 Hey, welcome everybody to another Parsons TKO event. We're just going to get started here in just a second. So we thank you all for joining us and we're excited today to welcome you to a discussion of the power of utilizing data to increase engagement and how that relates to the future of nonprofit data. And I'm really thankful to be here. I'm your host Nate Parsons co-founder of Parsons TKO. We're a digital transformation agency that focuses on helping organizations use technology process and empowering their staff to advance their visions more fully in this digital age. And we do a lot of work around business process improvement technology road mapping and planning and all sorts of other things that help organizations actually enable themselves to use technology not just acquire it. And I'm really thankful today to be joined by two illustrious experts in the data field and we'll start off with Amina Doss and I'll let her introduce herself. Thank you for joining me. Thanks Nate. Thanks Devin for having me. I introduce myself every time I try this in a new way in a new experiment. So these days I would say today I would say I am the one who reads really good books like living in data, and then turns them that those things that I learned into consulting and working on work shopping on advancing equity through data, or how do we build better donor engagement with inclusion in mind so my focus of the focus of my work as an independent consultant for the master data is to always involve data with social equity. And that's what I'm doing these days. Very good. And I am Stefan bird Krueger. I'm the head of data strategy at Parsons TKO and building on on Nate's introduction of the company my role in particular as to help our clients leverage data as a strategic asset, really helping them understand the value of their work both as as something that drives their engagement and drives their relationships with their audience, but also something that's much more internally reflective something that helps organizations understand how their own staff are relating to their work how their staff are relating to their tools their technology their audiences, and really as a way to build a culture that is curious and and inclusive of people's ideas of about how to drive the mission forward. So really excited to be here and especially to talk with you Mina, I know it's it's been very fun, reflecting with you and learning about your approach to data in the sector. Looking forward to this conversation very much. Me too I appreciate it. And as you can imagine, what we're going to do today is have a little bit of a guided discussion where we hope to have a little bit of free form engagement and kind of engage in different things but we do have some topics we're going to work through to sort of really get at the heart of these questions. But we encourage you to participate in this conversation as well we have on the zoom chat open and you know our team is going to be watching that and I'm going to be trying to watch it too so that we can surface those questions and enter, you know, bring them into the conversation at the right places and so please if you have questions you have thoughts they're spurred from what you're hearing please feel like this is a participatory interactive event and we'd love to hear your thoughts in the chat so thank you for that. And yeah I guess we'll dive in and so you know Mina, you know I think we'll start with you, you know you've been a prolific publisher on LinkedIn and you have a fascinating sort of quote unquote origin story a little bit about how you built your own community and started to like use data to actually, you know, build your own community and to have actually really personal relations and I think that's sort of a counterintuitive thing to a lot of people. And you built a really large following, and you know you have this sort of very archetype of personalized engagement that scale on social media and so, you know I think it'd be really valuable for people just to hear a little bit about what you've learned, not just about how to do that process but what your audience cared about and how you actually kind of moved from the analytical data side of mid maxing to personal relationships and really building things that and connections that your audience cares about and that you care about. Well, thanks for that good question even during the prep and when I saw this question, it came to my mind, okay, these folks really think I'm doing something super great. That's not necessarily true I'm doing it with a lot of amazing people like yourself so the question I, I see it in two ways in two parts, one is what does my audience here and the other part is how did I do it, how am I doing this thing and I would say, in the first part how am I doing this how am I bringing a audience. It started almost like two years ago when we all, you know we're sort of grounded by the planet, stay at senior room don't talk to anybody don't go outside and I started to show up on LinkedIn, more because that was, that was a place where I felt comfortable. Tiktok is not my thing and Instagram is also not my thing and making quick videos of dancing and jumping so I decided okay I like to write good content and good posts and I want to show up on LinkedIn and I started to show up. And believe it or not my first way to show up was not about writing posts, it was just about, can I talk to one other human being who nods to me who says yes I understand you. Yes, I see you and I started to reach out and create space for having these conversations coffee conversations almost every Friday, one to three coffee conversations every Friday and that happened for a little while until I felt a comfort to start writing to start posting almost every other week. But to your other question, what does my audience care about, and a part of me always wants to write about mental health always wants to write about social justice. But at some point I started to say okay, I want to talk more about humanizing analytics I want to talk more about humanizing data, humanizing the AI that we are quickly moving towards. So necessarily everybody in my audience already has that that understanding that passion for and so part of my work and showing up in LinkedIn and building that audience and building that engagement has been about. How do I do that, how do I humanize it how do I create that curiosity how do I create that level of questions that I can get it back in return. What does this mean and so I have had some of those now my coffee conversations have moved from. Oh, it was a tough Monday to more like, okay, what does it mean then we put together this question with this kind of data, one or two questions and that to me feels first indicator that I'm creating an audience on these topics. The other other. I'm interesting way. I'm seeing my audience is talking about this this is the for example, if Nate you and stuff and you are, let's say, two people who do not know each other and our subscribers to my newsletters. I'm seeing this, this conversation happening between the two of you, Stefan and me about data equity without me in the middle so I feel like I am connecting to dots who were not connected otherwise now and talking about this and this feels like another good outcome of the audience. I know this question was for me to but I want to answer it to as, as one of me and as audience members, I can, I can attest to what I liked. I think the authenticity of your outreach, the humanity of it, I mean you are a person who talks about data, and you do it in such an open sort of transparent relatable way, you talk about the experience of working with data. I think one of the first posts that where we started actually talking to each other. You were just talking about the process that you go through to have these conversations. It's just so open and relatable. And, and I really liked that and I love seeing it in the data space where we default to talking about the technology so much. We always think of it as it's bits and numbers and it's tools and tech. But it's not like data is about the way we relate with our systems. It's the way we relate with with evidence of our work. And, and yeah, I think just the space you're able to create around data for people to talk about the way they work the way they feel about it. Yeah, I think it's wonderful. It's an example of what we should all aspire to. I actually remember the post you you are talking about. Now that reminds me, so I was doing this experiment last year, I wanted what I am trying to teach in my consulting and in my workshops I wanted to practice that and so I took all my LinkedIn connections as an experiment like they are my prospects they are my donors. And now I want to create a relationship with them. But how do I do that I have these 9,000 people in my network, something that comes up even when I'm giving it to fundraiser here's your segmented list but how do I help them to create meaningful relationships from there so I think that was supposed about it. And you're, you're absolutely right I mean humanizing data is, it can be approached in multiple ways, and I wanted to approach it in a way to not just talk about data to not just talk about technology, but as someone who also who has her own bad days who has her own working days sad days you know strange days and I still want to next day next day, wake up and work with data and how do I bring both the elements of good and the bad days into data as someone who is working and then creating narratives out of it and then taking that context out of it and sharing that with the stakeholders. I cannot keep away the emotions to reduce it to 95% out of this five out of 15 that has to be emotions in it so yeah I'm trying to that. I love it. Yeah, I think you know you know one of the things that you sort of subtly, you know, demonstrated there is like in terms of expertise that I just wanted to highlight for a lot of people is that you know a lot of people start out with data being a two way conversation like there's an organization and there's a consumer of content or whatever or donor, any of those things, but you've done is made at least a three way or a multi way conversation a community conversation where it's not just you and another person and that's the data that you're thinking about it's actually interconnectivity and like what do people in your network need to know about each other in order to have connections, not just what do you need to know about them in order to like talk to them about consulting or you know I think that's sort of a really powerful thing and it's a sophisticated thing and you know I think that kind of leads into my next question which is you know what do you think leadership looks like in the context of data in the nonprofit sector where you know I think a lot of nonprofits power or you know is their potential power is through these connections they make in that they bring people together and they coordinate you know a lot of people to do good you know what do you what do you think leadership with data is going to look like in the future. See that's what I'm working on it's only I knew all the things that my work is going to our work is going to bring forward well see. So I have I have been working in the industry for and I say industry I come from tech and I moved to nonprofit and I have been working for almost 15 years now, a lot of my work before in the tech has been as a program managers as someone who created has to be a dashboards has created KPIs metrics and I moved to nonprofit the work has been similar on analyze data for engagement do these surveys and there have been different lenses when I have had to apply. Okay, this is this project this is this work that I'm doing. And this doing this consulting, I want to throw all of those lenses of man program manager of building KPIs and metrics. I want to go back to the fundamentals and I'll come to the part where what does leadership look like and we have to that, I promise. But I want to, I want to take 100 steps back to the fundamental of data. Because I truly believe that it's like having a relationship in the kitchen and I have talked about this example with seven before in our conversations and I think you to me. One of us have not everybody in the family has some relationship with the kitchen that's one space where everybody has a relationship with some are cooks in my family some would get groceries and just you know stock the fridge. I am more of a fridge reader, nothing else, and then there are some who do it all. Everybody has some relationship data is the same thing everybody has a relationship someone someone collects it someone visualizes it someone analyzes it the leadership is the one who may be looking at some reports to create strategies and making decisions and next steps but everybody's doing something with it. And so the leadership, as of now, if the question is, what is its relationship with data to me it looks like right now it's very specific to okay give me a report here, we'll get together in a room and we'll talk about the next steps. What I want when I want this consciousness and awakening to happen is asking okay where's this data coming from who collected it why did we collect it let's talk three more fundamentals. So the next steps and strategies that we create is truly inclusive that we wanted to be is truly equitable that we wanted to be and to make that happen I want the leadership to look like who asked those fundamental questions. I mean, I love the analogy we're talking about farm to table data. And I think that is, I think that's spot on I, you know leadership there's a lot of room for leadership. There's a lot of room for for innovation and innovating on the way we use data. And I think that the the first place to continue with the analogy. The first step that I'm usually talking people to get to is just getting everyone at the table, you know leadership is setting that table leadership is making sure everyone understands their relationship to the family of data. And, and I like what you're saying which is you go a step further which is we don't just come there to be consumers of it. Although, although, you know, I will say there are serendipitous conversations that happen at the data dinner table. And so, not trying to be too prescriptive has its own benefit. Just let your team be natural. But I think while we're there taking a moment. Let's take a minute with the analogy, you know, a moment to say say grace, you know around our data and appreciate where it comes from appreciate its heritage, appreciate the relationship to the audience to what it represents to what it takes to gather this data why we have it what's the point of it. I think I think having those sort of reflective conversations about your data practice are super super valuable and changes the way people feel about the data that they're working with that makes it more meaningful more relevant, more delicious. And so, so yeah, I think, yeah, I think that's exactly the right approach. And I would, I would want to add there. It's not just about the leadership themselves right like if they would obviously they would have better relationship with data that's good but they are also the ones like you said are setting the culture. And so this constant. Give me fresh data, give me new data. It creates a culture of our expectations for the data collectors or the evaluators are the people who analyze Okay, let me create this new report send it let me create this next report and send it. There's no time in building that culture. Where is the curiosity about that data with this, the general feeling Okay, what am I doing with this data it's not just about answering a question that is being asked to the data so yeah, I think. Yes, that's the great step you're talking about that we can do. I think it does not always come from the top. I think it can come from anywhere in the organization, any side of the table. No, I think it's exactly right. I'll throw out one one hope I have nonprofit leadership and their relationship to date in the future which is that, you know, to take the kitchen analogy I think there is a need, especially as people are collecting more data and getting more sophisticated and the use of data for leadership to say, that's enough. And what I mean by that is like we've washed enough dishes we need to do something else in the kitchen, or the fridge is stocked with enough, you know, you know eggplant for the moment we need something else now you know like, I think that as leaders get more sophisticated and what they're looking at. And I think that the data collectors and the data operators and the data experts within the organization know when they've got enough right because, you know, in our own business I can tell you that sometimes I need to know things down to like a second decimal level and a lot of times I just need to know is it a big thing or is it a small thing like you know those are totally different in the data world. And I think that, you know it's often difficult for people to know what their leadership needs and the leaders because of their lack of expertise and knowledge and comfort with data as a subject and say, give me the very best thing you possibly can, regardless of how much effort, reality or anything else needs to be bended in order to make that actually happen, you know, I think is as nonprofits get more sophisticated, they'll know the right fidelity of data to ask for, and that's going to make the kitchen a lot happier place to be in a lot of ways, I think. Yeah, actually I give an example, I think two weeks ago to one of my clients I said, Okay, so this person this particular individual is becoming is in person life is becoming more healthier and so I used to ask these questions about okay where what are you doing the kitchen so I could use the kitchen example more. And he's becoming healthier and and you know, in a way he's bringing more choices of good food in the kitchen and so I gave him this example that okay, what, what do we do and for the first time you're looking at our fridge and we want to okay, let's get some of this fatty stuff out and let's pack it with organic stuff let's bring some good veggies let's pick that's the first level of your data cleaning, you have some bad data, clean it up you get some good data, but you cannot stop with too many organic veggies because that's not good either right you can have just endless carrots and endless fees in your fridge. Right, and so the next question then is, how do you know how many but how much wedgies do you want to stop them how do you know how many organic milk cans need, and that's a very day to day question it doesn't need us to take another webinar or another question is to learn how much food do I need in my fridge, or how many vegetables I need in my fridge, we can take the same feeling the same emotion the same understanding and same awareness, and bring it to data, clean it up, and then decide what do we need to do with it so it doesn't mean in a way we, we do know what are the things that are going to be good we just need a little bit more consciousness and clarity and purpose but then our own absolutely I want to latch on to that word purpose, you know I also very recent client conversation where they're looking to move to a new CRM, and they are coming to it with so much complexity so much data that they've collected over the years and all these different formats from all these different contexts, and the weight of that the weight of that complexity is is truly holding them down and holding them back. And, and they have this feeling that we need to keep this we need to somehow preserve every bit, every line every name that was every entered and all the context from it. The need is actually preventing them from moving forward. It's causing them so much stress that you know they can't even really work with it. And there comes a point where you have to do the cost benefit analysis like the potential benefit of this data that's preventing you from acting is not good enough for you to keep it. But really, we're figuring out when it's time to clean out the fridge. You know what can we call, and what we retain, what is the purpose of that and that I think that word of purpose is central. It's to so many things I mean not not least of which is compliance, you know with all the data privacy laws out there having purpose for your data is critically important to legally have that data. But I think beyond beyond that it is the question of what is the role of the data in our organization. You know I often say data by itself has no value. Data gets its value through its adoption through its use through us looking at it thinking about it having conversations at the dinner table about it. I think just yeah having having that purpose and using that purpose to guide both our operations on data, but even the very architecture, the very, you know, the inventory of what we track and what we keep, and what we get rid of. So important to figure out what you want to get rid of as well. I mean, it's making me think about change moments in general you know we just did a big, you know, educational push on how Google Analytics 4 is going to change the, you know access to data and the kind of data that a lot of nonprofits are able to get their hands on and you know what a big shift it's going to be. And we're also seeing quite a few organizations in the marketplace moving off of Luminate and last year this year and next year and you know trying to figure out what's next for them. And you know Stephanie was talking about a big project where people had a lot of legacy data and spreadsheets and other things that are trying to figure out a squish it into a new system. You know it was changing the air I mean you know both here in technology and in the profit land and you know in America and the world were broadly, you know what's what's new that people should be aware of what's what other ingredients are coming into the fold that you know people need to plan for as they're kind of trying to manage this change moment like you know are there other new things that are entering the mission driven space. I think Stefan do you want to take this first because I'm probably going to go a rabbit hole and ran so I'm going to let you go. I know what rabbit hole you're going to go down so I'm not in it. I, you know I think in terms of what's, what's new in the space. Again, I mean I mean sort of hinging off of what we've been talking about the newness is a little bit about technology there's lots of new technology and I think things like the Google Analytics for transition will change what's possible. You know, for better and for worse, I think, at times, because when you change things like with the Google Analytics example in particular Google Analytics three is going away. The nonprofit sector has a huge amount of experience with that tool that platform. And it's getting completely upended, you know, they're going to a whole new world. This has a very real implications. I think the technology changing matters. It introduces possibilities Google Analytics for can be built into all kinds of new and modern and creative and advanced solutions. But, but I think all of that, whether it's good or bad or which one's right for you, it all comes down to the day to day experience. What is our relationship with these tools. And so I, you know, when I talk about new and the nonprofit sector, I kind of don't want to focus on the tools of the technology and the disciplines, knowing that AI is out there knowing that you can use machine learning. It's really, it's cool. It's very exciting. Lots of new things are possible. I like looking at it because it helps us brainstorm the things we would do if we had that that's valuable. But that act of brainstorming I think is worth more than the actual tool and technology, because the things you imagine. There are lots of pathways to achieve that. And so I don't want people to get too hung up on the tool. I want them to focus on the what could I do if I had data like this or if I operated it in that way. I mean, I tried to stay out of rabbit holes, make it out of your way. You did a good job. You did a good job. I would, I would agree. I agree with you what you just said, Stefan, and I'm going to add that but a little bit more of a fundamentalist ethicist lens to say going back to the kitchen. Would it matter if I prepared chicken with a new spoon with a different looking spoon with a different looking dish that would it matter if I serve it on a white plate versus a flowery plate, because those are the things that I'm translating into work. These are the tools that's the technology that's enabling you to do your primary job of cooking and not cooking and probably hundreds. But doing your job with data, it's the primary thing. And so, when it comes to has asking this question what's new in data, I almost want to immediately say there is nothing new about data. It's fundamental. It's old. It's almost like, you know, this is one of my, and I said this before, this is one of my techniques is I don't want to use 105 emojis around the word data, new data. No, it's old. It's about, it's about people. It's about the things we are engaging with that matters to us and the data we have been collecting it exists already. Anything new that I would want to see amongst us about data is just the better consciousness and better understanding and purpose of what we are doing with it. And I know I have picked this book in front of you guys even before the call, but this is a great book living in data. And, and I'm learning a ton of stuff from that book. One of the things that I'm learning specifically is we have come to a place where 95% feels bigger than the number five. There was a, there was a page I was reading last night where the sentence was, we have come to a place where 95% is bigger than number five. How 95% okay, what does it, where is it coming from what is it what does 95% you present out of how many five what does that represent how many, but we just automatically placed so much value, just on the number itself, or the just those little digits that are missing, where we are coming into those conversations and what's coming out of those conversations so anything to new I would just go back and say that we need more understanding where's it coming from that the purpose maybe. I'll stick to that word. Yeah, I think you're fun, you're totally right I mean there's certain things that are just sort of earned fundamental and I think data is one of those things but um, you know we'll say one of the things that I see you know to answer my own question here a little bit in the space is related something I learned at the very beginning of my career and these things seem to be cyclical but the first book I read about data really outside of like my physics, you know background, you know like about like engagement and things was this book called people where, which was, you know, managing knowledge workers and these guys had done these incredible long investigative surveys of big companies figuring out what made people productive and what made people unproductive. And you know in the technology world, one of the things that developers were always trying to help leadership understand was this idea of the mythical man month that if a project took two months, and you added four more workers that didn't mean it now took two weeks right it really meant that maybe took three months because this work people actually didn't add velocity, just as like you know as like putting more gas in the tank or adding more, you know batteries to something like you know like it actually doesn't work that way you know and it was really trying to help leadership understand like how data worked and how knowledge workers worked in relation to that data. And you know one of the things that we're seeing industry wide right now is the consolidation of marketing and outreach teams and fun development and fundraising and grant giving teams together. And that there are people are realizing that that data isn't different right it's actually the same data just use different ways and often informed and improved by connecting those two people's views and understanding of that data together. And in fact, one of our clients, you know recently had their title change from like I think there's like a VP of comms or something like that to VP of engagement and you know we want to give ourselves a little pat on the back of the engagement architects but I think it's actually an industry, you know, trend more widely. But that's coming together and I think in a lot of ways what I see is the new thing in the nonprofit space in the mission driven space is a better understanding of the kind of data they have, you know, not that the data is different or that there's new tools or new software for the data it's that their relationship to the data is new, and I think that's really an important fundamental shift in the sector that I hope continues because it's so meaningful when you think about the first time somebody is met to a point where they might put you in their driving or their will or something like that and that's a long relationship often, you know, and in most organizations, still, there's a big, you know, sort of fire break that you know somehow that data and that relationship has to jump inside the organization if even if the person outside wants to have a building and continuing a deepening relationship so you know I do think the relationship with data maybe is what's new as much as any like you know fancy widget or whatnot in the space at least for what I could say, you know. So those long surveys made you mentioned it reminded me as you were talking about my previous and all those jobs where those really long surveys would land in my inbox, you know, or I would be the one talking to the teams who would be sending out the surveys and I think at this point and I say this, my work is evolving, I am learning so I am evolving and I'm sure we all are. And from the point when I used to do those jobs as compared to now, I see big problems with those long surveys that just asked for if the ad for more people this is going to bring up the operational efficiency to this match or that. It's almost like we're taking people as like units in that data, like you know, but the problem is it doesn't it doesn't differentiate between, let's say, for example, access needs. It's not just units who are same it's not having four oranges of exact same size weight shape and adding it to your juice it's like four different come for different people they're they they access needs to be different and I'm giving one example of how that's one of the problems where we cannot assume that I am going to be the same way productive as Stefan you are going to be as you are going to be if the three of us are added into a project, and we need to as managers as leaders we need to be aware of what my team needs and sometimes those standardized surveys have those problems so I'm probably very tangential but I just those surveys reminded me. This is all rings very true to me and me in a year example before of the 95% is bigger than five made me wince and brought me back to my own you know when I was young in my career and an analyst sort of responding to requests from people all over the organization. One of my favorite, favorite and most common questions I got was can you give me. You know, can you tell me about this campaign that we ran or this you know social media push that we're doing just the numbers just need the numbers. And it's such a ludicrous questions to ludicrous request and and it rings so it, it rings hollow and it speaks to the relationship that people have with data right now. Because when you're doing that you know what you know what's happening with it is going into a report maybe a spreadsheet or something. It's checking a bureaucratic box, and it's not being used what it's meant for, which is to help people understand the relationship with their audiences. And I think that's the fundamental thing and and and it's it's so easy to do this, I think with social media data in particular because there is so much of it. And the format that it gets delivered to us in and social media data you think about the dashboards that you can log into you think about the the roll up reports and things like that. It's very easy to just get that top line number how many likes how many followers how many posts. It's very well structured to spit that out. But and I'll actually give those dashboards some of this credit. People often look past that that roll up screen, you know the here are your five best performing posts. It's very easy to gloss past that because I okay well I don't want to know about the top five want to know about all of them give me the top line numbers. But there's so much value in digging into that there's so much value and just opening up those posts, just scrolling through the comments and seeing what people are actually saying. And this is something you know the numbers with no context are meaningless. And the best thing we can do when we're trying to talk about analytics is bring along the annex data. You know, give me an example of what these numbers look like what they represent actually show me and I think when people, you know, recent post I put up was about how data without UX is pointless. When you actually think about the experiences that you're creating for your audiences when you look at these numbers, it transforms how you interpret them transforms what you understand about them and it transforms what you will decide to do next. It just makes it puts your brain in a creative space as well, when you're thinking about you user experience. So, doing that adding that context actually visualizing what the data represents. And I think that's a real world way, not even just from a data visualization perspective is, I think I think that's, that's new and and leadership. And I think it connects back to the question number two that we had, um, what does leadership look like with this data right like so some of the example that you mentioned it kind of brings and to our topic of today's to engagement. So that that's the expectation leadership sets this example that you gave just now, your leadership would ask for these just the numbers to high level numbers, and you as a new analyst but produce them and share them that creates an expectation for the new analysts right that Oh my leadership wants these. So probably good leadership looks like this so I should do the same when I am moving forward and becoming a leader, I'm going to ask those same numbers. This is like a cycle and we want to break break that kind of a cycle. And I think that what looks new in the leadership should to setting up the coming up the culture. I think these are such wise things and you know reminds me of a sort of funny was sort of related story for back in my past so you know, a little in fact I used to work in the travel and tourism industry. I had a lot of connections with people who did social media campaigns for big brands and things and one of my friends went on to work for the agency that I managed to one of the big three automakers social media accounts, and you know things were popping along and then they had this particular week where their engagement was off the charts and everyone was like really excited. And then what they found out was that I'm, you know in the context of that's of looking at that data. They had never gotten to log out on their phone of the corporate account and had tweeted something about how terrible Detroit's roads were, and that had like really soured this big three automakers relationship with the current government of Detroit. But people thought it was hilarious that one of the big three automakers was trashing the roads in Detroit on the on the report it looked great it looked like they're having a great social media week. In reality, it was a terrible week and people end up getting fired over it. And that's just a good example of like data without context can be very dangerous you know and you can lead to a lot of erroneous conclusions you know. Yeah, and maybe that's a good segue another kind of thing that I think about a lot which is you know as we get more castle data as people get more involved with data. So there are more concerns about sort of put data privacy and equity, you know because I think one of the challenges with data, especially social media data and things like that is that it can be really non representative of the populations that you wish you were reaching or the community you wish you had right I mean, there's all sorts of things just three time access to that like you know the cost of the devices the cost of plans like where people have those things the time of day you're interacting with people versus the time of use of devices I mean it's all kinds of ways that you can introduce bias, or just misrepresent or get a different pie slice of the people you're trying to like, really have a good community conversation with so I'd love to hear a little bit about, you know just some of your experiences and thoughts around like, you know both on, you know, the access and management of data and you know to make it an responsible way that kind of you know ensures everybody's privacy and you know, you know, have sort of an ethical bet to it, but also the sort of focus of how do we make data really, you know, mean what we think it means when we say 60% or 30% of something is happening like is it the right 30% like how do we kind of, you know, manage that from an equity and a kind of diversity perspective. Ooh, it's a very, it's a question I could probably write an article on I'll take this down to question for my next newsletter edition. I'll show up on top and let probably stuff and chime in and then come back again. So I was thinking about equity and equity when I read this question I was thinking how should I respond to this question because it's a, it's a broad topic, it's, it's, it's tough and it's big and it's challenging question and we are all dealing with this. One statement or that came out of my observation was there are two different things I said so privacy and equity. We know we are familiar with the word data privacy we have seen whether or not how much and we understand or not we are familiar with the word equity with data is a sort of the new word, we haven't seen that much. So, the way I have observed this in my conversations and in my work is. We think and I say we I say sort of like all of us in the world, we think data privacy has 15 different ways to think about what we understand none for equity we think about there's probably just one some magical way which I don't know today but I learned from how to in one of the conferences, and then experimented and then it will be done, neither is true. The understanding we need has to be in the smallest portion of how the data is collected to all the way up to how it's going to be used how it's going to be leveraged was going to use it. There, I think we often circle these questions because almost with a hidden intention of, can I get a how to, and there is no how to. Use and ask these questions, every step of the work for me my work looks like doing let's say. Let's say I don't engagement survey, it has to look like from the point of view of designing questions, sending it out the settings to the analysis to the report to how that is going to plug back in when into the strategies every point has some portion of equity in included in it. Yeah, I'm going to farm more thoughts stephen chime in please. I mean no it's it's it's wonderful I think it speaks very much to my perspective on this, the equity in your data reflects the equity in your strategy. Your data is a reflection of the strategy. And I think it comes back to that point of what is the understanding the purpose of your data, understanding why you're collecting these things. What kinds of decisions you're going to make with them, what is the point of tracking this what is the point of studying it of analyzing it. And that is that's going to roll all the way back to how you collect the data whether it be a survey or passively on your website or in your email system. If you want to be able to manage to various aspects of equity, you need to know that very early on, because you need to build your tracking of that equity into the way you collect your data. So that means including things like questions about identity in your survey or in your, your event registration form, because if you want to ensure that your population is representative of the population you intend to reach. You need, you need to know that you know so often people will come to a campaign at the end and say tell me about how equitable it was. And all you have is well this many people wanted lunch, you know chose fish over the chicken. You know, if you don't know and you don't plan to manage around that you're just not going to have the right information in order to manage around that. So it does it I think it starts very early in the process. Before you even get to the data in order for the data to be able to be useful in that. And I think that that that point of purpose is also relevant from a privacy perspective. Because if we are dealing with things that are often sensitive information about people. You need to recognize that you need to recognize that sensitivity and figure out from the purpose how do I limit. When do I know it's time to delete the data that I've collected that sensitive information about people. When do I know, how do I know who needs this and make sure that nobody else has access to this data. And, and really making sure that our, our data management practices aligned to that purpose, and, and nothing more. And so, so I think I think those are, those are my perspectives there. You know, I, and I would I would add there what you just mentioned something like in this I think came out of one of my converted recent conversations. This is the social identity data, like we were talking about something. Why don't people why don't be included in our data collection and send it out, you know, all these standards seven eight questions, but it doesn't work that way. It doesn't work because this person I was talking to their responsible for collecting data for their institution, and they are being asked for from their leadership to collect the social identity data. And I think this is where we were talking about just adding those eight questions, and then creating reports and saying five out of 15 people who responded to so and so race or ethnicity or sexual orientation said this, and so we should do this. This can cause more harm if we don't dig deeper because, and one of the things I recommended and I think we all could wherever the social identity data has been collected we could do is, let's start tracking how much data. We are not collecting in those questions let's start tracking because that talks about trust that talks about the back to the privacy thing so if say I'm a trans woman and I have seen the harms that have been going on in the world. And all of a sudden I get a survey that asks me question to share my identity which is so personal to me, and I am no clue why you are collecting it how it would be used. I am not going to give you that information, no matter how many how well in a language you put that an asset question and now it's on you as an institution I would suggest start tracking how many years have you asked and not got back this response so I have not got it back. My data quality was 40% on social identity today next year it's going to be 38% the year after it's going to be 35 the lower it is the better it shows you are creating trust and environment of trust so I can share something that's so personal to me. But just looking at what we have collected and not tracking what we have not collected can also cause harm. Absolutely. Absolutely. And, and this gets back to me, you're very early point, which is all of this work that we're doing all of the work around data around audience engagement, our relationships. I think, when you first said it, you know you said it's not just a two way relationship. But it's also a relationship between peers. I think, I think we're lucky if we even get to the two way relationship part so many organizations just think you know I am, I'm a press release machine you know I'm just pushing out content. They're so inwardly focused about how they engage with their audiences and meaning your point about that if you're collecting data, be clear about why you're collecting it to yourselves, but also to your audience, you know let's talk to our people if you're going to ask about social identity to them why you're asking how it benefits them for them to share this information. And, and what you're going to do with it you know are you going to keep it forever you know are you going to report to your board about my identity. You know these are things that are relevant to people when you're when you're engaging in these sensitive topics. And, and I think that kind of transparency that clarity and doing it in a way that is authentic and approachable. You know it's not enough to put it in your privacy policy and you know your terms and conditions. Have a conversation with your audience help them bring them along in what you're trying to do. I think, yeah, authenticity. Yeah, and this may be a kind of an inarticulate, like you're not another very articulated way thought that's coming to my mind this. And I've seen this before. I think we need more grace and patience and the way we create our goals. So let's say we got created a post on behalf of a company on a social media page and that tracking back to that tracking engagement on social media. So I got like 15 good comments and 200 lights but no we were actually hoping for 600 lights because that's what usually otherwise would happen in, let's say other organizations or I think that's that's a problematic place to even though we have had some idea about how we are collecting data, how we are collecting data, unless we also keep some grace and patience with, you know, seeing the results and so the moment you see only 100 lights and you say, Oh, this is not working this you know this idea of bringing equity and doing it is not working let me go back to how things were working and let me take back the approach. That's not going to be helpful. So we need to have a little bit of a patience on what we are doing trusting this process because it's about including people it's it's making sure that it's not just about categorizing and segmenting and then, you know, being extractive about it and then sharing back that information so then there needs to be some as it was grace and patience, and how we see those numbers. We totally agree I mean yeah reminds me a little bit of one of the other things here is that you know language is so important and data is something that can help us improve our ability to communicate like but you know how we do at the format but actually even the language we use I mean, you know we focus a lot on the think tank industry and they are like a poster child for terrible language choices where you know they'll often have academic experts with PhDs talking to other PhDs who have been working on a subject for seven years. Write a post that they want the general public to understand the importance and value of and there's an enormous golf between the language they're using the technical terms the way that they slice and dice nuance in the you know the language they use and the readers ability to even comprehend the way they chose where they ever be and the importance of that choice you know like that there's such a big golf there. And you know there's another thing that sort of more broadly happens in the nonprofit community sometimes like this term Latin X which is you know has terrible polling that most Latinos do not want to be referred to as Latin X and yet, many people like to use it because they feel like it makes them progressive or that it's like the right terminology and people will come around or something like that and you know those things all create barriers to actual equity right because if you have someone to impart to someone or you want them to support something you think will be viable for the community or for the world. You have to meet in the middle and to have a good communication and really understand each other and listen to each other and you know I do think that's a really important part of data which is to like help people inside the organization, rethink and hopefully reimagine how they're communicating you know and I think that's something I really hope data will help do more in the future which is to get people off their communications high horses and get more to a like practical reasonable place where they're just like, what do we need to do to communicate this in a way that really resonates and how can we be, you know, empathetic and really listen when people are telling us they're not getting it. And even if it's through sort of anonymous data sometimes you know and I think that's, that's, you know, one of my little hobby horses for the nonprofit sector which is like be practical about your language, you know, and I do think data has an opportunity to help with that. Yeah, absolutely. I remember, you know, my first, in my first college, when I was doing my first degree. And one of my courses were marketing and statistics and I remember one of our projects back in and this was back in India, one of our projects was. And generally because the country speaks both Hindi and English and the official language and English is taught in a lot of schools. We were told because you can speak two languages. We can see a movie, any cartoon, any show that and see the translate watch the translated version and tell me how do you feel and tell because we were talking about the language statistics and marketing class and that was part of our project. I think I picked up with my couple of my classmates, Nickelodeon or something like that, to see the translated version from English to Hindi, and the same version back in English to compare what how it seems. And it missed a lot of context. It sucks, honestly, you know, I'm sorry for the language it sucks. And that's when it was one of the learnings for that project was language really matters and if you're translating something from A to B, B to C, ensure that the context stays it's not just about using a voice over in that case or taking a word and then using it in that another language. There are nuances because of which that language shaped up to be what it is. And so we need to save and protect that nuances and whatever the context is, we are putting that forward. Yeah, a poem in one language doesn't translate in a machine way to a poem and another language and there's poetry, there's poetry in a lot of our work is poetry in the way we write poetry in your data. And, and I think, yeah, that context makes all the difference. Wonderful point. Such a good point. Goodness. Yeah, so I guess we're nearing the top of the hour here so I know folks may have other places they need to run to but are there any other questions folks have from the field that they might want us to address or I don't know. If you have any thoughts verge from this that you know we haven't sort of like dive into that you wanted to kind of wrap up with but um, yeah. Yeah, I mean I'd love to hear from the audience if anyone wants to share but you know I think my parting thoughts may not just thank you so much for taking the time to speak with us and sort of share your ideas. Thank you to social media, social engagement, authenticity, purpose to the way you reach out to people. You've, you've demonstrated so much of what we talk about. You really practice what you preach. I respect what you've built respect what you've done tremendously. So I really appreciate this opportunity to sort of hear and connect ourselves to you and your good work. It means a lot. Thank you. Thank you so much. This is the first time we have had that coffee and I'm so thankful that I wrote that post and we got connected. And since then every engagement I have had that persons to go or that made you in our conversations with seven you I mean, I really appreciate this. Every day I find spaces where what I'm talking about comes back with like nods that make sense. Yeah, I'm getting I can see those nods here so I really appreciate being here in this space it's my pleasure to save me a couple spots from making up because I had a good conversation so We're really glad to have had you here and then stuff and I'm very thankful for you as well and this has been a really illuminating conversation for me so thank you so much for for for being here and for spending your time. Thank you. All right, well, recording this will be available soon and I think Mickey has some follow ups for us if folks would want to, you know, connect with you mean or read some of our blog posts on the subject or anything like that but Thank you everybody for coming we appreciate it. Yeah, there's our little little place to connect folks to. Yeah, and thanks for any questions for us I'm more available after this and you know, I mean as a prolific poster and we try and, you know, keep up with the least the fourth of her volume on our own posting and stuff so you know if you have any questions after this send them our way and I'm sure those will make their way into an answer somewhere so thank you so much everybody. Bye.