Centering Humans in Nonprofit AI and Personalization

By focusing on our employees needs happiness and well being, we ensure they can best support you, you’ll quickly realize that working with our team is like having additional members of your own staff that will always be there to help you. When you get started with Donor Perfect, we Read More

By focusing on our employees needs happiness and well being, we ensure they can best support you, you’ll quickly realize that working with our team is like having additional members of your own staff that will always be there to help you. When you get started with Donor Perfect, we provide a full data transfer and onboarding team to make sure your system is set up correctly, and that it matches your unique needs and ways of working. Our professional trainers will then make sure you get off to a fast start explaining what you need to succeed using a variety of training programs that cater to your preferred learning methods. Our customer care team provides ongoing support whenever you need it by phone, chat or email. They’ll answer your questions help you improve results and quickly become your best new work friends. While you focus on your mission. Our product managers and developers are incorporating your feedback and prioritizing your needs and concerns to deliver easy to use software that will enable you to achieve all your goals when our customers and employees are asked what do you like best about Donor Perfect they both say the same thing, the people you will to learn more about how Donor Perfect can meet your unique needs by speaking with your account manager or attending a product demonstration webinar.
Good afternoon everyone. My name is Shama clone, I am a senior Donor Perfect Training Specialist. Welcome to Luna dasa session centering humans in nonprofit AI and personalization. A little bit about Nina She is the CEO, consultant and facilitator of namaste data and data for ensure everyone. Namaste data focuses on advancing data equity for nonprofits and social impact agencies. Love data for everyone provides tools and resources to help people learn how to navigate the world with data. Mina specializes specializes in designing and teaching equitable research tools and analyzing engagement. She supports nonprofits in three critical areas, data collection, assessments on community surveys, and staff workshops on improving data Equity and Human Centered algorithms. Before I hand the presentation over, just want to address a few housekeeping items. All presentations are attached to the session and can be downloaded for your review. Please be sure to add your questions to the q&a tab that’ll be next to the live chat tab so we can see them and get them answered for you. All sessions are recorded and will be found on our Donor Perfect website post conference. Now let’s give a warm welcome to Mina and Lena, take it away.
Thank you so much, Sean. Good afternoon, everyone. Good morning. Well, is there a time for Good morning. I don’t know. My brain is all over the place. I am joining from University of Calgary right now. I am typically in Vancouver BC. But I am having back to back sessions and another conference for arts funders in Canada here. So I’m all over the place. But I’m really excited here to talk to you about data and AI. And before I jump on to go through these amazing slides that I have prepared bits that are a perfect team. I want to hear in the chat window. How are we all feeling about AI? I mean, go rant it. Use F words. Use the nice words. Go for it. Go on the chat window. I am giving you the loving permission to use AI Okay, let’s see. Okay, Madeline says definitely loving it. Okay, love it. Exhausted. Okay, Ashish, I hear you exhausted. Dana, my friend. You’re seeing it Ben. Cats love it. Okay. freaks me out a bit. Okay. Helpful. Mixed feelings uncertain. Gary, I’m getting that okay, Margo. Not sure. Yeah, that’s a new response. All right. Excited saves me so much time. Faith is just a tool that should go on a t shirt. Autumn nervous. All right, keep them coming, because I want to share what I feel and introduce myself. And let me go back to my presentation. Before I get into it, though, when one quick point I’m joining from a different space, this is a university setting. So I have one screen and I’m going to take a lot of support from Shawn. Thank you, Shawn. From for keeping an eye on the chat window and the q&a window. And I’ll talk a little bit about my end of the housekeeping things in a second. Okay. So who am I am Mina Das, my pronouns are she her and hers. I have a consulting company called the my state data. And if you have to remember for worse for me, I mean, Shawn was very kind To read a very long paragraph about me, which I gave, but I read clearly need to shorten it. If you have to remember four words about me that is data, AI, the woman who doesn’t stop talking about nonprofits, and how can they do good with data, and equity and inclusion. I work to I work to bring the two worlds together the lived experience as part of my world and the learned experience as part of my world. I want to get to a place to the forum where you and I can sit in a coffee shop and talk, how can we build more transparency and accountability? That’s data I want to sit with you face to face with the people you care about, to talk about. What does it mean to do good with data? Because it’s going to mean some things, we are going to have to change a few things. We have to unlearn a few things. We have to learn, learn some new things, how do we do all that? That’s a lot of things. And on this other end of the world. Our friend, Sam Altman is trying to change the world we don’t know yet in the villain way or in the Code Hero away. But some things are changing very fast on that end. So I want that kind of an outcome of this work. That is what I do every day. And you would have all the ways to connect with me. My one goal with these 30 minutes of time with you, is to give you some common language to ask questions. I don’t want us to come up with the perfect answers. And there will be questions that Bill left, that will be left unanswered. And that’s okay. I have one goal. Regardless of whether or not we go through all the slides here. And everything I prepared to talk is we can ask them good questions about AI, about holding ourselves accountable about holding each other accountable to build some power and influence in this conversation of AI. So my friends, use the q&a window to drop your questions. Shawn here will help me to read those. And if you have any feelings, I blend life coaching and data science together if you have any feelings throughout the session, also drop them in the chat window. I’m going to read them I promise. Okay, so we are going to talk about centering humans in AI and personalization. Lots of words in there. But we will boil it down to a few, three different angles in a few minutes. But before we get into that, let me do my land acknowledgement, which I always feel grateful to do. I respectfully acknowledge that I am fortunate to live, learn and provide my services on the unceded territory of the Coast Salish peoples, including the territories of Musqueam Squamish and slaver good nations. In other words, I am a person who is fortunate to live work breathe from the lands of Vancouver, BC, and these are the communities whose lands I’m on. And I’m very grateful as a first generation immigrant to be there. All right, so let’s set some context here. Why am I here? What am I doing here? You are here. Clearly because you care about the subject clearly because you want to make a difference. Why am I here? One? Yes, I want to make a difference with you. But to because AI is something that everybody uses. I took a poll two weeks or three weeks ago at another conference in Washington, and I asked the first question, how many of you use it how frequently do you use AI? And there were options like very frequently to not at all frequently and I have never used it. Those were the you know the scale based options. Guess what, out of the 88 people who responded 24 responded to anything other than I have never used AI everybody else 88 minus 24. You do the map selected. I have never used AI which is interesting to me because we are using AI in our personal life already so much. We are booking vacations we are picking up shows new movies to watch Friday night. A lot of that is coming through AI how we’ll talk about it in a second. But they’re using it and few of us care to talk about it. So the 200 close to 200 people we have in the room maybe right now. We are the ones we’re carrying to talk about it we want to know what is happening with this word AI like these are the two small letters to alphabets that can make us go afraid make us go over well make us go excited. Few of us can talk about it. And fewer fully understand, right like that blue box is really like a really tiny box. All the feelings that you shared. I want to unpack those maybe not in these 30 minutes before Shawn throws me out of This room, but hopefully at some time, in this blue box, what is happening? What does it mean to center humans and humanity in this conversation? Who are we trying to protect? When we talk about AI? Who, what are we holding ourselves accountable to when we talk about AI? So you don’t need someone to come and teach you. These are the five algorithms that exist in the world today. And these are the nitty gritty, you know, a plus b equals to c, you don’t need those formulas underlying. I mean, unless you’re a data scientist like me, then yes, yes, you do need that. What you do need is to listen to your communities, to be able to dream to be able to see a future where you can coexist with this technology in a really human centric way. That is what I want from us to move from the blue box to more owning this conversations together. This is why I am here. Okay. I have a homework for you. Take a minute to form your thoughts. You don’t have to jump in to complete this sentence for me. Today, I believe the future with AI is and I’m specifically adding the word today because the more we learn about this subject, that the our feelings will change, our outlook will change. So today, in this moment, I believe the future with AI is and Shawn, my friend, you are going to help me read a few responses or I can come back. Okay. I see. You do. Okay, go for it.
We’ve got Anna saying inevitable. Amy is unknown. Joanne is simpler. Wendy is excited. Brenda said going to be in the way. She said unbelievable. Patricia said brighter Latina says good, bad and ugly. Also excellent movie genossen. imminent. Patrick is saying I believe the future with AI is collaboration. Tammy says scary. Denise says helpful. I can keep going. Did you want a few more?
I am getting a sense. This is super, super helpful. Okay, um, I’m gonna come back to Beth, you mentioned lazy, which was different and super interesting. And I am getting a few others kind of in the camp of helpful. I think Michelle, you said it can be scary for the people from other communities or marginalized communities. Kimmy says just starting okay, I can really literally go on. But I need to go back to the presentation. Keep them coming. today. I believe the future of AI is because there are two words in this then the sentence that I want you to take after this session. The word believe the word future. And the word today that the highlight of this word is not AI. Yeah, we are talking about AI. Yes. But what we are talking about is what you believe, which is going to come from the things that you read, learn talk, unpack, that’s going to build that formula for believe in the future. So keep them coming and keep thinking about this question even after the session. But I want to take a few of those things and all of you shared. And pick a question that I’m often ask asked, the question I get asked is do you think nonprofit industries use of AI to profile donors? Right, will drive more donors into death? Because of their ability to keep the donation behavior private? It’s really asking what what is the possibility of AI and its impact on death for for our donors to just go drive more towards it because you know, they don’t want to be scanned and segmented and compared. Here’s something that helps them have helped me to unpack this question. When you think of AI in your organization’s and your missions. I want you to think it in think in three, you know, a three part formula. One, you’re thinking about AI start questioning what we do with the data. This is something that you need internal clarity between the all of your staff members, with your board members with your allies, right? That’s clarity, what do we do with this data? I want you to have accountability. I want all of you in your staff members in your team members, whether they are in this conference right now or not. I want you to go and go and talk. Why did we obtain this data? That’s accountability. Why are we doing what we are doing? And then I want you to think about and again, build collective understanding. How did we obtain the setup? That’s transparency and when that clarity, accountability and transparency meet That’s where the trust happens. That’s where what I like to call as you’re creating almost like your AI values or your AI policy, you need this, you know, that intersection of three things, to be able to go back out into the community to talk to your donors, and let them know, this is what we do with the data. This is what how we hold ourselves accountable. And these are the ways we obtained the data. And you are welcoming them at that point, by building that trust, to have them you know, see that you can get their data to do the work that you are doing, of building these this human connection. Right. If you do not do that, if we skip those parts, what is happening is we are sending out a very vague message that we don’t have that common language we don’t we are not on that common platform and understanding of what are the possibilities of AI. And so we are directly hampering the trust that we have with the community. So I want you these, I want you to have these three things as you’re thinking about today. I think the future with AI is think about the strength three things. Okay. One slide, if you plan to lead me in the next five minutes, I always include a slide at the top three things that you can take from here, next 30 years with AI, three things are happening. AI is here to stay. It’s not going anywhere. Regardless of how you and I might feel on a few days, AI will only grow in capacity from here, right? That means right now it is in your for applications is going to only go grow more. So your vending machine, for example. So I’m right now, in University of Calgary doing another conference, they have a lawn mower outside, which has an AI application fit in with the admin staff, the admin staff fits a lot of components. And then the lawn mower decides that it based on the weather and its predictive capabilities to how to mow the lawn, which is interesting. And can we hold different session after today, but it’s only going to grow in capacity from here, right? And the third thing is the growth rate of AI around us is expected expediting faster than we realize. More and more applications are going to come my point in giving this slide next 30 years with AI to make us feel overwhelmed, or scary or those who said scary. It’s not it’s not that point. It is here to stay. The question through this slide that I want you to have is if it is here to stay stay? What can we do as humans to build a good strong co relationship with this technology? And I mean it in a way not in a way? How to Protect us humans? No, I mean, I mean it in a way. Through this change that’s happening? How can we protect the humanity, you know, the good parts of our existence, the care, the compassion, the empathy? How, what can we do? What do we need to do? If we are going to have this technology for the coming years? I want you to ask yourself, the people around you the people who are selling you AI technologies, that people you are asking you to adopt these technologies to pass? What can we do to protect the good parts of our existence? If we are cohabitating with this technology? That’s the next 30 years for us. Okay, so what’s the goal here? In the next few minutes of this very short session, we are going to quickly look at how AI exists today. What is AI? The question of personalization, what can happen and what can go wrong? And then we do need to end the session with an action. So what can we do? And I want to establish in every conversation as I do, I’m coming into this conversation, as your storyteller, as your ally as your friend as the person you met in a coffee shop to talk about where and I apologize for my use of my language? Where the heck are we going in this world with AI and consciously optimistic in the power of what you and I as you’re talking about this, so I am allowing you giving you the permission lovingly to use the chat functionality in drop in your thoughts drop in your questions. Oh, by the way, use the q&a to drop in the questions by dropping your thoughts and questions make this a collective wisdom session where we both can bring the best of us if you’re talking about this, and then if we have time we will get into talking to each other. Okay. Personalization with AI It exists, even though 88 minus 24, some 60 ish people said, they do not use AI. But we do. That’s that’s the funny thing we have been using AI in. And when I say AI, I mean like some form of algorithm that has been working in the backend of the applications we use, like Gmail, or paper or Netflix that has been working in the backend. But we knew it to another different term, we call it user experience. But those user experiences are being built by some sophisticated algorithm on the back end. So Netflix, for example, you watch the movie, and you’re going to get recommendations of other shows and movies, Amazon, ever heard of that little company? Buy something, right, and it’s going to immediately show you what other customers purchased. Gmail, I feel like I have a complicated relationship with Gmail, you can finish your sentences that my partner doesn’t is you start typing a sentence, and it can autocomplete your sentences. Now, it can also take look for grammar, it can check for intent, if you mark those parameters, it can add shorten your emails, it can add another tone, there are a bunch of stuff it can do. That’s personalization, there’s happening, it’s learning about you. And when I say it, the algorithm is learning about you to create some recommendations to do some actions, which is very closely aligned to who you are. So you will feel comfortable in continuing to engage with that application, right. But I want to unpack that whole idea of of AI. And then we’ll talk about personalization in the form of a wheel. So what AI basically is and there are different flavors, you can you might have heard the word predictive modeling, you might have heard machine learning, deep learning generative AI, they’re all different flavors of the same six, five or six steps happening in the wheel, which I’m going to show you on the right hand.
You pick up some objective, it could be you want to figure out the largest donors in your database, or you want to figure out, you know, where the remaining homeless shelters should be created based on the demands in the society, or in the city, you’re picking up some objective, right? That’s the goal of AI, I’m going to walk you through how AI happens in this diagram. So based on your objective, let’s say you want to figure out who are the most engaged donors, you start collecting and storing the donor data, which probably you’re already doing, you are collecting it, you’re storing it in your database already. We often do better in many ways. But let’s say we are doing it, okay, you’re going to take that data based on your objective, and you’re going to make run that data through a few algorithms through a few different algorithms, you’re going to run it and see what the outcome comes. Depending on the objective, just to give some data science perspective, we pick and choose a lot of times every algorithm has a certain kind of output has a certain kind of input. And we can tune parameters, that’s the language I use, or we use of data scientist, you can tune the parameters of the algorithms, these models to make the best outcomes, basically, we are enabling the algorithms to give you the right kind of output, right? It would give you some output, it will give you some patterns and predictions, some strategies, right? So we got you know, our top donors, for example, right? That is going to lead to some actions that’s going to influence behavior. So you’re going to take in this example, that whole data, right of the sorted, segmented nice list of the top donors and you’re going to share it with your fundraisers, you’re going to create some strategies some actions would happen, that’s going to influence behavior. An example would be all of you would be trained and taught as to what data should you be keep collecting for your algorithm so that you keep getting this output again and again, right? That’s a behavior that I want to collect this data point in the right way, in the right quantity in the right quality so that my algorithm never stops running. That’s an action that influenced your behavior. And it’s going to create some form of an impact. Hopefully, you would be able to engage your donors, and you would bring them back into, you know, having more more engagement with you, right? That’s the wheel of AI. How does personalization look like them? In this case, you can have personalized communication, like you know, because you were interested in some activity, here are updates for you. You can have donation suggestions for them. You can have some in Hmm suggestions for them, you can have personalized future experiences. So because similar donors with so and so profile chose the font names, here are four font options for you. There are, here are some few most commonly used examples of where personalization happened doesn’t mean these are the only four once you can have more, right? Where can it go wrong? It can lead to if you’re if you’re not careful, it can lead to inappropriate recommendations, right? It can lead to unnecessary polarizations. It can lead to cause bias and discrimination, right, diminishing the spirit of generosity, we might be sending out a message that you are not important to us, or we don’t have enough data to make the best. Best guesstimate to this, this ask. So we might be sending like the wrong message around the word generosity. We can send misguided invitations, and the worst we can alienate or exclude someone by making them feel unseen, unheard and unvalued. So how do we send her humanity in this conversation, then? How do we send her that if you look carefully, I’m not saying how do we center humans, but again, the good parts of our existence in this personalization, four things we’re going to go through. First, we need to ensure the transparency piece we need to ensure and because I come from the data world, I’m going to talk through the data piece, ensuring data transparency, so being transparent with your donors, how that data will be used for personalization, obtaining explicit consent for the personalization, and you know, generally the data collection efforts. I want you to experiment with those personalization. Once you’re working on your transparency and building trust piece, start working on building curiosity piece, by enabling your team members to start experimenting with it. What does that mean? Enable your team members to collect relevant data to test and evaluate this personalization, right? Create in house guides and resources. This is not to add more work on your plate. But really, how do you build that culture of asking good questions and curiosity around the subject, right. Which brings me to the third point, investing in continuous learning, enabling the entire team with learning opportunities. And this means not just attending conferences, and webinars and sessions, it means going back and sitting next to the to the human in your team, every other human in your team and talking, Hey, what did you learn? And hey, this is what I’m on sharing that piece, right? And as you’re going through that, that whole three points, you’re at some point, you will feel comfortable and confident to start quite asking questions around evaluation. Is this personalization actually helping us or not? Or are we just sending out this personalization without any consequences? We need to hold ourselves accountable, right. So you have to ask, How is this helping in building engagement, and this will happen at some point when you have invested in learning you are have experimented you have working to build transparency, but it will happen I promise where you get to a place of evaluation. Okay. I want to come back to this slide. As we are wrapping up things here. Next 30 years with AI. AI is here to stay, it will grow in capacity, and the growth rate is going to be faster. So I want you to have two questions for yourself. One, what do I envy need to appreciate approach AI in a humanity first way, and I am intentionally choosing to use the words AI and we I want us to be accountable individually. But also trust the power in this collective wisdom. So what do I and we need to approach AI in a humanity first way? And I also want you to ask yourself, How can we move away from the sense of competition, fear deficit to collaboration, confidence and strengths? This has to be a come to a place of what would it take to use AI for me and you to collaborate in a way which supports our industry which supports this planet which supports the people in this planet. I want us to approach from that angle regarding these algorithms around us. So if you have to pull to questions of this entire 35 minutes, all these two questions. And with that, I’m going to leave you with few links. So if you are interested to learn about the benchmarks of where we are on data and AI equity as a sector, please participate in this link. It is a part of the deck. And I intend to build transparency on the data so I’m going to share back everything I can And from this study that’s ongoing and has is going on going for another three months. There are some links for you how you can reach out to me how you can connect with me, you. I, I told you there will be questions we might not be able to get to in this 35 minutes. But we’ll get to them once we start creating special sessions and spaces of conversation. Okay, what can you do now take this session, talk to each other. This is one of the things with AI, we are so comfortable, we are getting to a place of being comfortable and look, logging into a system and doing our own thing and then sitting with that output. If you’re using chat GBT, for example. Take that and go talk to another human, this conversation communication cannot be replaced. And so unpack the session by having those conversation. And then experiment with different things. Explore the links, there are wonderful resources that a lot of people are creating, you can use those, there is a group called fundraising AI, explore that. And see what what the FAI group says you will see a lot of different resources that you can use, and then reach out with questions, thoughts and ideas. I’m going to come back to our session to see if there any questions there. Shawn, my friend helped me.
Alright, so second here. So the the highest upvoted questions, so I’ll just kind of go with the most popular ones from Margo, we have to remember to ask is data accurate? Or is this one thing generated from a collection of data from unknown sources? And how can we know?
Excellent question mark. Okay, how can we know? Well, I’m going to give you two words, whichever doesn’t make sense, in this very small amount of time, please feel free to reach out to me data values, see data values, I pick up a use those words, values, because that’s something that connects you and me, you get to know me better, because you know, my values, who I am, as we start talking to each other, I would get to know you Margot better, as I get to know Okay, who Margot is that that’s what value sets, we need to have the same thing for data value. That’s step one in our organization, we need a common understanding with our staff members, with our board members with our volunteers to know how do we store data? How do we analyze it? Who visualizes it? What kind of reports we create basically, every word every action that we do with the data with the word data? How are we doing it? And do we have a common understanding of that that data values that is going to help you in answering this question? And in where are we collecting this data from for personalization? Whether it is somebody who looked into the signature of a person and just collected it in a passive way? Or are we actively going out building forms collecting the data taking consent? It all comes down for me to answer that question, Margo, it comes down to me for data values.
All right. This one someone anonymously is there. So I can’t give you any names. But do you have any tips for sharing human centric AI practices with staff who may fall in the category of people who don’t believe they’re affected by AI?
Okay, that’s a great question to my friend who asked here, I’m going to give you an example, actually two examples. One is an organization who wants to stay anonymous on my end, but the other one is furniture bank, out of here in Canada, you can look them up in in the internet, on LinkedIn, what furniture bank has done here in Canada is they they created a manifesto of what to do with AI and how to do with AI. It was not just a guide book, it was almost like these values as an organization of experimenting, learning. And if we fall, we are going to use those values. They created something very, very raw, very new, to hold each other accountable. And they started using it and bringing in one tool at a time to test and see how can we, you know, build more relationships with the donors were the volunteers with the program participants. So that’s one example that I really do appreciate. The other example. And this organization wants to stay anonymous is one that they use, we are virtual reality, which is very cool, and artificial intelligence together to create headsets, where they then take to prisons for women who are incarcerated and are going to get out of the prison in the next six months to create vocational training. And they don’t just create that AR and AI and VR technology in the headsets. They have a whole document for enabling the entire staff members of why they are doing it and how they are doing it. So the key really is if you have staff members who are not into AI for lack of a better term and I’m building spaces of conversation, holding safe spaces to ask questions. And then really being friends with your data to see the opportunities in it. That’s what’s going to help you.
This question, I’m going to combine two similar questions. So this is from Melissa and Joanne, do you have any free resources for nonprofits to learn and leverage AI? Slash resource suggestions for AI enabled accessibility features?
Okay, so, um, excellent question, Melissa. And Joanne. Okay, so here, I would say first three sewers, it would be fundraising AI. It’s a group of people who are passionate about this topic, they are working on this topic. Check it out on the internet, fundraising AI, Google it, you will get resources. You will also see on namaste data’s website, some of the, you know some language, you will also see some similar resources on the internet. But can it can I offer most about you and Joanne, something here, you are going to come across some excellent resources on the internet, but the Excellence in those resources are not who wrote it, or who drafted it, or what’s the content in it, the excellence comes from the conversation you are going to have, after you have gone through it with your team members, with the people in your organization. So wherever you get these resources from whether from you know, my website FAI, LinkedIn, other sources, read it, sit with it, be uncomfortable or comfortable. And then go talk to other people about it in your team. That’s what’s going to make these resources most helpful to get you the language of the questions you need to ask.
Alright, and I think we’ve got time for one more. So this is from Jane, if the data we have is very basic and not as granular ie just have email name, gender donation amount, etc. What can we do with these few data points at a small data set? I
love this question Jean. Okay, so I’m not one of those person who is going to give you you need the five things to cook this dish, I’m going to be one of those people who’s going to help you enable your kitchen with the right things and raw materials, so that you can cook your own dish. So you’re asking the right question. That’s great. step one. Step two is you’re collecting some good, good information right now, I want you to start with thinking about what are your data values? And two, with the data points that you have? What are the questions? Can you get answered? Are they aligning with the with your wife with your reason? And if they are, then start thinking about the possibilities of where you can bring in some artificial intelligence? And if you realize that, well, we don’t have enough data points, I can answer some of the questions. You don’t approach AI. With thinking about where can AI come you start with your why you start with listening to your community you start with? Where would AI be most helpful and beneficial. So by asking a lot of questions. And if you realize then gene that you know, you are not collecting enough data points, that’s when you go back to your data collection forms and start changing a few things to collect that data. And then think about AI. So really start with your data values and listening to your community. And then coming back to what can you do with the data points you have?
I think let me see just one more end because it’s, it’s a follow up from Jane, do you have any recommendations of AI tools beyond chat GPT and related plugins that small nonprofits can take advantage of?
Excellent question in Shawn, who asked his follow up question to James as well. Okay. All right. So to the week, your follow up question. Tools you’re asking. I will say there is an abrupt work in this space for many years, there is no perfect formula of the tools that exists. As much as I would personally also wanted a checklist of tools, they don’t exist. Where you can start though, is figuring out where are the places where you need more support, which technology, it could be your data collection, it could be your visualization, it could be sending out emails, figure out the scenarios and the use cases, the questions that that is taking most of your time. And out of those questions. Where do you want to spend more time and which are the questions you it’s not your strength, it’s not something you enjoy, but it’s something here where technology can truly help you. Once you have figured out those responses. That’s where you’re going to start looking for the tool. So if you’re thinking of, say, visualization, that’s where you’re going to choose, can I use Power BI? Can I use Tableau? Can I use something open source like shiny? Start with your use cases, and then you can build that this on your own? Trust me. I’ve seen that happen.
All right. Well, thank you very much. I think that’s all we have time for today. So thank you everybody for attending MENA session. We hope you had some great takeaways. I did see a lot of people are feeling more optimistic. So thank you, Mina. The next session is going to begin at 310. Eastern we have Dana Snyder talking about sparking a recurring first mentality with your supporters. And Natalie Anderson from Sage talking about sparks energy, aligning fundraising and finance to ignite mission impact. Again, as always, no matter which session you choose to attend live, you won’t miss any content because all sessions are recorded and will be made available later this