1 00:00:00,120 --> 00:00:05,250 How do you prioritize and organize projects in a way that moves away 2 00:00:05,250 --> 00:00:11,460 from being reactive into a proactive and even responsive approach? 3 00:00:12,030 --> 00:00:16,650 Well, my next guest, Ginger Band Dean says it's all about the data. 4 00:00:17,685 --> 00:00:19,785 After talking with her, I'm inclined to agree. 5 00:00:22,785 --> 00:00:24,884 Welcome to Passion and Profits Without Burnout. 6 00:00:25,185 --> 00:00:26,445 I'm your host, Jacob Moore. 7 00:00:27,044 --> 00:00:31,905 I'm a speaker, coach, childhood suicide loss survivor and filmmaker who left 8 00:00:31,905 --> 00:00:34,004 Hollywood to follow my heart of service. 9 00:00:34,915 --> 00:00:38,545 I've helped tens of thousands of people find the balance in their 10 00:00:38,545 --> 00:00:41,785 life between passion and profits. 11 00:00:41,845 --> 00:00:47,095 On the show, I'm gonna teach you how to build a trauma responsive, resilient, 12 00:00:47,125 --> 00:00:49,765 and impactful community and organization. 13 00:00:50,445 --> 00:00:52,214 All without burning out. 14 00:00:53,025 --> 00:00:53,955 Let's get started. 15 00:00:58,245 --> 00:01:01,635 So during this episode, there are three major takeaways. 16 00:01:01,635 --> 00:01:04,634 The first thing that you're gonna learn today is that 17 00:01:04,634 --> 00:01:06,465 data doesn't have to be scary. 18 00:01:06,465 --> 00:01:07,604 It's not your enemy. 19 00:01:07,995 --> 00:01:09,854 In fact, it's on your side. 20 00:01:09,854 --> 00:01:14,655 And she really digs deep into why that is and how clinicians and people 21 00:01:14,655 --> 00:01:18,735 who are in the service field really need to befriend data in order to 22 00:01:18,965 --> 00:01:19,774 do their job better. 23 00:01:20,375 --> 00:01:27,455 The second big takeaway is that not only people but data have biases, and 24 00:01:27,460 --> 00:01:29,435 that's not necessarily a bad thing. 25 00:01:30,545 --> 00:01:35,645 Data and our, our relationship to it evolves over time and 26 00:01:35,765 --> 00:01:42,035 learning how to adjust and to grow with it is really important. 27 00:01:42,815 --> 00:01:49,695 The third big takeaway is that data should drive your mission rather 28 00:01:49,695 --> 00:01:51,494 than your mission driving your data. 29 00:01:51,884 --> 00:01:56,175 There's an important distinction there that Ginger talks about, and it's 30 00:01:56,324 --> 00:02:01,574 really, I think, profound in an aha moment for me looking at the data to 31 00:02:01,574 --> 00:02:08,414 identify where are the gaps in service and how are marginalized people being 32 00:02:08,414 --> 00:02:14,084 overlooked and maybe not getting access to the services that we think they. 33 00:02:15,015 --> 00:02:17,565 It's all in the numbers and they don't lie. 34 00:02:18,165 --> 00:02:24,225 Ginger Bandeen comes to us after 15 years working as a 35 00:02:24,285 --> 00:02:25,785 licensed clinical social worker. 36 00:02:26,475 --> 00:02:29,415 She founded mission driven data. 37 00:02:29,820 --> 00:02:35,280 And does that from the perspective of quality improvement, advocacy, 38 00:02:35,310 --> 00:02:40,650 behavioral health, and she's really passionate about making data accessible 39 00:02:40,650 --> 00:02:44,730 to people, especially clinicians and helping people who would say 40 00:02:44,730 --> 00:02:45,810 that they're not data people. 41 00:02:46,140 --> 00:02:48,330 Be excited and find the joy in it. 42 00:02:48,870 --> 00:02:53,520 I gotta say, I'm excited about data now, so please welcome 43 00:02:53,520 --> 00:02:55,230 my guest, Ginger Banding. 44 00:02:56,280 --> 00:02:56,940 Well, Ginger. 45 00:02:56,940 --> 00:02:57,450 Hi. 46 00:02:58,545 --> 00:02:59,175 Oh, hi Jacob 47 00:03:00,855 --> 00:03:02,205 ! Thanks so much for being here. 48 00:03:02,205 --> 00:03:06,405 I appreciate you taking the time to share some knowledge with us today. 49 00:03:06,555 --> 00:03:06,765 Yeah. 50 00:03:06,765 --> 00:03:07,935 I'm so excited to be joining you. 51 00:03:07,935 --> 00:03:08,715 Thank you so much for the in. 52 00:03:09,720 --> 00:03:10,980 Of course, of course. 53 00:03:11,370 --> 00:03:15,420 So you are, I, I think I can say this. 54 00:03:15,470 --> 00:03:20,940 I, I feel like I know you well enough now to say that you are a data nerd, . Would 55 00:03:20,940 --> 00:03:21,390 you agree? 56 00:03:22,080 --> 00:03:22,410 Yeah. 57 00:03:22,410 --> 00:03:22,680 Yeah. 58 00:03:22,680 --> 00:03:25,860 It's taken me a long time to agree that that is one of my identities. 59 00:03:25,860 --> 00:03:27,450 I used to always say, I'm just a clinician. 60 00:03:27,450 --> 00:03:28,380 I don't really do data. 61 00:03:28,380 --> 00:03:31,410 But now that I have a whole company that just focuses on data, I can't 62 00:03:31,410 --> 00:03:32,760 really get away with that anymore. 63 00:03:33,670 --> 00:03:35,130 Yeah, right on. 64 00:03:36,239 --> 00:03:37,290 I'm not a data nerd. 65 00:03:37,559 --> 00:03:43,382 And so for everyone listening today, I just want to share that I'm going 66 00:03:43,387 --> 00:03:46,712 to be learning right alongside you. 67 00:03:46,952 --> 00:03:48,782 Ginger is the expert here. 68 00:03:49,262 --> 00:03:54,224 and I use data because I need to because people tell me that 69 00:03:54,224 --> 00:03:55,334 that's what you're supposed to do. 70 00:03:55,364 --> 00:04:01,424 If I could just work on like feelings and you know, ideas and that was enough, 71 00:04:01,429 --> 00:04:03,014 like all this qualitative stuff like 72 00:04:03,719 --> 00:04:05,189 I'd be golden, but you know. 73 00:04:05,729 --> 00:04:09,239 Well, the best data captures feeling and feelings and ideas, right? 74 00:04:09,239 --> 00:04:13,109 Like if you can capture your feelings and ideas with data, that's the best. 75 00:04:13,109 --> 00:04:13,949 That's the best kind of data. 76 00:04:15,299 --> 00:04:16,349 I like that. 77 00:04:16,829 --> 00:04:21,449 That is, that is a great segue into talking about like how you came 78 00:04:21,449 --> 00:04:27,059 today because you have, you have this background in like, as a clinician 79 00:04:27,064 --> 00:04:27,249 Yes. 80 00:04:27,509 --> 00:04:29,529 Where you were working and, and serving people. 81 00:04:30,054 --> 00:04:30,504 Yes. 82 00:04:30,534 --> 00:04:32,604 And now you're, now you're doing data. 83 00:04:32,604 --> 00:04:33,024 Yes. 84 00:04:33,144 --> 00:04:33,714 How does that work? 85 00:04:33,864 --> 00:04:36,834 Yeah, so I, I started off doing direct clinical practice. 86 00:04:36,834 --> 00:04:37,824 I'm a licensed social worker. 87 00:04:37,824 --> 00:04:39,785 I still actually have my license as well. 88 00:04:39,854 --> 00:04:44,874 So I started off working with individuals affected by homelessness in New York City. 89 00:04:44,904 --> 00:04:49,269 My first job after grad school and then did some kind of administrative e jobs. 90 00:04:49,299 --> 00:04:52,624 And then also did intensive case management in a rural 91 00:04:52,624 --> 00:04:54,304 part of Oregon for a while. 92 00:04:54,634 --> 00:04:58,114 And that was when I really got most familiar with community 93 00:04:58,119 --> 00:05:02,014 mental health, community services, that sort of delivery model. 94 00:05:02,019 --> 00:05:04,834 And I loved the work that I did. 95 00:05:04,834 --> 00:05:09,034 I love the people I got to work with, the, the individuals who were working 96 00:05:09,034 --> 00:05:10,774 alongside me to improve their lives. 97 00:05:10,827 --> 00:05:15,454 I really enjoyed working with With that population and back in those days, we 98 00:05:15,454 --> 00:05:16,924 were just doing most things on paper. 99 00:05:16,924 --> 00:05:21,124 We weren't really using electronic health records or data, and I was sort of part 100 00:05:21,124 --> 00:05:23,884 of the groups originally at the agencies. 101 00:05:23,914 --> 00:05:26,584 I worked at a couple different agencies that went to that transition to 102 00:05:26,584 --> 00:05:29,134 electronic health records, and so I would. 103 00:05:29,329 --> 00:05:32,389 I sort of ended up on the committee to help them figure that out. 104 00:05:32,443 --> 00:05:36,613 Maybe because I seemed like someone who could understand systems a little bit and 105 00:05:36,613 --> 00:05:38,930 I and I maybe just volunteered by mistake. 106 00:05:38,930 --> 00:05:43,000 And so anyway, I ended up learning about that process and then, When I moved into 107 00:05:43,000 --> 00:05:47,196 more quality improvement and compliance as my job I realized that those jobs were 108 00:05:47,196 --> 00:05:51,717 gonna require, looking at data was as we moved into electronic systems that quality 109 00:05:51,717 --> 00:05:53,157 and compliance were really about data. 110 00:05:53,547 --> 00:05:57,117 And so I just decided, well, I'm gonna have to learn this data stuff now. 111 00:05:57,117 --> 00:05:59,787 And so then I sort of picked everyone's brain I could think of... 112 00:05:59,997 --> 00:06:00,897 and you did, you did it! 113 00:06:00,897 --> 00:06:01,497 ...tried to learn it. 114 00:06:03,252 --> 00:06:03,882 Wow. 115 00:06:03,882 --> 00:06:04,392 Okay. 116 00:06:04,482 --> 00:06:04,602 Yeah. 117 00:06:05,082 --> 00:06:06,882 So, so that's interesting. 118 00:06:06,882 --> 00:06:11,378 You, you kind of, you know, found a way there naturally through 119 00:06:11,378 --> 00:06:13,088 the career, you know, that. 120 00:06:14,103 --> 00:06:19,503 You know, I'm, I'm guessing you got into, because, you know, something, 121 00:06:19,503 --> 00:06:20,643 something drove you there, Right? 122 00:06:20,643 --> 00:06:23,086 Some sort of heart of service brought you to that. 123 00:06:23,356 --> 00:06:23,536 Yeah. 124 00:06:24,436 --> 00:06:24,646 Yeah. 125 00:06:24,646 --> 00:06:28,336 And I think I, one, I felt like if we don't, and I still feel this way, 126 00:06:28,336 --> 00:06:31,816 that if we don't have people who have that connection to the work 127 00:06:31,816 --> 00:06:35,226 we do, Also involved in the data side of things, we end up with data 128 00:06:35,226 --> 00:06:36,426 that doesn't really mean anything. 129 00:06:36,516 --> 00:06:40,613 And so we really need people to bridge that gap between understanding 130 00:06:40,613 --> 00:06:43,493 the clinical side of things and the individuals we serve, and understanding 131 00:06:43,493 --> 00:06:47,333 people, and then also understanding the data or else we end up with this 132 00:06:47,333 --> 00:06:50,423 mismatch of like, Oh, we're tracking this data and it looks like we're 133 00:06:50,423 --> 00:06:53,663 doing bad at one thing, when really the people would say, We're doing good 134 00:06:53,663 --> 00:06:57,113 at that thing, or vice versa, or just focusing on things that aren't important. 135 00:06:57,613 --> 00:06:58,173 So, yeah. 136 00:06:58,683 --> 00:06:59,043 Right. 137 00:06:59,373 --> 00:07:00,453 Yeah, absolutely. 138 00:07:00,453 --> 00:07:04,533 And, and so enter mission driven data, right? 139 00:07:05,943 --> 00:07:06,633 Yeah, yeah. 140 00:07:06,633 --> 00:07:10,383 I started that company in, I started this company in 2019 and 141 00:07:10,383 --> 00:07:13,383 then started doing it full time in 2020, the year of the pandemic. 142 00:07:13,420 --> 00:07:15,740 And I've been doing that full time since then. 143 00:07:15,798 --> 00:07:19,488 I named my company very specifically mission driven data because I think so 144 00:07:19,488 --> 00:07:23,868 often our data is driving our activity because we have to track certain things 145 00:07:23,868 --> 00:07:27,858 or we have to report certain things, and I really want us to move the mission 146 00:07:27,858 --> 00:07:31,208 in front of the data so that we're really thinking about like, What is our 147 00:07:31,208 --> 00:07:34,538 mission and how could the mission we have drive the data we're looking at? 148 00:07:35,348 --> 00:07:35,618 So. 149 00:07:35,858 --> 00:07:36,428 Sure. 150 00:07:36,433 --> 00:07:36,698 Yeah. 151 00:07:37,058 --> 00:07:40,868 Let, let's dig into that a little bit because what does it look like 152 00:07:41,258 --> 00:07:46,298 if the data is driving the mission versus the mission driving the data? 153 00:07:47,183 --> 00:07:50,303 You know, I, I think a good example of that that happens all the time 154 00:07:50,303 --> 00:07:54,803 in agencies is a focus on what agencies often will call productivity. 155 00:07:55,013 --> 00:07:58,553 So it's like tracking how much time clinicians are 156 00:07:58,553 --> 00:08:00,653 spending, seeing clients, right? 157 00:08:00,653 --> 00:08:02,663 Which is a direct driver of revenue. 158 00:08:02,873 --> 00:08:06,233 So it's important, like agencies need to stay afloat, they need to make 159 00:08:06,233 --> 00:08:08,153 their payroll, so revenue matters. 160 00:08:08,723 --> 00:08:13,393 But if you focus purely on productivity and you're not looking at anything 161 00:08:13,398 --> 00:08:18,093 else about that data, you end up doing some weird things that are not 162 00:08:18,093 --> 00:08:19,863 aligned with most agency's missions. 163 00:08:19,863 --> 00:08:24,183 So like you might end up favoring clients who are more able to come in 164 00:08:24,183 --> 00:08:28,766 for appointments on a regular basis and it might end up like go sort of having 165 00:08:28,766 --> 00:08:33,331 the unintended consequence of clinicians being unwilling to take on clients who are 166 00:08:33,331 --> 00:08:36,541 maybe more complicated, who are gonna have trouble getting to their appointments, 167 00:08:36,751 --> 00:08:41,221 who are gonna have barriers to access, like we end up, like perpetuating 168 00:08:41,221 --> 00:08:44,996 things like health disparities you know or other things that make access 169 00:08:44,996 --> 00:08:50,276 difficult for people because we're only incentivizing one aspect or one layer of 170 00:08:50,681 --> 00:08:54,055 that metric, that data is only looking, it's very one dimensional. 171 00:08:54,355 --> 00:08:58,225 And so I'm really into, if we're gonna look at that, how could we look at that 172 00:08:58,230 --> 00:09:02,035 and also look at things that help us avoid those unintended consequences. 173 00:09:02,095 --> 00:09:04,105 And really that's where I think about our mission. 174 00:09:04,105 --> 00:09:07,315 Like is our mission to provide the most number of services? 175 00:09:07,645 --> 00:09:08,035 No. 176 00:09:08,095 --> 00:09:08,425 Right. 177 00:09:08,425 --> 00:09:09,775 Like most agencies wouldn't say that. 178 00:09:09,775 --> 00:09:12,895 They would say, Our mission is to provide the right amount of services 179 00:09:12,895 --> 00:09:14,605 to the people who need it the most. 180 00:09:14,955 --> 00:09:15,135 , Right? 181 00:09:15,345 --> 00:09:19,785 And so how can we put that piece into the data that we're looking at so that 182 00:09:19,785 --> 00:09:23,085 we're not just one dimensional, we're like looking at the whole picture. 183 00:09:24,260 --> 00:09:24,750 Yeah. 184 00:09:24,755 --> 00:09:32,060 So that really helps you to balance, you know, billable hours and the 185 00:09:32,060 --> 00:09:33,650 impact that they're providing. 186 00:09:34,280 --> 00:09:34,730 Yeah. 187 00:09:34,730 --> 00:09:37,910 And who's ac who's getting access to those hours would be the other way. 188 00:09:37,910 --> 00:09:38,120 Right. 189 00:09:38,120 --> 00:09:42,440 There's sort of the, the entry point, like, are people getting into services? 190 00:09:42,800 --> 00:09:45,740 I mean, it's really common, I think, in community services for us to be 191 00:09:45,740 --> 00:09:48,385 really good at serving the clients that are in front of us, the folks that 192 00:09:48,385 --> 00:09:51,745 come in the door and make it there and are here to see us and to not always 193 00:09:51,745 --> 00:09:54,925 remember that there are may be people out there who need those services, who 194 00:09:54,925 --> 00:09:58,765 aren't getting through our front door because it's so much, I mean, we care. 195 00:09:58,825 --> 00:10:02,365 We can only, you know, our hearts are big, but we can't, It's hard to be 196 00:10:02,365 --> 00:10:05,275 always thinking about all the people who can't even get in to get services. 197 00:10:05,275 --> 00:10:07,015 When you've got someone in front of you, you can help. 198 00:10:07,090 --> 00:10:08,320 So you help that person. 199 00:10:08,770 --> 00:10:12,730 But when we start thinking like agency wide and thinking about our community 200 00:10:12,730 --> 00:10:16,150 impact, thinking about like who's able to get in the door and not able to get 201 00:10:16,155 --> 00:10:20,410 in the door, who's getting dropped off and like falling through the cracks. 202 00:10:20,740 --> 00:10:25,439 So like in some ways data, it, it it allows us to see past, like the 203 00:10:25,439 --> 00:10:28,109 thing that's right in front of us and see maybe the bigger picture 204 00:10:28,109 --> 00:10:29,609 of the need in our communities. 205 00:10:31,049 --> 00:10:31,539 Yeah. 206 00:10:31,544 --> 00:10:31,649 Yeah. 207 00:10:31,649 --> 00:10:36,834 So, Maybe that allows you to better serve marginalized 208 00:10:36,834 --> 00:10:40,614 communities and ensure true equity. 209 00:10:40,619 --> 00:10:41,004 Yeah. 210 00:10:41,244 --> 00:10:42,954 In services that are available to people. 211 00:10:42,984 --> 00:10:43,374 Right? 212 00:10:43,734 --> 00:10:43,974 Yeah. 213 00:10:43,974 --> 00:10:47,064 Or really think about like, are we doing something unintentionally that 214 00:10:47,064 --> 00:10:48,984 is excluding people from services? 215 00:10:48,999 --> 00:10:52,653 good example I always think of is this agency where we had, I had a 216 00:10:52,653 --> 00:10:55,173 conversation with the team there and they were talking about whether to 217 00:10:55,173 --> 00:10:59,418 add a Spanish speaking phone message to their voicemail tree, you know? 218 00:10:59,418 --> 00:11:02,598 So you call in that there would be part of the voicemail message that 219 00:11:02,598 --> 00:11:03,918 you would get would be in Spanish. 220 00:11:04,218 --> 00:11:07,368 And they said, Well we never have anyone come in who speaks Spanish so we don't 221 00:11:07,368 --> 00:11:09,318 need a Spanish speaking voicemail tree. 222 00:11:09,348 --> 00:11:12,948 And I was like, Oh, well are there no people in your 223 00:11:12,948 --> 00:11:14,208 community who speak Spanish? 224 00:11:14,208 --> 00:11:17,178 Cuz if there are, if there are any and you never have anyone come 225 00:11:17,183 --> 00:11:18,528 in, that might be the problem. 226 00:11:19,583 --> 00:11:24,773 And, you know, but it's that, you know, whether it's just not seeing, 227 00:11:24,773 --> 00:11:28,043 you know, the, the force through the trees or, you know, unconscious bias 228 00:11:28,043 --> 00:11:31,672 or, you know, whatever's driving that the data illuminates it, 229 00:11:31,852 --> 00:11:32,122 Right! 230 00:11:32,122 --> 00:11:35,812 I mean, data can have bias as well, but I think that doesn't mean 231 00:11:35,812 --> 00:11:36,982 we shouldn't look at it, right? 232 00:11:36,982 --> 00:11:40,801 Like we can have, we can accidentally incorporate our internal bias into 233 00:11:40,806 --> 00:11:45,836 our data as well, but it does give us this opportunity to see patterns 234 00:11:45,836 --> 00:11:49,166 that we would not necessarily notice because of our biases. 235 00:11:49,166 --> 00:11:49,436 Right. 236 00:11:49,436 --> 00:11:51,866 Like it can, it sort of, Yeah. 237 00:11:52,226 --> 00:11:55,166 Like if we're open to seeing those patterns, they can become 238 00:11:55,166 --> 00:11:59,306 visible to us, whereas we might not just notice them anecdotally. 239 00:12:00,086 --> 00:12:00,356 Yeah. 240 00:12:01,676 --> 00:12:05,396 So you said data can be as well. 241 00:12:05,396 --> 00:12:11,166 Then how do we ensure that we're getting good data and that it's 242 00:12:11,506 --> 00:12:12,856 saying what we think it's saying? 243 00:12:13,456 --> 00:12:15,496 I think that is a very good question. 244 00:12:15,496 --> 00:12:20,896 I think how, What are some ways to think about that? 245 00:12:22,136 --> 00:12:26,206 , I think that we can get caught feeling like the data has to be perfect. 246 00:12:26,236 --> 00:12:28,126 The data doesn't have to be perfect in order to use it. 247 00:12:28,126 --> 00:12:28,156 Mm. 248 00:12:28,636 --> 00:12:32,446 I think imperfect data gets us somewhere and then we, when we start looking 249 00:12:32,446 --> 00:12:36,286 at it, sometimes that leads us to do a better job of making cleaner data. 250 00:12:37,126 --> 00:12:39,823 So like so I'm kind of in favor of an iterative process. 251 00:12:39,823 --> 00:12:42,433 So, So basically start with what you've got. 252 00:12:42,873 --> 00:12:46,353 And if what you've got is a hundred blank answers to a question that, 253 00:12:46,353 --> 00:12:49,413 so you don't know what, you know, you've got, let's say 500 clients, 254 00:12:49,593 --> 00:12:52,683 you know what the answer is to this question for 400, a hundred or missing. 255 00:12:52,863 --> 00:12:56,133 Start looking at that data and then people will start caring enough. 256 00:12:56,253 --> 00:12:59,673 People will notice it's being looked at, and that will drive them to 257 00:12:59,678 --> 00:13:02,493 maybe fill out that blank answer the next time they see that person. 258 00:13:02,523 --> 00:13:02,613 Mm-hmm. 259 00:13:02,687 --> 00:13:04,247 . But you know, biases are. 260 00:13:04,597 --> 00:13:08,707 I think one of the ways you can account for or sort of approach that is to have 261 00:13:08,707 --> 00:13:11,737 a diverse group of people who are looking at the data, people from different 262 00:13:11,737 --> 00:13:13,387 backgrounds with different perspectives. 263 00:13:13,432 --> 00:13:17,572 And that those folks can then help bring out biases that are maybe 264 00:13:17,692 --> 00:13:20,212 being included that you can take out. 265 00:13:20,238 --> 00:13:22,934 So it's always good to have someone involved in looking at the data, who 266 00:13:22,934 --> 00:13:24,507 knows how that data's getting entered. 267 00:13:24,507 --> 00:13:27,431 Because they can say like, Oh, well I know why that always looks like that 268 00:13:27,431 --> 00:13:32,836 cuz we told them to always put that answer, you know, for example, So one 269 00:13:32,836 --> 00:13:38,236 way is to make that data accessible to a larger, more diverse group of people. 270 00:13:38,236 --> 00:13:42,196 So I'm in favor of, you know, getting the data down to the folks who are, 271 00:13:42,346 --> 00:13:45,556 who are, you know, entering that data so they can have impact, you 272 00:13:45,561 --> 00:13:47,866 know, have input on how that data is. 273 00:13:48,601 --> 00:13:49,381 Understood. 274 00:13:49,386 --> 00:13:52,321 And then, you know, down the road I, we're still working with 275 00:13:52,321 --> 00:13:53,791 agencies to get there, I think. 276 00:13:53,791 --> 00:13:59,521 But I mean, I think having consumer groups looking at the data would be so exciting. 277 00:13:59,641 --> 00:14:02,731 You know, like what, what do people who access services want 278 00:14:02,731 --> 00:14:04,381 to know about the, the data? 279 00:14:05,491 --> 00:14:06,061 That'd be cool. 280 00:14:06,511 --> 00:14:06,781 Yeah. 281 00:14:06,781 --> 00:14:09,841 That, you know, that reminds me of another conversation I was having 282 00:14:10,291 --> 00:14:14,632 with Ryan Goodrich, who's an HR specialist and has worked in the 283 00:14:14,632 --> 00:14:17,122 corporate HR space for many, many years. 284 00:14:17,512 --> 00:14:21,308 And he developed this really fantastic internship program and 285 00:14:21,313 --> 00:14:26,391 one of the core principles of it is designing an internship program 286 00:14:26,391 --> 00:14:28,551 that you would want to be a part of. 287 00:14:28,881 --> 00:14:29,254 Right. 288 00:14:29,271 --> 00:14:35,421 Creating something that people actually, you know, like actually 289 00:14:35,421 --> 00:14:39,981 meets people's needs or that people are interested in, or, you know, speaks 290 00:14:39,981 --> 00:14:42,051 to who they are, their personality. 291 00:14:42,561 --> 00:14:47,211 And, you know, that reminds me like we can do that same thing with data, right? 292 00:14:47,301 --> 00:14:47,841 And. 293 00:14:49,081 --> 00:14:54,181 Ask the questions or make sure we're speaking to the groups that, you know, 294 00:14:54,271 --> 00:14:59,508 maybe get left out sometimes to ensure that we're accounting for different 295 00:14:59,508 --> 00:15:03,078 perspectives, different cultural, you know, beliefs, different outbreaks. 296 00:15:04,138 --> 00:15:04,293 Yeah. 297 00:15:04,743 --> 00:15:05,103 Yeah. 298 00:15:05,103 --> 00:15:08,253 I mean, some of the data tools we develop, we like to include various 299 00:15:08,253 --> 00:15:10,053 ways of looking at that data too, right? 300 00:15:10,053 --> 00:15:13,383 So some people are very visual, so they wanna see data in like a nice 301 00:15:13,388 --> 00:15:15,153 colors and pictures and charts. 302 00:15:15,213 --> 00:15:15,243 Mm. 303 00:15:15,573 --> 00:15:17,763 Some people really wanna see charts of numbers. 304 00:15:17,793 --> 00:15:22,503 So, you know, having tools that kind of meet different styles of thinking. 305 00:15:22,557 --> 00:15:26,037 And then, you know, we try, we try to use data tools that are really interactive. 306 00:15:26,037 --> 00:15:29,157 I think that's the other thing that happens with data that sort of limits 307 00:15:29,157 --> 00:15:33,567 people's understanding and usability of that data is if it's very static. 308 00:15:33,567 --> 00:15:33,627 Yeah. 309 00:15:33,627 --> 00:15:36,957 Like they just get this piece of paper or essentially like a piece of paper. 310 00:15:37,612 --> 00:15:41,122 But it's nice if you can have data tools that let people like ask questions and 311 00:15:41,122 --> 00:15:42,562 answer some of their own questions. 312 00:15:42,562 --> 00:15:45,742 So if they're looking at the data and they can interact with that data. 313 00:15:45,742 --> 00:15:49,762 For example, say you're looking at services and number of individuals 314 00:15:49,762 --> 00:15:53,002 open for services and you wanna look at different service types. 315 00:15:53,012 --> 00:15:55,472 But maybe you also add some demographic information there. 316 00:15:55,472 --> 00:15:59,160 So like age groups or gender or racial or ethnic background or 317 00:15:59,160 --> 00:16:00,870 zip code or things like that. 318 00:16:00,870 --> 00:16:04,320 Then the people who are looking at the data can dig around and kind of 319 00:16:04,325 --> 00:16:06,420 look for some patterns themselves. 320 00:16:06,441 --> 00:16:09,984 And then when they find a pattern, they can also then look further and try to see 321 00:16:09,984 --> 00:16:11,694 like what's contributing to that pattern. 322 00:16:11,744 --> 00:16:16,334 So I love tools like that that really engage people in the process. 323 00:16:16,759 --> 00:16:17,449 I like that. 324 00:16:17,569 --> 00:16:21,109 Yeah, that's been a, you know, a challenge that we've had is, you know, 325 00:16:21,109 --> 00:16:27,589 we, we collected, we do surveys and evaluations, and then it, it goes 326 00:16:27,589 --> 00:16:33,259 into a spreadsheet and it lives on the Google Drive and that's where it dies. 327 00:16:33,289 --> 00:16:38,809 You know, maybe we extract some numbers and shared in an annual report or, you 328 00:16:38,809 --> 00:16:43,559 know, something of that nature, but, you know, otherwise, The actual, like what 329 00:16:43,559 --> 00:16:47,519 you're describing of making it usable. 330 00:16:47,519 --> 00:16:48,809 Like, Like living data. 331 00:16:48,809 --> 00:16:49,049 Yeah. 332 00:16:49,139 --> 00:16:49,409 Right. 333 00:16:49,619 --> 00:16:49,949 Yeah. 334 00:16:51,179 --> 00:16:51,389 Yeah. 335 00:16:51,389 --> 00:16:52,109 So interactive. 336 00:16:52,109 --> 00:16:55,679 So like in that example, if you had survey data sitting in a spreadsheet, maybe 337 00:16:55,679 --> 00:16:57,329 you pull it into a visualization tool. 338 00:16:57,334 --> 00:17:00,029 So like a user could say like, Do people who answer this question 339 00:17:00,029 --> 00:17:02,849 this way, How does that break down with this other question? 340 00:17:02,849 --> 00:17:05,369 You know, like, if they answered this question this way, does that change how 341 00:17:05,369 --> 00:17:06,689 they would've answered this question? 342 00:17:06,689 --> 00:17:07,107 Or yeah. 343 00:17:07,107 --> 00:17:09,747 Or over time are we seeing changes in the scores? 344 00:17:09,807 --> 00:17:10,657 You know, I. 345 00:17:11,367 --> 00:17:14,945 You know, you can also make a piece of paper more dynamic by 346 00:17:14,945 --> 00:17:18,455 having it show change over time or comparison of two different things. 347 00:17:18,725 --> 00:17:21,965 So even within a tool that maybe doesn't allow for some of that interactive 348 00:17:21,965 --> 00:17:26,405 stuff I was describing, you can make the data meaningful by having 349 00:17:26,405 --> 00:17:28,415 it compared to something over time. 350 00:17:28,415 --> 00:17:31,811 Something else, you know, thinking about data all by itself 351 00:17:31,811 --> 00:17:32,981 doesn't really mean as much. 352 00:17:33,521 --> 00:17:33,791 Yeah, 353 00:17:35,201 --> 00:17:35,591 yeah. 354 00:17:36,611 --> 00:17:42,596 So, What about, So say, you know, for an organization like mine, 355 00:17:42,926 --> 00:17:45,146 we're not a large organization. 356 00:17:45,146 --> 00:17:46,296 I don't have a ton of staff. 357 00:17:46,359 --> 00:17:49,659 We serve, you know, a, a pretty large population. 358 00:17:49,689 --> 00:17:52,399 You know, we've had over 10,000 people go through our program. 359 00:17:52,399 --> 00:18:01,376 There are so many ideas, so many projects, so many things to organize. 360 00:18:02,776 --> 00:18:03,766 Where do I start? 361 00:18:04,516 --> 00:18:10,001 How do I understand where, where to begin with all of this? 362 00:18:10,901 --> 00:18:14,861 What, what's, what's a tool or an approach that you use to start 363 00:18:14,861 --> 00:18:16,141 figuring that out with your clients? 364 00:18:16,147 --> 00:18:20,018 Well, you know, we've actually been thinking a lot lately about about a 365 00:18:20,018 --> 00:18:22,748 framework to help agencies because we work with a lot of agencies 366 00:18:22,748 --> 00:18:24,068 who are at that starting point. 367 00:18:24,068 --> 00:18:26,408 You know, they've, they've had some turnover. 368 00:18:26,413 --> 00:18:28,718 Gosh, a lot of agencies are having turnover right now. 369 00:18:28,794 --> 00:18:31,164 And so there's somebody new who started there and they don't 370 00:18:31,164 --> 00:18:33,864 really understand anything that's going that's been done before. 371 00:18:33,864 --> 00:18:36,324 And they, or they have their own ideas about where they wanna go. 372 00:18:36,324 --> 00:18:37,822 And so they're sort of starting at the beginning. 373 00:18:37,822 --> 00:18:39,813 And so we've sort of adapted. 374 00:18:40,368 --> 00:18:43,878 We've sort of taken a little bit from Maslow's hierarchy of needs to help 375 00:18:43,878 --> 00:18:47,538 agencies kind of think about like, you know, Maslow's hierarchy has like basic 376 00:18:47,538 --> 00:18:50,358 needs at the bottom, and then safety needs and then belonging, and then 377 00:18:50,358 --> 00:18:52,018 esteem, and then self actualization. 378 00:18:52,026 --> 00:18:55,926 So we, we've been taking that as sort of the beginning of thinking about this, 379 00:18:55,926 --> 00:19:01,206 but through that process we also learned that Maslow got a lot of his ideas. 380 00:19:01,206 --> 00:19:02,496 One could say maybe. 381 00:19:03,336 --> 00:19:06,576 Borrowed without asking a lot of his ideas from the Blackfoot nation. 382 00:19:06,597 --> 00:19:10,399 And so there's some great information out there if your listeners are interested. 383 00:19:10,449 --> 00:19:13,611 Just Google Maslow and the Blackfoot Nation or I've got this 384 00:19:13,611 --> 00:19:15,381 book here by Sydney Stone Brown. 385 00:19:15,381 --> 00:19:19,778 It's all about transforming transformation beyond greed, native self actualization. 386 00:19:19,808 --> 00:19:24,518 Anyway, the whole idea is that Maslow kind of misunderstood or misrepresented 387 00:19:24,518 --> 00:19:28,388 the Blackfoot nation's ideas, and I'm probably not gonna do it perfectly either. 388 00:19:28,388 --> 00:19:32,553 But the basic idea is, Self actualization is sort of the beginning 389 00:19:32,553 --> 00:19:36,483 of a process, and that next above that is community actualization, and 390 00:19:36,483 --> 00:19:38,343 above that is cultural perpetuity. 391 00:19:38,823 --> 00:19:42,003 So this idea that as an agency, we're trying to kind of get our feet on 392 00:19:42,003 --> 00:19:44,973 the ground and figure out if we're okay and can we make payroll and 393 00:19:44,973 --> 00:19:48,928 all those basic needs to get to the point where, As an agency, we're sort 394 00:19:48,928 --> 00:19:52,318 of quote unquote, self-actualized would mean that like you're a high 395 00:19:52,323 --> 00:19:55,588 functioning agency, you've got all your ducks in a row, you're doing great. 396 00:19:56,038 --> 00:19:58,798 But that's really not where I hope agencies will stop. 397 00:19:58,798 --> 00:20:02,338 I hope they will go past that to the point of like, are we having the impact 398 00:20:02,338 --> 00:20:03,988 on the community that we wanna have? 399 00:20:03,988 --> 00:20:07,318 Are we, you know, are we serving the mission that we wanna serve? 400 00:20:07,558 --> 00:20:10,468 And then above that, are we impacting the larger system, right? 401 00:20:10,468 --> 00:20:11,698 Like, are we changing the world? 402 00:20:12,118 --> 00:20:13,768 So like, I love that model. 403 00:20:13,768 --> 00:20:18,068 And so when we walk people through a process, To sort of prioritize. 404 00:20:18,848 --> 00:20:21,548 We're always trying to help people think about what are your basic needs like, 405 00:20:21,848 --> 00:20:25,748 and you know, if you're running like a small business like you or I run, you 406 00:20:25,748 --> 00:20:29,018 know, you probably have a general picture of like where you're at financially. 407 00:20:29,138 --> 00:20:32,348 Although sometimes nonprofits have that is not under their control either. 408 00:20:32,348 --> 00:20:35,348 And so that's tricky, but helping you get those basic things figured out. 409 00:20:35,348 --> 00:20:38,798 But then also each time that you're doing these things for 410 00:20:38,798 --> 00:20:40,478 a basic need, how can you like. 411 00:20:41,018 --> 00:20:43,028 Pull yourselves up that continuum too. 412 00:20:43,028 --> 00:20:47,078 So like, yeah, this is, you know, we're measuring our, let's say we're rev 413 00:20:47,138 --> 00:20:50,385 measuring our revenue, but let's, let's look at revenue a little more granular. 414 00:20:50,390 --> 00:20:54,315 Let's look at like by zip code, Like let's look at revenue by insurance type. 415 00:20:54,320 --> 00:20:58,095 Let's look at revenue by something else so that we're sort of also answering 416 00:20:58,095 --> 00:21:02,055 some of those higher level questions while we answer the basic question. 417 00:21:02,385 --> 00:21:05,595 Because it's very easy to get stuck on those basic questions and never 418 00:21:05,595 --> 00:21:08,835 go above it, you know, cuz those questions are hard and we're always 419 00:21:09,405 --> 00:21:13,395 bombarded with all these external, you know, factors like pandemics. 420 00:21:13,875 --> 00:21:17,175 So how do you kind of like, keep your eye on the prize in a way, right? 421 00:21:17,175 --> 00:21:20,925 Like you're still thinking above this like, sort of Yeah. 422 00:21:21,285 --> 00:21:23,835 Bot not bottom, but very basic layer. 423 00:21:23,844 --> 00:21:26,106 So yeah, I'm always thinking, so there's, we're always trying to think about how 424 00:21:26,111 --> 00:21:31,960 do we go up the pyramid, you know, go get higher, go more toward where we're aiming. 425 00:21:33,520 --> 00:21:33,940 Yeah. 426 00:21:33,970 --> 00:21:40,210 So there, there has to be almost this duality of the, the business 427 00:21:40,210 --> 00:21:47,740 base basics and doing the day to day with the eye on that mission, right? 428 00:21:47,740 --> 00:21:53,080 So what I would call a, a mission mindset, having that infused 429 00:21:53,440 --> 00:21:56,080 in every single decision yes. 430 00:21:56,440 --> 00:21:58,060 Then informs. 431 00:21:58,630 --> 00:22:00,070 Why are we doing this? 432 00:22:00,070 --> 00:22:04,600 Why are we looking at the revenue and what can we do with that? 433 00:22:05,050 --> 00:22:12,370 Or how does this data or information impact what we're trying to accomplish? 434 00:22:12,460 --> 00:22:12,520 Yeah. 435 00:22:12,790 --> 00:22:14,830 I really, I, I like that. 436 00:22:14,835 --> 00:22:20,894 That's I think a great perspective for growth and, you 437 00:22:20,894 --> 00:22:22,604 know, infusing that in a way. 438 00:22:22,680 --> 00:22:24,390 Is really sustainable, right? 439 00:22:24,420 --> 00:22:30,240 Because I think if you were to come to me and say, Hey Jake, I want to, you 440 00:22:30,240 --> 00:22:37,920 know, implement this, you know, this data policy that ensures X, Y, Z, and you know 441 00:22:38,100 --> 00:22:43,020 it's going to take this, this, and this to, to implement, to overhaul, right? 442 00:22:44,040 --> 00:22:45,840 I mean, that, that, it sounds daunting. 443 00:22:45,840 --> 00:22:48,360 It sounds like a, like a, a huge task. 444 00:22:48,360 --> 00:22:52,230 But if you say, Hey, you know, we're just gonna start here and just take a look 445 00:22:52,230 --> 00:22:54,870 at what is driving your business, right? 446 00:22:55,530 --> 00:22:58,290 What clients, what revenue is driving your business. 447 00:22:58,348 --> 00:23:01,486 And while we do that, we're just gonna take a look at 448 00:23:01,486 --> 00:23:02,926 this, this little piece too. 449 00:23:02,953 --> 00:23:06,068 We, when we add on it's, it's very similar to what we do. 450 00:23:06,703 --> 00:23:11,503 In five bridges with, with wellness, you know, the, the, the crash diets 451 00:23:11,503 --> 00:23:18,583 don't work, but small changes over time add up to huge gains in the long run. 452 00:23:18,673 --> 00:23:18,763 Yeah. 453 00:23:18,821 --> 00:23:19,258 That's great. 454 00:23:19,258 --> 00:23:21,718 That's, that's an aha moment that I'm having right now, 455 00:23:22,468 --> 00:23:22,828 . Yeah. 456 00:23:22,828 --> 00:23:25,348 And the other way you can infuse, just to add more ahas, 457 00:23:25,378 --> 00:23:26,698 infuse more of those layers. 458 00:23:26,698 --> 00:23:28,498 You know, one of those layers is belonging. 459 00:23:29,008 --> 00:23:32,908 And so I think sometimes just thinking about the process we use to roll out new 460 00:23:32,913 --> 00:23:35,218 products, new, new strategies, right? 461 00:23:35,428 --> 00:23:38,398 Like that can either contribute to more sense of belonging in our 462 00:23:38,398 --> 00:23:43,738 culture, of our agency, or it can cause disharmony or disconnection. 463 00:23:43,738 --> 00:23:46,258 So like that's the other piece that I think. 464 00:23:46,888 --> 00:23:50,488 We talk a lot in the works that I do about process, which sounds boring, 465 00:23:50,488 --> 00:23:52,168 but it's actually like the meat, right? 466 00:23:52,168 --> 00:23:56,278 Like if, like, if you have this amazing data tool, but everyone's gonna hate 467 00:23:56,283 --> 00:24:00,448 it because it's used punitively and it's not, not seen as a, like a, a 468 00:24:00,448 --> 00:24:02,318 strategy that's designed to help people. 469 00:24:02,329 --> 00:24:03,229 , you're gonna lose everybody. 470 00:24:03,270 --> 00:24:06,229 My favorite kind of data tools are the ones where and we have this happen 471 00:24:06,229 --> 00:24:09,259 sometimes where clinicians who are used to feeling like data's just this 472 00:24:09,259 --> 00:24:12,089 thing that comes after them and tells them they're doing their job wrong. 473 00:24:12,089 --> 00:24:14,891 Where instead we get to build these data tools that really help them 474 00:24:14,896 --> 00:24:16,481 do their job, you know, a job. 475 00:24:16,546 --> 00:24:20,176 And we've had folks come back and say, I feel like you 476 00:24:20,176 --> 00:24:21,736 invested in this thing for me. 477 00:24:21,766 --> 00:24:24,766 Like something that the agency bought was for me, and it was this 478 00:24:24,766 --> 00:24:28,126 data tool that makes my life easier and I can now do my job better. 479 00:24:29,336 --> 00:24:31,616 Anyway, I, Those ones make me really happy. 480 00:24:32,786 --> 00:24:36,086 , And you would never think of that like, Oh, data as like a staff morale 481 00:24:36,086 --> 00:24:37,676 boost, but it, it totally can be. 482 00:24:38,996 --> 00:24:39,446 Yeah. 483 00:24:39,566 --> 00:24:43,826 Well, and I, and I think, you know, again, like you're, you're sort of turning 484 00:24:43,946 --> 00:24:47,753 it on its ear a little bit by saying like, no data can advocate for you. 485 00:24:47,753 --> 00:24:49,313 It doesn't have to be punitive. 486 00:24:49,313 --> 00:24:52,973 It doesn't have to, you know, because that's the thing is I, I think 487 00:24:52,973 --> 00:24:54,233 we're scared of what we don't know. 488 00:24:54,263 --> 00:24:54,323 Yeah. 489 00:24:54,383 --> 00:24:54,773 Right. 490 00:24:55,133 --> 00:24:56,243 And so when I think. 491 00:24:57,038 --> 00:24:57,368 Data. 492 00:24:57,368 --> 00:25:00,938 I'm like, Oh my gosh, there's so much out there that can 493 00:25:01,148 --> 00:25:03,608 poke holes in what I'm doing. 494 00:25:03,788 --> 00:25:03,878 Right. 495 00:25:03,998 --> 00:25:04,718 And that's scary. 496 00:25:04,718 --> 00:25:04,778 Yeah. 497 00:25:04,880 --> 00:25:09,530 Because I know anecdotally I know, you know, the feedback that I'm getting from 498 00:25:09,530 --> 00:25:11,883 people, Hey, this works, this is great. 499 00:25:11,883 --> 00:25:13,431 Let's just leave it at that, can't we? 500 00:25:13,491 --> 00:25:17,392 Cause cause if we start to dig too deep, then you know, and that, 501 00:25:17,392 --> 00:25:19,072 that speaks to insecurities, right? 502 00:25:19,072 --> 00:25:24,142 Insecurities in, in each of us as, you know, whether it's an entrepreneur or a 503 00:25:24,142 --> 00:25:26,660 program provider or you know, a clinician. 504 00:25:26,667 --> 00:25:29,787 Of course we all have to some degree imposter syndrome. 505 00:25:29,937 --> 00:25:30,027 Sure. 506 00:25:30,087 --> 00:25:30,447 Right? 507 00:25:30,627 --> 00:25:30,747 Yeah. 508 00:25:30,777 --> 00:25:32,667 But I like that data doesn't have to be scary. 509 00:25:33,842 --> 00:25:34,027 , right? 510 00:25:34,597 --> 00:25:34,927 Yeah. 511 00:25:34,927 --> 00:25:36,997 And I think that's that piece too, of bringing people into 512 00:25:36,997 --> 00:25:38,047 the conversation, right? 513 00:25:38,047 --> 00:25:42,444 Because like when I was a clinician, I, I always felt like, gosh, I know, I could 514 00:25:42,444 --> 00:25:46,464 tell you how you'd know the folks I'm working with are doing better, but it's 515 00:25:46,464 --> 00:25:47,904 not the things we're currently measuring. 516 00:25:48,354 --> 00:25:51,494 , you know, like you'd know the folks I work with are doing better because 517 00:25:51,984 --> 00:25:55,224 they stated the same address for longer than they've ever stayed at the same. 518 00:25:56,139 --> 00:25:57,549 Like just change of address. 519 00:25:57,549 --> 00:26:00,489 Like, just look at how often their address changed and that means something. 520 00:26:00,489 --> 00:26:03,789 Or you know, how often they had to get a new phone because they had something 521 00:26:03,789 --> 00:26:07,799 go wrong in their life, or Or do they have an emergency contact listed, 522 00:26:07,799 --> 00:26:11,099 which is sort of an indirect sign of like social connectivity, right? 523 00:26:11,099 --> 00:26:14,399 Like they came in, they didn't have anybody they really trusted in their life. 524 00:26:14,939 --> 00:26:18,629 They reconnected with their family and now they, now they listed someone 525 00:26:18,629 --> 00:26:21,689 on their record as someone they feel comfortable with us talking to, which is 526 00:26:21,694 --> 00:26:24,329 potentially like a sign of huge steps. 527 00:26:25,709 --> 00:26:26,039 Yeah. 528 00:26:26,039 --> 00:26:28,799 In fact, that's one of the outcomes of our program, right? 529 00:26:28,889 --> 00:26:34,479 Is, is people getting a, you know, primary care physician or identifying 530 00:26:34,484 --> 00:26:37,559 who their, you know, their emergency contact is, their backup Yeah. 531 00:26:37,739 --> 00:26:38,442 Person is. 532 00:26:38,442 --> 00:26:40,848 That's, that's something that our program helps people do. 533 00:26:40,850 --> 00:26:44,900 But taking a look at that and saying, But what does that mean? 534 00:26:44,930 --> 00:26:45,290 What does. 535 00:26:46,355 --> 00:26:51,185 What does that outcome say about that person's stability? 536 00:26:51,515 --> 00:26:54,065 You know, and it's, it's interesting cuz as you're saying that, you know, 537 00:26:54,065 --> 00:26:57,875 I'm, I'm thinking of it as, you know, running a business, I'm like, man, I 538 00:26:57,875 --> 00:27:00,755 hate when people change their email address or change their phone number. 539 00:27:00,755 --> 00:27:03,155 Cuz then I have to track 'em down and try to figure out how to get 540 00:27:03,155 --> 00:27:07,188 ahold of them and instead of looking at it, you know, that that's that 541 00:27:07,192 --> 00:27:10,432 eye on the mission of saying, okay. 542 00:27:11,047 --> 00:27:11,977 What does that mean? 543 00:27:12,097 --> 00:27:13,027 What does that mean? 544 00:27:13,027 --> 00:27:16,627 If their number is changing, what does that mean if their emails bouncing back? 545 00:27:17,137 --> 00:27:21,007 And how can I use that information to, to bridge a gap? 546 00:27:21,037 --> 00:27:21,367 Right. 547 00:27:21,427 --> 00:27:25,147 I mean, we've been noticing how often there's just been so much 548 00:27:25,147 --> 00:27:26,527 turnover in the field, right? 549 00:27:26,527 --> 00:27:29,347 Because as part of our, you know, as part of one side of our business 550 00:27:29,352 --> 00:27:33,067 is we reach out to agencies and, you know, contact lists that were good 551 00:27:33,067 --> 00:27:34,597 two years ago are not good anymore. 552 00:27:34,987 --> 00:27:35,108 Everyone is. 553 00:27:35,697 --> 00:27:39,957 Changed jobs or moved around or you know, and, and that just tells us 554 00:27:39,957 --> 00:27:42,987 something about where the field is at, you know, and what, and what people 555 00:27:42,987 --> 00:27:44,337 are actually dealing with out there. 556 00:27:44,337 --> 00:27:48,147 Which is a very, for a lot of folks, a very new thing they're doing. 557 00:27:48,177 --> 00:27:50,097 You know, cuz most people are in those positions now. 558 00:27:50,097 --> 00:27:53,453 It's like a brand new thing and they're learning a new system and Yeah, yeah. 559 00:27:55,353 --> 00:27:58,693 Which has its own challenges in a number of ways. 560 00:27:58,721 --> 00:28:03,821 But I wanna speak, you know, a little bit about you as an individual, as 561 00:28:03,821 --> 00:28:09,447 an entrepreneur you know, with this, with this clinician background and, 562 00:28:09,597 --> 00:28:15,477 you know, coming into running your own business, how has that been for you? 563 00:28:15,477 --> 00:28:24,927 Finding the time, the ability to practice self care and balance your work and. 564 00:28:25,347 --> 00:28:26,067 The rest of your life? 565 00:28:27,837 --> 00:28:28,977 That's a very good question. 566 00:28:28,977 --> 00:28:33,237 I, I would say that that has changed even as things have changed with the pandemic. 567 00:28:33,237 --> 00:28:36,777 So I think it was in some weird way easier to start a business in the 568 00:28:36,777 --> 00:28:40,647 pandemic year because everything was shut down anyway, so you didn't really 569 00:28:40,647 --> 00:28:44,362 need to have a ton of work life balance cuz mostly like life had shut down. 570 00:28:44,368 --> 00:28:47,758 I think I noticed that more as things started to open back up again and I 571 00:28:47,758 --> 00:28:49,648 realized like, Oh, I work all the time. 572 00:28:49,648 --> 00:28:50,368 What am I doing? 573 00:28:50,373 --> 00:28:51,718 I need to have other things in my life. 574 00:28:51,988 --> 00:28:52,528 I will say. 575 00:28:52,763 --> 00:28:56,723 Throughout that whole time, I got in the habit of taking a walk every 576 00:28:56,723 --> 00:28:58,732 day, which I'm so grateful for. 577 00:28:58,732 --> 00:29:03,322 Like, that was my, my one thing with when the pandemic hit, I was like, Well, I'm 578 00:29:03,322 --> 00:29:06,562 gonna have to take a walk every day, cuz I can't just be sitting at my house all day. 579 00:29:06,592 --> 00:29:10,852 I already worked from home back then, and I already knew how isolating that can be. 580 00:29:10,852 --> 00:29:12,772 So I was like, well, I'm just gonna take a walk every day. 581 00:29:12,855 --> 00:29:16,005 So that was, I still do that as much as I can. 582 00:29:16,107 --> 00:29:18,492 Because walking is just really good for my brain. 583 00:29:18,492 --> 00:29:21,792 And it's kind of funny, I, I don't really think of it as like an exercise thing. 584 00:29:21,792 --> 00:29:25,422 It's really just a, like, I need a break from screens where I'm just outside 585 00:29:25,782 --> 00:29:29,652 and in my thoughts and, you know, saying hi to my neighbors and checking 586 00:29:29,652 --> 00:29:31,272 out the gardens and all that stuff. 587 00:29:31,272 --> 00:29:31,794 So yeah. 588 00:29:33,369 --> 00:29:34,479 That is a big one. 589 00:29:34,479 --> 00:29:38,709 And I'm, and I, you know, when I did eventually get Covid, because many of us 590 00:29:38,709 --> 00:29:42,339 have now, I was very glad that I'd had this daily practice of taking a walk. 591 00:29:42,344 --> 00:29:46,179 It helped me feel like, okay, well I know my, my lungs work okay. 592 00:29:46,179 --> 00:29:49,119 And I'll be able to tell if I feel really different and all those good things. 593 00:29:49,119 --> 00:29:49,669 So, yeah. 594 00:29:49,692 --> 00:29:49,997 That's great. 595 00:29:51,157 --> 00:29:54,259 But I would say that like it's I also rely on support from 596 00:29:54,259 --> 00:29:56,419 lots of, you know, other folks. 597 00:29:56,419 --> 00:29:59,910 It's the one thing that the pandemic made was, made things more challenging 598 00:29:59,910 --> 00:30:02,910 was it's a little harder to meet people who do similar things. 599 00:30:02,913 --> 00:30:07,695 Like Jacob and I, you and I met at a conference in person again after like one 600 00:30:07,695 --> 00:30:09,495 of the first back in person conferences. 601 00:30:09,536 --> 00:30:10,035 And. 602 00:30:11,565 --> 00:30:14,655 Yeah, I mean, I'm looking forward to more of those kinds of things because 603 00:30:14,655 --> 00:30:18,885 I think it really helps to have, I'm, I'm an introvert, Well, I maybe, What's 604 00:30:18,885 --> 00:30:20,925 that ambivert where you kind of do... 605 00:30:21,065 --> 00:30:21,755 ambivert? 606 00:30:21,755 --> 00:30:22,675 Yeah, yeah, yeah. 607 00:30:22,680 --> 00:30:28,136 So but for whatever reason, relationships that are only over zoom are hard. 608 00:30:28,136 --> 00:30:29,786 I like to have met people in person. 609 00:30:30,566 --> 00:30:32,403 So I'm looking forward to more of that. 610 00:30:32,943 --> 00:30:33,433 Yeah. 611 00:30:33,933 --> 00:30:35,313 Yeah, I, I would agree. 612 00:30:35,313 --> 00:30:37,173 I'm, I'm very similar in that way. 613 00:30:37,703 --> 00:30:43,443 I, I have learned to be very much an extrovert and to be outgoing 614 00:30:43,503 --> 00:30:45,463 just by nature of what I do. 615 00:30:45,463 --> 00:30:49,654 But if given the choice most times, like I'll stay home you know, but 616 00:30:50,254 --> 00:30:54,184 every time that I get out, like, you know, the conference where we met, I'm 617 00:30:54,189 --> 00:30:58,054 just like, Oh man, this is so great to be able to connect with actual human 618 00:30:58,054 --> 00:31:02,901 beings and, you know, have that I think however that looks for people, it's, 619 00:31:02,901 --> 00:31:04,251 it's important to have that connect. 620 00:31:04,886 --> 00:31:05,287 You know? 621 00:31:05,316 --> 00:31:05,496 Yeah, 622 00:31:05,856 --> 00:31:06,216 yeah. 623 00:31:06,276 --> 00:31:10,356 I mean, the internet and Zoom have given, have opened up the possibilities so much. 624 00:31:10,356 --> 00:31:14,916 Like I love that I can work with an agency in Minnesota or Kansas or Pennsylvania, 625 00:31:14,921 --> 00:31:16,086 that I don't have to go there. 626 00:31:16,086 --> 00:31:19,656 I can just help them and like bring my expertise to them through 627 00:31:19,661 --> 00:31:21,006 the, the wonder of the internet. 628 00:31:21,006 --> 00:31:23,024 But also I like to meet people in person. 629 00:31:23,029 --> 00:31:26,834 So, you know, it's on my list now to kind of go around and meet all these people 630 00:31:26,834 --> 00:31:28,814 that I've really only talked to over Zoom. 631 00:31:28,814 --> 00:31:32,094 So I'm doing Pennsylvania in the fall and I gotta get Minnesota in 632 00:31:32,094 --> 00:31:32,814 the fall when it's not freezing. 633 00:31:32,814 --> 00:31:34,345 It's gorgeous in the summer. 634 00:31:34,795 --> 00:31:35,185 Yeah. 635 00:31:35,185 --> 00:31:37,315 But yeah, I'll try to do it in a non winter month. 636 00:31:39,355 --> 00:31:39,805 Good call. 637 00:31:39,925 --> 00:31:40,405 Good call. 638 00:31:40,455 --> 00:31:40,775 Yeah. 639 00:31:42,145 --> 00:31:42,595 Yeah. 640 00:31:42,655 --> 00:31:48,654 So the last thing that I want to ask you about is the, are some of your outcomes. 641 00:31:48,654 --> 00:31:52,974 So we have, we share some clients in Virginia and. 642 00:31:54,149 --> 00:31:57,988 I'm just curious, you know, what, what sort of results have you seen when 643 00:31:57,988 --> 00:32:02,068 you've been working with companies and organizations for a couple of years? 644 00:32:02,533 --> 00:32:07,183 How are you able to help implement these changes that you suggest, 645 00:32:07,183 --> 00:32:10,603 and, you know, what, what are the results that are coming out of it? 646 00:32:11,263 --> 00:32:15,910 You know, I would say because it is kind of a journey for agencies that for a 647 00:32:15,915 --> 00:32:19,300 lot of the folks that we've worked with now for a while, what the main change 648 00:32:19,300 --> 00:32:22,810 you kind of start to see is almost in the culture of the organization. 649 00:32:22,810 --> 00:32:26,950 So you start to see, you know, a lot of folks we work with will start, 650 00:32:26,955 --> 00:32:30,100 like I said, with those like kind of foundational layer of reports. 651 00:32:30,445 --> 00:32:32,965 But we're also kind of, you know, sprinkling in some 652 00:32:32,965 --> 00:32:34,165 stuff about their mission. 653 00:32:34,435 --> 00:32:36,055 And then you start seeing things happen. 654 00:32:36,055 --> 00:32:39,507 Like a clinical director comes and says, Well, I really like how this report works. 655 00:32:39,507 --> 00:32:41,337 I'd love to have a report that looked at this. 656 00:32:41,697 --> 00:32:45,287 Or you'd have like a CFO who says, We're doing all these power BI things, 657 00:32:45,287 --> 00:32:46,937 could the financial data also be? 658 00:32:46,937 --> 00:32:48,961 And, you know, and, and so I think. 659 00:32:48,961 --> 00:32:54,312 Or we've had really cool things happen where agencies identify or have a 660 00:32:54,312 --> 00:32:58,092 hunch about something in their data that is something really relevant to 661 00:32:58,092 --> 00:33:01,620 their mission, like maybe a health disparity or maybe they're starting 662 00:33:01,620 --> 00:33:05,430 to wonder if a particular program is in the right geographic location. 663 00:33:05,430 --> 00:33:08,970 And then they'll come to us and we'll pull some data and they can use that to 664 00:33:08,970 --> 00:33:13,530 actually make a better informed decision about what they wanna do going forward. 665 00:33:14,350 --> 00:33:17,230 An agency was able to use their data to see that there was a racial 666 00:33:17,230 --> 00:33:20,680 disparity and the access to a particular evidence based practice that they 667 00:33:20,680 --> 00:33:24,478 really believed in strongly and really. 668 00:33:24,988 --> 00:33:28,468 Couldn't imagine that there could be a disparity there, but they were able 669 00:33:28,468 --> 00:33:32,068 to look at the data and they were like, Oh yeah, that's, that's real. 670 00:33:32,068 --> 00:33:33,328 There's there, there it is. 671 00:33:33,363 --> 00:33:36,285 And so then use that to implement some training and 672 00:33:36,285 --> 00:33:38,385 then check to see if that helps. 673 00:33:38,385 --> 00:33:38,775 Right? 674 00:33:39,015 --> 00:33:42,375 So often we do training in our field, but there's no way to 675 00:33:42,375 --> 00:33:43,965 actually measure if the training 676 00:33:44,420 --> 00:33:48,685 helped , you know yeah, I think about the big move toward trauma informed care. 677 00:33:49,015 --> 00:33:50,845 Like, I love trauma informed care. 678 00:33:50,845 --> 00:33:51,985 I'm a huge advocate of it. 679 00:33:52,015 --> 00:33:55,495 I always, I mean, there's a, another person I know on the internet that 680 00:33:55,495 --> 00:33:58,675 does this conversation around trauma informed data, which I think is a 681 00:33:58,680 --> 00:33:59,965 great way to think about it too. 682 00:33:59,986 --> 00:34:04,291 But could we kind of look at some metrics around like, okay, we, we did all 683 00:34:04,291 --> 00:34:05,731 this push toward trauma informed care. 684 00:34:05,911 --> 00:34:10,711 Are we seeing that as a, as an agency, You know, folks who are coming in with trauma 685 00:34:10,711 --> 00:34:14,791 are having better success, are getting access to services more, Are we providing 686 00:34:14,791 --> 00:34:16,867 more specialized services to those folks? 687 00:34:16,872 --> 00:34:19,597 Are we like, what's happening as a result of that training? 688 00:34:19,627 --> 00:34:24,437 You know, I mean, I'm into the feelings, which, but like, and I'm not trying to 689 00:34:24,437 --> 00:34:29,177 say data in place of feelings, but like how could we incorporate the two together? 690 00:34:31,112 --> 00:34:31,662 I love that. 691 00:34:31,952 --> 00:34:32,312 Yeah. 692 00:34:32,312 --> 00:34:36,452 And, and what you're describing is making that shift from being trauma 693 00:34:36,452 --> 00:34:39,062 informed to being trauma responsive. 694 00:34:39,452 --> 00:34:46,682 And as an organization and as a community looking at, you know, how can we predict 695 00:34:47,042 --> 00:34:53,222 and anticipate the challenges that people may face around trauma that they've 696 00:34:53,222 --> 00:34:56,472 experienced and then get into true. 697 00:34:57,092 --> 00:34:57,812 Prevention. 698 00:34:57,932 --> 00:34:58,082 Right. 699 00:34:58,126 --> 00:34:59,751 Which, which is fantastic. 700 00:34:59,751 --> 00:35:04,523 And I think using data as a tool for that of course it, it makes sense, but it, it, 701 00:35:05,213 --> 00:35:10,793 it seems so tangible and so accessible when, when you share it that way. 702 00:35:10,973 --> 00:35:11,033 Yeah. 703 00:35:11,273 --> 00:35:11,663 I love that. 704 00:35:11,843 --> 00:35:12,293 Ah, thanks. 705 00:35:12,473 --> 00:35:12,713 Yeah. 706 00:35:12,718 --> 00:35:13,523 I get excited. 707 00:35:16,583 --> 00:35:19,523 So I, I think truly you, you are a, a data nerd 708 00:35:20,073 --> 00:35:20,523 I am. 709 00:35:20,523 --> 00:35:20,923 Yes. 710 00:35:21,683 --> 00:35:22,643 That is the definition. 711 00:35:22,883 --> 00:35:23,843 Getting excited by it. 712 00:35:23,903 --> 00:35:24,053 Yes. 713 00:35:24,060 --> 00:35:27,898 Is there anything else that you wanna share with our listeners? 714 00:35:27,952 --> 00:35:30,844 About what you do or something cool that you discovered? 715 00:35:30,844 --> 00:35:33,560 I think, I think the only other thing I would mention is, and I don't know if 716 00:35:33,560 --> 00:35:37,039 this is something your listeners would be interested in, but we do host a free 717 00:35:37,044 --> 00:35:41,901 online community for other data nerds or data adjacent nerds where we host like 718 00:35:41,942 --> 00:35:44,272 guest speakers and we do free trainings. 719 00:35:44,328 --> 00:35:47,221 We're doing a whole training, this actually about, we're doing 720 00:35:47,221 --> 00:35:49,231 trainings around this framework I was talking to you about. 721 00:35:49,231 --> 00:35:52,301 And so if you're, You know, if one of your listeners is interested in that, they, 722 00:35:52,331 --> 00:35:56,406 they're welcome to look up at our website, mission driven data.com and check out 723 00:35:56,406 --> 00:36:01,438 our community cuz it's you know, our, our long term vision really is, I guess the 724 00:36:01,438 --> 00:36:06,560 mission for my mission, like our company's mission is to to change the conversations 725 00:36:06,560 --> 00:36:09,920 that we're having about data, to become more focused on data that is meaningful 726 00:36:09,920 --> 00:36:14,450 and to really define metrics that capture the value of community service. 727 00:36:15,915 --> 00:36:18,495 Hey, before you go, if you enjoyed that conversation with 728 00:36:18,495 --> 00:36:22,605 Ginger, I'd really appreciate you throwing some numbers my way. 729 00:36:22,905 --> 00:36:28,455 Could you please take 30 seconds to just forward this episode along to someone 730 00:36:28,455 --> 00:36:31,095 in your sphere who might find it useful? 731 00:36:31,305 --> 00:36:37,305 A coworker, your boss, a, a colleague who might want to learn about data. 732 00:36:37,785 --> 00:36:39,045 I'd appreciate it very much. 733 00:36:39,195 --> 00:36:42,105 We'll see you next time and until, Be well, 734 00:36:46,815 --> 00:36:49,725 Thanks so much for listening to Passion and Profits Without Burnout. 735 00:36:50,115 --> 00:36:53,115 I hope that you found some impactful takeaways, and if you 736 00:36:53,115 --> 00:36:54,795 did, I'd love to hear from you. 737 00:36:55,185 --> 00:36:59,475 Share a screenshot on your ig story, tag me or send me a quick message. 738 00:36:59,775 --> 00:37:03,225 This show is for you, so any feedback is welcomed. 739 00:37:03,405 --> 00:37:05,955 Hey, and make sure you're also subscribed to the show so you 740 00:37:05,955 --> 00:37:07,575 don't miss any of our new episodes. 741 00:37:07,905 --> 00:37:10,425 And if you could take a few minutes to leave me a five star. 742 00:37:11,205 --> 00:37:12,165 That'd be greatly appreciated. 743 00:37:12,945 --> 00:37:15,135 Thanks for listening and be well.