked of entry level jobs, and I don't know where they're 263 00:26:07,600 --> 00:26:12,560 going to get that. So I said, you can get it with me, you can work on my projects and have that 264 00:26:12,560 --> 00:26:17,360 experience. And then it's a little bit also of the Teach for America Kool-Aid, where you say get 265 00:26:17,360 --> 00:26:22,400 folks into social justice work early and they'll stay in the job. And I think that's a great 266 00:26:22,400 --> 00:26:27,680 way to get folks into social justice work early and they'll stay there. So partially getting folks 267 00:26:27,680 --> 00:26:31,680 to work with nonprofits as some of those first data tech experiences for them and hopefully 268 00:26:31,680 --> 00:26:33,200 stay in those roles. 269 00:26:33,200 --> 00:26:40,720 Right. And limited resources will definitely make those entry level jobs feel like they need to be 270 00:26:40,720 --> 00:26:45,200 ratcheted up on experience because they can't pay for the multiple roles. So it's like, well, we 271 00:26:45,200 --> 00:26:50,560 can't bring someone new into this and teach them everything. We need someone to come in and do work 272 00:26:50,560 --> 00:26:55,920 with them at that higher level. And so it all gets jumbled. But my favorite is when these job 273 00:26:55,920 --> 00:27:01,040 descriptions will list new tech and say, you know, give me five years experience in something that 274 00:27:01,040 --> 00:27:06,000 just came out last year. And it's like, they're so out of touch sometimes with what even is happening 275 00:27:06,000 --> 00:27:11,440 in this space. They don't know how to write the JDs. So it can be a big challenge for nonprofits 276 00:27:11,440 --> 00:27:19,120 when it's an ever changing landscape, the tech is so evolving all the time, and they probably don't 277 00:27:19,120 --> 00:27:24,320 have the resources to train entry level roles in this stuff. They need people with experience to 278 00:27:24,320 --> 00:27:30,160 come in and often take a salary cut from what they get out there in the for profit and big tech 279 00:27:30,160 --> 00:27:37,040 spaces. So these are real tension points that I think everyone in nonprofits and mission driven 280 00:27:37,040 --> 00:27:43,040 organizations tend to feel with the more technical a role becomes, the harder it is to find those 281 00:27:43,040 --> 00:27:47,040 people to fill those positions. So yeah, great conversation on that, Emily. 282 00:27:47,040 --> 00:27:51,680 You're helping me write the website copy for this apprenticeship program. That's exactly right. 283 00:27:51,680 --> 00:27:57,280 That's why the idea for this is you have someone like myself who has 10 plus years experience is 284 00:27:57,280 --> 00:28:03,440 technical can do both sides of it, that can act as the main tech person filling in right like that 285 00:28:03,440 --> 00:28:08,960 interim in imagine we're creating a role in an organization, right? It's your first data tech 286 00:28:08,960 --> 00:28:13,360 role. So who you're going to staff in it first, first, you're going to staff me at like maybe it's 287 00:28:13,360 --> 00:28:18,000 a part time 60% role. And I'm going to have an apprentice who's learning how to do this stuff 288 00:28:18,000 --> 00:28:24,560 alongside me, but within your organization's context and your data. So let's a year goes by 289 00:28:24,560 --> 00:28:31,280 with that happening. Now I can step back and this apprentice can come in as a full time role at a 290 00:28:31,280 --> 00:28:37,200 lower salary than then they will imagine because I'll stay in a support role for them. Then when I 291 00:28:37,200 --> 00:28:42,160 step back fully, all of that funding goes back into that role. And even in this conversation, 292 00:28:42,160 --> 00:28:47,920 it makes sense why and with your background, Emily, that curiosity becomes one of the core virtues 293 00:28:47,920 --> 00:28:53,360 of what it of what a tech person needs a data tech person, because it's about on the job 294 00:28:53,360 --> 00:28:59,120 learning. And it's about having the curiosity to ask questions and to innovate around what's 295 00:28:59,120 --> 00:29:04,560 happening with the technology, but then also with the programs and with the with the organization 296 00:29:04,560 --> 00:29:08,960 itself. And so yeah, even think about adult learning and learning on the job, like that's 297 00:29:08,960 --> 00:29:13,280 a curiosity thing. That's not I have lots of technical chops and I can step in. But no, 298 00:29:13,280 --> 00:29:18,160 I passionate and interested enough to figure it out and to learn. And I will recognize the 299 00:29:18,160 --> 00:29:22,800 challenge. I think we've said that there might not be a person in house on the team to provide 300 00:29:22,800 --> 00:29:28,000 that guidance. Right? The year every tech person if you're creating your first tech role, your 301 00:29:28,000 --> 00:29:35,040 manager is the CEO, the chief of the usually it's the finance person, right, the chief finance 302 00:29:35,040 --> 00:29:43,520 officer, finance and data and tech seem to have a lot of overlap in the world. So yeah, being able 303 00:29:43,520 --> 00:29:48,400 to also find the resources out of house to support that in house role is a challenge as well. 304 00:29:49,200 --> 00:29:52,960 Tell me about this was the other large bucket that you opened up earlier, I didn't get to go 305 00:29:52,960 --> 00:29:57,600 to but about program evaluation or really like, are we doing good work? And does it matter? 306 00:29:58,160 --> 00:30:03,920 Or how is it mattering? And obviously data has an intricate role to play in that question. 307 00:30:03,920 --> 00:30:09,760 So even thinking about how the organizations measure their success or evaluate how they're 308 00:30:09,760 --> 00:30:13,600 doing, give me some examples of what that would look like for an organization to start assessing 309 00:30:13,600 --> 00:30:18,800 that or to explore that with data. And I want to piggyback off of that to Emily and just say, 310 00:30:18,800 --> 00:30:24,400 describe the field you mentioned the field and I want to make sure that our audience knows exactly 311 00:30:24,400 --> 00:30:28,320 what you're talking about in your particular context when you're doing that kind of program 312 00:30:28,320 --> 00:30:34,400 evaluation. So I kind of wanted to merge that in there with what Sawyer was asking. 313 00:30:34,400 --> 00:30:42,800 Sure, sure. Okay. So a lot of organizations when they're getting their foundation set up in their 314 00:30:42,800 --> 00:30:51,360 data, it's about definitions and processes, defining what you're trying to do, who you're 315 00:30:51,360 --> 00:30:57,440 serving, and then what data you need to track for that. So I mentioned this earlier, just being able 316 00:30:57,440 --> 00:31:02,080 to count how many participants you have, you have to define what a participant is in each year. 317 00:31:02,560 --> 00:31:09,520 So that set up is part of the field of program evaluation too, just really that foundational 318 00:31:09,520 --> 00:31:17,840 set up of understanding what data points we have, what we want. Then being able to use a tool like 319 00:31:17,840 --> 00:31:25,040 a logic model to define your inputs, outputs, behavior changes, and outcomes that you want to 320 00:31:25,040 --> 00:31:33,280 see in your community and from your work. And then layering data on top of that logic model to say, 321 00:31:33,280 --> 00:31:39,200 for us to know this, what do we need to know to say that we can prove, we can have the evidence 322 00:31:39,200 --> 00:31:44,240 that either, yes, we were correct that these outputs will lead to these outcomes, or no, 323 00:31:44,240 --> 00:31:50,640 that didn't happen as we expected and we need to change course. So that's a kind of high level 324 00:31:50,640 --> 00:32:00,080 view of the field of program evaluation in a nutshell. And the examples I can think of, 325 00:32:00,080 --> 00:32:04,960 just to talk to a few. So if an organization wants to start thinking about this and they haven't 326 00:32:04,960 --> 00:32:14,400 before, I would recommend looking into the book, Leap of Reason by Mario Marino. This is an old 327 00:32:14,400 --> 00:32:21,920 book. It's free online and Mario Marino has this leap ambassadors program, all about program 328 00:32:21,920 --> 00:32:26,800 evaluation. It's just a great resource. And they have a lot of videos on their website of organizational 329 00:32:26,800 --> 00:32:31,600 leaders talking about how they've introduced program evaluation and what it's meant to them. 330 00:32:31,600 --> 00:32:38,480 And the book itself talks about some organizations that have found that they weren't having the 331 00:32:38,480 --> 00:32:42,160 outcomes that they anticipated and how they wouldn't have known that and might have kept 332 00:32:42,160 --> 00:32:46,480 operating in what they were doing had they not evaluated what they were doing and what the 333 00:32:46,480 --> 00:32:55,120 outcomes were. There's also a new data tech platform called Sure Impact, S-U-R-E Impact. 334 00:32:55,120 --> 00:33:00,800 This is a program evaluation technology tool geared specifically towards nonprofits collecting 335 00:33:00,800 --> 00:33:06,080 the kind of data they need to report on their programmatic impact. So I think it's maybe five 336 00:33:06,080 --> 00:33:10,720 years out, it came out of another organization that was all about program evaluation research. 337 00:33:10,720 --> 00:33:16,320 That's a really one to check out, even just to learn like, what does it look like to do program 338 00:33:16,320 --> 00:33:21,840 evaluation in a physical sense, in a data sense, you can kind of see from their demos what that 339 00:33:21,840 --> 00:33:29,680 would look like for an organization. And on the culture side of program evaluation, it is not easy 340 00:33:29,680 --> 00:33:37,120 to put your heart and soul into helping other people and then be given the opportunity or the 341 00:33:37,120 --> 00:33:44,800 challenge to have to evaluate, were we doing right? I mean, you were doing right because that was in 342 00:33:44,800 --> 00:33:49,120 your heart and with all the factors that you could, you thought this was the way to go. 343 00:33:50,080 --> 00:33:55,200 But taking that next step of debriefing and evaluating and possibly being faced with really 344 00:33:55,200 --> 00:34:00,240 hard information that no, it wasn't working out and that doesn't mean nothing went well, right? 345 00:34:00,240 --> 00:34:06,400 If you're, there can be good that comes out of it, but that's so difficult and how many 346 00:34:07,520 --> 00:34:12,880 for-profit organizations are willing to do that kind of review of themselves. And for nonprofits, 347 00:34:12,880 --> 00:34:18,640 I think it is, where's the time? You just have to keep going, keep serving, keep pushing out. 348 00:34:19,520 --> 00:34:27,280 But it really is in a learning culture aspect of that. It is so valuable to have program 349 00:34:27,280 --> 00:34:33,600 evaluation, especially through data, be part of that culture because we will serve better. 350 00:34:33,600 --> 00:34:40,800 We will reach our missions sooner and better, or we'll change our missions for the betterment of 351 00:34:40,800 --> 00:34:45,440 the world and our staff members if we do participate in this kind of evaluative work. 352 00:34:46,240 --> 00:34:52,320 Yeah. It's that data plus soul you were talking about where if we're not willing to take a critical 353 00:34:52,320 --> 00:34:59,200 eye to our programs and evaluate, are we actually achieving our missions? And even asking the even 354 00:34:59,200 --> 00:35:06,000 harder question, is our mission the right mission? Did we actually get the right values into that 355 00:35:06,000 --> 00:35:11,760 mission that's serving this particular population? We could be missing the mark and we have to be 356 00:35:11,760 --> 00:35:20,080 able to allow the data to speak into that openly. So these are great, great topics to talk about, 357 00:35:20,080 --> 00:35:25,360 to know that we're hitting what we're really after in these missions that we're aiming towards. 358 00:35:25,360 --> 00:35:31,760 It's amazing to me that I think that the data technology side of this work sometimes is like, 359 00:35:31,760 --> 00:35:36,160 oh, that's not part, it's not direct mission work. It's on the side, it's overhead, whatever. 360 00:35:36,880 --> 00:35:42,240 The learnings that introducing good data tech culture can bring to the overall culture of 361 00:35:42,240 --> 00:35:49,120 the organization to me is so aligned. We talked about we don't know everything about our technology. 362 00:35:49,120 --> 00:35:53,360 It's changing all the time. So we have to keep on learning and evaluating our program. 363 00:35:53,360 --> 00:35:59,680 The challenge it is to change your CRM and how often do organizations change their technology? 364 00:35:59,680 --> 00:36:04,080 They are having to go through that all the time and do this kind of evaluation. 365 00:36:04,080 --> 00:36:08,320 I really think that the more we get everyone in the organization involved in that 366 00:36:08,880 --> 00:36:14,560 day-to-day act of their data and tech work, you can zoom it out to what the organization as a whole 367 00:36:14,560 --> 00:36:19,280 could be doing and take key learnings from that. So I think that the data tech side of a nonprofit 368 00:36:19,280 --> 00:36:25,040 has even more to offer than just the analysis and just the ability to report and talk back. 369 00:36:25,040 --> 00:36:30,560 It has these cultural values embedded in it that we can bring to other parts of the organization. 370 00:36:30,560 --> 00:36:36,720 Yeah. One of those values seems to even be humility of we're going to honestly look at the data 371 00:36:36,720 --> 00:36:41,680 and realize and see if we're doing what we thought we were doing and helping the way we thought we 372 00:36:41,680 --> 00:36:45,920 were and being honest enough to admit that we might have been wrong. And that's a scary thing. 373 00:36:47,440 --> 00:36:51,760 Whether it's a new organization, a young organization that has some innovative spirit 374 00:36:51,760 --> 00:36:57,680 to admit they're wrong or for these more legacy or long historical organizations that have just been 375 00:36:58,400 --> 00:37:03,600 charting this mission for decades or centuries and to look at the data and start to evaluate 376 00:37:03,600 --> 00:37:08,160 with enough humility to say, maybe we're not doing the impact we thought we were. 377 00:37:08,160 --> 00:37:13,440 And that sounds like a data culture thing right there of like, we're all embracing the data 378 00:37:13,440 --> 00:37:17,760 because we care enough about these people or these communities or this mission that will 379 00:37:18,560 --> 00:37:20,640 evaluate the data well. It's beautifully said. 380 00:37:21,200 --> 00:37:26,480 Emily, I'm curious if you could share a little bit more about the services that you offer or even 381 00:37:26,480 --> 00:37:30,880 like this apprenticeship program. Tell us a little bit more about like, yeah, your consulting firm 382 00:37:30,880 --> 00:37:39,360 and what you do. Sure. So, you know, it's still evolving. I have a young company and I want to be 383 00:37:39,360 --> 00:37:45,840 nimble and evolve with the needs of nonprofit organizations. So we offer a kind of fractional 384 00:37:46,560 --> 00:37:52,880 tech team approach where you don't have the resources to have a tech person on the team, 385 00:37:53,440 --> 00:38:00,480 but you want and understand the value in not doing quote unquote project-based technology 386 00:38:00,480 --> 00:38:05,840 work, which I think is really not a, this is a whole other podcast topic, I'm sure. 387 00:38:08,160 --> 00:38:12,320 Technology isn't always really project-based in a nonprofit. It's long-term, 388 00:38:13,120 --> 00:38:18,160 in perpetuity kind of work. So organizations that can understand that and know it, we are 389 00:38:18,160 --> 00:38:24,480 very happy to partner in and be that resource for them and become part of their team, right? 390 00:38:24,480 --> 00:38:28,800 It's almost like having an in-house team and get ready for potentially actually having an 391 00:38:28,800 --> 00:38:33,280 in-house role in the future. So we serve as a fractional tech team for organizations. 392 00:38:33,840 --> 00:38:41,280 We also have a sort of larger service that's our, we call it our Learn, Use, Love service, 393 00:38:41,280 --> 00:38:48,640 which is to help data culture and technology culture go from wherever it's at now to where 394 00:38:48,640 --> 00:38:55,920 it could be super impactful for the organization. And those three areas are our key areas that we 395 00:38:55,920 --> 00:39:01,760 introduce into organizational culture. It's a culture of learning, right? We've talked about 396 00:39:01,760 --> 00:39:08,080 that a lot. Culture of using, which is just to say that your job functions aren't the only way 397 00:39:08,080 --> 00:39:12,960 that you can use the data and technology. There's more to it and you can be partnering on different 398 00:39:12,960 --> 00:39:18,960 projects. So we do a lot more of introducing use across the organization of their tech. 399 00:39:18,960 --> 00:39:26,880 And then I believe that the relationship between humans and technology can make or break use of it 400 00:39:26,880 --> 00:39:32,320 at the organization. And so that's why we include love in this kind of approach. It doesn't mean 401 00:39:32,320 --> 00:39:37,440 you actually have to love, but you have to look at your relationship with technology. 402 00:39:37,440 --> 00:39:42,000 Okay, it's loving technology. Okay, this is our relationship. 403 00:39:42,000 --> 00:39:47,280 Yeah, it's a lot. You can, I personify technology as much as possible. 404 00:39:47,280 --> 00:39:53,360 Okay. 405 00:40:17,280 --> 00:40:21,000 would be sort of like a two to three year commitment, 406 00:40:21,000 --> 00:40:23,040 where at the beginning we say we're 407 00:40:23,040 --> 00:40:26,120 wanna get ready in our budget and in our culture 408 00:40:26,120 --> 00:40:29,200 to have our first tech role in-house. 409 00:40:29,200 --> 00:40:31,640 And the pathway that we want that to go on 410 00:40:31,640 --> 00:40:33,480 is to have someone like myself 411 00:40:33,480 --> 00:40:36,900 who is a little more senior in their career 412 00:40:36,900 --> 00:40:39,480 to serve in that sort of fractional, 413 00:40:39,480 --> 00:40:42,920 or in some cases it could be full-time tech role 414 00:40:42,920 --> 00:40:45,240 to get it ready, get it prepared, 415 00:40:45,240 --> 00:40:48,160 and have an apprentice learning alongside, 416 00:40:48,160 --> 00:40:50,720 building relationships, getting the contacts, 417 00:40:50,720 --> 00:40:52,640 building them up into that role. 418 00:40:52,640 --> 00:40:54,880 And then that's the first year. 419 00:40:54,880 --> 00:40:57,160 In the second year, the apprentice takes over 420 00:40:57,160 --> 00:40:59,120 in the full-time role at the organization, 421 00:40:59,120 --> 00:41:00,880 but I stay on as support. 422 00:41:00,880 --> 00:41:05,280 And so where the money distribution is shifts then 423 00:41:05,280 --> 00:41:06,720 to the apprentice also. 424 00:41:06,720 --> 00:41:09,680 And then in the third year, I can step away completely 425 00:41:09,680 --> 00:41:12,920 in terms of that day-to-day interaction, 426 00:41:12,920 --> 00:41:14,440 but still be a resource. 427 00:41:14,440 --> 00:41:18,560 Even when I also sort of like build quick tech tools 428 00:41:18,560 --> 00:41:20,620 for folks with spreadsheets or air table 429 00:41:20,620 --> 00:41:21,660 or some things like that. 430 00:41:21,660 --> 00:41:24,040 And I always offer lifetime support on that 431 00:41:24,040 --> 00:41:26,920 if it's like small changes, big projects, 432 00:41:26,920 --> 00:41:29,040 it's a different question, but I just can't, 433 00:41:29,040 --> 00:41:31,880 I can't leave a project behind fully. 434 00:41:31,880 --> 00:41:33,160 I just never can. 435 00:41:33,160 --> 00:41:35,120 And I'm not ashamed of it anymore. 436 00:41:36,560 --> 00:41:37,400 I'm always gonna be there. 437 00:41:37,400 --> 00:41:39,280 So there's always, I feel like I can't let go 438 00:41:39,280 --> 00:41:41,400 of some connection, but the idea is really 439 00:41:41,400 --> 00:41:43,720 to hand it over to the apprentice. 440 00:41:43,720 --> 00:41:47,120 And at that point, they're the resource on their team. 441 00:41:47,120 --> 00:41:49,760 Yeah, well, we're gonna have to have like a check-in 442 00:41:49,760 --> 00:41:51,200 in like two or three years, Emily, 443 00:41:51,200 --> 00:41:52,440 because I know this apprentice thing isn't new, 444 00:41:52,440 --> 00:41:55,080 but like I wanna see two or three years from now 445 00:41:55,080 --> 00:41:56,880 what that's looked like and hear the stories that come out. 446 00:41:56,880 --> 00:41:59,400 Because I think that's a really compelling model 447 00:41:59,400 --> 00:42:01,920 of slowly helping organizations grow 448 00:42:01,920 --> 00:42:04,280 and mature their culture while not having the budget 449 00:42:04,280 --> 00:42:05,460 right away to make that happen, 450 00:42:05,460 --> 00:42:07,720 but can slowly bring on board 451 00:42:07,720 --> 00:42:10,200 what the technical skills and ramp up. 452 00:42:10,200 --> 00:42:11,160 That's beautiful. 453 00:42:11,160 --> 00:42:14,520 Emily, what got you into the nonprofit world? 454 00:42:14,520 --> 00:42:17,280 What matters and why have you stayed in the nonprofit world? 455 00:42:17,280 --> 00:42:18,720 Why does it matter to you? 456 00:42:18,720 --> 00:42:23,720 I have always been really lucky to have opportunity 457 00:42:24,280 --> 00:42:27,280 and resources that I've been able to use 458 00:42:27,280 --> 00:42:28,880 and take advantage of. 459 00:42:28,880 --> 00:42:31,400 And I've from a young age been introduced 460 00:42:31,400 --> 00:42:35,200 to the fact that that's not everybody's life situation 461 00:42:35,200 --> 00:42:38,880 and was taught early on that everyone is there 462 00:42:38,880 --> 00:42:39,800 but for fortune. 463 00:42:39,800 --> 00:42:41,960 Everyone's situation is there but for fortune, 464 00:42:41,960 --> 00:42:44,920 everyone is equal and worthy and deserving 465 00:42:44,920 --> 00:42:46,940 of good and happy life. 466 00:42:46,940 --> 00:42:48,840 But that that's not the case for everyone 467 00:42:48,840 --> 00:42:50,600 in terms of resources and opportunities. 468 00:42:50,600 --> 00:42:55,600 And so I have always felt that it is something 469 00:42:55,740 --> 00:42:58,820 that we should work towards as a human culture 470 00:42:58,820 --> 00:43:01,960 to have everyone have those resources and opportunities. 471 00:43:01,960 --> 00:43:05,960 And then just more like specifically in career path, 472 00:43:07,020 --> 00:43:08,760 when I first moved to New York, 473 00:43:08,760 --> 00:43:10,420 I was working in book publishing 474 00:43:10,420 --> 00:43:13,120 and it was a fairly easy job 475 00:43:13,120 --> 00:43:15,240 and I had a micromanaging manager 476 00:43:15,240 --> 00:43:16,960 so I didn't have a lot to do. 477 00:43:16,960 --> 00:43:20,120 And I was volunteering around New York City 478 00:43:20,120 --> 00:43:22,280 with a lot of different small nonprofits 479 00:43:22,280 --> 00:43:27,280 and we were making very good local individual impact 480 00:43:28,480 --> 00:43:30,040 in the ways that I was volunteering 481 00:43:30,040 --> 00:43:31,980 and seeing what the organizations were doing 482 00:43:31,980 --> 00:43:34,260 but they had really big missions 483 00:43:34,260 --> 00:43:38,500 like end domestic violence or end poverty. 484 00:43:38,500 --> 00:43:42,560 And I felt like we weren't doing activities 485 00:43:42,560 --> 00:43:44,240 to get towards that mission. 486 00:43:44,240 --> 00:43:47,240 And I had a lot of passion and time and energy. 487 00:43:47,240 --> 00:43:49,040 I was in my twenties. 488 00:43:49,040 --> 00:43:53,160 So to give but I didn't see that I had any skill 489 00:43:53,160 --> 00:43:57,120 to bridge that gap between what we're doing 490 00:43:57,120 --> 00:43:59,080 and getting towards these big missions. 491 00:43:59,080 --> 00:44:02,720 And so I ended up going back to school at night, 492 00:44:03,640 --> 00:44:05,880 just looking, I had come from a music theater 493 00:44:05,880 --> 00:44:06,760 literature background. 494 00:44:06,760 --> 00:44:09,120 So I went looking for a skill set I didn't have. 495 00:44:09,120 --> 00:44:10,360 I went to business school. 496 00:44:12,360 --> 00:44:14,680 I got introduced there to data and tech 497 00:44:14,680 --> 00:44:18,080 for supply chain logistics, 498 00:44:18,080 --> 00:44:19,820 really kind of dry stuff. 499 00:44:19,820 --> 00:44:23,080 But somehow it seemed like since I was volunteering 500 00:44:23,080 --> 00:44:26,000 and learning about this and also actually I went to a school 501 00:44:26,000 --> 00:44:28,640 that had a really good social entrepreneurship program 502 00:44:28,640 --> 00:44:33,160 and saw that this was a tool meant to efficiently 503 00:44:33,160 --> 00:44:36,640 and effectively move processes towards their end goal. 504 00:44:36,640 --> 00:44:39,680 And that's what I wanted for social justice 505 00:44:39,680 --> 00:44:41,240 and mission-driven organizations. 506 00:44:41,240 --> 00:44:43,840 And so marrying those two things in a career 507 00:44:43,840 --> 00:44:45,000 felt like the right move. 508 00:44:45,000 --> 00:44:48,520 Well, that story, thanks for sharing a little more insight 509 00:44:48,520 --> 00:44:50,200 into your background. 510 00:44:50,200 --> 00:44:52,840 And we're coming up on time here. 511 00:44:52,840 --> 00:44:55,680 So Emily, if someone wanted to reach out to you, 512 00:44:55,680 --> 00:44:59,320 find you online, find out more about what you're doing, 513 00:44:59,320 --> 00:45:01,540 where can they go online to find out about you more? 514 00:45:01,540 --> 00:45:03,040 Yeah, they can go to my website. 515 00:45:03,040 --> 00:45:06,920 It's called maketechworkforyou.com. 516 00:45:06,920 --> 00:45:08,840 And that comes out of the fact 517 00:45:08,840 --> 00:45:10,760 that not everyone needs to be a coder. 518 00:45:10,760 --> 00:45:13,280 You don't need the shiniest technology. 519 00:45:13,280 --> 00:45:14,620 You just need to make tech work 520 00:45:14,620 --> 00:45:16,660 for what you're doing right now. 521 00:45:16,660 --> 00:45:18,440 Yeah, and they can find me on LinkedIn, 522 00:45:18,440 --> 00:45:20,040 Emily Hicks-Rotella. 523 00:45:20,040 --> 00:45:25,040 And I love to talk to folks and hear what's going on. 524 00:45:25,560 --> 00:45:27,640 So anyone that wants to find the time 525 00:45:27,640 --> 00:45:30,580 to just brainstorm, thought partner, 526 00:45:30,580 --> 00:45:32,200 talk about these topics, yeah, 527 00:45:32,200 --> 00:45:34,160 that's my favorite thing to talk about. 528 00:45:34,160 --> 00:45:37,320 And thank you both so much for having this conversation 529 00:45:37,320 --> 00:45:39,360 because you ask really great questions 530 00:45:39,360 --> 00:45:40,560 and you have really great insights. 531 00:45:40,560 --> 00:45:44,240 And I've learned some really interesting ways 532 00:45:44,240 --> 00:45:46,240 to sort of think about this from you. 533 00:45:46,240 --> 00:45:47,520 So I appreciate it. 534 00:45:47,520 --> 00:45:52,000 Super, super technical, practical, tactical episode here, 535 00:45:52,000 --> 00:45:55,600 conversation, really appreciate it so much. 536 00:45:55,600 --> 00:45:58,280 And Emily, I gotta ask a question. 537 00:45:58,280 --> 00:46:01,360 So if data was to be found in the field, 538 00:46:01,360 --> 00:46:02,400 like we were talking about, 539 00:46:02,400 --> 00:46:03,720 what kind of crop would it be? 540 00:46:03,720 --> 00:46:05,180 If it was found in the field, 541 00:46:05,180 --> 00:46:07,840 what kind of crop is it gonna be? 542 00:46:07,840 --> 00:46:09,560 Oh boy, see, I'm a city person. 543 00:46:09,560 --> 00:46:11,400 I don't have a lot of good nature stuff. 544 00:46:11,400 --> 00:46:13,920 Okay, I'm gonna go in. 545 00:46:13,920 --> 00:46:16,040 I'm gonna go in. 546 00:46:16,040 --> 00:46:17,080 Oh God, this is so terrible. 547 00:46:17,080 --> 00:46:18,160 It's not even a crop. 548 00:46:18,160 --> 00:46:21,200 Data are the redwood trees, okay? 549 00:46:21,200 --> 00:46:25,080 So they are big, right? 550 00:46:25,080 --> 00:46:28,800 Unmoving in some ways, but like always growing, 551 00:46:28,800 --> 00:46:33,320 no matter what, they provide oxygen 552 00:46:33,320 --> 00:46:35,000 and they have a symbiotic relationship 553 00:46:35,000 --> 00:46:36,480 with humans and the earth, right? 554 00:46:36,480 --> 00:46:37,600 We can't live without them 555 00:46:37,600 --> 00:46:38,440 and they can't live without us. 556 00:46:38,440 --> 00:46:39,280 This is going deep. 557 00:46:39,280 --> 00:46:41,360 The oxygen, carbon dioxide. 558 00:46:41,360 --> 00:46:45,200 Yes, I think that's that relationship part. 559 00:46:45,200 --> 00:46:48,520 Maybe there's some idea that data technology 560 00:46:48,520 --> 00:46:50,240 is separate from nature, 561 00:46:50,240 --> 00:46:55,240 separate from the ecosystem of the divine 562 00:46:55,560 --> 00:46:58,360 and humans are in that. 563 00:46:58,360 --> 00:47:00,400 Some people even think humans are separate from nature 564 00:47:00,400 --> 00:47:02,780 and these mindset bridges, I think, 565 00:47:02,780 --> 00:47:05,280 if we can see ourselves as part of nature 566 00:47:05,280 --> 00:47:08,800 and data technology as part of that natural ecosystem, 567 00:47:08,800 --> 00:47:13,280 I think we'll find the symbiosis among us 568 00:47:13,280 --> 00:47:17,080 to help each other grow in positive ways. 569 00:47:17,080 --> 00:47:17,920 Wow. 570 00:47:17,920 --> 00:47:19,120 That's a lot in that redwood tree. 571 00:47:19,120 --> 00:47:21,520 That was a lot in there. 572 00:47:21,520 --> 00:47:23,320 And I was just thinking this whole time, 573 00:47:23,320 --> 00:47:26,360 I wouldn't know it, but it's gotta be some kind of grain. 574 00:47:26,360 --> 00:47:28,640 It's a basis for bread. 575 00:47:28,640 --> 00:47:29,960 Yeah. 576 00:47:29,960 --> 00:47:32,400 You know what else it could be at some point? 577 00:47:32,400 --> 00:47:34,920 Like, you know that, where is it? 578 00:47:34,920 --> 00:47:38,840 Maybe in Switzerland where they have a grain vault. 579 00:47:38,840 --> 00:47:40,520 So in case of oblivion, 580 00:47:40,520 --> 00:47:42,720 then we can regrow all of our grains. 581 00:47:42,720 --> 00:47:43,720 The storehouse. 582 00:47:43,720 --> 00:47:45,280 Yeah, the storehouse. 583 00:47:45,280 --> 00:47:47,720 I think it'd be really interesting to see 584 00:47:47,720 --> 00:47:49,720 what data points about humanity 585 00:47:49,720 --> 00:47:51,600 or even if this be an interesting thing 586 00:47:51,600 --> 00:47:53,440 as a data culture exercise, 587 00:47:53,440 --> 00:47:55,640 if you were building your grain vault 588 00:47:55,640 --> 00:47:57,360 for your organization, 589 00:47:57,360 --> 00:47:58,960 what data points would you put in it 590 00:47:58,960 --> 00:48:02,200 that you would need to have if everything blew up 591 00:48:02,200 --> 00:48:03,040 that you could start over with? 592 00:48:03,040 --> 00:48:05,400 Or you could just do a time capsule kind of thing. 593 00:48:05,400 --> 00:48:07,760 Like, hey, what data would I throw in a time capsule 594 00:48:07,760 --> 00:48:09,320 for a hundred years down the road 595 00:48:09,320 --> 00:48:10,720 and what would you find in there? 596 00:48:10,720 --> 00:48:11,560 I don't know. 597 00:48:11,560 --> 00:48:13,120 Oh man. 598 00:48:13,120 --> 00:48:15,280 Gotta unpack that stuff in another conversation. 599 00:48:15,280 --> 00:48:16,120 That's good. 600 00:48:16,120 --> 00:48:17,400 Yeah. 601 00:48:17,400 --> 00:48:18,240 All right. 602 00:48:18,240 --> 00:48:20,600 Well, thank you, Emily, for being here 603 00:48:20,600 --> 00:48:23,120 and thanks listeners for joining us 604 00:48:23,120 --> 00:48:25,680 on this episode of Making Data Matter. 605 00:48:25,680 --> 00:48:26,520 Signing off. 606 00:48:26,520 --> 00:48:53,520 Thank you, everybody.