California Demographic Data Collaborative Webinar Series (Part 3) - The How

0:00:07.5 Kelly Brown: Good afternoon, everyone. Welcome. As people come into the Zoom. So I
wanna reintroduce myself. My name is Kelly Brown, I'm with Viewpoint Consulting. I would like
to welcome you to the third in a series of five webinars being offered by the California
Demographic Data Collaboration. A collaboration between the James Irvine Foundation, the
Weingart Foundation, and the California Endowment. As we've mentioned in the past, this data, this
series is designed to strengthen nonprofits in California with respect to their use of data in general,
but specifically around demographics and demographic data. Primarily to increase learning and
opportunities, to reduce the burdens on the nonprofits that they're mutually engaging, and to
strengthen their shared commitment to equity and justice. So as folks are coming on, as I
mentioned, my name is Kelly. I will be guiding you, I've been facilitating these conversations.
0:01:15.4 Kelly Brown: And will be guiding you through today's conversation in partnership with
my colleague, Jason Alexander. Again, he is a consultant and the CEO and founder, co-founder of
Capacity for Change, along with my former colleague, Meghan McVety. He's worked with many
foundations and nonprofits, and has been a HUD consultant on one of their key projects. I'm
looking around for his full bio, but I think he can give you an update once we get closer. But Jason
has worked closely with a number of public and private organizations around collecting data, and
we'll be in conversation this afternoon around some key strategies and takeaways. We're also joined,
obviously, by our colleagues at the three foundations. It looks like Kelly Martin and Joyce Ybarra
from Weingart. So today, as I mentioned, we're gonna be talking about data collection strategies.
And but before we move into that, I just wanna give a quick summary of the first two sessions.
0:02:20.5 Kelly Brown: Again, a reminder that all of these sessions are being recorded and will be
shared at the end of the full series in conjunction, in addition to supplemental materials. So you
don't have to take a lot of notes that will be shared for you. But we did want to just give some
recaps of where we started in the first session, which was focusing on the why. Why is
demographics important? Why is it important to what you do? And how you do what you do? And
how do you understand how to use it and deploy it to strengthen your programs? But also to surface
that demographic data is essentially data or conversations around identity, and that can sometimes
be charged. So we really want to support people in their efforts to engage critical, but often
sometimes complicated questions. In the second session, we went over the actual taxonomy, how
you categorize the data, how you categorize people with terms to use, how to think about
demographic terms.
0:03:17.3 Kelly Brown: How to strike the balance between designing data collection tools that are
inclusive of as many people as possible, but are also not burdensome to folks who are responding.
And to remind folks that these things change over time because communities and people and how
they identify change over time. And so as you're designing your mechanisms, be prepared to make
changes. As I mentioned, I also wanna say before we go over the agenda, is that we're gonna
transition in kind of the second half or maybe the second two-thirds of the conversation today to be
in conversation between Jason and myself. But we do encourage folks to utilize the Q&A function
to ask questions of... As we go along, we'll pause in the middle to surface questions that will be...
That you might put in there, but we'll ask if people use that function to surface your questions. And
my colleagues and I will be watching and monitoring that so that we can engage you in the
conversation as well.
0:04:21.7 Kelly Brown: So what we wanna cover today is collection strategies and really trying to
help people differentiate between the kinds of data and the processes of collecting demographic data
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from organizations on behalf of others and demographic data from individuals. We'll lay out a
couple of frameworks to just kind of help you think about how that data flows. And hopefully it will
help you not only think about your own processes, but also engage your peers and funders in terms
of what it takes to engage this data. We also wanna stress the different kinds of data, make sure that
people understand what different kinds of data can tell you and do for you and some pros and cons
of each and helping folks understand what is the best way, depending on your needs, to engage
demographics. And then we'll transition, as I mentioned, into a conversation with Jason about actual
strategies and good practice around collecting information.
0:05:22.7 Kelly Brown: And around actually engaging a range of individuals around these
questions. I wanna start today just quickly with a little bit of a framework around what is the
process of engaging organizations around data in general, but particularly on data that is requested
of individuals and to be reported in aggregate. And some of this may seem sort of intuitive, but we
both break down this framework and flow so that people can really understand what goes on within
an organization when they receive a request for information. And all the steps that happen before
they can actually share it out through to whoever is requesting it or even whoever is using it. And
this happens, this process happens, whether you're an organization that's the size of three or 300.
And I found this framework helpful so that people can understand that all along this process there
are opportunities for people to have roadblocks or for there to be drop-off.
0:06:31.5 Kelly Brown: Or there to be barriers to getting the data that you need and that is being
requested of folks. So this is just a flow chart to help you think about that process. The other aspect
that it's important to sort of lift up and again can seem implicit and taken for granted is that
particularly when you're collecting demographic data or characteristics of individuals and then
reporting it in the aggregate, you really are trying to balance a number of different things. One,
you're trying to get as much data as possible from as many people as possible, often whom are very
different kind of people. So a general population is your potential audience. You're trying to capture
the dynamic debates around demographics that we touched on in the last session, and how that can
sometimes be evolving and may differ between individuals even within the same organization.
0:07:28.4 Kelly Brown: And then you're trying to get that data, organize it, categorize it, and then
share it out on behalf of your organization. So again, if this is a framework to help you think about
how to balance all of these imperatives, and given these imperatives, what is the best way to get the
data that's being requested. I want to just go through again, the different kinds of data. And we
wanted to tease this out a little bit is because people often kind of conflate these things, or are not
often sort of really clear on the difference between data you get from a survey and data that that you
get administratively or as part of a process. So data, survey data, we've all done surveys, we've all
responded to surveys, and it's really data that is collected by asking a specific group of people some
specific questions at a specific point in time. And when you do that, some people will respond and
some won't.
0:08:27.4 Kelly Brown: And it's really unimportant to understand that when you send out a survey,
you'll get a response rate, you almost never will you get 100 response rate. And unless... We'll get
into more detail about the sampling, but unless you've done a lot of work to understand the
population beneath the who you're sending the survey to, it really is only data on the people who
responded to the survey. It's often collected by researchers or organizations, for a specific purpose
and to ask a specific question. Administrative data, on the other hand, is data that's routinely
collected as part of a process, and it applies to everyone. So for example, it's people's personnel
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records, when organizations or clients are coming into some kind of service process, people ask
these questions when you go to the doctor, the EEOC requires this data for compliance. So it's
really data that is attached to an individual.
0:09:21.4 Kelly Brown: And it is collected and the kind of data is collected, has to do with either
regulatory requirements, or things that tell people something about the process of doing business.
So your income, for example, is administrative data, because it's information on everyone, and it
can be used, again, for reporting or for fundraising, and for operational purposes.
0:09:44.7 Kelly Brown: The difference between this kind of data is, as we've laid out, but there are
pros and cons to both, when you're thinking about particularly collecting demographic data on what
tools is gonna be most appropriate for your purposes. So again, if you're using a survey to collect
this data, the pros are that it's easy to deploy, you can ask questions in a format, you can get a list of
people that you wanna send it to, you can share it out and get information. And the other benefit of
getting... Of using surveys is that it can also get information on attitudes or sentiments.
0:10:21.0 Kelly Brown: You can get sort of some other general feedback or answer other questions
using survey. The problem with the survey, however, the cons, as I sort of alluded to before, is what
you... The data that you have really depends on who responded, and there can be a lot of bias and
differentiation between the people who respond, and the people who don't. And so without sort of
really having a good understanding of that underlying population, and doing a lot of sampling, and
an organization in terms of who you deploy the survey to, and how you do follow up and what have
you, you really have to be careful and almost can never make inferential claims around survey data.
It's really, you can only say use the data, and make claims about those who actually responded to
the survey.
0:11:12.4 Kelly Brown: Another key issue around using surveys is that how you frame questions
and how you frame the questions can significantly reflect the responses. And as we know because
we've all done a million surveys, or not done a million surveys, we've been bombarded with them.
Using surveys can often get much lower response and much lower levels of data because people just
are fatigued with them and don't respond. Administrative data, on the other hand, is more accurate
and complete because it's collected from everyone as part of a routine process. So again, like upon
hiring or joining the board, people get this kind of data and attach it to an individual. So it's a little
bit more complete, and it allows for a flexible kind of analysis so that you can ask... You can use
this data to ask different kinds of questions and also to... I'm sorry I'm getting enroute to the chat.
And also the analyze this data over time.
0:12:20.7 Kelly Brown: The cons of administrative data is it's limited in scope, because you can
only ask so many questions, personal questions or individual questions of folks along the way. And
if you don't update it regularly, then obviously that becomes out of date. So you have to require or
make sure that the data is updated as new people come into your situation, as you get new clients, as
you get new employees. Be sure that it's that you're requesting that data to keep it up to date.
0:12:58.6 Kelly Brown: So we want to share with you that often things that surface when people
are requesting demographic data is that there is... There can sometimes be resistance for a number
of reasons to sharing this data. And one good way to get around or to mitigate that resistance is
really to share the purpose, why you're asking these questions, whether you're asking it through a
survey or whether you're asking it administratively, and how you're going to use it.
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0:13:24.7 Kelly Brown: So that it's known, that clear and transparent around your request around
this data will reduce non-response. It's always, always important to emphasize that always, under
any circumstances, whatever tool we're using it's voluntary for individuals to provide this kind of
data on themselves for any reason. But we wanted to just also surface that there are some reasons
why people resist. Sometimes it's privacy concerns in terms of not being clear or not maybe trusting
how the data will use. And this sometimes surfaces, even when you're transparent and you use
disclosure, people can sometimes not trust that. Sometimes people resist thinking of themselves in
certain kinds of categories, in categories in general, the category that you laid out. Sometimes the
resistance is association of these kinds of questions with partisan or political issues.
0:14:19.0 Kelly Brown: But most of the time, most people are very used to answering these
questions. We've all answered them in a variety of settings, sometimes we answer them, sometimes
we don't, but usually it's not a big deal. And point of fact, a lot of research has shown that people are
much more sensitive asking than when being asked questions about their wage, their earnings, or
their income than they are about certain characteristics, demographic characteristics. And we wanna
be sure that when people come across resistance, again, whether it's through a regular process, as
part of your organization and administratively or intake or through a survey, to recognize that while
some people may resist being asked these questions and getting this information, most people will
respond. And most people also want to be seen, and many people wanna be seen and counted. And
so you shouldn't assume that because some people resist.
0:15:10.8 Kelly Brown: And it will usually be a relatively small number, that most people find is
these questions intrusive or inappropriate. Most people understand the need for them, and why it's
important, and we wanna point this out is because sometimes the resistance and the pushback can
be quite intense and very vocal, but we have to... You have to be sure that you're balancing that
vocal resistance with many other individual's desire to be seen. We also wanna point out too, that

when people are queried and they provide answers to these kinds of questions, particularly open-
ended questions that people are thoughtful about reviewing those responses. Sometimes they can be

personalized, especially in an intake session, and Jason will say more about that when we
[0:15:57.7] ____ in conversation, to be thoughtful and respectful about those open-ended questions.
But be sure that you're mitigating and being judicious and sharing responses that might be
personalized or offensive.
0:16:10.9 Kelly Brown: So while I've given you this kind of all [0:16:14.6] ____ You've gone
through it quite quick, I saw some questions of whether or not these will be shared, it absolutely
will, the deck and all the webinars will be shared. But I want to just do another poll and see how
folks on the call are collecting this data. We know from previous polls that many of you are engaged
in demographic data in some way, whether it's about your constituents or your stakeholders, and
there may be some variations, how are you collecting that data now?
0:16:52.0 Kelly Brown: So we're getting some responses, and I see some questions. Before we get
to the formal pause for questions, I'll just ask, is there anything pressing? That's...
0:17:03.4 Speaker 2: Just one question about right now, are there captions available? If not, I'm
sure we'll make sure we have that next time, but just wanted to ask you that as well.
0:17:13.7 Kelly Brown: Perfectly. There probably is captions and if I can turn it on behind the
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scenes in real time, I will, but thanks for raising that. So let's see what's happening in the survey.
Most people ask, a few people guess, and some people do something else. I'm curious if people can
put in the chat, what else they... How else they get this data, if they don't ask, guess, or just no.
[pause]
0:18:05.5 Kelly Brown: The chat is disabled. You can put it in the Q&A, in that case, that might be
helpful.
0:18:28.8 Speaker 2: Oh, Kelly, just to raise that we're hearing that the chat is disabled.
0:18:32.8 Kelly Brown: Yeah, I saw that. So I will disable it. But in the meantime, if people can
put their responses in the Q&A, there's [0:18:41.4] ____ else responses. I'm gonna end the poll
because it looks like most people have responded. That's your results. Most people ask, a few
people guess, a few people do something else.
[pause]
0:19:17.0 Kelly Brown: Before we go to questions in the chat, you wanna summarize a little bit
about the key... Aspects of collecting demographic data, one emphasize, we wanna emphasize
explaining the purpose for which you're requesting the questions, how you're gonna use it. The
prepare... [0:19:35.9] ____ ask the questions that we have to ask them.
0:19:40.3 Kelly Brown: Emphasize how data will be... That may be personally identifiable, will be
used and that all data will be kept secure and shared in the aggregate. Stress consent, ensure that
whenever you're asking these questions and we saw that most people are asking them, asking
individuals as opposed to guessing, that it is voluntarily provided and self-reported, differentiate
between confidentiality and so anonymous [0:20:06.1] ____ is people can know how the data is...
Who the data is associated with, but there's an agreement about how it'll be shared and with whom
anonymous of data that cannot be associated with an individual.
0:20:23.1 Kelly Brown: And then we'll get into these last points more with Jason in terms of
collecting data in respectful and culturally responsive ways and being conscious of collector bias.
We do wanna emphasize, again, while we saw that some of this in the poll that some people do
guess, most people ask. And it's really, really critical that when you're getting this information from
individuals that you really ask and allow them to self-report. Because it's just out of pure respect
that you don't want to assign or assume anything about one's identity. And you also don't want to be
in a situation where you are pressuring people to respond to questions that they don't wanna respond
to. So be sure that establishing processes that allow you to ask and when people don't share their
information or choose to decline to answer that is actually a data point that that's valid. So let's see
what's in the Q&A?
0:21:35.6 Speaker 2: Sure. So, we have a few responses on what are some other things that they
do. So we have, we mostly guess, but do ask some questions when people register for events. We do
policy advocacy on behalf of classes of people, we access publicly available demographic data, but
most often US Census. Someone else also gets the US Census, or partner with other organizations
who collect data. Another one, we utilize a number of survey platforms and instruments. And lastly,
we use reports that exist that are done by other groups. For example, center for Nonprofit
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Management or Pew.
0:22:15.1 Kelly Brown: Those are helpful uses and it's really helpful that folks shared that, is
because when we did the research with nonprofits, we really did see that a lot of folks were
accessing kind of larger data sets and publicly accessible data sets. And I think Jason can also share
a little bit more when we transition now into conversation with him about how those data sets are
accumulated and how organizations can connect to their data those larger data sets to make use of
them. Are there any other questions that people have?
0:22:52.6 Speaker 2: We have a few more. One, there are some data sources where they assume
people's races/ethnicities, like the RIPA data, police stop data. What about working with that type of
data? So I guess they're just trying to asking about, how do you feel about working with data that
other people collect, but it's assuming people's race and ethnicity?
0:23:15.1 Kelly Brown: Well, I think, obviously we're trying to give best practices to people who
are in the data collection process themselves. But I think this is also why we stress the importance
of understanding and having data literacy. So you know what is good data and what is bad data.
Because if you don't have good data and people are assuming then what you do with that data will
be effective. Obviously, if it's not really accurate and not self-reported, then the limitations are...
The limitations on the reliable use of that data are real. And so obviously you can't change someone
else's data source. But I think my advice and Jason can maybe jump in when we move to that is to
be cautious about making too many decisions based on that data because you can't really trust its
viability or its validity when people are guessing because people guess a lot of different things
around people's particularly their race and ethnicity and certainly their sexual orientation.
0:24:22.4 Speaker 2: Do you wanna hold for now or take some more questions?
0:24:25.2 Kelly Brown: Let's have some more. Is that okay?
0:24:28.8 Speaker 2: All right. We have one more. How are folks navigating gender identity? So
far our numbers are so low, they are barely registering. We think it's important, but not sure how to
navigate.
0:24:39.8 Kelly Brown: Right. Well, we this is an interesting kind of arena that I think we're in
now in terms of people feeling comfortable asking about a gender identity. I think what we shared
again in the last session around how to frame those questions, and that they are much more
prevalent is some indication that people who identify as transgender are much more open to being
seen and captured in these processes. And so what researchers are learning is how to phrase
questions in ways that are respectful and appropriate and intensive. But again, I think it's one of
those situations where you provide the opportunity for people to be seen, but you make sure that
you are not requiring people to report if they don't want to and are not comfortable because there
could be a lot of reasons for that.
0:25:36.1 Kelly Brown: And when someone says the numbers are so low, the numbers are actually
quite low. The point is that now while they may be low, 10 years ago before we were asking these
questions, they were non-existent because they weren't even in the in the query process. It is
another... It's an important arena to understand as well that it is a very, very active evolutionary
space right now where folks in those communities are really actively trying to grapple with how to
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be seen and how to be referred to and how to be known. So we encourage people to ask the
questions, but to still expect that there may, at this point not be a lot of responses.
0:26:13.1 Speaker 2: Another question, or you wanna hold for now?
0:26:18.4 Kelly Brown: Do we have a lot?
0:26:21.8 Speaker 2: Yeah, two more. They're coming.
0:26:24.5 Kelly Brown: Yeah. Yeah.
0:26:28.5 Speaker 2: Okay. All right. I'll do one more. So this one's about limited English
proficiency. So if a funder requires data on people with limited English proficiency, what is the best
way to get that data? Should we assume that if they're receiving services in a non-English language
and have not identified English as one of their proficient languages, they are limited English? Or
should we ask every applicant. So basically similar to what we've talked about, but in regards to
English proficiency, should they kind of make assumptions or should they ask?
0:26:55.0 Kelly Brown: I think you should always ask. I think that's the default. Because those are
just areas where the assumptions will be faulty and unnecessary and it just will not necessarily
yield, appropriate data. And again, people, it's better to ask and know, than to not ask and not know
or to ask, and then people don't tell you. They always have that option. So I don't think people
should be concerned or afraid of asking because if people are concerned, they will tell you. But they
will also give you an answer, and then that way you'll know when you won't have to assume or
guess. So if there's one more question, let's get in conversation with Jason. And some of those
questions I think might be able to be answered through Jason's conversation. As I mentioned, he
works closely with many government agencies in the Philadelphia area, collecting information from
individuals through a variety of platforms and mechanisms and his background and experiences,
how to ask these questions of vulnerable people... How to frame and build the questions in ways
that don't that... Provide a burden, and just different strategies for how to do it respectfully. He will
also cover some topics around privacy and coalitions.
0:28:17.7 Kelly Brown: So Jason, I wanna give you just an opportunity to say a couple of things
about your background and what you bring to this, and we'll just get in conversation and we may,
some of the questions in the, in the Q&A will be, maybe best answered by you.
0:28:31.7 Jason Alexander: Great. Thank you, Kelly. I'm really, excited to be here and talk about
this with all of you. As Kelly mentioned, I'm a principal and co-founder of Capacity for Change
with my partner Meghan McVety. We actually work both regionally and nationally with public,
nonprofit, philanthropic organizations as well as public-private partnerships, coalitions, and
collective impact initiatives. And this is all we do. We work on strategy, partnership, and data. And I
would say in the 20 years I've been doing this, it's a constant evolution of learning how data can

really inform and drive your strategy. And so there really isn't anything I work on that's not data-
driven, and in many cases I am responsible for overseeing data collection processes for various

types of organizations. So I'm just really here as a practitioner, not necessarily a theoretical expert.
0:29:33.7 Jason Alexander: But I think what I've learned over time is that collecting demographic
data is really hard. The more you do it, the more confident you are, the more you understand the
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nuances of how to do it well and thoughtfully and usefully. But it's like many other things. The
more you do it, the more you learn about the next layer or level of nuances and challenges and
doing it well. So for us, data is something that many organizations and I imagine all of your
organizations use, not only to drive your strategy and your planning, but also understanding what
sort of impact you're having, either in direct services or advocacy or in partnership as well as
advancing equity. And a lot of the data we work with is to help organizations and coalitions
understand where there are disparities in terms of access and outcomes.
0:30:30.6 Jason Alexander: You can intuitively know that, you can personally experience that, but
you can't quantify it unless you have the demographic data that allows for that kind of analysis. And
finally, data, of course, is a requirement that often drives fundraising and grant writing and grant
reporting. So it just benefits every organization of every size and scope to be really thoughtful about
collecting data and to the point of this conversation, doing it in a way that is person centered and
trauma informed. And there are definitely ways to do that regardless of whether you're collecting
survey or administrative data and whoever you might be working with. So that's just kind of an
overview of kind of how I think about data. A lot of the project, it sometimes it's survey data to
drive equitable programming. For example, I've been working with, Tony Hawk's Skate Park
project nonprofit for several years, and over time they've gone from a commitment to advancing
equity and making skateboarding and skate parks inclusive spaces. But they've over time learned
how to gather, collect, and analyze data to identify specific programs and initiatives that can
actually take that intuitive value commitment and transform it into concrete programming, which
they're now doing today. All the way... Sorry.
0:32:06.7 Kelly Brown: I was gonna say, can you just sort of say more, Jason, about that, about
sort of something that you mentioned in terms of sort of person centered and trauma centered. And
so when people are thinking about how can I get this data from someone who's coming into a
homeless shelter looking for welfare and in addition ways to ensure that you're getting the kind of
data where the process isn't so burdensome and overwhelming. I think that a lot of folks struggle
with that.
0:32:33.7 Jason Alexander: Right. Yeah, and I was gonna try to jump to that other side where a lot
of the data collection work I do and support is working with people in crisis. And I think your
opening slide around how do you collect data when people are in vulnerable situations? And some
of the reasons people don't want to give you demographic data is magnified or compounded when
you're actually trying to collect data from people in crisis. So, whether I work with systems around,
988 mental health crisis response and the recent transition of the National Suicide Prevention
hotline to a broader three digit toll-free number for anyone experiencing not only a suicidal crisis,
but any mental health or substance use or behavioral health crisis. There's only so much
demographic data you can collect.
0:33:27.5 Jason Alexander: So while we would love to know the demographics of people calling
or texting 988, that's not a reasonable moment to be asking a lot of significant questions. So you
have to kind of scale what you can ask to the situation, but on the other hand, the US Department of
Housing and urban development does require every community's homeless crisis response system
to ask very significant and detailed demographic questions. And that's a space I'm in a lot. And,
again, you're dealing with people in multiple forms of crisis, but yet we're still able to, across the
country, collect significantly detailed demographic data on race, age, gender, sexual orientation and
other... Even other things, mental health crisis, disabling conditions, other experiences. And what's
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amazing about that as a model is not only can it be done, it is done every day.
0:34:34.8 Jason Alexander: It can be done in a trauma-informed way. There are a lot of strategies
that are used to do that. But what it leads to is there's actually a national database of comparable
data across the entire country that defines reports, collects and analyzes demographic data in the
same exact way, and is able to be reported and analyzed at national, state and local levels, and
talked about the same way. So I'm happy to go into more strategies about how that actually works if
that's valuable. But I think, just to be clear, yes, demographic data can be collected in a secure
private person centered trauma informed way, even with vulnerable populations or people in crisis.
0:35:22.0 Kelly Brown: And is that process kind of a post hoc process, but potentially, in other
words, when people are out of immediate crisis or trauma is when it's more feasible to get that kind
of data?
0:35:37.3 Jason Alexander: So this is a very local decision and it's up to a local, elected board of
representative leaders to decide when to ask those questions. But I will say in most of the
communities I work in, they're asked at the first point of contact, there are some things that are
required to be asked at the first point of contact. And demographic data is part of that required set.
Again, this is homeless crisis response, not mental health crisis response. But there are ways to do it
that I recognize that often people... It's not just that people... I mean, everything you talked about in
term or shared in terms of, or why people don't wanna share data. Let me just add on I things I see
like, the fact that they're being asked demographic data at a moment where they're in crisis or
they're traumatized or even the questions can be triggering depending on who's asking them and
why.
0:36:37.4 Jason Alexander: But I think the other nuance is sometimes people are... I think
struggle. And I know this is administrative data, or participant data, in many cases, but I think
everyone struggles with, what'll the data be used for and what's the best answer I can give to make
sure I get access to the services I need? Or that my voice matters in an advocacy movement or
coalition. So, because unfortunately I don't think systems are always clear, and unfortunately they're
not always... They're premised on some historical injustices that I think people have good reason to
be concerned about sharing their demographic data.
0:37:22.8 Kelly Brown: Right. And so it sounds like what you're pointing to is still informed
consent is being clear, even whether it's around a trauma point is to be clear about why these, these
questions are being asked to give people at least the opportunity to assess exactly what you said is
this what do I say that's gonna get me what I want, but also how can I share information that will be
helpful. There was a question I think in the Q&A about similar kinds of questions around immigrant
status. That's something that's really, really, really critical, but obviously some very, very sensitive
when you're in a point where you're maybe trying to provide services and wondering if you have
any suggestions around that. And then I think it would also be helpful to sort of specifically around
sort of what is collector bias? And maybe that points to people who were referring to data sets that
are assumptions. But kind of speaking to some suggestions around getting data from populations
like immigrants and how do you check your bias?
0:38:29.0 Jason Alexander: Yeah, so there's so many things. I wanna go back to something you...
A question that was asked earlier in the Q&A round, which is, I wanna echo what you said, Kelly.
Self-reported demographic data is the best kind of demographic data, and I'm leery of any
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demographic data in which someone else guesses what someone's identity is. And I'd be leery of
data sets. I know sometimes we have to use them, but the best data is self-reported data. And to
make a person centered, you have to make sure people have options. So most of us don't define
ourselves by categories. We define ourselves as individuals. And so one of the pitfalls of collecting
demographic data, which I'm sure you've talked about, is what happens when we ask a question, but
we all have a different definition of what the answer is. For example, if I'm asking someone, do you
identify as Latino or Hispanic, and they identify as Puerto Rican, but I don't give them an option,
I'm limiting them to categories that are predefined that people don't relate to.
0:39:43.1 Jason Alexander: And I also wanted to kind of loop back to one of the other Q&A
questions that I think points to a strategy for being trauma and culturally informed, which is, I'm a
big believe... I believe I'm a content expert, but not a context expert. So I know that we need to ask,
for example, questions around LGBTQIA+ identity, but how to ask them, what I do is I go to
context experts. I go to people who identify and say, how should we ask this question? How should
we give this definition? I've learned a lot of things. I've learned things that many of you probably
know, but I didn't know, like when you ask someone their legal name and they're forced to give you
what they consider their dead name, that's information that they're kind of... It doesn't relate to who
they are.
0:40:34.6 Jason Alexander: So I think if you're building or using your own demographic tools and
you have the ability to customize them, you should actually share them with people who are going
to be asked or people who are context experts or have lived experience with those demographic
questions so that you can first arrive at the best set of questions and share definitions of what the
answer choices are. But also finally, that my third kind of tip along this line is there should always
be an open-ended response. There should never be, you must pick one of these seven race identity
categories and that's it. Or I mean, multiple is better, but best is how do you identify and be able to
collect that and analyze that.
0:41:23.0 Kelly Brown: That's super helpful. And I'm wondering if these sort of general good
practices are also could be really helpful for answering questions like the person who raised her and
asking questions around immigrant status. It's like, once you have relationships with people who are
undocumented, what have you, to ask them, this is why we need to know this information. What is a
good way to get at it? What is a good way to engage it? What is respectful and appropriate around
capturing that so that we can provide the best kinds of services to you without putting you at risk?
And at what point? So I think that suggestion around when you're designing the tools under, and we
talked about this the last time, you're gonna have to, sort of walk a fine line between being inclusive
and being accurate and being burdensome. But this is a real opportunity for you not to always have
to guess or assume, but to engage your stakeholders in meaningful ways around these questions. I'm
wondering, people do often talk about the issue around privacy and security. And so I'm wondering
if before we get to the additional questions in the chat, if you can talk a little bit about what are
some platforms and tools that people can search out or that we can link them to that help people
keep folks data private and secure?
0:42:51.7 Jason Alexander: Absolutely. So as you have on this slide there, you don't have to spend
a million dollars on a data system for it to use a secure data system. There are... The tools range
from Google Forms, SurveyMonkey Smartsheet is basically an online collaborative spreadsheet
program. That's a reasonable subscription-based model. It has HIPAA compliance and other privacy
protocols built into it. So when you look at that range of solutions, those are reasonably low cost
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solutions, even for a smaller organization. But many organizations and coalitions also use some
customized project products that are intended for collecting and privately and securely managing
confidential data, including demographic data, whether it's anonymous or not. And those systems
include Eccovia, which has a product called ClientTrack, social solutions, there's Apricot, there's
Clarity. Some publicly funded systems of care are required to report into these kinds of tools. But
for, I think probably many of the folks on the... Around this meeting, they aren't, they may not be
required to.
0:44:10.6 Jason Alexander: So if you're doing it internally as a nonprofit, as a foundation, as a
coalition, you really can use a range of tools that would allow you to collect the data. And if you
can't collect it electronically and store it electronically in a secure way, it's really hard to analyze it.
So I think that just has to be... I think even, I would like social sector organizations to think about
this as part of their infrastructure, not something that it'd be nice to have if we could afford it. But of
course, I understand that there's not... There's often not funding to realize that commitment. And
then strategies to protect personally identifying information. There are just a... I mean, when you're
surveying and it's truly anonymous survey, you're not even asking, there are still ways that the
survey fielding organization can identify you, especially if it's an electronic survey. But there are
really good strategies to protect personally identifying information that are built into most of these
systems or can be built into them as well as strategies that are a little more homegrown.
0:45:22.4 Jason Alexander: So for example, in a lot of the homeless crisis response system
databases I work with, we provide options for people to give... To remain... Well, first of all, there's
always a consent. There's always a standard consent. And one of the consent options is to opt out of
consent. It's harder to connect people with services, but not impossible. But another great option is
anonymous protocols. So we de-identify information that is potentially personally identifying. And
in some cases, we allow participants or end users to generate their own coding system. So they can

generate their own basically username and password for their data in the system and it's self-
generated, only they know it and whoever records it. But these are all the different strategies that

even, particularly with people who are not only vulnerable, but at risk. So for recent immigrants, for
undocumented individuals, for people fleeing or surviving domestic violence or human trafficking,
most of these systems have protocols in place to securely protect their personal data. And we
actually... You can be very creative in workarounds to protect it even further.
0:46:49.2 Kelly Brown: That's helpful. And I just wanna... Because of the time, I wanted to be sure
that we see in the chat that some people have shared a couple of resources and some feedback
around different platforms and how secure they are. So again, what we're talking about is security
that has to be assessed for your particular purposes. But in the Q&A, folks have shared some
resources and some information around not only other platforms, but also some tools and issues and
forms around potential demographic surveys. What we're trying to get across here is that this is,
getting this data and securing it is doable without a massive amount of investment.
0:47:33.1 Kelly Brown: Having said that, it is important that people understand what it takes,
understand what the kind of data that they need, what kind of consent they need, how to keep it
secure and private, and then that they communicate with their funders and supporters what that
actually takes. But it can be done, and we just want people to understand that there isn't... That
because there are steps that people have to take in order to secure that data, it doesn't mean that it's
data that isn't important. And just real quickly, I don't know if you just wanna mention how
important it is, because a lot of folks really operate in coalitions and in settings. How do people
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think about making sure that they're aligned with their peers and make the data meaningful and
useful?
0:48:21.1 Jason Alexander: Yeah, so that's a really great question. If you are participating in a
coalition, a collaborative, a public-private partnership, or a network or system of care, for example,
these, again, 'cause I work within the Homeless Crisis Response so much, I share this image. If
you're not uniformly collecting the data the same way, it is very hard to analyze the data
collectively. So I believe this applies to individual organizations and coalitions. There should be an
agreed-upon set of questions, of answer choices, of definitions, as you see here. These are the
forthcoming gender definitions from HUD for Homeless Crisis Response systems across the
country that are HUD-funded. And not only do there need to be, again, a clear set of definitions,
questions, and answers, but they need to be regularly updated. And I think actually HUD is
excellent at in the last few years of, first of all, soliciting input every year from grantees, service
providers, and community partners around how should we update particularly our race, ethnicity,
and gender questions, definitions, and categories.
0:49:51.4 Jason Alexander: And then making sure that everyone's aware of that and everyone's
doing that the same way. So there's really... My concern when I work with coalitions is that
everyone's collecting demographic data, but they're all doing it slightly differently than they're
doing... They're doing it the way their organization does it, and then trying to share and compare,
and that's just very difficult.
0:50:15.6 Kelly Brown: Yeah, and it really makes the data much, much less useful. So again, trying
to build a capacity and awareness is how do you sort of observe good practice with respect to asking
these questions and deeply engage the stakeholders and the people that you're engaging, and be sure
that you're as much as in alignment with your peers and colleagues as possible. So I'm aware of the
time. We're at 3:50. I'm gonna go through the rest of the slides real quick because I do share some
information about some emerging field-wide efforts that are out there to standardize the process of
collecting data, but we'll understand if people have to drop off. Be sure that the rest are assured that,
again, we are recording, and we'll share the whole deck for everyone at the end, but one effort we
did wanna mention is that you may have heard of CANDID, which is the merger of the Foundation
Center and GuideStar has launched demographics via CANDID where they're trying to encourage
nonprofits to input their demographic data into GuideStar in one standard way so that it can then be
accessed by a range of consumers so that it reduces the burden in terms of constantly responding to
lots and lots of different requests and also ensuring and increasing the value that comes from the
aggregate and analyzable data.
0:51:39.5 Kelly Brown: There's still a lot of work going on to understand the best practice and how
nonprofits are experiencing, but so far, a considerable number of organizations have already started
to put at least part of their data in, so we wanted people to be aware of that and to be aware of
opportunities that people may be reaching out to you for your input on how that's being
experienced. There's also Philanthropy Data Commons and GivingTuesday Data Commons, which
are entities largely driven by funders that's trying to streamline the data that flows into and around
foundations, again, with a mind to reduce burdens, and a lot of that is focused on sort of sharing
aggregate data and allowing technical barriers to sharing it, but again, it's early stages and a lot of
research is going on to understand how impactful these tools will be, but we wanted to be sure that
you were aware of them.

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0:52:32.8 Kelly Brown: It looks like we took a lot of questions as we went along, but we do
appreciate the comments and the additional resources in the chat. We're gonna take some of these
questions and try and answer them in the supplemental materials going forward, for example,
getting information from people who don't have a lot of technology, so we've captured your
questions and we'll try to address them as we move forward in various ways, so stay tuned to going
forward. The deck, again, will have a summary of what we covered today, so I bet you'll be able to
go forward and I'll just now, if you can just drop the link in the Q&A for the evaluation of this
session, again, that feedback is really, really important to us because we're trying to use it to refine
and improve and strengthen the sessions as we go along, so the link should be in the Q&A. People
can take that survey and give us some feedback.
0:53:35.2 Kelly Brown: And then just a reminder, two weeks from now, we're gonna have one last
substantive session on using the data. We're gonna have some participation from another foundation
outside of California who's been collecting and using data for some time. Hopefully, we'll have
more opportunity for folks to be in discussion with her and with colleagues, and then at the end of
the session of the series on June 20th, it will be an open town hall for people to engage with the
collaborative partners around the motivation for this process, how they're gonna be using the data,
what they'll be asking you from, and just also as an opportunity for you to learn from each other as
folks have been doing by sharing resources and ideas and thoughts in the chat. So, once again...
0:54:22.6 Speaker 2: Sorry. I won't be able to post it in the Q&A. I can only answer the questions,
but we'll have to just send out the survey to all the entities.
0:54:30.1 Kelly Brown: Okay, we will do that. We will send that to you, so watch your mailbox for
that survey and answer it, and we will see you in two weeks, hopefully. Thanks to everyone.
0:54:52.6 Speaker 4: Thanks, guys. That went really fast. That was a lot of information. Again, I
don't know how you do it, but that went really fast.
0:55:03.7 Kelly Brown: Yes, it did. It did go fast.
0:55:07.6 Speaker 2: Lots of questions this time, though, so the engagement was there for sure.
0:55:15.0 Jason Alexander: I did my best to answer four of the questions in the Q&A that we
didn't get to, so I typed the answers even though people might have already left.
0:55:24.4 Kelly Brown: Great.
0:55:25.1 Jason Alexander: There's just so much to talk about.
0:55:27.4 Kelly Brown: There's so much to talk about, and we will respond to these questions, and
there's still folks along, so you're hearing our little backstory, but we're gonna try and capture the
questions in the Q&A so that we can cover them going forward and supplement the materials, but
we will send out the survey, and thanks, everyone who's still here. Stay tuned. We'll see you in two
weeks.
0:55:53.7 Speaker 4: Thanks, guys. Bye.

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0:55:55.3 Jason Alexander: Thank you for the opportunity.
0:55:55.5 Kelly Brown: Okay.

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