These regions were particularly of interest for the project from the very beginning. As you know, the project got a large grant from by the International Science Council. And the International Science Council is very keen to support science in developing countries. And so it has three regional offices. One is in Africa. Another one is in Asia and Pacific. And the other one is in Latin America and the Caribbean. And so at the beginning of the project, we had regional, I mean, for the three tasks of the survey, we had regional workshops in 2017 in these regions. One was in Asia, in Taiwan. Then the other one was in Colombia, in Latin America. And the other one was in Africa. And the idea was to bring in some regional flavor into the three tasks of the project. So now, before I move on, let me give you a little bit of a disclaimer of what I'm going to show. All these regions are very different. And there are many differences within each region. And particularly in Asia, you have very large countries with large scientific communities and other countries with rather small communities. So the results of the survey are an average of all those situations. Then not all of them were equally represented because of the size of their scientific communities, the countries. And then I am going to give you what I have and a little bias in Latin America because I have more results on that. But anyway. So most of the results will come from the global survey. And some from the other two tasks, particularly task three. So this is the regions in which the global survey analysis was performed. So concerning the three regions I had to look at for this talk, Asia Pacific actually is Western Asia. So the global survey did an analysis in sub-regions of those three regions. So one is Western Asia, another is Central Southern Asia, another is Eastern Southeast Asia. The Latin American, the Caribbean, was analyzed in two groups. One is Central American Caribbean that includes Mexico and South America. And then Africa is Africa is one. So this is the number of responses that the global survey received from these regions. So from Africa, it was almost 4% of the total. Asia Pacific 20 and Latin America and Caribbean 12. And we had some expectation of how many responses we could receive from each of these regions. And for Asia Pacific, we only received 33% of what we were expecting. But the other two regions would receive many more responses than what we expected. So these are the countries that, not all of them, these are up to when they make up to 90% of the responses to see how spread the responses are when they talk about African results. They are mostly dominated. Let me see what is the thing. I guess this is this. So if you look at the countries in Africa, so South Africa and Nigeria, they make up almost slightly over 50% of the total responses of Africa. And then the rest is more or less spread among a bunch of different countries. In other regions, it's more dominated by very few countries. Like, for example, in Asia, Japan had 2,500 responses. China, 1,200. And so that counts for 76% of the responses from Asia. And if you add Republic of Korea in Taiwan, it's 92%. Then other areas of Asia also, Central, Southern Asia is dominated by responses that came from India. It's 80% of the total response. And then Western Asia is Israel, Turkey is 86% of the responses. So yeah, I'm giving the proposal of women over the total. So it's here. Let me go back. I forgot to mention. So in Africa, it was 61% over the whole number of responses. And in these regions, it was 36%, female, 45%, and 59%. So this is mostly men responses. And then in the other Latin America, Central and Caribbean, it was mostly dominated by Mexico with 77% of the responses. And then these other countries. And then in South America, it was Argentina and Brazil accounted for by like 70%. And then these are the other countries that I came up to almost 100%. It's not 100%. Anyway, so to give you a flavor that we are giving you averages of these regions, but they are slightly dominated by certain countries. So let me show you some results from the survey. Some of them have been shown yesterday. So maybe I repeat myself. So these are shown. I don't know if you get to see. These are all these sub regions of the survey. And so the first two bars correspond to Africa. Then it's North America that doesn't enter my talk. Then it's Caribbean, Central America. Then it's South America, Western Asia, Central, Southern Asia, East, Southeast Asia. And then it's Europe. And we don't go to there. And then the way this is shown is if there is a significant difference between. So there are two bars because one of them is male and the other one is female. So the first one is female. And the second is male per region. And then if there are significant differences between male and female's responses, then they are colored, so like green and orange. And if there are no significant differences given the number of responses, they are both gray. So this is about the question here is saying that they had a positive relationship with their advisor doing their doctoral studies. And so most of both female and male had positive responses. And the lowest was for women in Eastern Southeast Asia. And that's lowest for the regions I'm looking at in this talk, basically. And then there were no significant differences in the positive responses that came from Africa and Latin America and the Caribbean. This is about my program, Treated Everyone Fairly. And here there are significant differences between women and men in South America. And in this region women were less likely to indicate that the programs treated everyone fairly. So this I found interesting because I have more details about Latin America in particular. So this is about interruptions. And one thing I've noticed during your doctoral studies is that, for example, in Africa and in Western Asia, there were no significant differences between men and women. And I was wondering, but there were large percentage of people who said they had significant interruptions. So there were interruptions, but they were equally likely to occur for men and women. So I was wondering if that's related to conflict in these regions that affected equally men and women. While in other regions where you don't have conflict, you have other, I mean, the interruptions due to childbirth or other personal things affect mostly women. So you have the significant differences are mostly in, well, it's like no one. There are not very many significant differences, not in the regions I have to look at here. So I have a question. Yes. Yes. Three male with 39% male, 20% male is not colored. Yes. And that depends on the number of responses and Rachel should be able to tell how you came up with whether there is a significant difference or not because it has to do with p-values and it's the sample size, I guess, that affects. So I guess it's very few responses there. So maybe you have large differences in percentage, but it's very few people, actually, because it's very few responses. So I guess it's based on p-value. I don't know what the criterion was, though. Yes, yes. So it's a chi-square statistics that they used to decide. OK, but it's both it's not that, but the source of the pop is that there is just two people. Yeah, well, in Western Asia, we had very few responses in Africa as well. Other regions, they were larger samples. Then about career progression, well, this is saying that their career progression was how it was compared to colleagues. If it was a compare, if it was, well, these are more quickly. The responses are more quickly about the same and more slowly. And so at least one third of the women reported slower career progression compared to colleagues who completed their degrees at the same time in Latin America and the Caribbean, Central and Southern Asia, and Eastern and Southeast Asia. And then in all regions, here is indicating their work or career did not change significantly. And this is funny because there are significant differences between men and women in all regions here. So men were significantly more likely to say that their work or career did not change significantly because they had a child. OK, so these are some results specific to Latin America. We had a closing activity like this one in San Paolo. And Laura Merner from the AIP Statistical Research Center presented some results on Latin America. So I'm going to show you some of them. And this is about the interruptions during their doctoral studies. So you see, no, it's the majority, both in men and women. But 20% of women said they had significant interruptions and only 13 men. And these are the reasons. And so if you look at the main reason for male responses saying they had significant interruptions is because of financial constraints, while for women is pregnancy or childbearing. And then kids don't show here. And here, I don't know. Well, there are illness and other things. But there is this large difference there. And then whether this break slowed down or this change slowed down significantly affect your career. And so this is from a great deal to the left, to not at all to the right. So the first two lines are men. And the other two are women. And so it affected my professional credibility is the second of the two lines. It affected my professional credibility is the first two lines. So it's first men, second women. And then it affected the type of work that I do. It's first men. Yeah, it's first women and then men. So women are more affected. Yeah, that's true. That's true. I don't have the M and F. So you see that women are more affected than men, both in the type of work they do and also the credibility of their work. And then how did your work change because you are a parent? And so men say my worker career did not change significantly in 51% of the responses. And I chose less demanding or more flexible work schedule is the answer that is 45% of the women chose. And then whether your career, you made decisions differently depending on your personal decisions differently based on your career. So the green thing is yes. The gray thing is no. Male is the first line. Female is the second line. And so you see that women answer yes more often than men. OK, so now we go to the three regions. And this is about career progression and also prestige. And this is another thing that I found interesting because it's whether you served on important committees. And there are no differences in Africa and also in central, southern Asia. And so it's more or less the same percent of women and men who said that served in prestigious committees. And then the significant differences show up in South America and in eastern and southeast Asia of the regions that we are. Yes. Is east, yeah. OK, so this is about employer and co-workers. And I think I'm going to skip this so that I can move to the other tasks. And then about salary, over 27% of the women reported lower pay than colleagues with similar qualifications in Africa, Asia, and Central American Caribbean, and 22% in South America. And then this is about comparing their salary to their partner or spouse. And so in all regions, women were more likely than men to say that their partner or spouse earned more money than they did. And 36% of the men in Central, South Asia said that had a partner that was unemployed and only 3% of women. So it's very different, the personal situation of men and women who answered the survey. And so this is whether your spouse or partner is in the same field that you are. And so it's very large. The percentages in Latin America is between 43% and 44%. This is because we have separate Central and South America. And it's 45% in Central, Southern Asia that have spouses that are employed in their field for women. And that's not so common for men. And so there are significant differences between men and women across all regions here. OK. And then this is about discrimination. And you also see whether you felt discriminated against in the assessment or evaluation of achievements because of your gender. And so the green is female, and the orange is men. And you see huge differences here. And here is people who never experienced discrimination. And so again, orange is men and green is women. So there are significantly differences across all regions here. These are the sources of discrimination that people in Latin America chose. So the first one is gender. So you see almost 50% of women said that they felt discriminated because of gender. And the other reasons are not so there are significant differences, but they are not that large. Although age accounts for 23% of the responses in the case of women. And then if we go again to the three regions, this is about harassment. So whether you personally encountered sexual harassment at school or work. And these are significant differences everywhere, but in West Asia. One is 19% women and 7% men said they encountered that. So here I have some details about that in Latin America. And this is have you ever experienced or encountered sexual harassment? And there were different options. One was no. Then yes, I heard about it happening to someone else. Yes, it happened to me. Yes, I witnessed happening to someone else. And so the first response is no, both for women and men. Although for women, it's 45%. For men, it's 63%. And the second is I heard it happened to someone else. But it's 34 women, 31 men. And then it happened to me is 24 women and 6% to men. OK. And this is an interesting thing also. Who most encouraged you in your studies? And so if you look at what's the most often chosen response in the case of men is your own determination, willpower, and hard work. And in the case of women is parents. So it's the perception of whether it was your own effort or it was you had help from someone else. So I had some accounts about the role of mothers for women in Latin America. But I'm going to skip that because I have only five minutes. And so then about the study of publication patterns. This is the list of academic disciplines and geographical regions were differences in self-reported submission numbers to top journals from men and women are found to be statistically significant. And so you can see that in these regions that I am supposed to talk about in South America, we have in mathematics and then computer science, biology, and then in Asia is mostly in chemistry. And then this is the map that Helena didn't show yesterday, but I found funny because Argentina is there like an outlier where women, you have single authored mathematical papers by women. So I found that amazing. I don't know anyway. So let me tell you a little bit about task three. And this is the number of good practice initiatives that were found in the regions. And these are the list. This is the list. And so I chose bit. This is one in Africa, which is mostly computer science and addresses different stages. That's an interesting thing. So for example, it has one month intensive boot camp for youth to learn how to produce apps for their communities. Then also a summer camp for school girls, primary and secondary schools, then also a reach program, a training program for people to do outreach, which is an interesting thing, and also scholarships for university students. Then this is one from Asia. This is a tool for coding also. This is in India. And this is the one that I know the best, which is the red mexitec, which means the Mexican network for science and technology and gender, which is an interdisciplinary network of social scientists and scientists in STEM. Lilia Mesa, who is here, she was in charge of, she was the director of the network until very recently.  They have meetings. They have publications, journals, books. You can go to the web page and look for it. And in particular, this network was very active in the type of activity we've been organizing in Latin America for quite a while, which is workshop for young scientists. We had one in Puebla, in Buenos Aires, in Bogota, in Lima, in San Paolo a few weeks ago. And at some point, we combined them with activities related to the Gender Gap in STEM project, in particular in Bogota and in San Paolo. And so we have a website where we also collected our database of, but it's not a database. It's just bullets with lists of good practice initiatives in different regions. We also produced a book, Web Funds from the Project. The book was published by the Mexican Physical Society. And it has contributions of all the people that went to the Bogota workshop with initiatives in our countries, in our societies, scientific societies across the region, et cetera. And we have social networks, Facebook and Twitter. Then if you want more details, you can go see the poster. And one problem that is quite pervasive in Latin America is gender violence. There are many women that are killed in Argentina. They are at least one every 30 hours. These are numbers from Mexico. They've been growing very fast. So this is Chile. This is not gender violence. It's not killings. It's a strike of women that lasted for 100 days, starting with a sexual harassment case that was not handled properly at the university. And so all of this led to a movement that is called NUNA Menos is in Spanish. Not when any other less would be something like that. So that most of our universities in the region, at least in Argentina, they have protocols to handle sexual discrimination and violence, basically. And then one thing we learned in our workshops with young people, because it's not that we just empower young people. They empower us. We learn quite a bit is all these social network activities. This is a woman, a young student in Chile who has over 4,000 followers on Twitter and does all this outreach. People who have YouTube channels to show scientists that don't fit the usual stereotype. These are the astrotubers. Then you have Instagram. In Brazil, they also share YouTube channels, Instagram. And there are many, many things, 500 queer scientists.  Anyway, so let us turn the world upside down. Thank you. Thank you, Silvina.


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Good morning, ladies and gentlemen. It's a pleasure to attempt in 14 minutes and 50 seconds to describe some of the potential of predictive analytics in the area of food science, especially in the area of food safety. So I won't waste any time. But in walking into the RDS this morning, I was reflecting on the fact that the first time, the last time I was here, was as a 16-year-old for the Young Scientist exhibition. And it was at the time sponsored by Erlingus. And I had a data problem. I won't tell you when it was, because it was a very long time ago, but I had a data problem. It was a project, an observational project on water pollution. And I had to crunch some data. I didn't want to do it by hand, there were no calculators. I had to go to Cork to rent a calculator that was capable of calculating standard deviations. That's how far we've come since Erlingus sponsored the Erlingus Young Scientist competition a long time ago. So with that, I'll move on to give you an outline of what I want to talk about. So I started my professional career, my postdoctoral career in predictive toxicology. And I worked in computational chemistry as a postdoc. And so one of my favorite topics is predictive talks. And I want to use that as a case study this morning. And then the second area is in relation to text data, which is in early warning, food safety early warning systems in the food industry. And I'll give you a brief outline of that. The changing face of lab data, and this will be your entree to Professor Fanning's talk, who's going to talk more about that. And then life in the dry laboratory, because nowadays, many of us exist in the dry lab. We don't connect with the wet lab anymore. And that's where some of the most exciting stuff is done. And then some caveats, challenges, and solutions. So we're living really in an area of unprecedented change. We have more data than we know what to do with. We can't process all the stuff we have. So the models are taking time to catch up with the availability of data. And so that's where most of the work will have to be done. And in parallel with that, we've got formidable, double challenges, especially in the area of food safety. We've had FRIP for a little in the last few weeks. We've had the chicken problem now in the UK again. So every week brings a new problem. So the problems are not going away, despite the fact that it's often said we're at peak certification right now. We've got BRC and ISO certificates and any other kind of certificate you want. And yet, the problems are still with us. So let's talk a little bit about what we're doing in my company and other companies in the area of predictive toxicology. It's very basically starting with chemical structures, classical two-dimensional chemical structures, and transforming those structural data into different types of representation of the molecular properties. That could be volume and surface potentials. It could be hydrophobic and polar regions of the molecules. It could be hydrogen bond potentials, hydrogen bond acceptor potentials, and then electrostatic molecular potentials as well at the end. This last one, of course, being based on molecular orbital calculations, gives you a more of a picture of the electronic environment that the molecules are generating and experiencing. Food safety early warning. Well, as I said to you, we've got a lot of issues coming down the line. And what we have to do is try and look further ahead. We've got to try and be on the front foot and anticipate what's coming next. So how do we go about that? Well, we use Patrick Lagodak's approach, which is very much looking at the different types of evidence that if you don't process or don't attend to, turn into incidents and ultimately into a crisis. So if you look at the early signals, you process the early signals, you can sometimes prevent these crises happening in the first place. So that's the whole principle of it. The way we do it in the Nestle organization is to start with a human network, 150 people. Much has been said this morning about the value of judgment. So judgment is still important. The data are important, but you need humans with the data to optimize what you do with it. So you need actionable data. And to do that, we use an early warning expert network of 150 people. It's a multidisciplinary team. It exists in R&D and operations. And that's complemented then by web scouting, which looks at about 8,000 websites a day. About 300,000 articles are scouted per year. 10,000 articles are kept on a database for future reference. And then we disseminate the information throughout the organization. Much has been said this morning in the panel discussion about social media. Yes, many companies are monitoring the social media. We can track what's going on. But again, we're more or less looking backwards. We're not yet very good at actually using it for future decision making. So it's all really about continuous change needs continuous thinking. And then you need goal alignment. You need aligned organization so that common team thinking is needed throughout the organization to keep things together. Just to give one example of where this type of thinking is employed, we had a problem in Malaysia where we wanted to support farmers who wanted to increase their yield of rice from about 2,000 kilograms per hectare to 8,000 kilograms per hectare. And what we actually started to see, of course, was that we saw arsenic levels in the rice getting higher. This was a trend analysis of existing data. And we can do that because every company generates a lot of analytical data. In the case of Nestle, it's 130 million data points per year. And the data are volatile. We use them, we make a decision, and then they're left there in a database. So the question then is, can we use the data? Can we mine the data more effectively to prevent future problems? And this was what happened with arsenic. We saw the trend, it wasn't in the danger zone, but if it continued, it would have been. We would have ended up being either out of compliance or in an unsafe situation. So working with the farmers, we actually identified the fertilizers to be the main cause of the problem. And working with the farmers, what we were able to do was to allow the increase in yield without actually causing a safety problem. This was a classical win-win situation, optimized yield for the farmers, optimized safety for the manufacturer. Steve Jobs said that we cannot join the dots looking forward. We have to always join the dots looking backward. Bill Gates made a similar observation. So when we are actually talking about early warning and looking to the future, we actually have to look in the rear view mirror a lot of the time. They say that foresight is hindsight plus insight. We've got also to address the history and analyze the history before we can actually make good predictions about the future. So effectively, this is what we're doing much of the time. What about the lab data? You've all heard about Moore's Law. How many of you have heard about Carlson's Curve? Carlson's Curve effectively is the equivalent in the biological sciences, and it refers to the decreasing cost of genome sequencing. Here's the curve. It's decreasing very, very dramatically from the early point when in around 2001, it was about 100 million to sequence a genome. 2008, I think it was still around 10 million to sequence a genome, and today it's about $1,000 to sequence a genome. So again, it brings about huge, huge access to data, and Professor Fanning will actually go into more on that. Also, of course, the equipment is getting cheaper. We've got min-ion. We've got the high-sex and my-sex, which are becoming routine lab equipment, but we've also got personalized equipment that you can take into the field, and of course the Ebola crisis used these in West Africa to do a lot of the field work. So you can get the data very easily, even if the data are difficult to analyze sometimes. Then we've got genomics tools like the ion torrent from thermal systems. These are nice because you can go on a single chip. You can actually analyze 340 samples at a time. There's about 3 million wells on the chip. Each of them is effectively a small pH meter, measuring a release of a hydrogen ion. So again, you've got a phenomenal potential here to generate data. So we're moving into dry lab, and what do you do in the dry lab? Well, people have talked already this morning about literature-based discovery. There's examples where IBM's Watson was used to mine. I think it was 300,000 oncology papers and actually made a new discovery based on rereading those informatically. So there is potential for literature-based discovery. If you want to look at the actual potential, you can look at the Avicen, a roadmap online, and for them, they're looking at in-silico clinical trials that can help to reduce, refine, and partially replace the human trial.  You can use a model instead. There's a lot of interesting discussion right now about post-diction analysis, where you add data and you re-crunch the data after the actual trial is done. So you can do a lot with all data. You don't have to throw it out once you've actually completed the study. Prediction is a word I have great difficulty with. I've said this to Kian already. If you want to be on-kind, prediction is fiction. In fact, what we're often talking about is extrapolation, and probably have to be more precise about what we're actually doing here. Are we predicting or extrapolating or interpolating? But in-silico, we can actually do a lot of help. We can clarify. We cannot express it with a number. We don't know much about it, as Lord Kelvin said. So we can actually crunch more numbers. We can actually optimize the experimental design before we commit to the expense of a clinical trial or a lab study. We can actually test hypothesis in-silico. We can test the sensitivity of our models. Biology is complex, and I think increasingly with food and biological sciences, we don't have to actually manage all of the complexity. We can actually take out some of the complex elements and variables and actually do the use models instead. I can give us qualitative insight. In-silico is cheap. It's fast. It can be used, of course, as well with experts to elicit the judgments of the experts bring. We're doing a lot of safety evaluation now based on in-silico. As I mentioned with predictive toxicology, the same is true with predictive micro, where you model outcomes. And we're getting to the notion of the virtual asset increasingly. If you've got enough data, you can actually model your biology. Now, moving to the caveats. As I said, I would discuss caveats and solutions. Well, unfortunately, it's been said already, data are not information. It's not the same thing. So having more data doesn't mean we know more. And as Einstein said, information is not knowledge. And more importantly, knowledge is not action. So actionability is important. We cannot live in a world where we simply know things. We must do things. That's what's important. We're not doing something with what we know and what we have, then we're not actually being successful. And I think increasingly as we have more data and more knowledge, we need to organize it better. So the models and the structures are going to be of paramount importance. Edmund Spencer was a British philosopher, lived around the turn of the 20th century, beginning of the 20th century. His famous observation was that if a man's knowledge is not an order, the more of it he has, the greater will be his confusion. That's where we are sometimes. We're confused by having too much information. And the decision-making ultimately will be based on knowledge, not on numbers. So what we actually have to encourage now are data translators. We need a narrative on our data. Some people talk about let the data tell a story. The data can tell a story. The story is told by the people who actually interpret the data. So the data narrative is going to become much, much more important as we generate more data. And many of you have read The Black Swan, I guess, and Fooled by Randomness, both of whom were written by Nikola Taleb. I think it was in The Black Swan that he alludes to the illusion of understanding. You know, sometimes we fool ourselves in thinking that we know a lot more than we do. Richard Feynman famously observed that the first rule of science is not to be fooled by not to fool yourself.  So sometimes we fool ourselves with our own data. And so the solutions then, coming to the end of my brief presentation, the question we have to ask is, are we really predicting or are we extrapolating? Cronin actually said this morning, he mentioned the issue of traceability, transparency and validation. We need to ask those questions. Is the process traceable? Is it validated and transparent? We need to be aware of the realities of p-hacking and regression to the mean. Anybody had a p-hacking here? Some of you have. Well p-hacking is when you have a lot of data and you look for the p-value that's significant and you'll find them. If you're crunching off, you'll find p-values that are significant. Well so what? People call it p-hacking. It may be intentional, it may be accidental, but it's a problem. And then what's the framework for interpretation? Again, you know, the structure, the expertise structure. And is there a data narrative and do we understand the underlying biological systems? And to share with you my final slide, much of the opportunities I think in terms of solution finding will be around systems analysis and network science. These are two examples where the complexity has been reduced into visual examples. One is the obesity map produced by the Department of Trade and Industry in the UK. And then on the right hand side was a paper written by Joseph Barani and his colleagues at the Institute of Food Research in the UK where they're looking at mapping food trade systems. And again, it helps to visualize data so that decision makers can actually make sense of it. So that concludes my presentation. Thank you very much for listening.


Hello, and welcome to all of you joining us here. Another episode of the nonprofit show. Excited to have our guest with me today, Muhee Kwaja joining us. Muhee has a lot of letters behind his name, which I like to acknowledge, MPA, CFRE, CFRM and trainer with Fundraising Academy. So each and every Friday is brought to you by our amazing sponsors, Fundraising Academy at the National University. I'm Jarrett Ransom, your nonprofit nerd, and Muhee and I are going to nerd out with you on some of these questions that you, our viewers and listeners have shared. But first, we want to thank our amazing presenting sponsors. I'm gonna give a verbal shout out for those of you that might be listening via podcast. So thank you to our best friends over at Bloomerang, American Nonprofit Academy, Fundraising Academy at National University. Again, today we have Muhee with us today. Be generous, which is the donate now, pay later platform, your part-time controller, staffing boutique, nonprofit thought leader and the nonprofit nerd. These companies are here for you and they also show up each and every month to be of service and conversation. So thank you to our sponsors. Hey, we are marching towards, I cannot believe this, our fourth year, March will be our fourth year, I know of broadcasting. You can find our previous episodes on Roku, YouTube, Amazon Fire TV, as well as Vimeo. I mentioned podcasts, we are in podcast forms, so go ahead and queue us up wherever you stream your podcasts. You can hear this show just a few hours later today. So Muhee, before we jump into this question from Pat, I would love to just say, welcome. Thank you so much. Yeah, no, I'm so very excited to be joining you and I really love these sessions and I'm so grateful to be a repeat guest with you guys and I think everybody at Fundraising Academy really enjoys these because it really shows what's on the mind of our nonprofit leaders and the communities all across the country. So we love being able to just share our thoughts, whether they're right or wrong, we will share that. And you get to decide how you want to implement it. So yeah. Yeah, I'm glad you mentioned that. So you are a repeat guest, so glad to have you here as I refer to it as the hot seat. So each and every Friday, Julie and I take turns with a representative from Fundraising Academy. And we go through the questions that Muhee mentioned. You provide us, so our viewers and our listeners send this in, there's many ways you can get that to us, even carry your pigeon, I'm sure is a way that we would accept it. But every Friday we address questions and over the last three and a half years, Muhee, I have witnessed the change in the evolution of the questions and some of them, albeit similar questions to maybe what we've received before, I believe how they're answered and responded to now and like current day, current situation is interesting how there might be just a small little change that impacts the delivery of today's question. So without further ado, I'll clear my throat and get us started here with Pat. So Pat in Fort Worth, Texas wants to know some ideas. So specifically, can you give us some ideas on what our donor retention number should be? We're starting to track this metric, but frankly, we don't know what a target goal or even a range should be. Great question, Pat. Take it away, my friend. Hey, Pat, this is an awesome question.  I'd love to encourage you and your organization to look at the last five years of what your giving data tells you, you know, download all of the gifts coordinated by year cumulative amount. So see if the donor gave in 2021 and if they gave in 2022 and then divide that by the total number of donors that you have and that's your retention. For 2023, increase it by a little bit. That's gonna be the best for your organization. But the national average for nonprofits is somewhere in that 40 to 45% range. And you know, since there's so many different nonprofits of different organization styles, different budgets, how long they've been in business, all of those factors are gonna come into that 40 to 45% factor. So don't freak out if yours is 30. If yours is 60, you're doing an awesome job. But somewhere in between, look at the last three to five years of your data and hopefully that metric continues to increase and you can do so many things to focus on those donors where you have attrition and how you can retain them again. And that's a whole other podcast. So... That is a whole other podcast and conversation. Mui, I'm gonna put you on the spot and ask you maybe a clarifying question. I heard you say five years and I'm curious if that's always been your standard practice or maybe if that's changed due to COVID. So would you be willing to share a little bit more about that five year versus maybe a standard three? Yeah, I love that point. Donations through COVID surge in 2020 is gonna look different than the anomaly before that, right? So 2019 donations are gonna be different. 2018 to 2019 is gonna be very different than 2019 to 2020. And 2020 to 2021 is gonna be very different because of COVID. Maybe you didn't get those repeat donors again, right? So yeah, I'd say five years is there just as a best practice to really look into your historical data and try to mark those trends for your organization.  Work with what you have, but I think a five year retention, pool of data and metric will give you a better understanding of your database. Yeah, great point, Muhy. I love how you answered that for Pat. And then I also wanna just, we're gonna touch on this later in another question. So four warning there. It really to drill into some other KPIs, key performance indicators. So a donor retention is a great KPI or key performance indicator to track for the fundraising department. I think there's so many others that you can track as well, Pat. I don't wanna overwhelm you, but really looking at what are some opportunities for other metrics of success, how many new donors as you talk of bringing that level in, you talk of, did they increase their second gift? That's another kind of measurement, but there's so many measurements out there, Pat, I do think a quick Google of fundraising KPIs or key performance indicators would warrant a really good read for you. And again, we're gonna touch on that a little bit later with another question, or at least I know I'm gonna bring it up, but such a good question. I think as we look at our information from Muhy's perspective, that five years is really good to say, okay, where is our baseline? And let's increase it by just a little bit. So. And just so that we're clear, like calculating your donor retention, you're gonna look at the number of previous years donors that gave this year. Yes. And then you're gonna buy that amount by the number of total donors last year. And then of course, multiply by 100, and that is your donor retention rate. So it gets confusing when you start looking at that five-year mark because if you're pulling from Excel, this cell is going to do that cell, it's like crazy when you think about it. That's right. Works. Just make sure you're pulling from the right cell if you're doing things that way. You know, that's a good point. And Muhy, I do think a lot of donor databases are CRM systems. They start to track that for you. It's typically year over year, but you can customize that report and do three years, five years. So hopefully, Pat, you have a fantastic CRM system that's able to help you calculate that. So good point on that, Muhy. So we're moving over to Alabama. And then my Southern accent might come out when I say read this for Char in Huntsville, Alabama. We are bringing in a new fundraiser and we're trying to set some target goals. Should we separate out contacts or donor touches and quotes here from dollars raised? Any other things we should be measuring from this team member? Question mark says Char. So, you know, this is a great question. And Char may be a manager, a director, somebody who is in charge of metrics and making sure that the fundraising bottom line is met. And that's important. We need to be tracking these things. I think the accurate number of donor touches needs to be based on the donor themselves, their preference. So I don't think just because you have a donor metric of saying we need to have 150 donor meetings in your portfolio a year or we need to have 200 phone calls this year or 300 letters sent or 500 emails. I don't necessarily, those are the things that are going to encourage your donor to give. What will is the impact that they're gonna make. What will is how you tell that story. So it's hard to put a metric on that, but I'm not convinced that just because we have a touch point metric, that that's going to increase donor's ability to give. That's interesting, Muhy. I know early in my career when I was assigned a portfolio and I had a certain number of donors or constituents within that portfolio, I really was focused, I found myself focusing more on my metrics of which I felt I was being graded on by my boss as opposed to the authenticity exchange within the constituent themselves. So I love that you bring this up. And simultaneously as I'm saying this, I'm also checking my own self because I'm coaching a client now to create portfolios and to create moves management, actually using the true, the class selling education model using the different phases and stages. So it's a lot of fun. And I do thank you. I wanna thank you for reminding me and everyone watching and listening that it really is that authenticity exchange of what is it that that donor wants, right? How does that donor want to communicate? And maybe how many touches as you look for that is a really good just baseline knowledge of that donor touches from the dollars raised.  I think that donor retention, back to our previous question, that's another great goal to keep in track of. So good stuff, Muhy. I love this. Yeah, and I think again, like, yes, you have an event coming up, something they're interested in, something that is compelling mission statement, annual reports, something that comes across your desk and makes you think of that. Those are the things that you want to be sending your donor. Yes. I was with you as well, Jarrett, like early in my career, I was encouraged to get on the phone, write letters, send emails, do all these things. And I did it and occasionally I would get some responses back but I don't think that those were the things that inclined the donor to give or get more. My experience has been those things like we've said that have been the authentic, what we've made the donor respond. I have to bring up, because I know you and I had this conversation, Muhy, about the apple slicer. And so this is a previous conversation with us, but another point of the char is, you're looking at what the donor's interests are and I had shared in this apple slicer conversation that I had seen an article about like a regatta and I knew that this donor was really into yachting. And so I pulled that, it was a magazine, a printed magazine. I pulled it out, I wrote a little note, put it in the envelope and sent it to this donor. It was not an ask. It was not anything really about the organization. It was simply that authentic exchange of, hey, I know you're into regattas and yachts and you had shared similarly, Muhy, your apple slicer story. And I just love that, because that really is about the donor and not about your KPIs of being tracked as a successful fundraiser. So we'll have to go back and find what episode that was because that was a good conversation. That was a good one. All right, so Char, hopefully that's helpful. We're gonna move into New York. So we're going from Alabama to New York for those of you that are geographically tracking us today. Naya in New York says, we have been hearing about interim CEOs, but have you ever heard of an interim board chair? Our current board chair has to leave us due to a job transfer and we don't have anyone to take the chair position permanently. Have you seen this, Muhy?  There you are. I'm so sorry about that. So, yeah, I think interim board chairs is very interesting. I have seen full transitions of board chairs and interim board chairs with a few months to go before seats switch and things like that, life happens. So I think the flexibility there is key. And as long as there's a good exchange of knowledge of responsibility, vision, I don't see there being too many red flags with this or issues of concern. But of course, like who is the person that is stepping into that role and what are their expectations and what's their vision? Does it align with how the organization has been going? Those are all things that I would question. Yeah. Yeah. And I have definitely seen this happen and if you're structured, this would be a question where you have a chair and a vice chair. Perhaps the vice chair now steps in as that board chair responsibility that it might actually say that specifically in your bylaws. So you wanna take a look at that, in absence of the board chair, the vice chair takes that responsibility in absence of that person. Someone else steps up to take that responsibility. Similarly, I have absolutely seen that moohi around the board table where life happens, as you referenced. And maybe they do need to step away for some short temporary amount of time before those next executive voting term kind of placements come into effect, you simply ask for someone else to take the responsibility of that. And I've seen that honestly, not just with the board chair, but in all of those executive positions. So I don't think it's specific and structured NIA only to that interim CEO, or sorry, the interim board chair. I really think it's for just about everything. I've seen it also in the secretary role. So any of those executive positions that you are already a fiduciary, excuse me, governing agent of the organization, but if you're by and large above that and having more responsibility at that executive level, it's even more of a time commitment. So I see that exchange happen quite a bit. And I would, again, when in doubt, refer to your bylaws. And one thing I would love to share here as well is see if somebody can co-chair, what are the responsibilities, right? Get two people to step in and see what they can do to take the responsibility of being chair, because if you don't have anyone to take the chair position permanently, maybe some of the existing board members can add on to what they're currently doing to help out. Great point, yeah, that's a fantastic point. So NIA, I hope that helps. And your board structure is able to continue without skipping a beat there. So, okay, Jamie over in Dayton, Ohio, are you hearing about offering remote staff stipends from WFH or work-from-home offices? It could be used to purchase an office chair, desk, or shelving. We're considering doing this as an employee benefit, but we need some input. This is a really good question, Jamie. So, Muhy, you work nomadically. I know you travel currently, you're joining us from an international location. What are you seeing for this? Yeah, I know, I love this question. And I think it's something that every nonprofit needs to do. It's a perk, it's a benefit. You know, when we're in our offices, we had the luxury of all of these nice things, and they help with your productivity. And when you're at home, maybe you don't have access to those things, and it may stunt your productivity. So, if organizations want us to be functioning at home the same effective way that we do in the office, having access to those things like a second monitor, a nicer chair, a ergonometric keyboard, all of those things make a big difference. So, I'm a fan based within budget of what your organization's capacity is to make an exception to provide this stipend. And whether the employee chooses to use it or not, it's a nice additional amount to help the employee out. Maybe they'll say they'll buy it directly or they'll tell them that they'll reimburse the employee. So, there's no wastage of funds. Whatever the organization can do to help promote the productivity of their employees at home is critical. I'm curious if you've heard of and seen people being reimbursed or using that stipend for a walking desk. Have you heard of this one? Walking, walking machines, standing desks, all of it. I've seen it all, yeah. Yeah, and now I think you're right into your, or I really want to address kind of your work style. Like, you travel quite a bit.  So, you found a way that works for you as you travel nomadically. We had a guest on the other day. She works full-time, also nomadic from her recreational vehicle or an RV, right? So, she has a set up for that as well, which is really fantastic. And I do think that, you know, whether it's a stipend, whether it's a reimbursement, like to your point, it really is whatever helps them be efficient and effective in the task in which they're responsible for doing. And I remember, you know, honestly being really early in my career and just thinking whatever I had is what I had and that I couldn't ask for anything else. But if there's something that makes you, as I stand, as I sit up straighter, right?  A lot of people enjoy those as well. So advocate, I would say advocate for yourself, advocate for what you need to be a top, you know, provider of who you are and what you're doing. And if that means that you need a cushiony little gel thing for your wrist, like ask for that. Maybe that's part of the stipend that you're considering here. You know, I think that's important because so many of us have different ways in which we work. And Jamie, I think this is a great question. I love Muhe that you're like, yes, we should be considering this. Yeah, one thing to add too, is like increasing your internet speed, right? If both people are working from home, if they're kids online for school or you know, you're streaming friends while you're working, like whatever you're doing, that is a great use as well to increase your speed as well. Thank you for mentioning that. I think that was in the back of my head. I know that we've seen that kind of technology challenge, right, with our students early in COVID. And that doesn't mean that staff doesn't also have that same challenge.  And so, you know, they were really forced into an even more remote kind of situation than at home. So that internet is really good. Maybe even a phone, you know, if there's like a cell phone or a voice phone that you might need, you know, a cloud-based kind of service, all of that is really good. I'm curious. Yeah, I'm curious, Moe, do you recommend this? Like, should we be asking our staff on an annual basis? Or like, how often should we be asking them? Basically, do you have what you need to do your job? Yeah, I think that's a critical question. And maybe even coming up with recommendations of, these are the minimums you need if you don't have that. And like, what, you know, now are people using personal laptops? Or are they using their work ones? And like, what's the RAM? What's the memory? What's the whole stats on the product you're using? And does it meet the standards that you need to be running screen share on Zoom and having your email up and not freezing? No, that's not true. Yeah, so, yeah, I think that organizations should be forward with it as opposed to making their employees maybe feel shy about requesting it. So definitely the ordinance should take the lead on them. Yeah, good stuff, Jamie. And I hope that's very helpful. Hey, these conversations are always a lot of fun. As we shared earlier, we are answering your questions. They may not be right, but there are perspective and there are, you know, experience. So whether it's myself, Julia, Muhe, Jackalotto, Pearl, there's so many people that join us from Fund Rising Academy. We bring years, if not decades of combined experience. So we hope that we are able to provide a little bit of insight on how to best move forward. Today's guest in the hot seat was Muhe Kwaja, MPA, CFRE and CFRM trainer with Fund Rising Academy. So thank you. I also want to acknowledge and recognize that you are the co-founder of the American Muslim Community Foundation. So Muhe, thank you for all that you do. Such an honor to be here. Thanks for the opportunity, Jared. Yeah, it's a lot of fun. So thank you for being here with me and thank you to Fund Rising Academy at National University. Also want to give a shout out to our other partners and presenting sponsors. So thank you to Bloomerang, American Nonprofit Academy, Fund Rising Academy at National University, Bee Generous, your part-time controller, staffing boutique, non-profit thought leader and the non-profit nerd. Today has been fantastic. Thanks to you for joining me. Thanks to all of you for listening in, either through the video or maybe through the audio. So glad you're here. Monday, we're taking off in observance of Martin Luther King Day. So I don't forget that that is a no-show day. And we ask you to join us back here on Tuesday. Have a wonderful restful weekend. Muhe, have a fantastic visit, trip, all of the good stuff that you're experiencing. And I ask that all of you to please stay well. So you can do well. We'll see you back here next week. Thanks everyone.