Nonprofit Expert Episode 21: Ethical Data Usage for Nonprofits

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Julia GackenbachHost00:13

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DonorPerfectAd00:03

Welcome to Nonprofit Expert presented by DonorPerfect.

Julia GackenbachHost00:13

Hello and welcome to Nonprofit Expert presented by DonorPerfect. My name is Julia Gckenbach and I’m here with our guest, M Dawes. Mina, thank you so much for being here. It’s great to have you.

Meena DasGuest00:25

Thank you so much for having me, Julia.

Julia GackenbachHost00:27

Of course, Meena, you are a leading voice in the nonprofit sector, especially when it comes to donor data. I know you talk about a lot of other things, but today we’re going to really focus on how to best capture information about our donors, and this is something that causes a lot of fundraisers to maybe put their fingers in their ears and shake their head and close their eyes, which I, as a fundraiser, did. Do that a lot when it came to donor data, and so I’m excited to chat with you today and maybe break down some of those walls for fundraisers who aren’t really statisticians. But before we get into the nitty gritty of donor data, why don’t you tell me a little bit about yourself, how you got into fundraising and especially how you got into donor data? What made you interested in this topic?

Meena DasGuest01:16

You gave some good introductions, so I think this is a question that is probably going to be the hardest question for me. Tell me your story and my head immediately goes back to. Okay, where do I begin? Well, I grew up. I kind of include my personal story when I’m describing my work, so I’ll start there, probably.

01:35

I grew up in the beautiful country of India and I had my, you know, I grew up throughout the country. My dad used to work in a bank. We used to move a lot. Every two years we would pack up and go to a new city and in a way, I feel like I have always been an immigrant, moving from city to city, place to place. About seven years ago, I moved from India to States and from States to Canada. So right now I am talking from the beautiful lands of the peoples of Muscombe, squamish and Slavittuk nations, which is, in other words, vancouver, and my story is that I am the person a data scientist meets life coach-wide kind of a person. I am a life coach and I love data, and the reason is I’ve spent 17 years in working with different tech companies and in non-profit consulting companies, working with data, doing all sorts of things collecting them, creating dashboards, creating metrics, explaining it to the people who don’t usually talk to data, and doing all sorts of things.

02:51

And about four or five years ago, I met an accident and lost my teeth. I include this in this part of the story as a way of owning that part of my life, and that was one of the pivotal moments in my life when I started to question it is not enough to just collect data and become part of it. We need to be held accountable in this, because I needed post-surgery care and services that I could not access, even though I had numbers, case numbers, visa numbers, all these numbers which are supposed to be important numbers for your life, your documentation, your identification but I could not access some of the care services, and that’s when I wanted to question well then, what does it mean, even if I have these numbers? Why do I feel so powerless, even though I’m part of these databases? Why do I feel vulnerable? And that is when I started to change the way I work or what I work on.

03:44

So three years ago, I started my consulting practice called Namaste Data, which is basically, you know, everything that can possibly I can do to answer the question how can we do good with data. It sounds like a simple question, but it’s really not, because most days I am working with nonprofits trying to answer how should you think about data differently? What should be your values? What should be your practices around data differently? Or when it comes to saying goes for AI, because, as an extension of all the data things, we are now living in the world of AI and it comes to my work, I ask the same questions. What does it mean to think about AI differently? Not just in terms of, yes, it can do for you A, b and C. What’s our responsibility? So that’s kind of where my world is and how I came to donor data.

04:34

Well, I came first to the word data. That became my professional identity, almost identity, almost and over the last few years I’ve started to question what does it mean in this day and age to be living in data? What does it mean? Because you know, julia, you and I. It’s 825 on my end. It’s not, it’s still early in the morning, but I have interacted with at least at least seven systems that have data about me my microwave, my, my, my phone, my computer. There are devices that has data about me, who I am. There are parts that are in these systems. It should mean something besides it. You know, just having my day go on and so I came to that part of the world. This part of the world through data and donor data became one of the key things in the nonprofit sector.

Julia GackenbachHost05:35

So I think that’s what kind of my story is, why I’m here talking about data. I am so moved by your story, mina. That is amazing and complicated and beautiful. I’m really moved by that. Thank you for sharing that. You said something that really interested me and you said you work with people to figure out the value of data. I go back to my high school math class where we talked about numbers and values and all of these things. It really caused pause in me because I don’t know if I’ve ever really thought about assigning value to the data that I have. Can you dig a little bit deeper on that specific thread?

Meena DasGuest06:19

Absolutely. This is probably one of the things that I push for in every conversation is all of us, let’s take a step back. As humans, we have our own values, right. We have family values. We have our own values. We grow up. Our parents teach us how to operate in the society, how to operate in the world, correct. We have our kids and we teach them the same thing, and that is what constitutes our what we call as moral compass right, what’s good, what’s bad, the ethics of it.

06:50

We don’t necessarily have anything like that around the word data when we, with the word data, somehow our relationship is that we are going to be collected by different systems and we are okay with that Because you know, we never realized that we are being collected by systems. Once you are collected in these systems, you are being tracked, you are being measured, you are being compared, you are being segmented and one of the terms, one of my recent favorite terms is statistics-ified constantly. You are coming under mean median mode. All these terms. But really we never understood if we have any agency when we live in that data right, like, for example, going back to my life when I needed the care services and I had this case number of the Labor’s Department and the visa number that I had nine digits, and I was told from the day one you need to hold on to this document of your visa number and thing because this is your documentation. When I needed the care services, I could not access them for many different reasons that you know. This is services denied.

08:00

And so then the question um, then the question really is that when we speak of values, it’s the same idea. Who are we around the word data? So I speak of this as we do in our nonprofits. We do strategic planning, maybe every three years or five years, right? I suggest, I recommend, I highly recommend that we set up a process around the same time when we all sit around the table asking each other who should we be as humans, around that data in our organization? We are supporting and serving our communities outside right, and what can we do when we are cooperating with their data in terms of the values? How should we be collecting it? How should we be analyzing it? Whose voice should we be represented? So values really is who we are as humans that we use day to day to create our own moral compasses. The same thing, the same idea, is extended to the word data to say who we should be when we are operating with it. We don’t have that right now yeah, that.

Julia GackenbachHost09:05

That makes complete sense and it’s quite convicting. Like what value am I assigning when I ask a donor for their email address? Why do I need that? Why is that important? Well, let’s take a little step back. First. Let’s talk very high level about data. Why do nonprofits need data? And especially through the lens of what you’re saying. You know we want to value people. We want to value data. So how do we still see the importance in those data points? And what are those important data points? Like, is an email that important? I don’t know what kind of data should we be capturing.

Meena DasGuest09:50

There are two interesting questions in there, so I’m going to start with the first one. Why should nonprofits care? And, honestly, my answers change every time, the way I feel articulated. So now I’m going to share, probably in terms of time, because I’m reading a book about time and how it affects our history and society generally, so I’m probably pulling some thoughts from there. Okay, I believe we should care about data, because it is one thing that is capturing our past, present and future.

10:27

This data the nonprofits that they have it can tell them about the past of the organization. Where did they start from? Where did they come from? Who were they before so-and-so year, before so-and-so campaign, before so-and-so milestone? That allows us celebration, that allows us to remember who we are. That almost like having family photographs. We have it because we know who, our family, who was so-and-so ancestral people in our family, right, that’s almost like a document. That’s one aspect of that data. The second aspect is the present. So right now, who we are right now, in this moment, and one example would be who is in our community right now. Do we know? Are we serving more refugee and immigrants versus not, for example?

11:15

So, we get to know, through the data in the present, who is in our community right now. The future is one how are we going to co-create change with our community? And two, who are going to be our allies in making this change happen, so that one data point and I’m not talking about some random, one statistic or one metric or one percentage data truly can be your stories and numbers and digits and figures and all of it put together, that piece, that word data, is connecting you with your past, with your present and your future. It’s really creating a cohesive ecosystem with your community to understand how to create change. So it comes with a lot of responsibility. Going back to that values, again, we need that. Who are we going to be around this very important entity called data, so that it’s not just that byproduct of simply let’s keep collecting, keep collecting, but we also kind of understand what do we do with it? Now to your second question what should we collect? If it’s important and I usually answer that question for nonprofits in simple terms is think about the five questions. Think about collect about the who of your community or your work. Collect about the what. Collect about the how. So, because you’re talking about donor data. Let me boil down my answer to the donor data. If you’re collecting data about donors, think about the who, right? So who is your donor? That means their identity, that means their history, that means what constitutes them, that’s the who. Talk to them about what. So the programs they’re engaging with, the things they’re engaging with in your organization, that’s the what. The things they are engaging with in your organization, that’s the what, how, how are they engaging with? Do they come to you through emails, through your galas, through your campaigns? Talk to them about the how. The fourth question is why? Now? What is the interest and motivation and reason? Right? And the last one is what next? Kind of data and what next?

13:25

For me is a question like what makes you feel philanthropic, what makes you feel, um, generous? Ask those questions. Those are the data points the, the who, the what, the how, um, the why now and what next. Those are like the five buckets of data points which you collect. You understand a human. It’s pretty much like becoming friends with someone you know. We start with what made you my friend. I start getting to know you, you start getting to know me, we start doing things together and eventually we start learning about each other enough to motivate each other to do acts together, like we would go for a trip together. It’s pretty much like that, like we slowly understand what to collect. So I know we might I hope we might get into a question like what could we be doing differently? Because that’s when I want to come back to this answer a little bit more. But hopefully this is answering, julia, to what you were asking.

Julia GackenbachHost14:25

Meena, you are blowing my mind right now. This is so good. The concept of data being kind of a time situation is something I’ve never thought of in my life, and that is such a unique way, especially for fundraisers. I don’t know about how many people are listening, but when I was a fundraiser, I inherited. I started working at a nonprofit that had been around for 20 years.

14:54

There were so many things that I did not know walking in, and then I got to know my donors in their present and I was able to look back and see the journey they had through this nonprofit because of the data. And then you also mentioned the future of giving and we’ll get into this a little bit, but with AI and with predictive things, that data is going to be so important for the future of a donor. I’m just floored by that example. That was really profound. So thank you for sharing that. And then those five buckets, like you said, the who, what, how, why. Now, what’s next?

15:37

I just want to sit down with my donorPerfect CRM and pull out my top 10% of donors and answer those questions for them. That would really add value to those donors. Like what we were talking about before, you can pull your data out of DonorPerfect, but sitting down and answering those questions will add the value to these real people, these humans, not just these data points, but these humans, which is. I love that. Thank you so much for sharing that. And you mentioned a lot of this people analytics, so I want to dig deeper on that. How could a fundraiser like myself, or like someone listening, make a data point look more human? I don’t know if that makes sense, but sometimes my eyes glaze over because I’m looking at so many donor reports. How can I make that more personalized and more human?

Meena DasGuest16:42

Julia, you ask good questions and I am so nerdy about this topic I have like my brain gives one answer, I want to give another one, my words want to say another answer and then I want to combine it. Anyway, I won’t go into my whole ADHD kind of thing when that’s okay.

Julia GackenbachHost16:57

This is a safe space. You know the safe space um I appreciate it.

Meena DasGuest17:02

I’ll’ll probably share two things about that. One is an example from a book that I am reading one of the other books I’m reading and the other one is coming from my own work. So the example is how do we make these data points more human-centric? Right, that’s the question, and one of the examples I’m reading is this person, this author. He wrote this book. He wrote it about data and he wrote this book during COVID times, so the early 20s, mid-2020s, I think. He wrote it right after George Floyd and he speaks of how he took a lot of different data points during COVID times and made sense of it, and one of the examples he wrote was New York Times, I think, posted 100 names of people who died because of COVID at some point in 2020.

18:01

They printed their names, they printed their job description and they gave all that data. They also printed at some point in their materials how many people require health insurance and how it has grown from May 2020 to September or October 2020. So they have been like giving these data points, and this author collected all those data points and started to dig deeper. So what he did was he took the 100 names and then he went online to collect about their available background information and context. And he tabulated that data and he took all that and found out that there were places where most of these, a lot of these 100 names, were non-white. They were either Black communities or they were coming from other BIPOC communities.

18:55

And his point was, when we speak of terms like we are in this together, the reality is we are not.

19:05

We are not in this together because you dig deeper into these hundred names and you realize that some communities are disproportionately way more affected because of this and are going to be in the future, because of the aftermath of this COVID, differently than one part of the community or one part of the population. So when we speak of such generic statements that we are in this together or it’s our responsibility, or the word we sometimes, sometimes it diminishes that risk that comes for some communities way more than the other. Sometimes it reduces the impact that can happen if we were not to look at everything under one bucket of we, under one lens of we. So I’m giving that example. To come to my point of what I do in my work is if you want to really make your data points human centric or if we want to look at that data points not just as nearly as statistics or data points, but more as humans. We need to become friends with our data. That’s, that sounds simple, but it’s, but it takes commitment. That’s not simple.

20:19

Commitment right, like when you said I went back to my job or you started your job as a fundraiser and you inherited all these data points right, which is pretty practically what happens in every job. We get into a job, we get some data points. We don’t inbuilt some time to become friends with data None of the jobs do Neither you know we have in our job descriptions. You have to be friends with the data that you inherit in your job description and I’m not probably suggesting in this episode that we should have all that, because I’ll probably be talking a lot more other things other than to your question. But 101 is we need to understand who lives in that data. We cannot become friends with someone unless and until we understand who our friend is, and we can’t just go and bombard them Like you have a friend, you haven’t spoken to them for, say, a million years, and then suddenly you just send them a bunch of texts that this is what we do, this is amazing, that’s what we do. That’s amazing. And no, they are not going to be suddenly be so receptive and responsive to it. So the way I translate that into three things and this is like maybe the second part to the same question we answered. We can do three things with our data we can make sure that we are being non-extractive, we can make sure that we are being transparent. And we can make sure that we are being transparent and we can make sure that we are not adding our own biases.

21:43

And the examples I will give is of a survey, because I do a lot of data collection projects. I do a lot of survey projects. So non-extractive data means you are sharing back with the community why you collected that data. That’s one way to become human centric by making it a two-way conversation, by not just saying take your 20 minutes and fill up my 50 question survey or 30 question survey and then you will not hear back anything from me for the next three years and then I’ll come back and ask you once again. This is the same thing. That’s being extractive. We want it to be non-extractive, to build that trust.

22:20

That’s step one. Step two of making it human centric is being transparent. Tell them why are you doing all this data project? Tell them why are you collecting all this data? You are asking probably some of the most personal information to your community what’s your ethnicity, what’s your racial identity, what’s your gender identity? What’s your immigration status? These are some of the most personal questions. Why should someone trust you to give you that information? Right? That’s setting up that context. That’s transparency and biases I see that every single time happen.

22:55

Biases means in data collection. When you choose by yourself this 10 people are much better than the 40 people, because you know what 10 people are the top donors. I’ll only send them the data collection. That’s our own bias. In there, we immediately alienated a bunch of people saying they might not be so good for my project or for this campaign or for this fundraising thing. So why can’t we move, step away from that biases and set up some good context and set up and make sure that we are being non-extractive. I think those are some of the things that makes it human centric.

Julia GackenbachHost23:29

That’s incredibly helpful. I love those steps and just calling that out is so important. I don’t know that I ever checked myself that I wasn’t being extractive, you know. So that’s really helpful. I want to ask one a few other questions, but one really important question how does data help people become a better fundraiser? How does human-centric data help someone to become a better fundraiser or to make a bigger impact in their community? I think sometimes it’s easy to separate data and impact, but do you have any tips on how fundraisers those listening could use this data to make more of an impact in their community, I would say well, I don’t know if you are going to ask me or not to define what this equity and donor data means.

Meena DasGuest24:27

Sure, yeah, let’s do it, because I have a definition that I usually have on a piece of paper that I want to read and I included most of the conversation. If that’s okay, I would love that. Yes, and then I would get you some tips too. I would love that. Yes, data that ensures fairness, inclusion and respect for all donors, regardless of their background, identity or resources, how much money they have, where do they come from.

25:14

And equity in donor data involves operating with acknowledgement that our data is not perfect and perhaps it will never be. It will never be perfect. So instead of seeking that, the question really is how do we ensure that our donors feel valued and represented by being extremely intentional with data? And I think I want to take that all those words and turn it into tips and say we need to understand who is again probably going back to who is in our community. What do they care about, how do they care about it, why now do they care about it, and what kind of change do they want to see in the world?

25:58

The whole purpose of donor data and fundraising. If you put these words together, the key is not donor data. The key remains the fundraising job description, which is going out and inspiring someone to connect with their reasons of generosity and to enable that in the world to create a better world. Data is an enabler in that process and so if we want to use that human-centric data, or a human-centric way to operate with that data, to create a change, we really need to go back to the fundamentals of understanding who is in our data.

26:32

And the data is going to support you in that.

Julia GackenbachHost26:35

That’s very helpful. I love that, and it goes back to what you said about we need to be friends with our data and without knowing our friends, how do you have a conversation with them? You got to know them, you know. I love that.

Meena DasGuest26:47

I pretty much give an example, if you want to pick this as an example is you get into a bus, right, you have a stranger sitting next to you. You look at them and you feel like, oh, they might be good for my mission, they might be good ally, they might be good supporter or something. And then you get to talk to them about their name, you share yours, where are you going, and all those, some of the basics and then you ask them okay, well then, what’s your gender identity? And then what’s your racial identity? What about your? Are you married or not? And so we skipped some of the relationship building part and went straight to asking the most personal questions. How freaked out would they be? And and not, you know, we, we kind of reached the trust. We haven’t even built the trust yet, and so we need to remember that and avoid that whole situation of we need to remember that and avoid that whole situation of scaring our strangers around us before we can make them friends. That is so important.

Julia GackenbachHost27:49

Again, these are things that I just had never thought about. You know, when we send out a survey because somebody came to a gala, I hadn’t even thought about asking two personal questions. And I’m sure there has been many times about asking two personal questions, and I’m sure there has been many times, whether via survey or whether in person, I’ve asked an overly personal question. So that is a great point to really build the foundation first, and you mentioned earlier in this interview about some of the mistakes that someone might make in collecting data, and you’ve touched on a few.

28:18

I think one skipping some of the foundation, to not seeing the human behind the data and, you know, really, really treating people as numbers and not as people, and that’s a danger for sure. Hit on was the future that you’re able to see. By knowing a donor, knowing a friend, you’re able to see what’s next and where you’re going and where your stop is after you get off the bus, you know. So those are really important pitfalls to avoid and I think that that’s practical for those that are listening. I have one more question for you. There are so many exciting things happening in the nonprofit sector. I think there are incredibly wide open meadows for a lot of things. What is something that you are looking forward to or are excited about when it comes to the nonprofit sector and data?

Meena DasGuest29:27

Nonprofit sector and data. Okay, well, I would say two things One is coming out of my work and one is coming out of my hope.

Julia GackenbachHost29:35

Oh, I love that.

Meena DasGuest29:37

Okay, the first part is the hope part. You know, I came to the nonprofit sector maybe like seven or eight years ago. I started working in this space and the more I work with it probably every day, with the fundraisers and the programming committees and other committees of the nonprofits I am realizing every single day this feeling just gets renewed is I work in one of the hardest sectors in the world. Tech was easier than this Tech had. Like you know, two plus two equals to four. You do step one, step two, step three and there you go, you have your product, go collect that data.

30:15

Non-profit is one of that very unique sectors and probably the hardest one, because our job description is to go help inspire someone to understand what makes them feel generous and then help them build a vision to co-create, change it. It sounds words, but they’re really difficult. They they’re really challenging. How do you inspire someone to understand what their version of generosity is, to co-create change in a world when constantly the world is burning, by the way, with wars and inhuman practices and injustices? It’s difficult and, by the way, we are working in a sector which is underfunded and we don’t. We are not paid enough as a sector and we still want to do this. We still are willing to get our hearts broken, but try again so that you know we can make a change, we can make an impact. That’s incredible.

31:14

It’s exhausting, but it’s incredible, and I think that what keeps me in the work that I am doing every day. Again, it sounds basic, but that’s my hope that I continue that same energy that I feel every morning. I want to do this again, as much as I am exhausted and angry and feel all the heavy things. By the end of the day, I want to do this again. So I think my hope is just if I can stay true to my fundamentals, if I can stay true to my truth. I’m excited about that.

31:45

And the second would be probably my project that I’m taking outside of all my consulting and workshopping is this research called AI Equity Project. My goal, my vision, is to give this sector a bunch of stories and a quantified number to say this is who you are when it comes to AI equity, so that we can do a good job together of bringing more funds about artificial intelligence in this sector. We need more funding. We need people who can talk about these things comfortably, who can play with these things comfortably, without feeling constantly scared and overwhelmed around these words. And so, with that hope, I’m doing this research with 708 nonprofits and I would be sharing a report in a couple of weeks which will go live, and I really hope people you know, they read it, they think about it comfortably, find a place in their rabbit holes and we do something with it.

Julia GackenbachHost32:48

Yeah, that’s wonderful, mina, and I think you’re just living in this space of equity for all of nonprofits, whether that’s in your data or in your use of AI or in your communications, and I think that that is such a valuable pillar that, when I was deep in the throes of fundraising, I have to admit it wasn’t something that I was talking about or focusing on, and I’m so grateful that you are taking the time to communicate this to our listeners so that we can be more mindful of the people who are making a difference in our community through their philanthropic giving.

33:27

It’s such a new, dignified view and I just really appreciate that you’re taking the time to care for our fundraisers and for our givers. So thank you so much for the things you’ve shared. I feel like we could talk for another hour and a half about all of these things, but for now, we’re going to wrap up this episode and I hope that you’ll come back and share more about this equity project with AI, share more about just the dignity of the giver, and I’m just so appreciative of the work that you’re doing and of the time that you’re spending with us today. So thank you so much.

Meena DasGuest34:02

Thank you so much for having me here and thank you to everybody who will take a minute to listen to this episode. So thank you.

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