Hello, everybody. Welcome to another episode of Engaging Ideas, the Parsons TKO podcast where we talk to leaders and luminaries in the mission-driven sector about their experiences to gather insights and ideas to increase our perspective and understanding and to always make sure you know you are not alone. We are all in this together. Today, I am joined by Lindsay Nadeau, who is currently serving as the Vice President of Data, Insight and Campaigns at UNICEF USA and as a board member of APRA. Welcome, Lindsay. Thank you, Tony. I'm so excited to be here. Thanks for the invitation to come join your episode. I'm super looking forward to our conversation. Excellent. I am so delighted you joined us. I'm going to dive right in because Data, Insights and Campaigns all within a title, within a program. Sounds like a lot, but I'm curious, what can you tell us about how your team is organized, what your primary focus and objectives are within the organization? Yeah, okay. Let's start with how we're organized. Our scope is mostly what would be considered advancement services. If you're familiar with that sector of fundraising conceptually, it's like data in, data out. We really set up the intersection of if it's fundraising and it's data, it's us. The actual structure of the team, I have about five different workflows that roll up into what I call philanthropy insight, which is kind of the name of my team because my title is a little bit of a mouthful, as you noted. I like to think about prospect intelligence kind of being the origin of a lot of the work that we do. They are responsible for lead generation, conversion tracking, capacity screening, due diligence, landscape analysis, feasibility assessments, answering questions like if we're going to add more fundraising staff, where should we put them based off the geographic landscape of our prospect base, et cetera. This is what you might call prospect research more traditionally in our field. Another team that I lead is relationship management, which has kind of been an involved form of prospect management. This team does a lot of frontline KPI design and tracking. They lead what we call portfolio optimization and partnership with our prospect intelligence team. Projections and forecasting is very deeply rooted in the work that this team does. They also do a lot of business rule documentation. A lot of what they do is documentation and on-the-job training for fundraisers, really coaching them about how to use some of the data and systems and processes that we build out. Then another team that flatters up to me is our data strategy and management team. I see them very much as the custodians of our data model for philanthropy. They are looking futuristically at where all the different fundraising teams on the data maturity continuum and how do we get them all to the most evolved and mature state? What are the projects? In that sequence, I get them to more of an even footing because we have different relationships with each team. Then they also do a lot of business intelligence work, ad hoc reporting and analytics, project management with our centralized tech team who are really the brains of the operation. The scope of this team is just absurdly vast and they become a catch-all for things that don't fit in prospect intelligence or relationship management. This team also includes a sub-team called gift processing, which every home profit probably has this workflow in a team. They're responsible for getting all of the revenue into our system as quickly and accurately as possible. Last year, they did about 2 million transactions. We got a pretty big operation. I very much see them as the first step in donor stewardship and making sure that our donors are acknowledged quickly and accurately. Then the last workstream that I'm currently leading is campaign planning. I see that very much as a cross-cutting team because there is a role in every comprehensive campaign or special campaign that we're running for prospect intelligence and relationship management and data strategy management. It's like what we're talking about here are the eight, nine, 10-figure fundraising opportunities or campaigns, whatever you want to call them. We have some funding priorities or we have a comprehensive campaign or we just finished our campaign in the pandemic. Those are the kinds of campaigns. It's not a marketing campaign or a brand campaign. These are really massive scale where the entire organization is working together concertly under one campaign umbrella. That's kind of how the structure of my team and our roles are set up. We've got about 20 roles right now. Some of them are freelance but full-time workloads. Our team is fully remote as well, which I actually take as a huge point of pride. UNICEF USA has been very forward-looking and allowing us to hire the right people for the right job. One of the cultural touch points that our team uses to kind of figure out how we work together and what do we expect of one another is something that we develop called the standards of collaboration. It outlines those kinds of expectations and guidelines for how we work collaboratively across different time zones, about being in person, without being able to drop by each other's desks, etc. That's a really important part about how we're organized. Then also I would say one other thing that we have developed over the last three years is an adapted and kind of pared-down agile scrum sprint methodology for project management. That's kind of how we're organized.  I see my team's mission. If we can help fundraisers raise money smarter and faster so that then UNICEF can relentlessly pursue a more equitable world for every child. That is our job, smarter and faster. Let's use technology. Let's use process improvement, automation, change management strategies. What we're trying to do is equip leadership with data to make data-driven decisions in particular, but also the entire rank and file staff. How can we get them to use data more? I love it. I wrote a lot of notes down while you were talking. I'm curious. The three, there was the prospect and there's the group that does the design optimization work. You got into documentation. I heart documentation. I had some questions about the coaching side, the data strategy holder. When you had mentioned KPIs, is it one part of your unit that makes the KPIs for the whole broader team that you're all working on? How are the KPIs conceived and then how are they communicated back out to everyone? There's the commander's intent, if you will. We all know these top-line goals we're working towards. We probably have three different levels at which we're setting KPIs. We do it for our division, for the philanthropy division. Those are KPIs that roll up into the organization's strategic plan. I work with our chief philanthropy officer and some of our most senior fundraising staff to gut-check what the data is telling us we should set. I also work with our executive office on those.  Where we get into more of the weeds with KPIs is when we do individual fundraiser KPIs. If you are a major gift officer or a director of major gifts or a senior director or all the way up the ranks or you work in the corporate partnerships team, how are your KPIs different than how a major gift officer, like a more traditional version of major fundraising would be situated? The process for designing those is done in a little bit of data analysis. When we started this program, we benchmarked what have we seen in the last couple of years in terms of face-to-face visits with donors, the number of solicitations at what thresholds then yield outcomes that we want. Or as we transition to a longer-term planning model for a major gift operation, we are putting more of an emphasis on multi-year asks as opposed to an annual recurring amount that we just keep asking for every single year and really trying to think longer. We think from a leadership perspective for the head of major gifts or the head of corporate or whatever, what are you trying to achieve with your team? Where do you see the opportunities to grow? What matters is what we measure. There's that mantra in our field. Let's put those into the KPIs. We then have to develop the reporting definition. How are we actually going to use the data in our CRM to be able to quantify this? Then we have a training and communication plan where we launch it to the frontline fundraisers. There's usually a lot of dialogue, a lot of clarifying questions because this is important to them. This is one aspect of how they are going to be evaluated by senior leadership. It's one of the data's part of the conversation, not the entirety of it. We do a lot of work to socialize and help people feel comfortable with them and that they are there as a tool for them to equip them to advocate for themselves and the results that they're seeing or where they need more support or the effort that they put in and yet the cookie just didn't crumble their way this year, but they're doing the work to make the asks. That's how we roll it out. Then eventually once we've finalized everything, socialize it, we start on building a power li or any kind of business intelligence dashboard so that there is automated real-time and transparent and equitable analysis around progress to go for each fundraiser. Then the other version of KPIs we do are our own KPIs for philanthropy insights. We have a lot of data driven ones for ourselves about how many prospects we identify, how accurate our projections are, etc. Thank you for that. The follow-up on that just when you're getting those KPIs and numbers and these discussions with the frontline fundraisers, how do those conversations go when you're trying to socialize it? I think it's that get everybody comfortable with it and so they feel supported by it, not burdened by it. Yeah, what does that feel like? How do you lead those? Is it one big group at a time? Is there a lot of one-on-ones that go into it? Traditionally, what I've seen not just at UNICEF USA but across many of the nonprofits that I've worked at, it's one big launch with a large group conversation. Now, we've vetted it through leadership, so maybe the top tier of that team has seen it and fed into it. In some cases, very much the decision makers and our Chief Philanthropy Officer has weighed in as well. For some teams, they've been doing this for years. It's more about what are the KPIs this year? We've done the change management work to you will be evaluated in a data-driven way, at least as one component of the evaluation. We did just recently launch KPIs for our institutional philanthropy teams, which include corporate and foundation partnerships and a member-based organizations team that we call Global Cause Partnerships. For those three teams, for some, it was very, very new, so we are very fresh in the change management work with those teams to help them understand why feeding their data into our CRM is important for institutional knowledge and leaving a digital footprint about past strategies considered the cadence of outreach that it takes in order to secure a grant or corporate partnership to demonstrate how they are collaborating with other teams outside of the team that they work on because the intersectional fundraising asks help maximize our fundraising success. We've done a lot of work in small focus groups and we were able to kind of hone the KPIs that they thought would best represent them, which is hard because every team is a little bit different with those different lines, corporate foundations and this member-based organization one, and they want to prioritize different things, and so we have to start from a very basic level. We have to walk before we can run, and my CEO, I distinctly remember him telling me as we went on that journey with our institutional philanthropy colleagues, we just need to start somewhere. Once we start it, once we build a report, once we have everyone looking at it, then we can like layer on refined approaches, but let's start with the basics, and that was so encouraging to know that we have that kind of understanding of what it takes to build something that is data-informed at least so that we can then evolve to a truly data-driven place because some of our institutional philanthropy teams have a very rigorous data entry process, and some have less, more in like flat file and spreadsheets, and so we're accounting for a disparity among even this cohort of people who raise funds from organizations. It's been a lot of reemphasizing, reinforcing, reinforcing as one of those letters in the ad car change management, you know, structure and your work is never done there ever, and even from the beginning to the first couple of months to the end, we're still constantly focused on reinforcing, and it actually also took input from our chief philanthropy officer speaking directly to those teams about how she viewed it, and I think it was important for them to hear directly from her. Some folks thought that this was like a Lindsay thing or the CPO, you know, was coming up with the idea of doing KPIs, and I think it really took her saying this is a normal thing and fundraising. I know that it is a big change, it can feel threatening and it's emotional perhaps, but this is not different than what other nonprofits are doing, and we are going to be a best in class one, and so I can't even say how important like that level of leadership is when you're trying to make these large data driven changes. Oh, thank you for all that. Yeah, I hope everyone's listening, and yeah, getting that by and from the top is extremely important in the backing to make sure that you could push the work forward. Just for people listening too, I mean, a lot of times you, I feel like if people get, where do I start? And I think, you know, based on what your CEO was saying, it's not where it's now, like just start, pick a few things like the blank page is the worst thing ever when you're writing, right? And then once you get some stuff on it, now you're like, Oh, I can edit. Oh, I didn't like that piece. Oh, I can move this around. Oh, now we see how it works. Yes, definitely. You know, the first year that we piloted it at UNICEF USA with our major giving team, we had some huge lessons learned, and it's okay when you were being innovative and piloting things to say, Well, that didn't work. And you're to make it even better, but you've just got to keep that it's an experiments. We're going to build it better. It is iterative. We and like I actually had my chief philanthropy officer say to the institutional philanthropy teams, it's not going to be perfect in year one. It's not. And we expect that it won't be and we invite your feedback, but we want you to engage with the process, right? So I think that's a helpful way just name it. And that can help disarm people from being like, Well, this is terrible. We should just scrap all of it right away. Yeah, so important. Yeah, that's that that evolution that we're all going through, right? I mean, so much transition and change and transformation of the rod on us by COVID and all the things we're trying to keep pace with and technology so fast. And yeah, how to get those inputs, keep learning, keep working with it. I'm curious to just what does, you know, you had talked about there's sounded like there was like a almost like an open dashboard that's stored somewhere that everybody can see is that now there's no, if you're remote, there's no board in the office. So is it like on a wiki or an intranet? And if that's one way of re, I'm going to talk to questions in here. Sorry. If that's, if I'd like to know how, how people look at that, how often they look at it, where you store it. So other people listening might get a sense. And then two, I'm curious, just like, what do report readouts look like? What does it look like when you're doing the analysis? Are they, do you have group conversations? Do you sit with each group? Is it just something you email over and they look at? So two questions in one. Sorry. Alright, so yes, it is open to everyone who is in the philanthropy division, we're not necessarily like our marketing communications team. Right now, what we have in place is for major and principal giving and we are building out the specification requirements for the institutional philanthropy one since we just came out of setting those KPIs. So for the major and principal giving anyone in the philanthropy division can go into our Power BI, BI portal, we have all of our revenue reports, our pipeline reports, board giving reports, like all of the things in this one portal, one stop shop. And one of them is the KPI dashboard for individual staff members. And so anyone can view it. And I think that that provides equity. It can be challenging for some. And I did work at an organization. I did not agree with this. But HR said we were not allowed to share that information because it is related to people's performance goals. And in the same way, you wouldn't have an HR portal about your goals open to everyone that they had us do permissioning so that only the supervision pillar could see the individual staff members performance to go. So those are two different approaches you can take. I personally very much value the transparency and the equity that having an open source KPI dashboard provides to an organization just because two people with the same title or same level of experience have different KPIs targets doesn't mean that it's inequitable. You have to take context into consideration. And so the most important things when we're doing goal setting is to understand what the rationale is for why one target might be higher than the other. So when we set our KPIs, they are considered thresholds. We want to see everyone hit this baseline, but they can always be higher. It could be higher because they have a donor who is being receptive to a $10 million conversation. And that might not be typical of that title level, but we then want to set an ambitious aspirational goal for them. And so having it open allows people to see whether it's equitable or not, but then also for supervisors and other leaders to have conversations about the reason why. Maybe someone's on leave for six months of the year because they're on print to leave. And that's why someone's goal is lower. And then your second question, I'm blanking on remind me. So what does it look like when you're doing analysis or report readouts or sharing it? Yeah. Yes. So that would go back to our portfolio optimization meetings that I briefly mentioned. Our relationship management team leads those. And right now the prospect intelligence team also sits in on them, but it's really what we're talking about is more relationship management. Because they're the ones who create the KPIs and the fundraiser is invited and their supervisors usually in most cases also invited. And so it's like the peer coach who's from my team, who's the neutral. I understand the data. Here's what the data is telling me when I look at how on track you are to meet your KPIs. And then the supervisors there to help reinforce or underscore other management impressions or observations as someone who actually has been in the frontline position before and knows how to think about challenges or successes from their view. And obviously they have the management authority. Like we do a lot without the authority, right? We're really, we see ourselves as the peer coach to say, you are really strong on the number of asks you've made. But I'm seeing that the, you know, average ask amount is actually kind of low and not hitting that. What big asks do you have in your pipeline? Let's look at that together to see if you focus on these three, that's going to get your average ask API, you know, up or, hey, you told me you just went on a trip to such and such region, your visit count looks a little low, or we may be just a little behind like what visit should we make sure we're getting in the system for you. So it's a little bit of like accountability, buddy. But in a very benign, like we want you to be successful and we're here to help be your backup spotter for where there might be some gaps in what we know you've done or how to prioritize your work to make sure that you hit the key if you're supposed to. Accountability, buddy. I love it. Hadn't heard that before. That's so good. Accountability, buddy. So that last question on this follow up and then I'll, I got another one for you after this. But the way you're phrasing it at the end, you use the word spotter. And so I, I work out and I get it and I do like the metrics when I'm doing it and I like them delivered to me because then it does make me think how I could be doing better. It's like a lot of people, you know, if runners or athletes would talk about perceived exertion versus actual exertion, you know, if you've got something where you're watching your heart rate and like you feel real tired, but then you look down, you're like, I'm really not working that hard. Is it, is it that kind of like the accountability where, you know, when people are seeing it, they're like, Oh yeah, you're right. I had a gap there. I didn't realize it. Like are people like, is it that type of utility to someone who's then out back in the field just trying to be getting the conversations going?  So is it kind of like that a little bit? Yeah, I would say that's pretty accurate now. I will say like every fundraiser is different. And so the approach that we take, we're sometimes more hands on, we're sometimes more hands off. Like there are actually some fundraisers who used to work in like my data sphere who are my best friends when they go into the front line because they ask the best questions. Are they come in and they're like, I saw the KPI report. I know these were my three gaps. I've loved them. What should we talk about now? And like those are the unicorns of fundraising and from my perspective, obviously. But yeah, I would say that that is generally how it operates. Yeah. Okay. Well, thank you. This is super enlightening. Your last, in the intro there when you talked about the team, you talked about campaign planning. That was a lot more cross organizational.  So yeah, how do you map KPIs to the main organization? And then when these campaigns come in that you have to plan against them. I imagine they're coming from a CEO level type of decision, unless I'm wrong, but correct me. Yeah. Well, the board, the board, the CEO and our chief philanthropy officer. And then of course the CFO holds a lot of the first strings. So yes, that's a very critical bot group to help us determine when we do and don't activate on large campaigns. So I can answer the org strategy one first, I think, because that's a little bit higher level. And then like the outreach a little nitty gritty at times, but I've got a couple of examples that came to mind. So for org strategy, the strategic planning process is something that our executive office leads, obviously. And I helped feed into that because one of the goals, we've got five strategic plan goals. One of them is like a more integrated and maximized fundraising operation. And so I work with my chief philanthropy officer and our executive office, we have an executive vice president who helps lead on some of the planning work. And they have certain objectives. Like sometimes the strategic plan comes to me with like, these are at least three of the KPIs the board wants to see you focus on. And for instance, one of those was a longer term solicitation pipeline. I'm like, that is my jam. I'm here for that. I'm going to go all out and make sure that this organization can do a little bit longer term planning and have a sense of where we might land. So we get some of them that way. But then there are others that we are electing to add to that. And so one of my chief philanthropy officers initiatives and priorities is breaking down the silos that used to exist because we are fundraising operation used to be split across three different divisions, and she's integrated all of them. And so she wants us to work together outside of our teams cross pollinate. When we are soliciting an individual who is a corporate executive, how are we also blending in a corporate partnership ask? For instance, like that's how she wants us to be thinking about it. And so we set a KPI for our division and at the team level for each team. And then also at the individual fundraiser level to reach a certain number of integrated strategies where they are co creating blended ask strategies. So the KPIs can be done in different ways. And then I work with our tech team on saying, well, how are we going to define what is the data marker and our data structure for an integrated collaboration? So that's kind of one of those big broad things that we're working on. And then there is a dashboard that will show our progress to the board on them with like traffic light colors and whether we're on track or not. And a lot of the work that we do with our strategic plan is underpinned by something we developed, I think we're in our third year now, which is our four year financial model. So our strategic plan is four years, so therefore we have a correlation with our four year financial model. And since a lot of the forecasting and projections work at UNICEF USA comes from the philanthropy division, because we are like the primary source of revenue for the organization, that starts with me and my team. And we look at industry growth rates by fundraising sector, we look at our actuals and the trends and the growths that we've experienced. And we take into account outlier things that may not be replicable, repeatable. We do the data. And then we blend that with our gut, right? So my chief philanthropy officer and I are aware of where we plan to invest, where we think certain sectors have expansion opportunities, who we think a campaign might resonate with, and I mean audiences, who the audiences are. So we take the industry growth rates, we take our actuals and the trends, and then we blend in a dash of gut and stink throughout, we think we can push that stream more, or we just invested there, or we think that they're going to resonate more with a campaign. So we're going to push that one up, we're going to flatten that one out. And so that's another way that I think we are having an impact on the future of the organization, the long-term planning, because then we use the targets from that financial model, which gets updated every year for goal setting. So if we're thinking about our next fiscal year, how much are we expecting that our foundation team is going to be able to raise? And it's the starting point instead of just like throwing darts at a wall, right? And then one of the aspects of our strategic plan, or one of the goals of our strategic plan rather is digital transformation. I'm sure a lot of nonprofits have this as a huge priority. We're not alone. And we have a tremendous partnership with our centralized technology team.  And we have piloted a lot in partnership with them. So we were the first division to pilot business intelligence. We used to be like, when I got here, we used to be running Excel reports for like how much we've raised and we'd only do that once a week.  It's not great. They said, hey, we've got, you know, a dashboard platform now and we're like, sign us up. Like, how do we get there? Show us the path. And so we partnered to pilot that for them and now other divisions like our marketing communications team, which they have dashboards in like different platforms, the digital platforms that they use for direct response work or digital strategy, but really bringing it in house and being like master and commander of our own data is something that I think the technology team has really helped us think about the strategy about how we do that. And we were the first to really set like data driven KPIs because, you know, all of the different divisions at UNICEF USA, they're at different levels of their own data maturity and how much is in a centralized data repository versus things that are just manually tracking. And so we're lucky that we're positioned that we use a CRM, right? It's all there for the taking. And I've really been working closely with the technical, my team, the smart people, they have been working very closely with the technology team trying to push for a multi-source data repository and like build out what does that roadmap look like and what data do we want in there so that we can start to cross functionally to still down the insight instead of just thinking about the fundraising lens. Let's pull in information from the marketing lens, advocacy, call to actions, etc. Like, let's blend this all together so that we can get more robust 360 view of our constituents. And then, you know, we're also thinking through with the technology team, what would a self-service model look like in terms of reporting and analytics? And we're at the beginning of this, like we've kind of outlined what we think some use cases would be from like super advanced and like you're plugged into the warehouse or the lake or whatever, the reporting resources to like preset data sets to more training on ad hoc query stuff. And we're helping, I mean, they're the experts, they're going to help us actually like we would give them a concept, they're going to then help us like refine that and really fleshed it out for what they use cases are. But those are some areas that my team specifically gets to think very broadly, very futuristically and kind of be on the innovative cutting edge of UNICEF USA because we are so lucky to have such strong partnerships with our executive office team and our technology team. And then you also asked about outreach. So there's a lot of different examples and we've talked a little bit about optimization meetings that we lead that are very data driven. I think that that's one of the biggest forces that we have on guiding outreach on a micro level, like helping a fundraiser prioritize their work and who they should reach out to and why is one of the most impactful things that we can do to kind of influence the outreach strategy. We're also doing a lot of data analysis of say our direct response donor program, which is under $25,000 generally, and trying to identify who is right for an upgrade. Who should we try and see if they will be receptive to a leadership giving or a major giving or a principal giving strategy? And so we're screening for capacity, we're looking at their affinity indicators through a composite score we have, and then we are trying to move those prospects up the continuum to deepen their relationship, to build more connection, to give them one-on-one tailored information about the impact that they're having as a donor and then eventually hopefully solicit them for a larger gift. One thing that came up recently actually, and I know we're not unique here, either every non-profit like the Holy Grail of funding is unrestricted, right? That means we can use it for anything that we need, right? And when you think about it from the donor lens, it means that they trust us to make that decision. It's really what it's about. It's about trust. We're always thinking about how we raise more unrestricted funds. And my senior vice president of individual philanthropy came to me, she was like, we need to help our fundraisers understand who has given to unrestricted in the past who hasn't renewed this year an unrestricted gift. So it's like a lie button or side button if you're familiar with those acronyms like last year, but they haven't done it this year. And so we built a Power BI dash. Actually, we had one already built. That's a gift table, which kind of shows donation amounts in brackets and how many gifts are in those brackets and then what the total value of those gifts are just so we kind of understand the distribution of gifts. And we just very quickly layered in the fundraiser who manages that donor and a dropdown for the type of funding so that they can default to unrestricted and themselves. And then they see across the last 10 years who's made unrestricted gifts in the past, but haven't this year. And therefore we're driving the outreach to prioritize donors who give us the most flexible funding and we have the strongest trust with essentially is what they're saying. Then okay, the last example I thought about this, our corporate donors are unique in that they have like a hierarchy structure of subsidiaries and parent companies. And their gifts kind of come in from various sources and various points throughout the conglomerate. And so our constituent record management around corporate partnerships is a challenge and it's a challenge for many nonprofits and keeping them clean and not having duplicate records or making sure that they are linked as a subsidiary to a parent company is something that we don't have the human power to stay on top of as much as we would like to. So one of the things that we're trying to do is partner with someone who has a big data set on the corporate linkages and can use address and other attributes like metadata about the corporation to overlay into our CRM. Here's the structure we have for this corporate entity. Can you tell us how to link them through the relationships table of our CRM? And I think that that will actually help unite our outreach strategy as we interact with these corporations because then if everything rolls up the way it's supposed to, to the parent record, we can say, hey, mega corporation, you have like 50 records in our database. But what that tells us is you've actually given us this huge amount in totality, but before it was all fragmented and fractured, right? So the way that we steward them and prioritize them because we can see the whole totality is really just going to be the outcome of a data cleanup project that my team is working on. So those are some examples. I love that. I hope anyone listening heard that data cleanup example. Why do you get in there and do it? I, you know, I took some notes too in the early part. You had mentioned, you know, data and context when you're working with the chief philanthropy officer and putting a lot of heuristics as well in there, you know, it's like, hey, this is what we think we can get done because the team might be feeling good this year or the winds have changed. Maybe COVID's at our back, you know, we can try to go forward. So I think that's important for people to hear because when I run into a lot, you know, and when I worked in House of Future Artificial Trusts, we tried to do this a lot too, which was, it's not all just quantitative like qualitative matters too. And you're working there because you're not an AI robot and you do have experience in heuristics and life lessons that you're bringing forward to how this works. So just because when I was in DC in December chat, GPT just released sort of publicly and everyone who was in the communication space, it was just like, what, this thing's going to write everything now, what do I do? But it's like, it's still just a machine, like it doesn't have your doesn't have that gut instinct for where things could go. It's the self-service model too. I might want to dive in a little bit. Everyone, Stephen Berkeger is on my team, chief analytics officer here always talks about democratizing data.  Where are they accessing it from? What are they doing when they get it? Are they putting something back into the system that now changes the system? Like, how do you manage that? And we're in conversations with organizations from large ones like yours to smaller ones. And they, they are all thinking about how can I get this so these folks can get in there and access it more easily? So I'm not just servicing them all the time because they care about it a lot. But I don't have a good way to do it. So yeah, if you could talk just a little more about what y'all are thinking in terms of self-service models and what kind of protocols, governance needs that you're thinking about to go into that. I'm sure there's a lot of people in the audience that would be really interested in hearing about how y'all are thinking about it. So a couple really smart people on my team who are way more technically inclined than I am. They, when we previously were using a data lake as our primary data storage, they had direct access into that and were able to build their own Power BI dashboards. Now we try to align like, if this is a report that tells us exactly how much money we've raised this year, let's let the tech team make sure that they have like validated, vetted it, et cetera. They'll do the primary build. But if it's something like my prospect intelligence team has their own KPIs for how much they prospect, the conversion, you know, lots of different things, but it's not going to go to the board, et cetera, right? Like we try to think about fit for purpose, who's designing it given the risk of that data perhaps not being 100% accurate. Someone on my team was able to go into the lake and build their own dashboard for that. Or like the gift table report that I described was something that someone on my team actually built. And so I've got folks who we would put in our metaphorical, and this is not like fully vetted and launched it, but the tier one, like the super users who are not in central tech team, but who have some skills and know how to leverage that kind of a resource. So I think like that it's a decentralized model because, you know, nonprofits resource constraints, we're never going to have enough people in the technology division that we would all want to make our data dreams come true. And so how do they then empower people who have the aptitude, desire to lean into that and become their super users? So we've got that as tier one, we're switching over to the warehouse. And so we're thinking through like, how do we get them plugged in to that now? And how do we scale the two people I know on my team who like actually know how to do that work? How do we scale that to a few other people and create maybe four to five super users on my team is kind of where I'm thinking, tier one is going to go, but it's going to be a very small number of people in the philanthropy division.  They're going to be the data people in fundraising. Tier two, I believe now I'm like, my team is probably going to be like, well, that wasn't quite right. But tier two, I believe is having prepackaged data sets. So to your point, how do we get the data so that people beyond my team can do like can answer their own data inquiries at times without having to know how to use the ad hoc tool in our CRM to do their own query design? How can they pull data sets down and be able to look through for what they are seeking to answer? For that, you know, I've worked in shops where I'm able in a self-service capacity, I'm able to filter like our overall constituent base down to the population I'm looking for. And then I'm able to use that population, that segment to run it through a standard output and say, I want like the constituent report or I want the transaction report or whatever it is based off the population that I little down on my own parameters. And I didn't have to have any coding and I have not a code or I'm not a programmer, but I love data. And so that's the model that I hope that we can kind of explore as people who are the data focal point for major giving or the data focal point for corporate. So it'll be probably a handful of people on each team who then know what they're looking at with these data sets, they know where to go to find them, they understand what's in there, and they then understand how to manipulate the data in Excel, right? So they're going to be like the end user, super user, which is different than what I've got on my team. And then I believe that tier three is a little more an all open form, like who can just access the data in a little bit less sophisticated of a way. We're trying to put data at the fingertips, but I think the most important part about the self service model is that it helps nullify some of the needs of data literacy. And I don't mean that like in the pure way that I just said it, but when you go into our CRM, there are like five different amount fields. And if you use the wrong amount field, you've got the wrong data and you don't stand a chance of getting the right answer, right? And so even people on my team actually learned in the last year or two that all of these different variations of amount exist. And so in order to have the data literacy you need in order to use the self service query tool in our CRM, there's actually a really high bar. And so we do get a lot of requests that are technically someone could do in our CRM and you don't need a fancy self service option. But if we want to limit the risk around people not knowing what the right amount field is, for what purpose, then having more prepackaged like datasets would allow people more confidence. And we will spend less time saying, how come you guys got a different answer than I did when I pulled the same data, right? So it kind of helps put everyone on a little bit more of a equal footing on how we use data and kind of can speak the same language about something. That's how I see it. If you brought in the folks on my team who have a way more technical skill, they would have a lot more commentary to add on that. I think that's wonderful. Actually, thank you for that. That's really eliminating. I hope people really listened to that is just there is that nuance, right? There is these five different fields and they mean five different things.  Yes, because I think that's the curiosity that I find for a lot of groups is they don't want to know what all the five fields are. They want to know the thing that's impacting whatever they're trying to figure out at that moment. So I really like the idea of the prepackaged queries as part of that self service model. Thank you for that. I'm going to switch us up just a little bit, but keep rolling here. So in an earlier discussion we had, you pointed me to APRA's Ethics and Compliance Committee Diversity Equity and Inclusion DEI Data Guide, which is an incredible resource. We'll make sure it is linked for everyone in the ship notes here. But just real quick, for anyone before we dive in, so you can give us a little bit of what that guide was all about, sort of what's in it, how it came to be. Just for anyone who isn't familiar with APRA, who is APRA? So what is an APRA? So for 30 years we were known as an acronym APRA. First we were the American Prospect Researchers Association, and then we became the Association of Professional Researchers for Advancement. And now we're just APRA. We've taken away the meaning of each of those individual letters, and we are now in noun, so it's capital A, lowercase PRA. The reason that we did it was because we are more than the sum of those letters, and Prospect Research is not the only discipline that APRA, the APRA community, represents. We have members who definitely do prospect research, but also do relationship management, data science, campaign planning, and often some of our members, their roles are like they wear five different hats at a smaller shop. They might be data management, annual giving, CRM administration, stewardship, etc. So we no longer wanted to feel tied and wedded to those four letters. You just can't sum it up really that way. So that's kind of like the background on APRA. The elevator pitch for APRA, like who are we as an organization, is we're the premier professional association for individuals who strategically harness information and data to drive fundraising for their nonprofits. And what we try to do, we do a lot of education, but we provide tools, educational resources, and networking that help those that work in that prospect development field, which is kind of the umbrella term we use for some of those domains that I mentioned earlier, to achieve the missions of their nonprofits. And I just have to underscore that like, if any of the things I said about prospect research relationship, if any of that sounds like you, APRA is an amazing giving resource and a community, and we are the type of people that is like, Hey, can I see that KPI report that you just mentioned? And yes, screenshot here you go. Like we are so transparent with one another and really want to drive innovation and cross-pollination of ideas. So please, please go join the APRA community. You can have a guest account. We've got a new associate membership level. It's the best thing I ever did. And like once I found APRA, I realized that it wasn't a job. It was a career path and a community. So I hope that underscores for folks how important APRA has been to many of us. Oh, that's powerful. And yeah, just going on the giving of that community, I mean, the the DEI data guide you all created has been very impressive. I'm curious, what prompted it? How was it developed within a group as big as APRA or how many voices were involved in it? If you know any of this. Oh yeah, yeah. So conveniently, I do know this. I was on the board when it was created. It was my first year when I was on the APRA board and I was actually the liaison to the committee that created it. So I got kind of like a front row seat how it was created, although I wasn't one of the office of it. So the Ethics and Compliance Committee is the committee, there was probably about 12 to 15 members at the time who helped develop it. And they just, they sat and built an outline and took accountability for certain sections of it, like different authors focused on different sections and it went through a lot of review. We had other, like we have a, we had a, at the time it was DEI. Now we call it DEIBJA for justice, belonging and accessibility or the other three letters that we've added. They layered in their feedback, their thoughts, their advice as we were building that out on the Ethics and Compliance Committee. So that was kind of the process that it went through and how it came to be. I mean, I think APRA does this really well. We just recognized a need in the market for a written best practice on the ethical, you know, collection, storage and use of identity or diversity related data. I think we had been having a lot of conversations in our industry, the nonprofit industry, the data industry about the philosophical approaches to like why we needed to change what we were doing with data. From my experience in the field, prospect development is often the field that gets things done. Like we will write the documentation, we will brainstorm the ideas, we will innovate something new. We're kind of like the activators of fundraising and I think that that's how a lot of chief development officers see us and they come to us for things like that. Our association work very much reflects that. Like the industry had a need and we are often the ones who sit at the intersection of ethics and fundraising or data and ethics. And so it was a very natural thing for us to do. And you know, when we created it, there just wasn't anything that provided like a tangible resource to support data driven decision making and advancing and supporting diversity, equity, inclusion, philanthropy. And so we usually hold ourselves to the highest standard in trying to represent professional ethics in the work that we do day to day as APRA professionals. So the guide was just natural for us. Yeah, it's incredible resource. And so, you know, to tie it into your work at UNICEF USA, I mean, what have you been doing in your current role, you know, having that frontline experience being a part of it, getting to see it, getting to see it come together, you know, what are y'all doing in terms of data ethics and DEI? I think with the UNICEF USA, like scale that we operate at and being like UNICEF being a partner of choice for so many donors because of the credibility of our brand and our programmatic expertise and the scale at which we operate, it comes with a lot of trust and immense responsibility related to data. And so we try to hold ourselves to a very high standard. And I think there's some work that we've done, but we are not perfect. And we have more work to do. The work is never done in this regard. So at UNICEF USA, we've got an amazing, compelling, moving vice president of diversity, racial equity and belonging. And so we don't use DE and we use DREV because that racial equity piece is very, very important to and being like naming that very explicitly is very important to the work that we do as a nonprofit and to our leadership. And so they are centralizing the overall effort at UNICEF USA. And then within that, if we take it down a notch to the philanthropy division, one of the things that is being developed right now and is not fully fleshed out, so I can't really share a lot, but is an inclusive philanthropy strategy. And it's going to very holistically look at things like how do we engage different diaspora communities and how do we do that authentically and how do we diversify our board pipelines and things like that as we're thinking about potential candidates to nominate and my work gets intersected with that, which makes me very happy. But our chief philanthropy officer's office is the one who's leading the inclusive philanthropy strategy, but I think that it will have a significant impact on any campaigns that we launch in the future and how we approach our work. So that's really exciting. But those strategies are still very much like coming together in a coordinated way. So me and my team, what I have put in place and I putting in place again is a misstatement. I have an amazing team member who her passion is DE and I work and I'm so very lucky to have her on the team. She has spearheaded a lot of the work as we think about how we can do better with data within the realm that we command and she has helped us draft an identity data management policy, which offers guidelines around how the prospect intelligence team conducts prospect research and outlines the ethics around we are only going to use self-identified information. We are not making assumptions. We are not looking at a photo or a name and inferring racial markers or ethnicity, etc. And so that has been a real educational touch point for the prospect intelligence team. And I would really love to be able to start expanding and scaling that document, the guidance that it provides to the entire division, which is something that I talked about with our fundraising leadership. So I hope that that is on the horizon soon. Another thing that I have been able to do is put it on the radar of our data governance committee. So we have a data governance committee at UNICEF USA and I think it's about in its second year at this point and some of the things that we do on that committee are a line around what do we mean when we say campaign and we outline different kinds of campaigns so we can all speak with the same vocabulary. And so one of the things I'd ask is that we think about who has access to what information and why. Like if we have a data append, which we do, on the back end of our CRM, does everyone need access to that? And all of the indicators that could be demographic, race, ethnicity, etc. Do we need everyone to have access to that? Can we think about the ethics around it and how we agree as an organization to leverage that data or not? So that's a conversation that I hope we'll have soon as well. But then one area where we've actually already been able to start seeing some good results in is our work with consultants. So the vice president of DREB partnered with our general counsel to create a procurement policy. We didn't have one for a while, but they really pulled it together quickly. And a big focus of that is making sure that partners that we choose to contract with have that they align with our organizational values around DREB. And so that was like wind in my cells as I was looking at some new contracts we were going to bid out. And we were able to make it part of the scoring rubric and a big focus of the interview with some of our finalists. And you know, the consulting world is kind of all over the place on like some are like, yes, it's our jam. Here's everything we do. Others, it seemed like an after that perhaps, or like they had tried and it hadn't moved forward. And so during the interviews, we were able to dig in quite a bit with some of the applicants, the consulting firms that had applied and really pushed them on like, well, how like a lot of this work scope that we were contracting for involves analytics and modeling. How do you root out systemic bias and predictive modeling? Like, what have you tried? What have the results been trying to help them understand what it means for us and what we would like to see. And then actually the candidate that we went with, like, they had a pretty robust DREB program at their own firm. And I think that did factor in to why they were the right fit for us. And then when we got to the contracting phase, I didn't see anything about it in there at all. And I was like, hold up, reflect back to me, everything that we talked about in the interview about how you are going to bring the values of our DREB work into the data analysis work that you're going to do. So we had the opportunity to do that, they added it, which is great. But another area that we're thinking about, it's a little bit forward looking, but I think it's a really good opportunity as we're going to be doing a constituent survey, a digital e-survey for our constituents in probably the next six months. And I very much want to put more than just my one team member, probably, you know, someone from our central DREB office, the person who will lead inclusive philanthropy, I want all of their input on how do we design that? How do we communicate the intent of the survey and who's going to have access to it and how we're going to use it? And so I see that as like a very tangible way that we can make sure that the survey design is equitable, that we are not marginalizing people just by the way that we set it up. So that's an opportunity that's coming up that's very much top of mind. I'm lucky enough that I have this amazing resource of the head of DREB at UNICEF USA that I have been able to partner with them. We're in the initial phases of it, but this fiscal year was the first year that my team and I are starting to engage with the team and like the centralized DREB team. And really think about like, what would it look like if we start to integrate that way of thinking, questioning, sharing different lived experience in our day to day work? And what would the intention be? I mentioned the standards of collaboration earlier as like a really important cultural touchstone for my team. There's no mention in there that we embrace the DREB values of our organization and that we try at all times to be explicitly anti-racist. So how do we do that not in a performative way, but how do we put that at the center of the way that our team operates and how do we engage in the work that intentionally blends personal professional and just the growth that we have that we all need to do. So that's a little bit of a rundown. Like, I think we have a long way to go still, but we're starting and I think that's the most important part. Oh, you said it earlier, right? This works. It's always ongoing. We've got to do it, but it's great to hear you have so much going on is it's that operationalizing part that really starts to matter. You know, a lot of statements were made over the last couple of years and then how do you actually start weaving it into the operations of your organization where it's not just the thing we talked about. It starts to become the standards and then eventually that's just the way you operate. Like, here we are. It's like, this is normal and it's all flowing and it's great. But thank you for that. I hope a lot of people took some lessons from that. And one thing I hear a lot, maybe it's because I run a consulting company, but is that work on data is never something that has an allocated budget in a lot of nonprofits. I think there is a clear value of having good data that is well understood, though, some curious about advice you might have for other organizations on how to structure teams and our practices or their data practices that help define that they're not just a value or I think people see the value, but that there is a need and there's a lot of pieces that go into it. And if you're not budgeting for it, you're not going to get to the place you want to get to. What advice would you have for folks that I don't know? How can they convince people that this is something to start thinking about as a line item? That is a really hard question. That's the biggest question out there. And, you know, with nonprofits in general, we have such fixed resources. It's really hard. I'm super lucky to have a forward-looking leadership team at UNICEF. I'm not paying lip service, I swear. Super forward-looking, super empowering around the importance of data and data strategy. And that hasn't always been my experience elsewhere. And so, if you are at a nonprofit where you are struggling to convince leadership, that is very challenging. And I have at times left shops because we haven't gotten the vision alignment around what our team is capable of in the prospect development sphere. And I think your question really gets at the nonprofit starvation cycle a little bit. And I remember this moment where I saw Darren Walker, the Ford Foundation president, do a 60-minute interview where he was like, who wants to invest in a nonprofit with no infrastructure? Who? Why would a donor do that? Why are we forcing the nonprofits to work without the data budget that they need in order to be on the cutting edge and work more efficiently? And that really resonated with me. And so, I think that this question is very much in that lane. I'm glad that people are starting to realize, like, more and more, that nonprofits do need the types of resources that the for-profit sector has. I think that's incredibly important. How I would structure it, there's a couple of different resources that you can consult in our field to advocate for more resources. My team has grown a lot, actually. When I started about three years ago, we had five staff. We're up to about 20 now. Some of that is new headcount. Some of it is a restructure. So the gift processing team, that reports to me used to be in finance, so like six positions were moved over. But the rest, the difference is all net new headcount that they've chosen to invest. So I'm very lucky. One of the ways that I was able to advocate for new resources was by looking at the APRA salary survey, which is a terrific member benefit. And it gives you, like, industry standard benchmarks that there are usually seven front-line fundraisers to a prospect development staff member. Now, we currently, if you just look at the staff who comprise prospect development on my staff, which is only a fraction of the entire team that I lead, we're at 14 to one. So that gives you a sense. Like, we are understaffed by half. And yet I actually think compared to some other shops I've worked in, we're pretty lucky. So I don't know who the mythical unicorns are out there that have seven front-line fundraisers to one prospect development staff member. But using data points like that and helping senior leadership understand that if they're going to invest in the front line and grow those teams, that they have to help you keep pace with it. We should never lose ground on resourcing of the front line versus the operations or data side of things. So that's been helpful to me. I'm also a fan of asking for what I want and settling for what I need. And really having like that growth mentality set out and saying like, what I really want is this and it's like five new headcount. But if you give me two, here's what I can do with it, right? I also think a lot about, you know, I've been talking about headcount. Everyone always wants headcount. And that's the hardest thing to come by projects. So like that corporate data cleanup project that I mentioned carries no benefits, no retirements. You don't have to worry about whether you lay that person off of something or the turn in the economy, right? So what kind of data projects can you advocate for instead of headcount has been really helpful? Also meeting your senior leadership halfway. So I could have said, no, my team is not going to build Power BI dashboards. That is text responsibility. But strategically, I was like, it's in everyone's interest if we meet them halfway, we're not going to get all the headcount we need for our tech team. We're not going to get all the headcount I need for my team. Let's try to do some cross training and help each other out. And that builds my team's data expertise and fluency. In the past, not only in SFUSA, but in the past, we've also looked for, especially if you're in higher education fundraising, looked for grad students who are pursuing data science or business intelligence degrees. And they are a very economical way to come in and help you do data transformation and cleanup and visualizations. So that is one strategy that's worked elsewhere for me. Part of my success in like trying to advocate for either the role of my team to expand or the size of my team to expand or funding for specific data projects has been positioning leadership at the center of the data culture. And so when I had my chief philanthropy officer go talk to the institutional philanthropy team about their KPIs, I am using her visible leadership, right, to frame the importance of data. It's not just coming from Lindsey, whose job it is, to make sure that the data is strong and accurate and that we can use it. This is the culture that our very top leadership is setting. And so I think if you do that, it lends more credibility to it. It makes people sit up and pay attention a little bit more because it's not Lindsey the squeaky whale who loves data. I also think in terms of like, how do you demonstrate the ROI of a data project? You can do this. It seems hard, but I promise like, and it's never going to be perfect, but you use it as a directional indicator that there was a payoff for it. So for instance, we did a project recently in the last year or so where the solicitation pipeline that we had in our CRM could not be trusted. Let's just say that it was not complete. The data that was in there was not accurate. When I arrived, not even all the teams were using it. They just had some flat files that they were keeping track of all of their asks in. And so we really went through a three year project to integrate everyone into that solicitation pipeline and have them feed into it. And we got to that point. And then the next thing we did was we inventoried all of the complaints about why that was really hard for them. What doesn't work for them in the structure of that form? And we analyzed how many errors are we seeing? Things that we know are obvious errors. Like they're saying they're expecting to make the ask. And that ask was six months ago. Something's not right. They either failed to update it, they weren't nudged to do it. They could have actually had a typo in the date too and put like an old date in. We do have the human error issues too, even if they're trying to do their due diligence and update it. So what we did was we compiled all of those requested changes and we completely streamlined the form that they input the data into. And we introduced the conditional logic. We helped them understand when to put something in and to feel comfortable doing that. And it helped increase or decrease rather all of the error rates significantly. Like I used to look at the pipeline and say 30% of this is inaccurate. And I can tell just because certain things are not the way that there's like data missing from different fields or like they've put one thing here that contradicts something else here. And now it's like less than 5% gives me pause. And obviously that's just an obvious data problem. There could still be things that are like out of date. And I just can't tell that from just looking at the data. It could be a little stale because it's been a week or two since they've looked at the record and it's not up to date. But we've taken the obvious data error rate down just by more intelligent design and getting everyone to use it and then repeat back to us all the things that they wanted improved. And so like that kind of an ROI of saying it used to be this and now it's that as a baseline is really helpful for them because now as we are like talking about the need to build a Power BI dashboard for the projections that we run which we actually have already accomplished. Thank goodness. We're now looking at like well how do we retrospectively say this was worth the investment from our tech team and the licensing team so all that stuff because we can tell how accurate our projections are now because we have this like centralized resource where we can go back in time and say at this time we projected that based off this information and it landed within this percentage of accuracy. So I think that like there are definitely ways they're not perfect. They're directional but you can demonstrate the value. The time saved. My team's not having to nudge the fundraisers about their pipeline being inaccurate. It's easier and more straightforward for them to use it. They complain less about it. We spend less time talking about it now. Those are all like the qualitative things that you mentioned that I think being able to switch to the quantitative can also help demonstrate the value that you're having. And now I'm like trying to advocate for a headcount to basically shut the fundraisers out of the pipeline. I don't know if this will ever happen but like I've worked in some places where my team would be the only people who can update that data because we basically just take like a qualitative download from the fundraiser about well this is what happened with this solicitation and then my team just translates it into all of the data so that the accuracy can get within like one or two percent of projecting. My last shop was able to do that. So if I can demonstrate like okay we did this and that was the ROI. We did this and that was the ROI. Now give me one more resource and we will get you here. It's kind of like approach that I take. Excellent. Thank you. I like that idea too. I mean find the projects, show the value, make it a little smaller, then build on one ask, get the next one, keep the winds flowing. That approach. Thank you. This has been very illuminating an instructive conversation and for anyone who's a regular listener knows I have one question that I end every podcast with and so Lindsay what is your go-to song when you need a boost and why? This was so hard to answer. I love music and like I love prioritizing things but I don't like prioritizing my song list. So a lot of time thinking about this probably tension. There's an artist that is missing from your light up because I did look at the full playlist that you have. I'm going to go with the throwback. Missy Elliott moves control. I want music I can dance to that gets me going. I love it. Missy Elliott is coming to the playlist and I hope everyone out there if you need a good boost grab that playlist on Spotify. Give it a listen. Lindsay, thank you for this conversation today. I really appreciate your time. It's very illuminating. We're going to go through all of this conversation grab a lot of links for the show notes for everyone. A lot of things you mentioned here I think especially a lot of the APRA resources will make sure they get up there for everyone and thank you so much again. I really appreciate it. Thank you, Danny. All right, bye now.


Hi friends welcome back to my channel and a special welcome if you're new here. My name is Jen I'm a certified weight loss and nutrition coach and I'm on WW personal points. Happy Monday it is Monday so it is meal prep day. Let me just tell you how good these three recipes are. I am so excited to share them with you so if you're excited to give this video a big thumbs up. Subscribe if you're not and make sure your bell is turned on because I upload five days a week and Monday is always meal prep. Check out that description box down below for my recipe website nutrition coaching where I offer personalized to you macros and calories as well as one-on-one coaching if you would like to chat with me directly. Links discounts to my favorite things and my Facebook group definitely come join us there that's kind of how you can keep up with me a little bit more day today and of course we'd love to have you so let's jump into these three amazing recipes. For breakfast this week I am making meat lovers breakfast pizza. I love breakfast I love pizza so pairing these two things together I am super super excited about so let me show you what's in our recipe. For sure going to need milk you can use any milk that you have on hand we're only using a couple of tablespoons. We are going to make two ingredient dough so you'll need some self-rising flour and some non-fat Greek yogurt, jimmy dean turkey sausage crumbles, light butter, garlic salt I'm using that just to season up my two ingredient dough. I do like to have the crust have a little bit of flavor and then I have some turkey bacon that we had cooked up this last week for BLT's so I'm going to use that you could use center cut bacon whatever your preference is some shredded cheese and some green onions. So the first thing we're going to do is make up the dough for our breakfast pizza. So I have one cup of self-rising flour with two ingredient dough it's usually a one to one ratio so we would do one cup of the non-fat Greek yogurt but what I like to do is add my yogurt in slowly because too much yogurt will make your dough really really sticky so I like to add just a little bit mix that together and then continue to add in yogurt until I get a dough consistency. Once you have your dough go ahead and add whatever seasonings you're going to use I just like a little bit of garlic salt mix that in and then we're ready to roll it out. I always add a little bit of flour to my working surface go ahead and get your dough on to whatever you're going to roll it out on and then with your rolling pin roll that out to your desired size and thinness. Now I want mine a little bit thinner since it is going to be the crust of more of a breakfast style pizza.  So the original pizza recipe calls for eggs I am not going to put eggs on mine I'm just going to do strictly the meat and the green onions so I am not going to use the whole milk this is if you are going to scramble up and cook some eggs. I did though melt a tablespoon of light butter in the microwave and I'm going to just brush that on top of my two ingredient dough just to give it a little bit of butteriness and it'll also help it crisp up and brown because we are going to put this into a 425 degree oven for about 10 minutes 15 minutes we just want the crust to cook a little bit before we add on all of the toppings. While the crust is in the oven I'm going to chop up some green onions and four slices of the turkey bacon. I just pulled out the crust it is cooked about halfway through now we're going to add on the toppings so I'm going to start with the sliced up four pieces of turkey bacon or center cut bacon whatever you decide to use and then I have half of a cup of the fully cooked Jimmy Dean turkey sausage crumbles and then a quarter cup of the shredded cheese this looks so good we're going to put this back into the oven for about five to seven more minutes I just pulled out the pizza it smells so good this could even be lunch or dinner because we didn't add the eggs this would be a really good lunch or dinner as well remember you can go ahead and add the eggs to give it a little bit more of a breakfast vibe but I really wanted to focus on all the protein that comes from the meat so the last step is to take our green onions and sprinkle those right on top so here is the pizza doesn't this look so incredibly delicious I am really excited for this the crust is a perfect perfect thickness it's a kind of earing on the side of a thin crust and a regular crust the bacon the sausage the cheese the green onions it's going to be so good I will go ahead and put serving size points calories here on the screen for you and this week we're having pizza for breakfast for my lunch this week I am making barbecue muffin cups I'm really really excited for these they're going to be such a perfect lunch pair this with some vegetables fruit and you have a very well rounded lunch so let me show you what's in our recipe first you're going to need salt and garlic powder sour cream all-purpose flour light butter baking powder sugar-free barbecue sauce of your choice shredded cheese one pound of 96 or 93 ground beef and of course some eggs so the first thing we're going to do is brown up our pound of ground beef once your hamburger is cooked completely through we're going to add one cup of our sugar-free barbecue sauce stir that in remove it from the heat now we're going to add in one third cup of all-purpose flour garlic powder and one quarter teaspoon of baking powder mix that into the liquid mixture I have one cup of shredded cheese total I'm going to add about half of that to the mixture and then stir that in and we're ready to get these into the muffin pan make sure you spray really really well or use a muffin liner because this is a silicone pan they actually come out really easily but I am going to spray with some non-stick cooking spray really really well now we're going to scoop the mixture into 12 muffin cups as even as you can get it and then with your meat mixture with the barbecue sauce we're going to top each of our muffins with some of that again you want to make sure that you have enough for 12 and then lastly we're going to top them with the remaining half of a cup of shredded cheese they are going into a 400 degree oven for 15 minutes the barbecue muffin cups just came out of the oven again everything smells so good these look incredible lots of protein in these let me go ahead and put points calories serving size everything here on the screen for you and this is lunch for a sweet treat this week I am making low carb peanut butter balls this is a no bake recipe so you don't have to heat up your kitchen when it's hot outside and these are going to be such a well-rounded protein packed sweet treat let me show you what's in our recipe first you're going to need light butter chocolate chips of your choice I'm just using the lilies chocolate chips powdered sugar alternative I've of course I am using Lakonto it is my all-time favorite to me it tastes the closest to powdered sugar with all of the points and calories as always I'll link Lakonto down below with 15% off for you you're also going to need unsweetened peanut butter mine is from imperfect foods by the way this peanut butter is so good super super affordable and some vanilla extract to get started on our peanut butter balls this is such a simple recipe you need one half of a cup of light butter softened at room temperature one half of a cup of unsweetened peanut butter mine is actually unsalted as well so I'm going to taste my mix before I roll out the balls I may end up adding a little bit of salt since my peanut butter was unsalted 400 of the lilies chocolate chips vanilla extract and the recipe said that these can become overly sweet really quickly so to slowly add in your powdered sugar so I'm going to start with one quarter cup of the Lakonto powdered I'm going to mix this all together taste test the mixture and I can always add in more powdered sugar and again add in some salt if needed I am going to add another quarter cup of the powdered sugar and one more quarter cup so I ended up adding three quarters of a cup total and that should get me the perfect sweetness and I did not add any salt the salt from the butter was plenty so I was testing this out and I think this is what I'm going to do I'm using my little mini muffin tin instead of rolling these out into a ball with my hands I'm going to add the mixture to my little mini muffin tin pop it in the fridge until they set and then repeat because we want 24 peanut butter balls or peanut butter mini muffins total and here they are I'm going to pop them in the refrigerator until set you can also pull them out midway and roll them into a ball I was also thinking you could spread them in a baking dish and make bars out of them lots of different ways to utilize the peanut butter ball batter they are all set so I'm actually going to store them in a glass container I'll pop all of these out and then we'll do the rest of the mix it should make again 24 total it's a little hard to see them in the container here are the peanut butter balls I'm going to go ahead pop them back into the refrigerator allow them to continue to set again we made 24 peanut butter balls total calories points are here on the screen for you thank you for joining me for this week's www meal prep I hope you are as excited as I am for these three recipes and don't forget you can find all three of them on my recipe website which is linked down in that description box along with nutrition coaching links and discounts to my favorite things and of course my facebook group we'd love to have you join that community as well thank you guys so much for watching happy Monday and I'll see you all in my next video bye


You know better than I do, what the military spouse employment statistics are like. They can be pretty grim sometimes. Yeah, sadly, it's been 24% for nearly a decade and that number only continues to rise. And that's, it's exactly what I said, you know, it's really hard to piece the puzzle together. And oftentimes companies don't understand that. It's really sad that it took COVID for more companies to see that you can work remote and have a successful career. I am proof of that. I had a very awesome and amazing corporate career working full-time remote, making a great salary. But I started to see that a lot of my fellow spouses just weren't able to figure something out like that. So we're making steps in the right direction, but entrepreneurship is great because it, you know, bridges the gap to doing what you love while living a life on the go.


I have had the extraordinary opportunity to actually be a satellite. These amazing views just dazzle you completely. Everything you've probably heard other astronauts say is true. You see shapes you recognize from maps and things like that. You don't see the typical country borders. You get a very different sense on a lot of scales of the planet. Exclusive economic zones, territorial boundaries, things that animate our news and our awareness every day are not present and a whole other dimension comes through. But at the same time as this picture shows, you also clearly see the hand of man. And this is what we are the first generation of humans to ever do. Not only to be able to see the Earth from this perspective, but as I'll show you in a moment to develop technical means that let us take very sophisticated snapshots, essentially of the whole globe, essentially simultaneously, and develop the computational and scientific techniques that let us turn that into very powerful situational awareness about what's happening in what the state of the planet is at any given moment. And even more importantly, to transform these data into foresight, into practical predictions about what conditions are likely to be some time down the road. And that's where we see really novel things happening in our era of society as that gets incorporated into decisions from the mundane. One of, did you bring an extra hat or scarf today and did you realize it was going to snow? You know, to decisions of much greater import. So without further ado, this is, by the way, space shuttle altitude, Italy and the Adriatic, the kind of thing you see once around the Earth every 90 minutes, a sunrise and a sunset twice, 16 each in a 24-hour period. It is quite a stunning experience. It's a very novel technology, as I'm sure you all know, and so let's take a look a bit at the history of this. These are some of the earliest satellite-based images that were taken. They're from a system called Corona that the United States launched in the early 60s. When we sent these images to the forum staff to mount them up for this presentation, several times each they came back and said, oh, these are really very bad resolution, don't you have a better version? In their time, they were three times better in resolution than anything that had been taken by aerial photography before. The motivation, as you appreciate, was initially the classic high ground motivation of humankind to have a sense of the lay of the land and of the actions of other parties that might be competitors or might be adversaries, military intelligence, troop movements, missile emplacements, those sorts of things, pivotal role in the Bay of Pigs. It was an astonishing and revolutionary capability at its time, and just as an aside, to me maybe even the more astonishing bit was how you got these images back. You actually popped them out of the rocket at very high altitude, huge spools of film. You think your old film camera was complex in the back the way it was on the film out. You ain't seen nothing yet. Run it through, take the picture, spool it again in the nose cone, pop it off, and then fly an airplane by at the right place in the right time to snag the parachute out of the air and bring it back to the lab. They got pretty good at that, but obviously some fair amounts of data were lost. And it was not timely, it was not low latency. It took quite some time to get that to the earth and then to the lab and then through the lab and then to you who perhaps wanted to use it. And obviously data rates were low and obviously data reliability was low. And I point that out, obvious though it is just to remind us that in this, as in other things, it is transformations through every link of the chain from the taking of a measurement to the use of it. Transformations all along that chain are what transform our use of them and the way they can engage society and shape our lives. Looking where people are and what people are doing was one dimension, weather has always been a factor in human affairs, both economically and public safety and militarily. So very shortly after implementation was to try to see what can we learn and can we begin to develop better forecasts of the weather if we have an overhead view. These are two of the earliest weather satellites and the kind of imagery they took. Truly very simple, black and white, quite like your film camera. Just simple cloud images. But if you live in California and you're dependent on weather systems that are coming in from over the Pacific Ocean, if you live in Europe where you've got 3,000 miles of ocean upstream of you, the ability to see what cloud formations are over that ocean, because they're going to sort of stay intact and come your way three or four or five days later, that alone was pretty revolutionary weather reconnaissance in its day. Just figuratively this graphic shows some of the United States overhead observing systems. These are all civilian scientific systems, NASA, NOAA, the US Geological Survey. They do things ranging from measure cloud properties to measurements that help us understand the water cycle, soil moisture, Landsat for land cover, solar irradiance, all sorts of different measurements that we developed over these decades, specific sensors that can provide the raw data that we can convert into those attributes. Satellites don't measure those attributes directly. They measure radiance. They measure light that's scattered or emitted from the Earth. And it is a body of scientific knowledge accumulated through a lot of research that lets us translate those radiances into these particular properties that are of interest to business planning or to agriculture. So quite an array now. Europe has a number of satellites that do similar functions, Japan and others are joining this club and adding to the stock of satellites and sensors that help us understand this Earth. And as I've alluded to, there are different kinds of eyes, if you will, on each of those satellites. And I show here just a few global representations of the kinds of measurements we now can make. You have in the upper left sea surface temperature. Again, how revolutionary was it to be able to see the whole globe almost simultaneously for sea surface temperature? It's that data. It's the global view of those data in the early 1970s that transformed our understanding of El Nino from a local fisheries crisis that hit Peru occasionally to understanding it's a global oscillation in the Pacific Ocean. It's one of the fundamental heartbeats of the planet. It affects the ocean and the atmosphere and it drives about half of the weather variability of Europe or the Central United States. Transformational understanding of what was going on there. In the upper right you see the gravity field of the Earth, again measured from space, measured by satellite. The lower left is chlorophyll, land cover vegetation on the land surface, and chlorophyll or the biological productivity of the ocean in the ocean areas. And finally on the lower right, the sulfate aerosol content of the atmosphere, one month's worth of aerosols in the atmosphere. To be able to see these properties on a global basis, near simultaneously is scientifically quite transformative. But again you're measuring radiances that also are quantitative and rigorous and so it lets you not only make a map but proceed into other analyses that are even more useful to society. We've been able to monitor change over time which is important on a number of levels for different planning. On the left here you see the human development, the civilization, habitation, settlement patterns around the Las Vegas area, you see Lake Mead sort of fluctuate through these seasonal cycles. And on the right you see the flow of sediment and nutrients, fertilizer, chemicals into the Gulf of Mexico, through the Mississippi Delta. 41% of the United States land area drains down that river shed into that one point in the Gulf of Mexico. We sometimes have dead zones there that are without oxygen that are the size of several of our states. We can watch sulfur in the atmosphere, that's what the image on the left is.  On the right again with a gravity measurement monitoring the state of the entire ice mass on the continent of Greenland. And in each of these cases there are not only pretty loops and fascinating movies, but expert scientists in these phenomena when we can see that whole picture come to recognize features and aspects of the dynamic system that were not apparent before that we then can hone in on and refine our understanding of. And of course we can zoom in and look at more narrow phenomena like the dynamics over time of a single glacier, in this case in the areas of Alaska. You commonly hear people say, well satellites give us this, satellites tell us that, satellites don't. There is a global measurement, a global raw data of sunlight that can be translated, that can be transformed because of the decades of research and understanding how sunlight interacts with the earth and of going and collecting signature libraries so we know we can recognize the fingerprint of a conifer forest versus a different kind of forest. We can recognize the fingerprint of a healthy wheat crop versus a distressed wheat crop. We can recognize the fingerprint of chlorophyll that is abundant plankton in the ocean. So when you see these pretty loops and images, again to appreciate, it takes all of that and it also takes measurements on the ground to anchor and calibrate those sensors. Understanding the El Nino in a way that we can turn to practical forecast takes the satellite temperatures but also an array of buoys in the ocean that give us the third dimension of the ocean itself. At NOAA, the agency I lead, this is our business, to take knowledge of the earth with all the timely, useful information we call an environmental intelligence to deliver it to our citizens to inject it into the business for their decision making. That's what we do for a living. And here are a couple of the kinds of products that we put out from long range climate predictions decades to half centuries out into the future to the drought monitor. This is a recent image about a year ago actually of the drought conditions in the United States. It's now much more intense in California than a combination of on the ground measurements and overhead measurements that give us this synoptic view. The conventional ones we all know of course are hurricane measurements and daily weather forecasts. 80% of the way your weather forecast is made, 80% of the data that are involved in making your daily weather forecast come from these overhead satellites that measure vertical profiles of the atmosphere. You crunch that through a numerical model and you produce a prediction of what the state of the atmosphere will be 2448 so many hours out. That is how it is done. This is India. India in monsoon conditions. Flooded ground, heavy downpours. The monsoon usually starts around the June timeframe in India and its rainfall is absolutely critical to the food crop, to the wheat crop and its yield. In 2009 no as a bilateral exchange exchange climate and weather model capability with the Indian Meteorological Service and training and capacity building. If the monsoon doesn't develop as expected if the rains fail the wheat crop fails and you find yourself a few months later in a pretty significant food security crisis. Well, the whole world by this point is seeing the failure of crops in the drought affected areas and prices have begun to spike. So by the time you know you don't have a monsoon you also cannot afford to go to the market and buy enough wheat on the open market to feed your people. Climate predictions based on these satellite information now allow the Indian government to have an affirmative signal months ahead of that whether they will have a monsoon or not and to know whether they need to go preemptively to the commodities market and hedge the food risk by buying wheat in advance. Satellites taking a major national country out of the spot market for wheat because of the foresight that practical prediction lets us develop. These applications are beginning to develop in many, many, many different arenas. So we're trying to understand how to use these principles as human beings when it comes to really understanding how to infuse this kind of environmental intelligence into social and commercial and economic decision making and having the tools that connect the data to the information to the insight in ways that are useful to those practitioners. It doesn't do to write the fancy science paper and put it in a journal and leave it on its shelf. That strands the insight. How do we get it into these decisions? Water stress in particular but also crop health and forecast as in India? Renewable energy siding boundary layer winds, where are the favorable areas? Consistently favorable areas? The retail environment on the bottom right we know half a dozen major consumer retailers just in the United States alone that look always at the Tuesday weather forecast that we put out to make their Thursday shipping decisions for their weekend shopping. So optimizing your supply chain and optimizing production across a simple thing as consumer retail. Obviously transportation weather, both the operational weather but also the hot and cold that can affect installed systems like rail tracks these forms of information are increasingly getting into stock trading and commodities trading desks. In the United States the model is that NOAA produces the foundational data the basic underpinning and the basic underpinning products and we stop there and these sorts of analytics and tailored services are done by private sector companies. They are more nimble at meeting the evolving need of both society and the consumer but the underlying data are treated as global public goods. They are an open innovation platform for anyone to use and that also helps ensure that the basic governmental proposition of ensuring the safety of our public is never something that becomes a fee service it is always that basic public good that we can deliver. What might be a next in just this arena like the earth and turning it into useful information? Science and technology will continue advance sensors will be able to see finer spots of ground and they will be able to see with finer spectral resolution so to distinguish more signatures of key features and phenomena. I think a companion trend that will revolutionize this enterprise in very fundamental ways is densifying sensor networks on the ground so that you can take this kind of insight and bring it down to more localized and specific impact levels, urban environments for example, this is a big data arena NOAA produces 20 terabytes of data per day, twice our Library of Congress but architecturally we can only get 10% of that out into this enterprise that I've been talking to. How can we turn that around so that this tailored analytics enterprise is working on all 20 terabytes and what new applications might that enable? The kind of forecasting and work that we're talking about here today is not something that will ever be just a data mining proposition. It's so dynamic on so many levels you will always have to be taking the pulse of the planet. There are companies now at least in the photography arena that find it financially viable to finance the satellite themselves and sell the data to different customers who want it. Should that migrate into this domain or are there benefits and virtues to global data as a global public good that we should work to preserve? So with that let me leave you with a couple of other fun glimpses a little blue marble that we live on and turn the podium back to Aachen.