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Katy Hussey-Sloniker:

Welcome to Delivering on Diversity and Equity with Data. My name is Katy Hussey-Sloniker, and I'm the 

Learning Officer for the AmeriCorps Office of Research and Evaluation. Today, let's talk about data 

stewardship and equity.

In our contemporary society, we hear a lot about the need to measure diversity in our own 

organizations or communities, but how do we make sure that we're achieving those goals as well as 

sharing information in an equitable way? Our objectives today are to, one, understand the difference 

between data and equity versus equitable data. Two, learn about the federal subcommittee findings on 

the importance of creating equitable data that can be accessed by various stakeholder communities. 

And three, demonstrate AmeriCorps' historical work in these areas to include initiatives currently 

underway on this topic to create a more equitable work environment.

At the AmeriCorps Office of Research and Evaluation, our vision is to understand how and what 

AmeriCorps does that can make a lasting and sustainable impact across the four domains of our 

AmeriCorps impact framework. Next slide, please.

We conduct our work in four areas associated with our mission. We identify national service and 

volunteering trends. We conduct research and build scholarship on civic engagement. We measure 

national service impact. And we promote evidence-based models and program expansion. Our mission 

essential work can increase knowledge and evidence that improves the member and volunteer 

experience, strengthen an organization's capacity, support a community in their localized solutions, and 

contribute knowledge to the civic health of society. Our AmeriCorps research and evaluation work 

leverages relationship and trust that fosters the powerful synergies between the impact framework, 

interconnected domains of participant and alum, partners, communities and society.

We're excited to share this webinar with you, because data forms the basis for evidence, and evidence 

and data are only useful and ultimately used by others to generate lasting and sustainable impact when 

trust is reinforced. Today, we'll learn about engagement and accountability for data and walk through 

applied efforts at AmeriCorps to support open, transparent and equitable data management practices 

and use. The session will be packed, so we're encouraging participants to place questions in the chat 

that will be compiled and answered in our panel Q&A discussion towards the end of the webinar. Please 

be sure to place questions and comments you might have in the chat throughout the webinar. And now 

I'd like to introduce my colleague, Kyle Brees.

Kyle Brees:

Thanks Katie for that wonderful introduction. Hi everyone. My name is Kyle Brees and I am a research 

analyst here within Office of Research and Evaluation at AmeriCorps. Next slide.

So today, we'll be discussing two different aspects of data and equity, starting with data or equity or 

data that could be used to measure equity. This type of data is probably what you think of when you 

hear the term equitable data. This could include personal identifiers such as race and ethnicity, sexual 

orientation and gender identity and SCS status. But it can even look further down to your neighborhood 

level data points, such as air quality and neighborhood access to services. All those different data points 

combined together can help us measure equity within organizations and nonprofits.

You have probably heard of some of this work as it's been really in the news lately, and it's been more 

talked about in the mainstream media. It's being done across the country by different businesses, 

nonprofits and governmental agencies to accurately measure diversity within their own organizations or 

the beneficiaries of their services. We all know that measuring different aspects of equity are important 
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for an inclusive and diverse office space. But what has this led to most currently and the best practices 

for measuring this type of data?

Maybe surprisingly, this research goes back on this subject back to 2001, where a study by Trosky and 

Barrington discovered that, "Diversity is either positively associated with productivity or there's no 

significant relationship between diversity of productivity." So what that just really means is that anytime 

you create a more diverse workplace, you're going to lead to more production in your work office.

While we know productivity is not just the main focus of it, there's also been a strong policy push from 

the presidential office that Robin will be talking about later. This little excerpt from the Subcommittee 

on Equitable Data, "The principal Equitable data is about dis-aggregating and analyze the data to identify 

disparities in federal policies and programs, using levers of the federal government to address those 

disparities, and then enabling members of the public to hold government accountable." So it's not just 

about collecting this data and using it. It's about making it transparent, so that every citizen is able to 

hold their federal office responsible, and we're able to kind of see what was going on beneath it.

We could already see this executive order take place throughout different federal agencies. The census 

has put out demographic data, diversity metrics and data tools for visualizing data, visualizing the census 

data on their website. They created a whole section about it. I have the link below. We could probably 

share it in the chat. But basically, if you Google data equity incentives, it has a lot of great different 

metrics, visualization tools that go down to your county level that you can investigate at your own level. 

Next slide please.

So while all the tools being put out by different federal agencies and nonprofit are great for promoting 

data on equity, how can we make sure that it is being used in an equitable way? That is the other topic 

that we'll be diving into today, as we look into equitable data use. So what is kind of this difference 

between data on equity and equitable data use? I mean, they're pretty much the same. They pretty 

much sound the same. But while the creation of tools and publishing data is a great first step, so data on 

equity, that cannot be the end goal for agencies when sharing data or any findings really. I found this 

quote to be really powerful, "Equity is both an outcome and a process." So it's not just about our end 

goals or what we produce or what we share. It's about making sure that it is embedded in our process 

every day in our work. And this quote came from the Data Feminism book by Catherine D'Ignazio and 

Lauren Klein.

So also, while data inequity helps benchmark and measure diversity in an office, equitable data use 

promotes the dissemination of that information to all parties, particularly those who have been seen 

just as participants of studies or less resource rich than typical data users. The Forge Foundation 

produces a paper on equitable data use and has said, "The set of principles and practices to guide 

anyone who works with data, especially data related to people, through every step of a data project 

through a lens of justice, equity and inclusivity. Next slide, please.

Okay, so as we go through this presentation today, I just want to provide some examples. So I just want 

you to be able to think about these. These are not all encompassing. These are just some things I drew 

down as they take place in my work. There may be other examples in your work, but we want you to 

think about these and think, are these more data on equity or is this more equitable data use? Or could 

it be a little bit both? So these are different activities or data collection methods that you may be doing 

within your own office or your own organization. And as we have our speakers go through, I want us to 

all kind of think about how we could use these or what they might fall under, and then we'll be coming 

back to these later on at the end of the session. So there will be a quiz. Next slide please.

Okay. So a little bit of table setting, what you expect today. At the end of the day, we hope you can walk 

away having learned the difference between equitable data and data on equity, learned about the 
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executive order from the president and the importance of creating an equitable data use that can be 

accessible by various communities, that will be Robin talking about this. And knowledge from 

AmeriCorps' historical work in these two different areas and what initiatives are currently underway to 

move this work forward and create a more equitable work environment. That will be Andrea and Nate 

talking about those.

So as I kind of introduce, we have Robin Ghertner from the White House Equitable Data Working Group 

to talk more about equitable data use. And then we have Andrea Gibbons and Nate Benjamin to talk a 

little bit more about AmeriCorps' effort in this, before we go into summary to you. So without further 

ado, Robin.

Robin Ghertner:

Thanks a lot Kyle. Welcome everyone, and thanks for having me to the AmeriCorps team. I work in the 

Department of Health and Human Services at the federal level in the Office of the Assistant Secretary for 

Planning and Evaluation, or that's ASPE as you can see there on my title. But I am actually an AmeriCorps 

alum. That was one of my first jobs out of college, really was foundational for my whole career. And I am 

also a former researcher in the AmeriCorps Office of Research and Evaluation. So I have a lot of affinity 

for the work that that team is doing and the work that you all are engaged in whatever sphere you're in 

your community, at your state level, et cetera. So I'm going to talk to you today about a working group 

that I co-chaired under the White House National Science and Technology Committee. So why don't you 

go to the next slide.

Okay, next slide please. This working group focused on how federal agencies can work with community 

partners to advance the use of equitable data. And equitable data, Kyle talked about it a little bit. I'm 

going to give you kind of the definition that we've been using at the federal level or one of the 

definitions of course. I like to think about equitable data as data in action for equity purposes. So the 

difference between data and then equitable data is just plain data would be data that is just there and 

you may use it for different purposes. It becomes equitable when you are employing that data for 

purposes to address, say, disparities in policy outcomes, in program participation when you're using it in 

an action-oriented way to design interventions or design policy changes to address those disparities. Or 

when it's being used by members of a community or state and local government to hold the federal 

government accountable for equitable or for inequities that communities may be experiencing. Go 

ahead, next slide.

Okay, so a quick piece of background, and I'm going to try to drop this in the chat when I get the chance. 

This working group that I co-chaired was based off of a report that was delivered to the president that 

you see the picture here, Recommendations from the Equitable Data Working Group. This was an initial 

working group that was created, I want to say, really close to day one of the Biden-Harris 

Administration. The president put out an executive order asking federal agencies to take a hard look at 

how equity is being threaded through different federal programs and policies. And one of the things that 

came out of that executive order was a charter to start a working group to study how agencies can 

better use data for these purposes. So one of the recommendations from this first working group was to 

look at how federal agencies can galvanize diverse partnerships across all levels of government and be 

accountable to the American people through the use of data.

Now I'm going to be talking about what we found at the federal level and what sorts of considerations 

we have for federal agencies. I understand most of you participating in this webinar are not in a federal 

agency. You may be at a state agency, a local agency. You may be in a non-for-profit. These findings 

however, I think do have implications for other organizations, and also have implications for how you 
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may interact with your federal colleagues, either here at AmeriCorps or at other federal agencies that 

you may have relationships with. So let's go to the next slide.

A little more background on how the working group operated, and then we'll dive right into some of the 

things that we found. So the working group was comprised of people from 13 different agencies, 

AmeriCorps included. You can see there on the screen which agencies participated. Agency's selfselected. They chose to participate or not. We didn't go out and recruit them. But what you see is this is 

a diverse group of agencies that represents a wide array of federal programs. It's important to keep in 

mind, the federal government has its fingers everywhere. We do everything in the federal government, 

every part of Americans' lives.

So what we tried to do is create a vision and create some ideas that could be applicable widely across all 

the things the government does. We had regular meetings as a group. We had what we call a formal 

Request for Information, which is a tool that the federal government uses to request responses from the 

general public on specific topics. And we held what we call a listening session or simply like a webinar 

with experts from local governments, tribes, think thinks and universities to hear how others are 

thinking about what the federal government can do differently. Next slide.

So now, I'm going to talk about some of the main barriers and challenges that we as the federal 

government face when we are trying to engage community partners, many of you, on the use of 

equitable data. So let's dive right into them. The first main challenge that we came across, and I think 

this might not be a surprise to many of you, if you've tried to do this with a federal partner. We operate 

under many, many rules. We have a lot of requirements. We have statutes, which are laws that govern 

what we can do with data. We have regulations which implement those laws or statutes. And then we 

have many other program requirements that are not documented in the formal code of federal 

regulations, but are things that we have to abide by. All of these rules shape what we can do with 

federal data.

So a lot of times, outsiders will say things like, "The federal government needs to release its data. They 

need to give us access to data." The problem is, every agency that operates a dataset, that operates a 

data system has to abide by many, many different rules that not only say who can access the data, but 

how that data can be accessed and how it can be used. Navigating all these requirements is really 

complicated. Sometimes, it's way more complicated than we think. We think, "Oh, we just have this 

dataset, let's just make it available, or let's link it with another dataset." It can be really complicated. It 

takes a lot of time and planning. And that in turn of course means that if we're working with partners in 

the community, we have to really think about how we can do this most effectively and whether we can 

even do it at all.

This of course, we recognized in our working group that this actually exacerbates inequities, because it's 

going to be the organizations that are most equipped, that have lawyers, that have statisticians, that 

have people that understand data security and IT's infrastructure, who are going to have an easier time 

navigating these complex rules. Whereas the communities that don't have all of these resources are 

going to have a lot harder time partnering with us as the federal government.

The second main challenge that we face is, of course, resources. The resources that both a federal 

agency has and the community, the resources that a community has limit what we can do, how we can 

collaborate. Sometimes, we like to pretend that getting on the phone and starting a partnership and 

collaborating, it's free. You all know that that's not free. It takes time, it takes staff, it takes expertise 

that you may not have. At the federal side, for example, if we're going to be working with a community 

that say, speaks a language other than English, we've got to have people on staff that can speak that 

language. We have to have an understanding of the historical and cultural background and 
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circumstances of that community if we're going to successfully communicate and engage with them and 

understand their needs.

The last challenge on this slide is about trusts and the historical relationship between the government, 

the federal government and different communities. So any partnership requires trust, anytime you're 

working with anybody. If you think about an interpersonal relationship with a spouse, significant other, a 

neighbor, even at that micro level, you have to have a level of trust. If I say I'm going to do something, 

I'm going to do it. If you say you're going to do something, you're going to do it. If I tell you my secrets, 

you're not going to share them with other people. That extends to data-related relationships. And we 

know that there are legacies of harm that the federal government has exacerbated or created and built 

over time with different communities. And that harm is not only through programs or system 

involvement or enforcement activities, but it's also related to data.

We saw this during the pandemic when the federal government, when the CDC was trying to collect 

COVID prevalence data, positive test cases, hospitalizations, things like that. And we saw that certain 

communities were reluctant to share that information with the federal government because of these 

historical legacies. And that in turn meant, you may have heard the phrase, bad data in, bad data out. 

Garbage in, garbage out. If we get bad data, our analysis leads to wrong conclusions. And that happened 

at certain times in the pandemic when resource allocations, which dependent on reliable data. If we 

didn't get reliable data about community prevalence of COVID, it then meant that we couldn't allocate 

resources to those communities.

So there are real implications for this level of trust or mistrust. And as federal agencies, we recognize 

that it's incumbent on us to build those bridges, to repair past harm, and do what we can to kind of 

move forward and build trusting relationships and partnerships. That's a barrier, and some of the things 

I'm going to talk about, considerations coming up are meant to kind of try to address some of those 

factors. Let's go to the next slide.

Okay. Now I want to talk about some of the different roles that community partners can play in an 

equitable data initiative that an agency could have. And one way to visualize this is to think about the 

federal data life cycle. This may be kind like a new concept for many of you, but this chart here 

symbolizes how data live and operate in the federal context and probably in organizations that you work 

in. So in the middle of this circle is the term governance. Governance means, basically simply said, it 

means decisions about data. Who decides who can access data, how data are presented, what we can 

do with the data. And communities are actively involved in different governing or advisory boards that 

federal agencies have set up to guide research projects, program evaluations, data collections, et cetera.

At the planning and collection stage, community partners can help us inform how we're going to 

measure certain things, how we're going to define data, what does poor mean in your community, what 

does identity mean for your community. They can all, community partners, help federal agencies in 

conducting outreach or awareness about data efforts. When we keep going around the cycle to how 

data are used and accessed, partners can help us figure out the most accessible way, format that data 

can be presented so that people in a community can actually get the data, use it and know how to 

interpret it. Similarly with dissemination, if we do an evaluation for example, how do we make those 

results and present it in a way that people actually understand what we're talking about? Because 

sometimes data can be very complicated. If you haven't had a PhD level training in statistics or social 

sciences, some of these concepts can be very complicated.

Lastly, the last piece here is disposition or sustainability. And this is important to think about. A lot of 

times at the federal level, we have competing priorities and we may start an initiative that's targeted 

data at helping communities use data in a better way or helping the community inform how the 

government can use data in a better way. Our priorities may change or our resources may change, and 
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we may not be able to continue that initiative. So community partners can help find other resources to 

extend that initiative further or advocate to the federal agency, "Hey, you can't stop this, 'cause we rely 

on it and we really need it." Next slide, please.

Okay. This is kind of more of the same that I just talked about. Okay, the last two bullets here that I want 

to just to mention that I talked about trust before, particularly for communities where there's a 

historical legacy of mistrusts between the federal government and the community. As a federal agency, 

we rely on our partners in the community to identify. We may not know where those legacies exist. We 

may know where they exist but not know how to build bridges to overcome them, and we rely on 

community partners to help us with that.

The last point is related in terms of incentivizing participation. The federal government is good at a lot of 

things, but one thing it's not often good at is getting people to buy into certain things, particularly like 

answer surveys, to participate in community meetings, to utilize certain data tools. And so partners can 

and do play a really important role in helping us get communities to participate in what we're working 

on. Okay, next slide.

I am going to now talk about some of the considerations that we had for us as federal agencies. And 

again, our target audience here was federal agencies, but you, if you're not in a federal agency, you may 

see these as opportunities for you to engage in the federal government. So the first thing that we 

recognized we need to do as agencies is to take a hard look at what our goals are and what community 

partner's goals are, and to recognize that those goals are not necessarily the same. When we want to 

work with a partner, they may be trying to get something else out of that relationship. And if we are 

dead set only on our own goals, there's no way we're going to have a lasting relationship.

The second consideration was about capabilities. That was one of the challenges that I talked about 

before, resources and capacity. We need to understand what our capabilities are and what our 

community capabilities are. Are we able to identify communities? Do we know what community needs 

are? Do we have the time and the staffing? Do we have the linguistic and cultural competencies to 

engage communities? And on the other side, are community staffed to work with us? Do they have data 

literacy? Do they have the sort of expertise we may be looking for? Do they have the ability to work with 

a federal partner?

So once we understand what those capabilities are and what those limitations may be, then we can 

decide, do we have the resources to support that or not? And this is really important, particularly 

related to the trust factor. If we set ourselves up to have a partnership, but we don't have the resources 

to support it, the partnership is going to fall apart. And instead of developing trust, it's going to add to 

the mistrusts. So we really need to take a hard look at what we're capable of engaging on. We need to 

review and understand our governance structures. So what can we actually do with the data? Are there 

things that we need to change there?

The last point I want to just mention is data standards. We understand that non-federal partners, nonfederal organizations look to the federal government to establish standards and principles. And so 

oftentimes, as federal agencies, we're focused on our own self and what principles do we need to follow 

without recognizing that the decisions that we make are often examples for what others are trying to 

do.

My last slide is just about, you can go ahead and move on to the last slide is about opportunities for 

further work here. We didn't do everything. We just kind of opened up Pandora's box and now there's a 

lot more to look at. But I'll stop here and want to thank you all for your attention. And now, I'm going to 

pass it over to Andrea. Why don't you go ahead and take the mic and start your presentation.
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Andrea Gibbons:

Great, thanks Robin. So I am the Chief Data Officer here at AmeriCorps. I'm the First Chief Data Officer at 

AmeriCorps. It's a relatively new role across the federal government, and we're really focused on 

unleashing the value of our data. Next slide please.

So when we talk about the mission of my office, it's really to foster innovation, mature Enterprise Data 

Management best practices and methods to strengthen and enable the AmeriCorps community to 

discover, understand, access and use and share data in support of our mission. It aligns to the Evidence 

Act as it requires federal agencies to modernize their data infrastructure and to really leverage data as a 

strategic asset. Next slide please.

So when you talk about data management, it's a combination of the data infrastructure so that you can 

derive the value of your data, but a lot of it is about culture change and data literacy. So what we're 

looking to achieve is to make sure that data is discoverable and that it's accessible and understandable. 

So it means that your data is described, it's contextualized and you understand what it's fit for purposes. 

And that data's responsive, meaning that it's validated and that it's relevant to the business 

requirements or its purpose. It's trusted so that you understand that data is of high quality and it's 

documented, and that it's transparent to whoever the end user is. And that it's governed from an 

enterprise perspective, meaning that you have collaborative working groups that are working to make 

sure that the data is described correctly and is a fit for purpose for the agency. And data has clear lines 

of accountability. Next slide.

So the goals of Enterprise Data Management, which is what EDM stands for, is to reduce and reuse. So 

we want to optimize the data that we have, get rid of what we call ROT, which is Redundant, Obsolete, 

or Trivial data, so to stamp down the noise and only keep the data that you need. That you build trust 

and agility through both the data management capabilities and you have cross organizational line of 

sight. And that can be, when we're talking about that, staying within the agency or line of sight to the 

federal government or to our community partners. That we share and collaborate, so we unify our 

ability to both share data and that it's described and ready for use or reuse across the agency and with 

our partners. And that we build capacity and knowledge to ensure that we have the right skillset, both 

for the end users in data literacy and for the people using the data for analytical purposes. Next slide.

So the Enterprise Data Management Principles are really to centralize our data management practices, 

so that subject matter experts can spend more time on doing analysis, that we follow proven-industry 

best practices for both the management of our data and the analytics. We implement this culture 

change as to not disrupt the mission commitment, but also to allow or to educate people in data literacy 

so they know how to use the data. We apply modern data architectures and tools and automation to 

reduce the burden on technical staff. And leverage our existing investments in our environment and in 

our tools. And that we utilize agile methodologies to reduce the risk using a data centric approach. So 

think about the data first and for it not to be a byproduct of our IT systems, but to be on the forefront as 

we are designing our systems. Next slide please.

So there's really four pillars. That's the data governance and stewardship, which establishes those 

capabilities related to the data strategy and the federal data strategy, our data governance framework, 

how we define and implement policies around our data and that we continuously improve and for our 

measuring of our success. Our capacity building establishes capabilities for the workforce around 

planning our data skills and literacy to develop a data culture, our data centric culture. Our AmeriCorps 

Information Model or AIM establishes a modern data hub architecture and enterprise data management 

tools that decouple the data from the application layer. So through describing and contextualizing the 

data, individuals that are looking to use the data or share the data, don't need to know what source 

system it came from. It's described and contextualize independently of that. And data in practice 
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established support for variety of data products that we can know that we have confidence in that data, 

in order to use it either for analytics or just transactional use. Next slide please.

So when you look at the capabilities that we talked about, data stewardship and governance, what does 

that really apply to or what does that support? So it supports both our strategy and planning, our 

Enterprise data Management and governance operations, our policies, methods and guidelines. The 

capacity in building for data is really about the workforce, learning skills development and data culture. 

And our AmeriCorps Information Model is about the data hub and trusted data, and how you can link 

disparate data sources together in a methodical way. And data in practice is to make data available and 

discoverable as a product, to enable self-service analytics and to really enable the data user to have 

confidence in their data, whatever data they're going to use. It's already well-prepared and describes 

and contextualized. Next slide please.

So when we look at our roadmap when we talk about things, it's how do we build things for capacity and 

building a data workforce. So that's clear expectations according to the needs of the agency. Master 

Data Management, Lineage and Profiling really allows us to look at the data that we have historically 

and new data that we're bringing in, and understand both its quality, how it links to other data sources 

that we have so we can derive value from the data that we have within our data landscape. And then 

lastly, it's data in practice. So preparing data ready for use, so people can have confidence in the data 

that they have and they know they can replicate the trusted data and to ensure that staff can identify 

what data sources are set, are trusted to create both reports, dashboards, or predictive analytics. Next 

slide please.

So data governance is really about the combination of people, process, and technologies that come 

together in order to care and feed for our data, and make it usable, accessible and trusted. So they're 

related to the formal orchestration of the people, processes and technologies, and establishes cultural 

principles and participation and responsibility to Enterprise Data Management, transparency and 

inclusivity. Next slide.

So data governance and stewardship as it relates to strategy and planning. It enables the understanding 

of how our programs, our department's efforts contribute to the advancement of our strategic goals. It 

enables an ongoing awareness of Enterprise Data Management's current state and our obstacles to 

success across the agency to support the growth towards a data-driven culture. And a lot of times, when 

you talk about data governance, people think of one person being that responsible for the data, but 

these are really collaborative working groups that stretch across the agency in order to manage data at 

that agency level rather at an individual need or departmental need.

Thank you. So for enterprise governance or Enterprise Data Governance, it really establishes what I 

already talked about. It enables transparency in the management of assigned Enterprise Data 

Management roles, responsibilities to streamline implementation of data management practices. It 

standardizes our application and communication of those procedures and processes, and it harnesses 

standardized processes from the Data Stewardship Framework to support collective problem solving, 

decision-making to meet the evolving agency data needs. Next slide please.

So the capacity building for data is to foster and sustain our capacity and use of trusted data. So that's 

just education on how to find things, what it means for data to be the fit for a purpose, how you can and 

cannot use that data, understanding the scope and the temporal scope of the data. And the capacities 

really in this pillar, ensure informed agency input and alignment to both our workforce plans, data skills 

and communication practices. Next slide please.

So workforce planning benefit, it really talks about to change to a data-driven culture or to think about 

data as the asset that it is. It ensures the inclusion of Enterprise Data Management into agency-wide 
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needs and plans. It affirms agency's value of data, data literacy and related capacity expectations for the 

growth of staff. And it enables a sustainable growth of data literacy and content delivery to data 

champions across AmeriCorps. Next slide please.

Our learning and skills development really underscores the foundational data literacy among all of 

AmeriCorps staff. And enables strategic rollout of data learning pathways across the agencies. And 

support scaffolding of data skills and development according with role expectations, with attention 

towards ongoing agency changes. Next slide please.

Data culture is really about enabling and sustaining support and involvement for this capacity building 

for data, and that strategic communication to shift and sustain and staff beliefs and practices related to 

data. Also, it serves as building a network of data practitioners learning together and enabling agencywide participation. And it's really a springboard for innovation as it comes to our data. Next slide, please.

Our AmeriCorps informational or information model, it gives a one-stop shop for data that's visible, 

accessible, understood, trusted and governed, and it supports our strategic objectives. Next slide.

Part of the AmeriCorps information model is a data hub, which is a data catalog which allows individuals 

to search for data in business terms that best fits their needs. It improves the communication of 

enterprise data by establishing a data vocabulary for business users. So it really enables a semantic 

layer, so people don't need to know what table or column data is in. They need to know or they can 

search on it by a number of business terms. And it enhances our security measures to safeguard 

sensitive data by automatically classifying all of our data sources that are brought into the data 

landscape. And it supports the transparency and lineage of data, meaning that you know from what 

source system it came from and where it ended up in analytics, which is really instrumental when we 

were doing any kind of impact analysis for changes that we're going to make to either our data or our 

data collection. Next slide, please.

Trusted data is establishing trust for the core domains and linking all of our data through Master Data 

Management. So understanding that the data is of quality or what that quality is of that data, what its 

scope is, its temporal scope, its update so that individuals that are trying to use the data understand 

that it's actively steward and that they can trust that data has been prepared properly, and it improves 

the transparency for the quality of our data across the enterprise. And it's an ability to update our 

source system with corrected data, so we don't run into the same data quality issues time and time 

again. It's a more comprehensive catalog for publishing our data out to our open data portal and 

data.gov. Next slide please.

When we talk about data in practice, it's really to deliver data to the relevant people for report making 

analysis and making data-driven decision. It's a central focus or the central focus of this pillar is to 

manage data as a product. Next slide please.

So data as a product is really to increase the usability, quality and ability to integrate data products and 

create new data products. Data becomes more valuable the more it's reused. And the ability to 

quantitatively measure data products and quality and contributions to the AmeriCorps strategic plan. 

Next slide please.

All of this really enables self-service analytics. So it enables power users throughout the agency to 

quickly and easily build reports and dashboards to meet operational needs and enable data-driven 

decisions. It also is a foundation to be able to utilize and leverage AI and machine learning capabilities, 

just as Robin mentioned, "Garbage in, garbage out." If you don't have a strong governance practice, you 

can't be assured that you're utilizing data that's appropriate for both AI and ML. And the ability to share 

data quickly and securely within AmeriCorps and with our partners. Next slide please.
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Then again, it's all about the data user support. So the enablement of the AmeriCorps staff to better use 

the enterprise reporting solutions and quickly resolve any problems that the staff encounters while 

using data products with traceability back to the source systems. Next slide please.

This is just a layout that really shows kind of the how data can travel throughout the agency. So starting 

with our source systems on the left, which don't need to necessarily be AmeriCorps data. It can be other 

enrichment data or reference data, so including zip codes or congressional census data, our own 

member and grant information that is brought into our environment, which in a raw state. And then we 

apply data quality and Master Data Management practices and rules to it. And it is moved over into the 

trusted data area, which is ready for use and reuse for people within the agency. Also, we can publish 

out to our partners from there or out to our open data platform, which then eventually goes to 

data.gov. Next slide please.

This is my last slide, but you can go ahead and yeah, circle through the animation. So when we talk 

about the context of AmeriCorps and what I touched a little bit on data literacy, it's just important to 

realize that everyone has a role in data and data management at an agency in the country, in the world. 

We all use data in our day-to-day life. Others of us have data that are within our role that we need to 

understand and other people have data is their role, right? So when we talk about the foundational 

pieces, the ability to read, write, and communicate with data in context.

So we have a training program now that brings in everyone from the agency to talk about these 

different lenses of data management, data responsibility and data literacy for people to really 

understand what it means to think about data and to help ground them in the principles of data literacy 

as we continue to educate. And as you can see, certain people just need the foundational information to 

help them and be more comfortable with data. A lot of times, people are a little bit scared and they're 

like, "Oh, I don't do data things." But when you start to talk to them about even the data that they use in 

their personal life, they realize that they are a data-driven person. And how do you look and widen your 

data literacy? So we all are talking about on a common ground.

Then there's other intermediate that data is within their role and there's a different more advanced 

education. And then when data is your role, we're talking about more of the data science and more 

advanced skillset around deriving value from their data. I think that's my last slide and with that, I will 

turn it over to Nate. Take it away, Nate.

Nathaniel H. Benjamin:

All right, well, good morning everyone. I'm Nate Benjamin, I'm the Chief Diversity and Inclusion Officer 

here at AmeriCorps, and I'm really excited to have a conversation with you all. This conversation is really 

rooted around data, equitable data, but specifically around culture in data. And so, I am going to go to 

the next slide. Thank you.

So as a people professional, one that has executive led human capital, diversity, equity, inclusion and 

culture programs, I fundamentally approach the work with identifying what are the organizational 

needs, the gaps and the opportunities. And the best way to do that is through the use of data which 

helps propel and advance the conversation. And so to anchor this conversation, I always consider one of 

my core principles, which is, what is my why? As you see on the left side of the screen, Harvard Business 

Review captures it best in that, "Diversity boosts creativity, innovation, and leads to better business 

decisions and results in a more professional and an enriching environment."

Now you might be thinking, "Nate, I thought we were talking about equity data, but you were telling us 

about what HBR says about diversity." But if you understand, diversity is the underpinning of equity and 

inclusion, and you will never have true equity and inclusion unless you maintain an enriching 
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environment. So in other words, to create and foster an enriching environment, the setting must be 

principled in leading a belonging centered culture. Without belonging as a part of the organization's 

culture, the equity and inclusion will not exist. So Dr. Laura Hamill, she's the Chief Science and People 

Officer of Limeade, specifies that, "Inclusion can be measured by a sense of belonging, connection, and 

community at work." Another way of stating this could also be that equity and inclusion is a part of the 

culture through engagement. Next slide.

Now that we have a grounding in diversity and equity and inclusion, let's jump into three ways to lead 

culture using data, and how this generally drives mission outcomes and is especially key in service-based 

organizations. We must first embed DEIA transformation into our business practices, our policies and 

our procedures. Forward-leaning organizations recognize that for this work to remain a part of its 

arsenal, it must be directly connected and embedded into the mission. Gartner's research frames its 

strategic planning hypothesis that by 2026, 50% of organizations that do not have equitable and 

inclusive work environments will fall behind, slowing down innovation, productivity and financial 

performance.

While the federal government is generally not an entity for producing revenue, nor are nonprofits and 

service-based programs, we are either employed or financially supported by the federal government, 

who is the United States number one employer. So falling behind in innovation, productivity and 

meeting the needs of the American people is simply not an option. Next, we must increase equity 

opportunities for those we serve. Forward leaning organizations continually assess and put measures in 

place to ensure that equity is simply the fair treatment and equality of access to opportunity, 

information, and resources for all. And then lastly, we must develop the inclusive organizational 

cultures.

So inclusive organizational cultures create sustainability, it decreases attrition and turnover, increases 

retention, maintains knowledge management, and outperforms non-inclusive organizations. Now, when 

we consider culture, often it is mis-characterized as the niceties, where we think about pizza Fridays, 

happy hours and free coffee at places of business, but that is farthest from the truth. Developing an 

equitable and inclusive culture is the foundation and springboard for supporting success.

So if you are a grantee that is challenged by retention of persons working within your program, have you 

considered the why? How do you assess and evaluate the impacts of retention, turnover and attrition, 

or access to information and resources? And how much does not monitoring these data trends cost and 

reduce your mission's impact on an annualized basis? Implementing and synthesizing exit surveys, stay 

surveys in trend data alone are ways to evaluate the equity of your program and identify ways to 

improve, to meet or exceed mission outcomes.

According to Glassdoor 2020's Diversity and Inclusion Workplace Survey, 76% of employees and job 

seekers reported that diversity was important when considering job offers. 89% of people want their 

companies to be inclusive of individuals with intellectual disabilities. Racially and ethnically diverse 

organizations have a 36% higher likelihood of financially outperforming less diverse organizations. And 

then, companies with a high level of engagement do report 22% higher productivity.

So I want to remember these fundamental truths as we delve a little deeper into the three guiding 

principles in an even more simplistic line of thinking. To simplify what I've just covered, three ways to 

lead equitable cultures through data are through the use of business, access and culture.

Now, I thought that DEI was about the hearts and mind work. Well, that can be one motivator, but 

rooting this work in a business model with key identifiable metrics will always demonstrate its 

importance, while connecting the greater work back to mission outcomes. Author and chief scientist for 

workplace management and wellbeing at Gallup, Dr. Jim Harter says, "Engaged employees are more 
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attentive and vigilant. They look out for the needs of their coworkers in the overall enterprise, because 

they personally own the result of their work and that of the organization."

So while in some breaths I'm mentioning the term employees loosely, this is applicable for grantees and 

volunteers as well. The communities where we get to serve are equally important and we should pay 

close attention. So inclusion can't be exclusive tarts in mind work, but it must be built into the fabric of 

an organization strategy. And now I'll share with you a brief snapshot of how we've done this at 

AmeriCorps. Next slide, please.

So federal agencies were charged by the Office of Personnel Management, otherwise known as OPM, to 

develop two particular plans as a part of this administration. And one was an equity action plan, which is 

in support of Executive Order 13985, which is Advancing Racial Equity and Support for Underserved 

Communities through the federal government, and strategic DEIA plans in support of Executive Order 

14035, which is DEIA to the federal workforce. At AmeriCorps, I had the pleasure of being a part of the 

agency that recognized the importance of the integration and supported not only the development of 

the DEIA strategy, but ensuring that DEIA was embedded at the agency level to support both our 

workforce and our members, grantees and volunteers.

To familiarize you with the tenants of our equity action plan, they include these top five line items, 

which are to advance civil rights, recruiting and retaining a diverse core, partnering with Native 

communities, addressing limited English proficiency and ensuring language access and increasing equity 

in our procurement process, which are all ongoing equitable goals to further advance equity and service.

So going back to the strategy, I'll give you a quick snapshot of how we integrated strategy into how we 

function. To ensure its alignment, we aligned our outcomes back to what we discussed early, which was 

organizational success and ensuring that we had fiscal budget to support the bottom line. Next, we 

engaged with stakeholders immediately and communicated and marketed DEIA to our workforce as well 

as to our external partners. These steps were paramount into supporting DEIA transformation to our 

organization's business.

Strategic planning is the foundational work required to intentionally embed DEIA into our business, 

which undergirds how to lead and support equitable organizations. And so this is a part of the business 

side, but the business in how I'm mentioning it, is truly no different than how business models are 

integrated across the board. DEIA is a business model. It's a part of the work that we do and equity 

drives outcomes, and so it's not the nice to have. It's necessary in order for organizations to continue to 

perform, so that we do not fall into the hypothesis based on 2026, that if we don't have equitable 

organizations that we'll start to lag behind.

Further leaning back into access again, forward-leaning organizations are being intentional in what's 

known as organizational introspection, which is taking an inspection of its current practices to ensure 

that there aren't barriers in place that disenfranchises communities. Ways to inspect your current 

processes lean into areas, such as how equitable are your current systems. For example, as federal 

agencies, are your performance management and award systems equitable that the data demonstrate 

that demographics are overly compensated or under-compensated as opposed to your overall 

population? And if so, why?

This can be for grantees as well as federal agencies as well as companies. Are your hiring practices and 

recruitment strategies intentional in targeting more diverse pipelines? Do our recruitment strategies 

align and reflect our talent pool? And does our talent pools demonstrate consistency with our hiring 

decisions? From a cultural standpoint, do employees have access to the same trainings, to the same 

mentoring opportunities, who's serving us the sponsors in rooms that they aren't normally a part of?
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These are all areas that we continually work through to ensure that equity is afforded not just to those 

internal, but also the American people that receive the benefit of the federal government, whether it's 

nonprofit supplying for federally appropriated grant dollars, support to tribal communities and Native 

nations. This equity work is bigger than just an organization. Introspection supports a nation of people 

that need us to think about how we most appropriately consider equitable solutions. Next slide, please.

Just to wrap this up, I want to show that from a federal perspective, there is a way that we evaluate 

ourselves, and that's through what's known as the Federal Employee Viewpoint Survey. And we have 

what's known as the Diversity, Equity, and Inclusion and Accessibility Index. And this is 13 questions that 

are asked to evaluate agencies in the performance of the D, the E, the I and the A. And this index is 

serving as a baseline, which charges us to identify ways that we are doing, in particular, the equity work 

and best ways that we are advancing that, particularly from communities that we serve.

So this is of course, a quick snapshot into the way that we are doing the work. We recognize that there is 

a lot more work that has to continually be done. As I tell my team all the time, Rome wasn't built in a 

day, and so we won't have all of the answers overnight, but the way that you eat an elephant is one bite 

at a time. And so we have to continually press forward and incrementally do this work, so that we're 

ensuring that the populations that we are supporting and that we're serving are as best as possible 

reflections of the communities that we serve. So I'll pass it back to Kyle for a recap of what we have 

talked about.

Kyle Brees:

Thanks, Nate. I think we can maybe go up to briefly pass by next slide. I think everyone knows me at this 

point. But yeah, so thanks Nate. Thanks Andrea and Robin. Just so much great information shared by 

everyone. So I just wanted to take a quick couple minutes just to really revisit. So I've provided these 

examples at the beginning of our talk. I just want to kind of think about these now that you've had the 

chance to hear our speakers. Talk a little bit more about the subjects, to think about whether they fit in 

more into equitable data use, data on equity, measuring equity or kind of both, because while we 

introduce these topics as two separate things, they're most definitely integrated in the work that 

together. Some of these ones, I'm going to go through them a little bit quick, but some of these ones 

pretty obvious.

So the collection of race and ethnicity data, I think we could all say it was would probably data on equity. 

A lot of these examples, we kind of covered in our talk, but some other ones that might be a little bit 

new or different to people are data education. We heard Andrea talk about that. So it's important to 

make sure that your audience and the people that you are giving this data to, can understand it.

Participatory research methods, something that is very near and dear to our hearts in the Office of 

Research Evaluation, really involves getting the community and the participants involved, integrating 

yourself within the community, making sure that you're not just some outside observer. It's about 

building that trust that we heard Robin talk about. It's a two-way street for us. We've got to be able to 

the communities to trust us and we need to be able to trust them in order to kind of go through and 

create these really well-thought-out studies and get valuable information.

We've talked about community benchmarking measures that we've heard Nate talk about, some data 

tools that we've seen. There's a lot out there. I'd encourage you to just browse around, check it out. And 

then also, equity lenses and frameworks. And it's not just enough to measure this data, but it's a must, 

you've got to really integrate it in your work and your business processes or organizational processes. So 

while all these examples are a little bit different, they all incorporate them in their own unique way. So 

I'd encourage you guys to think about how you could incorporate what we talked about today in your 

work. The next slide.
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So here's just a quick recap of best practices. This is mainly from the equitable data use side, but I know 

we will be sharing slides out. So you could recap all the great work that people have highlighted here 

today. So some of the challenges, the federal statutes, regulations and other program requirements, 

they're very complex. They're difficult to navigate, I'm sure, as everyone knows. The federal and 

community resources to limit scope of collaboration. So as we all know, resources isn't just money, it's 

time. And we all can feel that pressure of not having enough time on our work or enough hours in the 

week to really accomplish everything we want to do. And then the trust and historical relationships 

between government and communities, this is about how you can integrate them.

I know a lot of people on here are part of community organizations. So you guys play a vital role in this 

part, in helping to kind of facilitate either the federal government or helping to raise up those 

community level issues that maybe central statutes kind of prevent them from, the resources prevent 

people from doing. And then also I think the biggest thing is building trust between the federal 

government and communities. That's something that's very instrumental right now, which is why the 

transparency is so huge.

So it's just some best practices. These are not all encompassing, but just a few for you to think about as 

we end today. So include and align community goals with federal goals. So obviously, it's a little bit 

different, but it all kind of trickles down. So the federal government puts up this executive order, it goes 

to different agencies. Agencies go down to community organizations, and eventually, it affects the 

people. Identified plan for specific capabilities needed by federal agencies and community partners. So 

it's about making sure you have these resources that we talked about involved, and then you have a 

well-thought-out plan so you don't get stuck kind of halfway through something.

Review existing federal data governance. So it's about incorporating not just the data into your work, 

but incorporating the practices into your work. And then establish data standards and principles to 

support non-federal data collection and analysis. So this is where you could really help. It's about 

collecting the secondary data. It's about making sure that your organization is set up, will help succeed 

as well, and not just the federal government. Because we know while it is big and we all have our 

[inaudible 01:06:01], it's not all encompassing and we rely a lot on these community organizations to 

really help support us.

So these are just some of the best practices, like I said, and I will be kicking it over to my colleague Laura 

Schlachter for a little bit of Q&A time before we end.

Laura Schlachter:

Hi everyone. Thank you so much to all of your presenters and for all of you for being with us today. My 

name is Laura Hanson Schlachter. I'm a Research Analyst in the Office of Research and Evaluation. And 

I've been kind of collating questions as they've come in the chat, and some of you submitted questions 

as you registered for the webinar. So I'm going to go ahead and just dive right in.

The first one, just a logistical reminder, it's in the chat, but in case you missed it, the slides and the 

recording will be made available two weeks after the webinar on the AmeriCorps Evidence and Impact 

Webinar pages and our YouTube Channel. And if you registered for the webinar, you'll receive a followup message right after this with direct links.

So first question, I'm going to start with Robin, but this is relevant for all of our presenters. Well, and just 

a recap of everyone. So Robin, in the chat, Chris asked about ensuring fairness and relationships 

between communities and the federal government. And so could you elaborate on some concrete 

strategies to build trust and ensure equity in these kinds of partnerships? Or maybe some lessons 
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learned from ongoing cross federal efforts that build on the White House Equitable Data Working 

Group?

Robin Ghertner:

Yeah, sure. So I talked a little bit about, I put up on the screen this image of the data life cycle as a tool 

to conceptualize the ways that communities can be involved. And that's really helpful for me, and it was 

really helpful for our working group as we thought about the different ways that communities can be 

involved. And that involvement I think is a key step that we found when we were brainstorming with our 

working group members of ensuring that there is an equitable involvement. I think the question is in 

large part about how do we make sure that we're not as a federal government targeting only certain 

communities that are easy to work with, which that's often the case. That's like, we're trying to get 

something done, we have in a short timeframe, so who can we just get to really quickly and just go in 

and out and get done what we need to get done? That, of course, sidelines a lot of communities.

So thinking about that lifecycle as a framework for how we can involve communities, there are key 

stages where certainly a high degree of capacities or capabilities are needed for involvement, but not 

always and not in every case. So one thing that we have found, we have several federal programs that 

have created advisory boards. I'm going to talk about tribes as a good example, because tribal 

communities have a long legacy of mistrust related to data in particular. We have the Bureau of Indian 

Affairs, for example, the Administration for Children and Families. They have projects where there are 

advisory boards that are specifically comprised of members and leaders from tribal nations. And what 

this does is this gives voice to those communities in some of those high level decisions about research 

studies, data collections, and the use of such data that have direct implications for how the federal 

government is engaging those communities.

So we found that advisory boards in particular, whether it's a formal thing or it's something that's a little 

more informal, is a great way to get community partners that represent those communities that don't 

have a history of regular positive engagement with the federal government on data.

Laura Schlachter:

Yeah. Thank you so much for that. Are there any other reflections on that question around building trust 

with communities, with partners from Kyle, Andrea, Nate, from your vantage points in the agency?

Andrea Gibbons:

Yeah, I think forKyle Brees:

I just say it's kind of ... Oh, go ahead, Andrea.

Laura Schlachter:

So Andrea first, and then Kyle.

Andrea Gibbons:

From the seat that I sit in, it is all about getting the data in a place where it can be used and reused, like I 

talked about. So it's about incorporating this institutional knowledge about the data that we collect, 

whether it's in or out of our agency. Talking about that data lifecycle, it's the care and feeding for that 

data as it moves throughout that lifecycle, and making sure that it's described correctly and that all of 
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the attributes are there. So individuals that want to use it, know how it can be used and how it can be 

leveraged, and more importantly, how it cannot. Yeah. Kyle?

Laura Schlachter:

Yeah, thank you.

Kyle Brees:

Yeah, I'd just say just quickly from the Office of Research and Evaluation's perspective, I know that we do 

a lot where we work with field working groups to try to really integrate ourselves into the programs, and 

making sure we understand much deeper level of how we can really make the data useful and kind of 

working in a partnership. And then also, a lot of our participatory-based research that we work, I know 

Laura, you work on some of these grants as well and a lot of our other people, but that's really the best 

way we're able to get into it. Because a lot of times, we don't have the time or resources to really dive 

into it, but we work with universities or other partners to do that community-based research where you 

can really integrate yourself in, and then you can get some really rich findings.

Laura Schlachter:

Okay. Yeah, thank you. My next question is more, I'm going to start with Nate, but Nate, feel free to pass 

the baton to others. In terms of thinking about kind of DEIA and collecting data on equity, we received 

quite a few questions about best practices for collecting demographic information that might be 

sensitive to certain groups. So thinking about race, ethnicity, gender, disability, veteran status, other 

characteristics. Some people might be more or less reluctant to answer these kinds of questions. So just 

wondering again, from your perspective where you sit at the agency, do you have any lessons learned, 

particular advice, and sort of embedding that kind of data collection in your DEIA strategy?

Nathaniel H. Benjamin:

Yeah, that's a good question. So I'm not necessarily sure if we need ... Well, let me frame that 

differently. Data collection in terms of equity does not have to be everything specific to RNO data. And 

one of the things that I'd mentioned before and I think it's very important, is the use of engagement 

surveys, when we think about stay surveys. Stay surveys is a best practice, because often we want to 

wait until people are traumatized by something that's happened in the organization and we find out 

why they're leaving. Now, we also want to know why people are staying. We want to know what keeps 

people engaged. And so we can ask and survey based on really a pulse, if you will. And this is a best 

practice, because we do this when you think about the ... I'll use an example, and I'm going to use an 

example from my previous agency.

As a federal entity, we use the FEBS, right? FEBS tells us what's going on. It's a benchmark across the 

organization. But we went one step further and in my last institution, we created what was known as 

the National Engagement Strategy because we want to keep people here before you walk out the door. 

And so we rooted a lot of the questions that we asked from a stay survey perspective around the same 

questions we were asking in FEBS. And so we had collective information which asked people what were 

the reasons why they were engaged, what were things that we could do better so we could help offset 

that. That work still went back to creating equitable opportunities.

So we didn't necessarily have to root everything into, you're a X person, your intersectionality says Y. 

This work at the core is culture work, it's belonging work, it's engagement work, and it's irrespective of 

your creed, your race, your national origin. So when we have these conversations, I like to up-level it a 
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little bit more. Diversity is a byproduct, but at the end of the day, without culture in place, we don't have 

outcomes that are met. So I say, let's use data from a strategic way and let's use it around engagement.

Laura Schlachter:

Okay, yeah. Thank you so much for that reflection. And I know that we're coming up on time, so I'm just 

going to send one kind of nuts and bolts question to Andrea. We've gotten a lot of questions about 

data.americorps.gov. How do people actually access data on the AmeriCorps open data portal? What 

are some resources they can use to kind of interpret what they're seeing? Do you have any ideas of 

examples of how they might use this kind of data to promote equity?

Andrea Gibbons:

Sure. So we just most recently reestablished our open data platform, and we created a DEIA dashboard 

for our members. And we included it out there, pardon me, on our open data platform, so that people 

can slice and dice the data and look at it in using their own perspective, rather than what we're 

committed to as a federal agency is to provide the set of data and the description and contents. But we 

went ahead and did the analytics and made the dashboards available, because what we heard from our 

partners are, "That's great, but we don't all have the necessary data school skills to derive value from 

that."

So far, and we just did this last week, we have it published out there and for people to use. We are going 

to create tutorials and more documentation, so people know how they can use it. And we are 

committed to just providing more and more robust analytics as well as sets of data for people that do 

have the skillset in their organization to download that data and use it for whatever purpose they want. 

So it's kind of, we're getting there [inaudible 01:16:56].

Laura Schlachter:

Yeah. Well, thank you so much for that. And I love how the open data platform just touches on so many 

themes that we've talked about today. And we're in our final moments of our time together, but I just 

want to take a minute to thank all of our presenters for your time and expertise and perspective, and for 

your care in these issues. I think that thinking about equitable data, data on equity is just so crucial in 

this moment. And with that, I'm going to reintroduce our Learning Officer, Katy Hussey-Sloniker, to close 

us out.

Katy Hussey-Sloniker:

So we're now at the close of our webinar. We'd like to thank each of our presenters for making the time 

to share their work with you today. We hope we hit our mark on the webinar objectives, and that you 

walk away from the session with some new information on equitable data engagement and 

accountability, data management practices and application to our DEIA work. I hope you join the Office 

of Research and Evaluation all year long, as we celebrate evidence and discussions on the evidence built 

that supports AmeriCorps' mission. Our next webinar will be December 14th, and will focus on 

AmeriCorps' return on investment studies and our contribution to the upcoming Council on Economic 

Advisors findings. Like this webinar, we'll have a special guest speaker from the White House 

subcommittee on cost benefit analysis in federal programming. Hope you'll be able to join us.

I'd like to extend a thank you to all our Guardians of Honors colleagues for their technical support and 

coordination. This webinar recording and support materials will be posted on the americorps.gov 

website under Impact and Evidence Webinars within the coming weeks. Please feel free to share with 
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your colleagues and networks. We'll also be sending out a post-webinar survey, so please let us know 

your thoughts on this webinar and for any future webinars. We hope you'll have a wonderful rest of 

your day and happy Halloween.