Latest / The Nonprofit Podcast / EP 126| How Does Data Equity Increase Donations for Growing Nonprofits?

Transcript

EP 126| How Does Data Equity Increase Donations for Growing Nonprofits? · The Nonprofit Podcast

• 0:04
  SPEAKER_01: The Nonprofit Podcast, powered by DonorBucks.
• 0:09
  SPEAKER_01: Have you ever wondered if the data that you're collecting truly represents everyone that you're working with and trying to help?
• 0:18
  SPEAKER_01: Or have you ever questioned whether your data practices are inclusive and equitable?
• 0:23
  SPEAKER_01: Welcome to the Nonprofit Podcast.
• 0:25
  SPEAKER_01: I'm Britt, nonprofit fundraising strategist at DonorBox.
• 0:29
  SPEAKER_01: We're here each week with practical actions that you can use today to increase donations and take your nonprofit to the next level tomorrow.
• 0:39
  SPEAKER_01: Today we're diving into how focusing on people and equity in your data practices can really help you better understand and serve your community.
• 0:49
  SPEAKER_01: I am so thrilled to welcome my dear friend Mina Doss, the CEO of Namaste Data.
• 0:57
  SPEAKER_01: Mina, it is so lovely to have you on today's show.
• 1:00
  SPEAKER_01: Welcome.
• 1:01
  SPEAKER_02: Hi, Brad.
• 1:01
  SPEAKER_02: Thank you so much for having me here.
• 1:04
  SPEAKER_02: I am so grateful for your friendship, for this work that you do, um, and excited to talk about all good things around data and AI.
• 1:13
  SPEAKER_01: So, Mina, I'm gonna switch it up because I want to put you in the hot seat in the hot seat.
• 1:17
  SPEAKER_01: And how the heck did you jump into this topic of human-centered, community-centered data inequity?
• 1:24
  SPEAKER_01: That's going to be a long answer, but you asked for it.
• 1:26
  SPEAKER_02: I grew up in the beautiful country India.
• 1:30
  SPEAKER_02: Growing up, we used to woo a lot.
• 1:31
  SPEAKER_02: I come from a time when there was no Facebook and not everybody had phones, and I used to never get a chance to really, you know, stay in touch with my school friends up until Facebook did come.
• 1:42
  SPEAKER_02: So thank you, Mark Zuckerberg.
• 1:45
  SPEAKER_02: Um, but until then, my way of connecting with people was, you know, taking a bus, doing some rounds of the city to talk to people sitting next to me.
• 1:54
  SPEAKER_02: And growing up, that gave me this chance to see that there is something awesome in everybody.
• 2:02
  SPEAKER_02: There is some power in everybody.
• 2:04
  SPEAKER_02: All I need to do to look at that power, to look at that awesomeness is just to lean into their stories with curiosity.
• 2:13
  SPEAKER_02: So I come from a background where I have spent a lot of time, a lot of years just listening to the community, just listening to the people with whom I haven't shared anything in the past other than a common country, a common city.
• 2:27
  SPEAKER_02: And all taking all that, I have spent years working as a data professional, about 17, 18 years now.
• 2:34
  SPEAKER_02: Um, and then I worked in the nonprofit consulting space, and simultaneously I also have my own school.
• 2:41
  SPEAKER_02: All this conversation with people that I had accumulated over the years growing up, I turned that passion into running my own school for sexual assault victims and um ex-convicts.
• 2:53
  SPEAKER_02: I wanted to offer a space of learning for people, and at the same time, I realized I don't earn enough to sustain this pool.
• 3:02
  SPEAKER_02: That was one of the pivotal moments when I realized, okay, I want to, I want to learn more, earn more, and bring back to this school.
• 3:10
  SPEAKER_02: And that's when about eight years ago, I came to Florida, started to go through my second master's program in computer science.
• 3:19
  SPEAKER_02: So, you know, hint hint, I'm a big nerd around data.
• 3:23
  SPEAKER_02: I started my second master's program on data, and from there came to Seattle.
• 3:28
  SPEAKER_02: This is again like eight or seven or eight years ago.
• 3:30
  SPEAKER_02: I was still in the tech back then.
• 3:32
  SPEAKER_02: I met an accident and I lost my teeth in the workplace.
• 3:36
  SPEAKER_02: I did not see the water on the floor, there was no signboard, there were hard floors, I fell down on my face.
• 3:43
  SPEAKER_02: It was it was a tough time.
• 3:44
  SPEAKER_02: I couldn't speak for a few months.
• 3:46
  SPEAKER_02: And then it became this kind of legal thing where my company did not pay for the surgery, saying, Well, where you fell during the work hours was part of a cafeteria.
• 3:57
  SPEAKER_02: So it should be them who should be paying.
• 3:59
  SPEAKER_02: And the cafeteria said, Well, it should be the company.
• 4:01
  SPEAKER_02: And long story short, that was one of the very key moments in my life when I realized that it is not enough to be in some powerful databases.
• 4:16
  SPEAKER_02: Like I was part of the database of that company, I was their employee.
• 4:20
  SPEAKER_02: I also had a visa status that allowed me to stay in the United States.
• 4:24
  SPEAKER_02: I was also given a case number after my accident in the labor's department.
• 4:29
  SPEAKER_02: I was part of some important databases, but I still felt very powerless.
• 4:34
  SPEAKER_02: I felt like as a first-gen immigrant, I do not understand this level of vulnerability that I am feeling.
• 4:41
  SPEAKER_02: What do I do about it?
• 4:42
  SPEAKER_02: And so I think that was one of the moments that made me realize that my lived and learned experiences around data do not align.
• 4:50
  SPEAKER_02: Like I have spent all these years in the past creating some amazing algorithms, some amazing programs, designing some amazing dashboards, but I don't feel like any of those circumstances, any of those projects, any of those jobs allows me to figure out or explore what my identity means there.
• 5:09
  SPEAKER_02: This this brown woman with black hair, that along with the fact that I spoke to the bunch of people growing up, all of that who made who I am, what does it mean when it shows up in data?
• 5:21
  SPEAKER_02: And so I could see this disconnect and it bothered me.
• 5:24
  SPEAKER_02: And that's when I left my jobs a couple of years ago and moved to nonprofit consulting.
• 5:29
  SPEAKER_02: Three years ago, pandemic, you know, through the pandemic, it made me realize okay, well, I am working in nonprofit consulting, but I still don't feel strongly that we have explored the answer to the question how do I do good with data by aligning my personal and professional identity, my lived and learned experiences.
• 5:50
  SPEAKER_02: So I started my business called Namaste Data.
• 5:53
  SPEAKER_02: This is where I work with nonprofits to draft AI policies, to do inclusive data collection, to teach and train about data equity.
• 6:02
  SPEAKER_02: So everything and anything to do with data and AI equity, I try to find answers around those.
• 6:09
  SPEAKER_02: That's kind of my story.
• 6:11
  SPEAKER_01: That's that's really beautiful, Mina.
• 6:13
  SPEAKER_01: I think your lived experiences, it really is an example that there are true human stories behind the numbers, right?
• 6:20
  SPEAKER_01: We got to look deeper.
• 6:21
  SPEAKER_01: And I think this kind of feeds into you and I know on LinkedIn, right?
• 6:26
  SPEAKER_01: Like human-centered, community-centered, equitable data, it's all the hot topic, right?
• 6:32
  SPEAKER_01: Consultants know it, nonprofit adjacent professionals know it.
• 6:37
  SPEAKER_01: But I wonder if people that are actually working at nonprofits, they're head deep, they're doing what they're doing.
• 6:45
  SPEAKER_01: It's a hard time right now.
• 6:47
  SPEAKER_01: Do you think for our listeners tuning in, do they really understand what these topics mean?
• 6:52
  SPEAKER_01: And if not, would you just kindly kind of break down in very simple terms, even for myself, Mina, not just what they mean, but why these concepts should really be top of mind for all of us right now?
• 7:05
  SPEAKER_02: You're asking some really good, beautiful questions.
• 7:09
  SPEAKER_02: Let's start with the first thing.
• 7:11
  SPEAKER_02: Do nonprofits know these things?
• 7:13
  SPEAKER_02: I don't think so necessarily.
• 7:15
  SPEAKER_02: Well, based on my all my three years of work in this in this space of talking about data equity, AI equity, I would say all of us, all of us in sector, we are capable of doing good things with data.
• 7:32
  SPEAKER_02: Sometimes we can, sometimes we do, sometimes we know we are doing some good things, sometimes it happens without acknowledging it formally.
• 7:40
  SPEAKER_02: So, no to your question.
• 7:42
  SPEAKER_02: We don't, not all of us know about it.
• 7:45
  SPEAKER_02: Even though we would be doing, we might be doing some good things with data, we don't know these concepts because we don't have a common language.
• 7:54
  SPEAKER_02: This was never a priority for any of us to talk about you should be doing good with data.
• 8:00
  SPEAKER_02: All that happened in the past or up until now is we need more data, let's collect more data.
• 8:06
  SPEAKER_02: Now that we have more data, let's make some decisions and let's go about it.
• 8:11
  SPEAKER_02: But that's not enough because data can be weaponized.
• 8:16
  SPEAKER_02: That same data, how you are collecting it, can bring a lot of barriers up front.
• 8:24
  SPEAKER_02: And let me give an example.
• 8:25
  SPEAKER_02: Let's say you are doing a survey, right?
• 8:27
  SPEAKER_02: You're doing some survey to do some data collection.
• 8:30
  SPEAKER_02: Um, the barriers can be uh who decided to ask some of the questions, right?
• 8:36
  SPEAKER_02: So let me also take a step back and give an example.
• 8:39
  SPEAKER_02: Like a real example of us, I do a lot of data collection, do a lot of surveys.
• 8:42
  SPEAKER_02: So one example is the organization supports, um, does animal advocacy across the country.
• 8:49
  SPEAKER_02: Um, they were doing a survey of the community, including their donors.
• 8:53
  SPEAKER_02: And what happened in the survey was while while designing, a bunch of the leadership people asked me to include questions about gender, ethnicity, and you know, demographic questions.
• 9:05
  SPEAKER_02: Almost said, Mina, can you add a question about genders asking male, female, or unknown?
• 9:12
  SPEAKER_02: Give them three options male, female, unknown.
• 9:14
  SPEAKER_02: And if they are unknown, add another option of asking for pronouns.
• 9:19
  SPEAKER_02: This particular example, this is a problematic instance of data.
• 9:25
  SPEAKER_02: One, we we immediately gave someone a message that you are alien to us by telling them that using the you know language unknown.
• 9:34
  SPEAKER_02: That's problem one.
• 9:35
  SPEAKER_02: Problem two, if you are using the unknown, if someone selected unknown, then you are placing more burden on them to give you more data.
• 9:45
  SPEAKER_02: So this little example is a problem instance of data.
• 9:49
  SPEAKER_02: This is what is at risk sending a message back to the community members saying that you don't matter to us.
• 9:56
  SPEAKER_02: You, your identity doesn't matter to us.
• 9:59
  SPEAKER_02: It's okay if we exclude you.
• 10:01
  SPEAKER_02: We are sending out that message through that work.
• 10:03
  SPEAKER_02: That's problematic because the core of nonprofit work is to build trust, as I understand it, to build relationships.
• 10:10
  SPEAKER_02: So it's kind of almost um, you know, at cross.
• 10:14
  SPEAKER_02: And now I want to define to your third point.
• 10:17
  SPEAKER_02: So data equity refers to the equitable access, representation, and use of the data within an organization and its interactions with the community.
• 10:28
  SPEAKER_02: So, in simple terms, let's say you have an objective about which you have to do some data collection.
• 10:34
  SPEAKER_02: I want you to think about it from community's perspective.
• 10:37
  SPEAKER_02: That means you need a representation of your internal team members who are supposed to be designing those data collection tools, but also get a representation from the community who are going to help you to understand if the objective is truly centering the community or not, or is it only supporting those who have um who have wealth or have power?
• 11:01
  SPEAKER_02: Then you are going to use your community's input to figure out what should be asked in your data collection, who should be asking it, who should be designing it, where should you be storing that data, what would happen after you have stored and collected that data.
• 11:15
  SPEAKER_02: Like it shouldn't be one-way street, you need to do something with that data.
• 11:18
  SPEAKER_02: That is data equity, that equitable access, representation, and use of data, that is data equity.
• 11:24
  SPEAKER_02: And the examples could are, you know, they could very vary.
• 11:28
  SPEAKER_02: Like if you do inclusive survey design, that's an example of data equity.
• 11:32
  SPEAKER_02: Talking about your uh survey results back with your community with transparency, that's an example of data equity.
• 11:39
  SPEAKER_02: Building informed consent and data collection, that's an example of data equity.
• 11:43
  SPEAKER_01: That's really great insight, Mina.
• 11:45
  SPEAKER_01: And it what it really actually kind of takes me back.
• 11:48
  SPEAKER_01: I was the executive director of a regional diaper bank in Seattle.
• 11:53
  SPEAKER_01: We were serving families experiencing homelessness.
• 11:56
  SPEAKER_01: And I will tell you, we kind of had an overly confident um ethos about us.
• 12:02
  SPEAKER_01: We really we kind of showboated, we had this really trauma-informed low barrier model where we cut through the red tape, the bureaucracy, and then we met families where they're at and we delivered immediately.
• 12:13
  SPEAKER_01: And that was great, right?
• 12:14
  SPEAKER_01: Because our model was really low barrier, our intake form was low barrier.
• 12:18
  SPEAKER_01: Families would call our crisis line, they place an order, and bam, they get they get what they need immediately.
• 12:24
  SPEAKER_01: But I think looking back, I realized this much later that we were completely missing a crucial segment of the communities that we were serving.
• 12:35
  SPEAKER_01: Why?
• 12:35
  SPEAKER_01: Because many families that are experiencing homelessness are completely invisible to the system, right?
• 12:41
  SPEAKER_01: They're not even tabbed into this network of human services.
• 12:45
  SPEAKER_01: And, you know, we completely missed the mark on reaching these families because a lot of families, understandably, they're worried about mandatory CPS reporting, distrust of bureaucracy, bad experiences with human services.
• 12:59
  SPEAKER_01: And I look back and I think to your point, Mina, what we were missing was that representation on our team, the people that developed our intake form, because the questions we were asking were completely not reaching the people that were most vulnerable, that we really needed to be reaching out to.
• 13:18
  SPEAKER_01: And so it basically is just kind of a real eye-opener about how biases and data collection, in terms of how we set up our intake form, created these inequities that we weren't even aware of.
• 13:30
  SPEAKER_01: And so I want to talk about some of the larger issues about data equity.
• 13:36
  SPEAKER_01: I think, I think there's a lot of fear.
• 13:38
  SPEAKER_01: I hear there's quite a bit of stigma.
• 13:41
  SPEAKER_01: I went to the nonprofit tech conference in Portland, and I remember a keynote speaker named Anna Suya Singupta.
• 13:49
  SPEAKER_01: She's a co-director and co-founder of Who's Knowledge?
• 13:52
  SPEAKER_01: And she was talking about how there are some real concerns about data and AI.
• 13:59
  SPEAKER_01: Who gets to create and control the data?
• 14:01
  SPEAKER_01: Who's included or excluded in the stories that we're telling?
• 14:05
  SPEAKER_01: What information is shared through these search engines, through GPT chat?
• 14:09
  SPEAKER_01: And she really did bring up some good points about the power dynamics, right, of data, of AI.
• 14:16
  SPEAKER_01: She was talking about how it really boils down to Eurocentric narratives, to sexism, to racism.
• 14:22
  SPEAKER_01: And so this really just has me thinking, given what you know about the current trends and data, and you know, people frankly, some people are really fearful that it's going to displace jobs.
• 14:34
  SPEAKER_01: Do you think that these concerns are justified?
• 14:37
  SPEAKER_01: How do you approach all of this?
• 14:38
  SPEAKER_01: Wow, Albert, you always ask like hard questions.
• 14:45
  SPEAKER_02: This has a this has a lot of things to unpack.
• 14:49
  SPEAKER_02: Like you are talking about how do you approach this, which is a complex topic of every possible ism happening out of um AI, right?
• 15:02
  SPEAKER_02: And then whether or not it's actually happening.
• 15:05
  SPEAKER_02: In my mind, this question has a lot to unpack.
• 15:07
  SPEAKER_02: And I'll probably start with the most human expression that it's exhausting.
• 15:13
  SPEAKER_02: As someone who is close to this topic, as someone who has designed algorithms, as someone who teaches now AI and data equity to nonprofits.
• 15:22
  SPEAKER_02: Um, this is exhausting.
• 15:24
  SPEAKER_02: But it is very, very important as well.
• 15:28
  SPEAKER_02: Why?
• 15:28
  SPEAKER_02: Because we already live in a world where data is of, you know, it's it's highly considered important.
• 15:35
  SPEAKER_02: It's very important.
• 15:37
  SPEAKER_02: AI is has already entered into the picture.
• 15:39
  SPEAKER_02: It always existed, but now it has this unprecedented attention from around the world.
• 15:47
  SPEAKER_02: I don't see for the next 30 years that AI is going anywhere.
• 15:51
  SPEAKER_02: It's only going to be here.
• 15:53
  SPEAKER_02: And the way AI systems are going to evolve is that they would be faster, they would be more capable, more robust, probably more intrusive.
• 16:02
  SPEAKER_02: And so two things.
• 16:04
  SPEAKER_02: One, all the isms that you talk about, yes, it is, it exists, it is possible, and it is only going to stay relevant.
• 16:15
  SPEAKER_02: I don't think we are really like getting rid of our data, our data system, the data that we have right now, it never considered to ask these questions like where's this data coming from?
• 16:26
  SPEAKER_02: Who is whose data is this?
• 16:27
  SPEAKER_02: Who collected this data?
• 16:28
  SPEAKER_02: We we missed those questions in the past.
• 16:31
  SPEAKER_02: So the data that we have right now, it has its own biases and issues baked into it.
• 16:37
  SPEAKER_02: We are feeding that data to AI to come up with answers, to come up with generated text.
• 16:45
  SPEAKER_02: So, of course, that text is going to have some of its own biases.
• 16:49
  SPEAKER_02: And then we are taking that AI-generated output to train AI systems.
• 16:54
  SPEAKER_02: Again, we are we will get to a point where human-generated data would be scarcely available when we have, you know, all of us have so much AI-generated content that's going to go back to teaching AI through their own language.
• 17:08
  SPEAKER_02: And I think that is definitely problematic.
• 17:13
  SPEAKER_02: Second to the fear of job replacement and displacement, that's also real, that's also true.
• 17:19
  SPEAKER_02: But the way I see it after the exhaustion, as someone who has engaged with these topics for a very long time, I have found a lot of joy.
• 17:28
  SPEAKER_02: As much as I fear some of these things, I've spent years working in them.
• 17:32
  SPEAKER_02: And there's joy when you work with them ethically, when you work with these things responsibly.
• 17:37
  SPEAKER_02: So the way I see it, our future, we have an opportunity today to approach these subjects from a place of strengths, not from a place of fear, deficit, competition, or lacking.
• 17:52
  SPEAKER_02: It's an opportunity for us.
• 17:53
  SPEAKER_02: This is a moment where we can make a choice whether we want to approach these topics with this place of strengths or not.
• 17:59
  SPEAKER_02: This is up to us.
• 18:00
  SPEAKER_02: And we can take that to talk about what to do with that fear of the job displacement or replacement.
• 18:07
  SPEAKER_02: The way I see it, we are in using this opportunity, tell our communities that in this moment our jobs are transforming.
• 18:17
  SPEAKER_02: AI is here to stay for the coming years, yes, but it is not necessarily going to take every job away or, you know, do everything by itself.
• 18:28
  SPEAKER_02: We are not there yet.
• 18:30
  SPEAKER_02: What we need for the coming years to use this moment of opportunity to execute this choice that we have to approach this topic from a place of strength is understanding that AI is transforming our job descriptions.
• 18:44
  SPEAKER_02: That means what we need to learn, instead of being afraid that it might take away my job, what we need to learn is how to build a good, healthy partnership kind of relationship with artificial intelligence.
• 18:59
  SPEAKER_02: We need to learn how to center humanity in these conversations.
• 19:04
  SPEAKER_02: Tomorrow, my job description would take away part of this data collection and analysis to AI, and that's okay.
• 19:11
  SPEAKER_02: As much as I still want to do everything by myself, the kind of nerd I am, that's okay if AI takes a part of it and does it.
• 19:19
  SPEAKER_02: But the question would be: when AI generates that output, when AI generates an analysis, how do I ensure that there is still humanity in that output?
• 19:27
  SPEAKER_02: How do I ensure that when someone interprets that output, when someone translates those insights, there's still, again, that humanity involved in it?
• 19:37
  SPEAKER_02: So to your response, I think we need this kind of an approach, taking this moment, taking this opportunity of making a choice.
• 19:45
  SPEAKER_02: How do we talk about these things from a place of strength, not from a place of something is going to be replaced, displaced, um, lost?
• 19:53
  SPEAKER_02: That's where we can, that's where we will find opportunities of partnership, not ad hoc use cases of using Chat GPT.
• 20:01
  SPEAKER_01: I love this.
• 20:02
  SPEAKER_01: It's really so before we even get to work on the strategy part of it, we really need to shift our mindset.
• 20:08
  SPEAKER_01: Again, a place of strength.
• 20:10
  SPEAKER_01: These are powerful tools for positive change.
• 20:13
  SPEAKER_01: They're opportunities.
• 20:14
  SPEAKER_01: And okay, Mina, so let's imagine nonprofits listening in, they're they hear you.
• 20:20
  SPEAKER_01: They're ready to shift their mindset, they're ready to work on implementing and adopting ethical data and AI practices.
• 20:28
  SPEAKER_01: It's doable, they're ready.
• 20:30
  SPEAKER_01: Can you touch on three simple strategies that any nonprofit, big or small, can start adopting right now to make sure that their practices are human-centered?
• 20:42
  SPEAKER_02: Okay, three things that someone can do.
• 20:45
  SPEAKER_02: Well, first of all, I want to acknowledge that anything, any three things that I share right now, please know that when you start living them, when you start implementing them, your answers to this question will change over time.
• 20:59
  SPEAKER_02: These are not prescriptive, do it once, and in we have solved the problem.
• 21:04
  SPEAKER_02: No, once you start living these things, you will come up with your own solution, own approaches, own answers, and you'll contextualize this question for yourself.
• 21:14
  SPEAKER_02: So stay flexible, stay open as you are thinking about answers to these questions.
• 21:20
  SPEAKER_02: My response to the three things.
• 21:21
  SPEAKER_02: If it's okay with you, I'm gonna make it instead of three, four things that I want the nonprofits to do.
• 21:26
  SPEAKER_02: One, definitely going back to the word transparency.
• 21:28
  SPEAKER_02: Everything you do with data, find out if you are bringing in enough context or not with transparency, and whether or not you are sharing things about that data back with your relevant community partners with transparency.
• 21:44
  SPEAKER_02: Take, for example, surveys.
• 21:45
  SPEAKER_02: If you're collecting data through surveys or any form of data collection, are you including enough context on why are you collecting that data?
• 21:54
  SPEAKER_02: That's consent, that's transparency, that's adding it to your context.
• 21:59
  SPEAKER_02: So that's one.
• 22:00
  SPEAKER_02: That every nonprofit can do right now is start investing time and energy and resources to build some education around these topics.
• 22:10
  SPEAKER_02: We are not used to talking to each other.
• 22:13
  SPEAKER_02: What does it mean to be living in data and algorithms?
• 22:16
  SPEAKER_02: We don't know that.
• 22:18
  SPEAKER_02: We were never given a chance to think about these things.
• 22:21
  SPEAKER_02: Some companies came to us, they told us this is how our app is, this is our product is, this is how your tech stack is, and we just went on from there.
• 22:29
  SPEAKER_02: We never asked, okay, what does it mean to make choices while living in the data?
• 22:33
  SPEAKER_02: So I want us all to do some education together, because that's the problem.
• 22:39
  SPEAKER_02: We don't have common language when we talk to each other.
• 22:42
  SPEAKER_02: How can we do good with data and AI, right?
• 22:44
  SPEAKER_02: Like we could be doing some good things, but we don't know how to talk to each other about these things.
• 22:48
  SPEAKER_02: So education, learning, that's number two.
• 22:51
  SPEAKER_02: Number three, what you can do right now is bring your community into these conversations.
• 22:58
  SPEAKER_02: Your community, that's a broad term.
• 23:00
  SPEAKER_02: It would change from you to me to someone else who's listening this um episode.
• 23:06
  SPEAKER_02: A community depends who's in your community.
• 23:09
  SPEAKER_02: And bring in different people from your community.
• 23:12
  SPEAKER_02: This diversity, when I talk about diversity, I mean going beyond the racial and ethnic diversity.
• 23:17
  SPEAKER_02: Bring in people from your community to talk the data pieces that you are working on.
• 23:22
  SPEAKER_02: Again, for example, if you are sending out some data collection tools to bring in some data to your organizations, bring in a voice or two from your community to say, are we collecting the right things?
• 23:34
  SPEAKER_02: What else can we do?
• 23:35
  SPEAKER_02: Don't just include the fiscal sponsors of the project, but include everybody who's going to be impacted and affected by that data.
• 23:42
  SPEAKER_02: And point number four would be probably humanity.
• 23:47
  SPEAKER_02: Everything that you do in your in your data collection, and I'm going back to the word data collection, everything you do with your data collection or data, ensure that the language that you use, for example, the language that is humanity-centered, humanity-driven, right?
• 24:02
  SPEAKER_02: You are no using words like unknown or other or things that can make someone feel isolated, excluded.
• 24:10
  SPEAKER_02: We don't want that.
• 24:11
  SPEAKER_02: These are some very simple things.
• 24:14
  SPEAKER_02: If we focus on building a good data practices, we are going to be much better ready for AI than thinking about how to get subscription to our AI tool and how to go stuff.
• 24:25
  SPEAKER_01: I love all of this, Mina.
• 24:27
  SPEAKER_01: I guess one final question for you.
• 24:29
  SPEAKER_01: We're talking about inequities with in how we collect and report data and also in the tools that we use.
• 24:37
  SPEAKER_01: But with these four takeaways, they're actionable, they're tangible.
• 24:42
  SPEAKER_01: Do you see any barriers or challenges in terms of who has access to them?
• 24:47
  SPEAKER_01: Are these are these four things applicable to anyone out there?
• 24:53
  SPEAKER_01: Absolutely.
• 24:53
  SPEAKER_02: Um, first bar, yes, it is applicable to anyone and everyone out there.
• 25:00
  SPEAKER_02: All of us are already in data systems of this world.
• 25:04
  SPEAKER_02: We are in our CRMs, we are in the databases of our phone companies, we are in the databases of a lot of different apps that we use, a lot of different technologies that we use.
• 25:15
  SPEAKER_02: Our communities, our families live in these data systems already.
• 25:19
  SPEAKER_02: So if we live in the data, this conversation is relevant to us.
• 25:23
  SPEAKER_02: So these four things are relevant to us.
• 25:26
  SPEAKER_02: Now, how much of an investment of our energy and resources are needed for each of these things, that is going to change from person to person, organization to organization, because this is going to take real commitment.
• 25:40
  SPEAKER_02: We have to spend time to build that transparency, to build that humanity.
• 25:44
  SPEAKER_02: We have to spend money again to understand how to do that continuous education, how to compensate the community when we are seeking their inputs and wisdom on our project.
• 25:54
  SPEAKER_02: So this work, it's relevant to everybody, but the amount of resources that's needed is going to differ from org to org depending on how much and where can we spend our energy, time, and money, because this is a serious, real commitment.
• 26:08
  SPEAKER_01: I love everything here, Mina.
• 26:10
  SPEAKER_01: It's always organic and natural.
• 26:12
  SPEAKER_01: And just to kind of sum it up for our listeners, I think what really stands out is we really need to put people at the heart of our data practices.
• 26:20
  SPEAKER_01: It's not just about collecting numbers, it's really about telling all of the real real stories, right?
• 26:25
  SPEAKER_01: And of course, really engaging our communities from the get-go, not just after the decisions have been made.
• 26:32
  SPEAKER_01: And a final piece of that is really kind of baking into our data practices that continuous feedback loop so that we're continually learning from our community and seeking feedback.
• 26:44
  SPEAKER_01: Mina, is there anything else I missed before we wrap up?
• 26:50
  SPEAKER_02: No, you did a great job.
• 26:52
  SPEAKER_02: That was fantastic.
• 26:53
  SPEAKER_01: That's awesome.
• 26:54
  SPEAKER_01: Well, Mina, I just want to thank you for joining me today, wrapping the Pacific Northwest.
• 26:59
  SPEAKER_01: And hey, one quick question.
• 27:01
  SPEAKER_01: If people want to follow you, where can they find you?
• 27:04
  SPEAKER_02: Okay, so well, it let's start with the in-person thing.
• 27:07
  SPEAKER_02: I live in Vancouver, BC.
• 27:08
  SPEAKER_02: If you are coming to Vancouver, BC, please reach out.
• 27:12
  SPEAKER_02: I'll take you out for coffee and a nice pastry because we have abundance of pastries here.
• 27:17
  SPEAKER_02: Virtually, I am preactive on LinkedIn, so you can find me by my name, Mina Das, or you can go to my website, which is Namaste Data.
• 27:26
  SPEAKER_02: So I'm gonna spell it out for you.
• 27:28
  SPEAKER_02: N-A-M-A-S-D-E-D-A-T-A.
• 27:32
  SPEAKER_02: Namaste Data O R G.
• 27:35
  SPEAKER_02: You can find me there.
• 27:36
  SPEAKER_01: Awesome.
• 27:37
  SPEAKER_01: Thank you so much, dear friend.
• 27:39
  SPEAKER_01: Thank you for joining us on the Nonprofit Podcast.
• 27:42
  SPEAKER_01: I hope you left with the confidence to take a small step today that will make a big difference tomorrow.
• 27:49
  SPEAKER_01: Be sure to click the download button on your podcast player and leave us a review.
• 27:54
  SPEAKER_01: And if you're listening on YouTube, give us a thumbs up.
• 27:57
  SPEAKER_01: Your feedback helps others find us.
• 28:00
  SPEAKER_01: You're here to help others, we're here to help you.
• 28:03
  SPEAKER_01: Until next time, stay inspired.
• 28:07
  SPEAKER_00: That warm feeling when you help someone, it's not just happiness, it's fulfillment.
• 28:12
  SPEAKER_00: And we believe it should be available to everyone.
• 28:15
  SPEAKER_00: From frontline heroes to first time fundraisers.
• 28:18
  SPEAKER_00: Our tools empower you to help others.
• 28:21
  SPEAKER_00: This is our mission.
• 28:22
  SPEAKER_00: This is DonorBox, helping you help others.