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Welcome to Making Data Matter.

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I'm your host Sawyer Nyquist.

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And I'm your co-host Troy Dueck.

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And today we're joined by guest Sam Elliott.

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Sam, welcome to the show.

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Thanks for having me.

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And for folks meeting you for the first time,

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haven't heard your name or run across you before,

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give us a little bit of background on Sam.

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Who are you and what do you do?

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Yeah. So I am currently the Director of Data Analytics

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at the Cowherty Homeless Foundation.

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I've been there for about four years now.

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And we are a funder of programs that support those

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that are experiencing homelessness.

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The role for data for us is we operate a record-keeping software,

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so the HMIS software that works for shelters,

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housing programs, outreach teams, you name it,

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as well as we have a business intelligence team

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that's building your classic sort of data browsing techniques

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and all that sort of descriptive analytics.

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And so leading those teams to help support

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our community at large fight against homelessness.

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I'd love to start the conversation here

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because I've never been to Calgary.

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Tell me just a little bit about, like,

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what's the nature of homelessness in Calgary?

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Like, why does your organization exist?

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What's the nature of the need there?

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Yeah. So I think we've been around for...

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I think we're on our 26th year.

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And, you know, it's...

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I don't know if we necessarily have

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the highest population per capita, but there is a need here.

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You know, any major city across the Western world these days,

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you're seeing that there is a challenge

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with dealing with homelessness right now.

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So we came around, yeah, 26 years ago,

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mainly focused on housing programs.

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So for those that don't know,

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we offer sort of recovery-oriented

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supportive housing programs.

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The idea is that if you experience homelessness,

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we get you into a housing unit.

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It's either a specific built unit

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or a landlord-run unit on the market

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and provide that case manager, social worker-led support

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to try and increase your capacities for...

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and life skills and try and really support you

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as you deal with some pretty serious, you know,

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typical physical or mental health,

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any of those sorts of morbidities,

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and get you back on your feet.

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Now, to support that and sort of ramp up

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beyond just the housing programs,

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we also offer prevention diversion programs,

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which is if it's your first day of homelessness

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or if you're showing up to a shelter,

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if we can just simply work with you

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to make sure you never experience homelessness,

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that ends up being a very cost-effective solution.

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So we offer things like that.

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And then for the more complex situations,

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so for those living in the streets or in encampments,

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we look to support programs that will go out in the streets

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and provide the services there

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with ideas better connecting them and trying to, you know,

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at all steps, make the individual's life better

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and get them better.

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And that's sort of where we play.

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So in Calgary, I think, going around, you know,

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our last formal number count was around 2,700 people

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were experiencing homelessness on any given night.

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Now that was in 2022.

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We're doing another formal count come October.

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We're likely seeing that number increase as well,

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especially in the more complex situations,

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which is a challenge,

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but that's sort of what we're trying to work on here in general.

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And so that would be, I'd say, the situation here in Calgary.

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You know, you see down in the States,

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some on the West Coast, for example,

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some pretty extreme situations.

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I wouldn't say we're into that,

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but it's not a light situation in the least.

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One thing you mentioned, Sam,

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that I'm particularly curious about,

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and I think it's something nonprofits can struggle with

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in general, is sometimes they just have difficulty

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collecting data.

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For whatever reason, it seems like there's these

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phantom metrics that people struggle to get their arms around.

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And so I'm curious, even just counting homeless individuals,

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I bet that can feel elusive at times.

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Well, how do you count them and how do you know where they are

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and how do you get those numbers and metrics about them?

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So I'd love to hear a little more about some of the strategies

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that you've employed to be able to just simply collect the data

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when it can be difficult to capture at times.

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Yeah, so we have two formal ways of addressing

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sort of the whole population number.

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The first is, and this is a Canada standard,

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it's now becoming an annual point in time count.

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So that is on one day, we coordinate a bunch of volunteers,

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typically social workers that work in the sector,

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as well as work with shelter providers

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and do a mass street count, all that sort of stuff.

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And you can imagine it's a very cost-heavy process,

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as well as, you know, as a human-to-human process,

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it's not necessarily the greatest feeling to just be counting

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an individual just for the sake of counting

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without providing them any sort of services.

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And so the other approach is this term by name list

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or, you know, those experiencing homelessness.

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And so with this approach is we actually use our record-keeping software

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across all of our programs, across the shelters that are using our software,

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as well as shelters will provide us their data directly on, you know,

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an automatic basis.

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And we'll be trying to use unique identifiers from the person's name

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to match them across systems.

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There's two challenges to this.

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The first one is we operate under client-centric data capture method,

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which the idea is that if you tell me your name,

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that is who I'm recording you as, you know.

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And so that could be a challenge if I'm Sam or if I'm Steve

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to do different providers, but also it can be if I'm Sam or Samuel.

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So what we're actually working there,

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and we've had some pro bono professor over at the University of Calgary here

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from the Electro Engineering School, is that deterministic matching.

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How likely is it that people that have given slightly different info

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are actually the same person?

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And so that's been a really interesting pathway.

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Even just by simply doing some quick distance matching,

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we've been able to reduce the duplicates by 35%.

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And so that's one pathway that we're pursuing for that piece.

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The other challenge is that when it comes to street outreach,

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there's only actually so much capacity with this approach.

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Our teams aren't able to go out into the city

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and support every individual that's in the encampment on a daily basis.

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And so then you're kind of doing some extrapital of pieces

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where you say, oh, and that's how 14, 30 or 90 days,

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how many different people were supported by these encampment teams?

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And so that's where it can become a bit tricky to say on any point in time

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how many people are experiencing homelessness,

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is you have those two factors that kind of play into the game.

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And so it's not a perfect solution,

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but it does give us some pretty good and deep insights

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into what's going on on a regular basis.

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How does even things like seasonality affect those populations?

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I think about like winters in Calgary

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being quite a bit different than summer experience

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and the impact that would have on homelessness

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and how homelessness is experienced.

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How does that weigh when you're taking population counts

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and the experiences there?

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Yeah, so we definitely, even on our program basis,

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ramp up during the winter.

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So we have our coordinated, oh, I've forgotten the acronym,

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CCEWR, I'm not sure what it stands for,

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but effectively it's our coordinated winter response.

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And the idea is that we actually ramp up

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and run warming centers during the winter here.

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As you can imagine, I think last winter we hit like a minus 39 Celsius day,

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which inherently you can opt out on the streets in that temperature.

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And so you don't actually see, in our seasonality,

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we don't see too much fluctuation in that total population number,

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but we do see the location shift,

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where shelter usage will be higher in winter,

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and especially during those cold snaps,

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and we have programs to encourage and get you into the shelter,

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versus when you're necessarily,

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if you want to be in an encampment type situation,

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it's a bit more appropriate in the summer,

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where it can get hot here, but it's very lovable.

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Yeah, you mentioned your foundation provides services to centers

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or homeless services throughout the city.

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Is that just in Calgary?

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Does that span beyond Calgary?

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And how many different organizations are you partnering with across your footprint?

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Yeah, so we fund all the folks that are providing the services ourselves.

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We don't actually run the services ourselves.

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On an annualized funding basis,

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these are agencies that we will continuously fund,

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because programs need sustainability,

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is about 23 different agencies.

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So there's quite a few players,

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and that's not 23 different programs in those agencies,

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that's just 23 different agencies.

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And then on a fluctuating basis, we support quite a few others as well,

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but those are sort of one-off grants, let's say.

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Largely focused in Calgary,

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we, you know, there's surrounding areas,

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and there is, you know, migration with the population.

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But up here in Canada, it's sort of each city is sort of,

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has either a community entity like ourselves,

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or is run by the municipality,

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as sort of leveraging their own approach.

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Pros and cons to that, you can localize the support in that setting.

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But then from a data perspective, as you can imagine,

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you're going to building a lot of, you know, either duplicate infrastructure,

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or it'd be great to unify some data sets and all that sort of thing.

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And so you do see some challenges that emerge from that data lens.

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One interesting thing, and one thing that I take as a point of pride

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for what we've built from both our record-keeping software

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as well as our data software,

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is that both our funded agencies that are mandated to use our systems use it,

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but as well as unfunded agencies as well.

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And so there's clearly to these frontline service providers

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a high value in having this data at the ready for them,

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and having the record software.

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So in addition to that direct funding that we provide as a service,

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I view our sort of data in our platforms as a service as well.

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Yeah, so from a perspective of the record-keeping as well as the data platform,

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what does it offer?

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Like what kind of information does this data collection offer these,

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these are for organizations and service providers.

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They're getting like names of people and records.

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What other types of data points are useful for them

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in terms of as they're offering their services?

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Yeah, so I look at it at three levels of that sort of that micro,

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mezzo and macro level.

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So at that micro level, if I'm a caseworker, I can use this as your classic,

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here's my case management software.

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Here are goals that I have for the individual.

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Here are the services that they need.

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Here are the referrals I need to make to other agencies or other platforms.

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And so that's where for that front level,

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if I'm a frontline staff, that's the value there.

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If I'm managing my shelter, I can see my shelter occupancy,

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who's in which bed, all that sort of stuff that direct,

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I'm providing the services, this is what I need to know.

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Then we go to that mezzo level at sort of that program manager,

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director level, if I'm at the agency,

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I can now actually get that broader picture of what's going on

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from my frontline staff, you know, your classic business intelligence.

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What is the volume of people that we're supporting?

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What are the services that we're providing?

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Are we seeing any spikes?

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All that sort of stuff that the program managers need to plan with all that.

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And then at that macro level, we get that broader picture.

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And that broader picture really helps our own internal system planning team

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when it comes to that funding decisions.

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We only have so many resources,

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and so we need to allocate them for the highest impact.

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And so that's where that data really comes into play.

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As well as we can then provide that our government funders

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with data on an immediate basis.

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And so that's one thing, you know, every nonprofit has the challenges

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of reporting to funders and all that jazz.

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The nice thing is that we effectively just roll up that micro level

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through that mezzo level up to the macro

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and just have automated that reporting to our funders,

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which is primarily government of Alberta, the government of Canada,

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as well as we receive some dollars from the city here in Calgary.

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And so we can automate there.

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And so that's sort of that value add across the board.

257
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And so, and I think that's the way you have to go with data,

258
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as much as there's always going to be the stick of,

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you need to report what you're doing in order for us to validate.

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There's also that carrot of making, you know, folks at all three levels,

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life's easier.

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And that's, and provide that information they need to make valuable decisions.

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That's where we play.

264
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That's such a neat tiered approach and where, you know,

265
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you're at that more really fine detailed grain data at that micro level,

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but then that can roll up easily to the macro level.

267
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And I think we all sort of aspire to that as we're architecting

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and building systems in the data space and easier said than done usually.

269
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So what enabled you to get to that level of maturity in the way that you've

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developed your data system there?

271
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Yeah, I'd say the first thing that to be honest would be

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we had executive level buy-in with this.

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Our previous VP of Community Impact came in and said,

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we need to do data right that he had come from, you know, a banking environment

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where data was ready at the fingertips when they needed to make a decision.

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They had it there.

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And so he said, let's get there.

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Let's build out order analytics looks like.

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Let's build out this modern BI.

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So I think that's the, that was the first step there.

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And it's a lot easier to over invest in the beginning and build out your governance

282
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and all of your architecture when you have executive level sponsorship

283
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from that standpoint versus, you know, sometimes you'll be cast adrift as a data person trying to

284
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and the executive will say prove that there's value here, which is really tough.

285
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We don't have any infrastructure.

286
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So it'd be that and then honestly, it's just your classic data warehouse

287
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and business intelligence and data governance and just constantly building on yourself,

288
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building on itself.

289
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And so the approach that we typically took was with every data product that we needed to create

290
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for an emerging need then is sort of what of these principles can we develop

291
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and really sort of horizontally scale across all of our different future data products

292
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one step at a time and just keep growing our capabilities and maturity in that fashion.

293
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So we had a great launch launching pad.

294
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And then from there, we had just kept approaching just, you know,

295
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nothing's reinventing the wheel here.

296
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And I don't think that I don't know of many nonprofits that actually do need to reinvent the wheel

297
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when it comes to this.

298
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You know, we're not tech companies trying to change the road through technology.

299
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We're trying to make data in a fashion that actually allows you to make informed decisions

300
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and, you know, make a greater impact.

301
00:14:46,440 --> 00:14:51,840
Yeah. So it was on that point that I wanted to talk about how do you define success?

302
00:14:51,840 --> 00:14:56,440
I think, of course, getting as many people out of that state of homelessness

303
00:14:56,440 --> 00:14:59,640
is going to be at the core of those metrics.

304
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But when you're defining, say, you know, those key performance indicators

305
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and you're sending those up to the core leadership to display,

306
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here's how we're doing.

307
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What are some of those metrics and measurements that you're doing in that homelessness space?

308
00:15:17,640 --> 00:15:22,240
Yeah, it's a good question because we're actually sort of in the process of reinventing

309
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what those look like.

310
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But, you know, right now it's your top level descriptive analytics.

311
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Here's the number of people that are experiencing homelessness.

312
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Here's the number of people that are in our housing programs.

313
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Here's the amount of people that are leaving our housing programs

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or leaving our system altogether.

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You know, that's sort of that home run.

316
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And all of that sort of jazz.

317
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Program utilization becomes a key thing.

318
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Those sort of efficiency metrics that lead up are sort of a bit more influenced

319
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and a bit more achievable by programs themselves.

320
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So how long is it taking you to work with a person or get them in your program

321
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or any of that sort of stuff?

322
00:15:57,640 --> 00:16:01,640
Now, because, and this is where I like to think of sort of,

323
00:16:01,640 --> 00:16:06,640
we're on the edge cases of edge cases when it comes to trying to measure this with folks.

324
00:16:06,640 --> 00:16:09,840
You know, we have extremely complex folks for a variety of reasons

325
00:16:09,840 --> 00:16:11,240
and you're trying to get them better.

326
00:16:11,240 --> 00:16:16,240
And up until now, we have effectively tried to measure, are you getting a home run?

327
00:16:16,240 --> 00:16:19,840
Well, what about like the singles or doubles or triples or any of that?

328
00:16:19,840 --> 00:16:24,240
And so our evaluation team is actually working with the programs themselves right now

329
00:16:24,240 --> 00:16:31,040
to sort of look and define principles, this principles based evaluation of our individuals getting better.

330
00:16:31,040 --> 00:16:32,440
And this is across four domains.

331
00:16:32,440 --> 00:16:35,240
So right now we've said, is someone getting housed?

332
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Which is a great improvement if you're experiencing homelessness,

333
00:16:38,040 --> 00:16:42,040
but we're not actually measuring is their financial situation getting any better.

334
00:16:42,040 --> 00:16:44,040
So is this going to be a sustainable improvement

335
00:16:44,040 --> 00:16:47,440
or are we going to need to support them very long term?

336
00:16:47,440 --> 00:16:48,640
Is their health getting better?

337
00:16:48,640 --> 00:16:51,240
You know, it's for folks that are complex in our system,

338
00:16:51,240 --> 00:16:54,640
it is not just a financial driven situation.

339
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Now the finances are typically aggravating their situation.

340
00:16:57,240 --> 00:17:00,240
You know, for example, your mental health and physical health will be worse

341
00:17:00,240 --> 00:17:03,440
the longer you are homelessness or experiencing homelessness.

342
00:17:03,440 --> 00:17:07,240
But when we support you, are those indicators getting better?

343
00:17:07,240 --> 00:17:09,440
And the last one is that community connection piece.

344
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Are you actually getting more connected with your community,

345
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either volunteer or events or any of those sort of things are really going to bolster a person's strengths.

346
00:17:18,240 --> 00:17:21,640
And that's sort of that framework of that recovery oriented system.

347
00:17:21,640 --> 00:17:26,640
And I'd love to say that we've cracked the code and have our KPIs rip in right now,

348
00:17:26,640 --> 00:17:29,640
but that's something that we're working in progress

349
00:17:29,640 --> 00:17:31,840
and really working with the programs that we serve,

350
00:17:31,840 --> 00:17:34,640
as well as those frontline staff to really define what those look like.

351
00:17:34,640 --> 00:17:39,240
I think, you know, trying to in our, you know, we don't provide those direct services.

352
00:17:39,240 --> 00:17:42,640
And if we were to try and define those KPIs entirely by ourselves,

353
00:17:42,640 --> 00:17:45,440
it'd be a sort of an ivory tower type situation.

354
00:17:45,440 --> 00:17:50,440
And we really look to sort of bring our community and those that are actually providing the services

355
00:17:50,440 --> 00:17:54,040
to really help refine how we do our approach as well.

356
00:17:54,040 --> 00:17:58,040
It's a non-answer to your question, but it's in the progress.

357
00:17:58,040 --> 00:18:00,640
You gave me lots of flavor text there.

358
00:18:00,640 --> 00:18:01,840
Thank you. That's great.

359
00:18:01,840 --> 00:18:05,040
Yeah, it's just a fascinating way you outline the problem

360
00:18:05,040 --> 00:18:09,640
because for a lot of organizations, it's like the more people in our programs is a good thing.

361
00:18:09,640 --> 00:18:12,240
And to some extent, that's true for you. You want people in the programs.

362
00:18:12,240 --> 00:18:19,440
But the goal is also for people to leave the program and to like be stable and successful and healthy on their own.

363
00:18:19,440 --> 00:18:25,240
The part I'm curious about is even thinking about the evidence of homelessness is one clear evidence.

364
00:18:25,240 --> 00:18:30,440
But the sources of that are so varied in terms of like all the different factors that could go into why.

365
00:18:30,440 --> 00:18:32,440
So it's like we're tracking like one end goal.

366
00:18:32,440 --> 00:18:36,640
We talk about just like the variety of things that could cause someone to be experiencing homelessness.

367
00:18:36,640 --> 00:18:38,640
How do you, how is that assessed?

368
00:18:38,640 --> 00:18:44,240
Or how do you track like the types of variety of reasons or ways someone might be experiencing homelessness?

369
00:18:44,240 --> 00:18:46,840
Or is that something you try to, I feel like you're trying to get your hands around it somehow.

370
00:18:46,840 --> 00:18:51,040
Like thinking about these are the different factors we're trying to influence that help somebody.

371
00:18:51,040 --> 00:18:54,240
But even not collecting that data or thinking through how that is assessed.

372
00:18:54,240 --> 00:19:00,840
Yeah, so in our assessments, when you know an individual is either being intaked into a program,

373
00:19:00,840 --> 00:19:06,840
like I remember apprenticeship diversion programs or being triaged into housing, we ask these sort of questions.

374
00:19:06,840 --> 00:19:12,640
So we act as sort of if you want to enter a housing, a supportive housing program here in Calgary,

375
00:19:12,640 --> 00:19:16,840
we first, we work through, you know, on the ground folks called housing strategists.

376
00:19:16,840 --> 00:19:19,240
They'll first determine if you're appropriate for a supportive housing.

377
00:19:19,240 --> 00:19:26,040
But then they'll ask some pretty detailed assessment questions to try and understand a bit more about you and which programs are effective.

378
00:19:26,040 --> 00:19:29,440
And so we really have these client centric ways of capturing this data.

379
00:19:29,440 --> 00:19:37,240
But that's, you know, it's effectively at that point, by the time we're asking the individuals already actually experiencing homelessness,

380
00:19:37,240 --> 00:19:43,240
you know, it's, you know, typically interact with our system until they're experiencing, which isn't, you know,

381
00:19:43,240 --> 00:19:48,640
and as I alluded to before, if we can prevent it from ever happening, that's better for the individual.

382
00:19:48,640 --> 00:19:51,640
It's better for the system in general, as it's way more cost effective.

383
00:19:51,640 --> 00:19:59,040
And so we're actually working and we've had some academic research partners try and point us into potential indicators.

384
00:19:59,040 --> 00:20:02,640
So obviously you have housing market indicators.

385
00:20:02,640 --> 00:20:09,440
You know, I know in the States it's pretty expensive, but in Canada, housing is really seen some major challenges.

386
00:20:09,440 --> 00:20:14,040
I think in Calgary, we saw year over year rent increases by 20 percent.

387
00:20:14,040 --> 00:20:20,240
And so we look at rent increases and then also the idea of a low income cutoff.

388
00:20:20,240 --> 00:20:25,840
So you have your poverty line, but folks are experiencing homelessness are typically even below the poverty line.

389
00:20:25,840 --> 00:20:31,440
So what is that cutoff point that if we see a large portion of people under that number,

390
00:20:31,440 --> 00:20:33,640
we're like they see an influx into homelessness.

391
00:20:33,640 --> 00:20:38,840
Other indicators, and I know this is an ongoing academic research project, so it's not done yet,

392
00:20:38,840 --> 00:20:43,640
but there's some promising results, is that we've actually combined anonymity,

393
00:20:43,640 --> 00:20:50,640
well, anonymized our data with the Calgary Food Bank data to see if we see a spike in food bank usage.

394
00:20:50,640 --> 00:20:54,440
Can we predict if that individual is likely to fall into homelessness?

395
00:20:54,440 --> 00:20:59,640
And so we're seeing some very interesting results there where they, you know, as most research shows,

396
00:20:59,640 --> 00:21:06,640
the answer is obvious. Yes, if an individual spikes up their food bank usage, they're more likely to experience homelessness.

397
00:21:06,640 --> 00:21:09,840
That will allow us to actually intervene with the program.

398
00:21:09,840 --> 00:21:13,840
And so that's sort of where a lot of additional data efforts are going right now,

399
00:21:13,840 --> 00:21:18,240
is sort of that connections beyond just our sector with the broader sectors.

400
00:21:18,240 --> 00:21:21,640
We're involved in the Community Information Exchange pilot project,

401
00:21:21,640 --> 00:21:29,240
which I ideally hopes to bring together sort of your 2-1-1 with your health, with your R data,

402
00:21:29,240 --> 00:21:34,240
and really sort of connect and also create those cross-system referrals and so on and so forth,

403
00:21:34,240 --> 00:21:37,640
where it sort of can we intervene before they even hit our system,

404
00:21:37,640 --> 00:21:42,040
or if they do hit our system, we have the programs in place to get them quickly out of it.

405
00:21:42,040 --> 00:21:49,640
For all the buzz of AI and LLMs, it's not like you can easily throw your data at one of those

406
00:21:49,640 --> 00:21:55,640
and get these predictive, you know, solutions to just come right out of them and say,

407
00:21:55,640 --> 00:21:59,040
oh yeah, this is exactly what you need to do to solve this problem.

408
00:21:59,040 --> 00:22:03,640
You have to do a lot more building and maybe even training of systems.

409
00:22:03,640 --> 00:22:10,840
I'm curious, is that a space that with all the hype that's out there around what AI can do for you,

410
00:22:10,840 --> 00:22:14,240
what's it looking like in your particular space?

411
00:22:14,240 --> 00:22:20,440
Yeah, I think the main premise of you have to build your data in a great spot before you can actually

412
00:22:20,440 --> 00:22:26,040
drop any sort of AI on top of it, it's kind of a range of really true for ourselves.

413
00:22:26,040 --> 00:22:28,040
But there is some interesting use cases.

414
00:22:28,040 --> 00:22:33,240
So, for example, before when I alluded to that client-centric piece and deterministic matching,

415
00:22:33,240 --> 00:22:35,640
can we find if two individuals are the same?

416
00:22:35,640 --> 00:22:41,440
If we now, you know, we've built a data model that has the person's longitudinal journey through homelessness,

417
00:22:41,440 --> 00:22:45,640
we combine that with this lots of our predictive or lots of our assessment data,

418
00:22:45,640 --> 00:22:47,040
can we predict their next step?

419
00:22:47,040 --> 00:22:48,640
If so, can we see where gaps are?

420
00:22:48,640 --> 00:22:50,640
Can we create an intervene?

421
00:22:50,640 --> 00:22:56,640
We don't want to get into ever a situation where we're trying to tell a caseworker what to do with

422
00:22:56,640 --> 00:23:02,440
an individual through AI just because they probably know way more than the AI system.

423
00:23:02,440 --> 00:23:05,240
And that's an area right for bias.

424
00:23:05,240 --> 00:23:10,640
But then it's sort of the can we predict what our system is going to look like in a year from now or any of that sort of stuff?

425
00:23:10,640 --> 00:23:16,640
And the last thing would actually be, and this is another place where we have to be very cautious about sensitivity or data,

426
00:23:16,640 --> 00:23:21,240
is with all the case notes that these caseworkers are writing or anything like that,

427
00:23:21,240 --> 00:23:27,240
can we plug a potential solution or even just like a word tokenization to be like,

428
00:23:27,240 --> 00:23:33,240
these are the flags of this case note or these are the actual like getting sort of more of those quantitative

429
00:23:33,240 --> 00:23:38,640
indicators out of that qualitative data that's there on the individuals could be another use case.

430
00:23:38,640 --> 00:23:42,640
Now, we haven't done that done any of these or operationalized any of these,

431
00:23:42,640 --> 00:23:44,440
but we've all done some exploratory efforts.

432
00:23:44,440 --> 00:23:50,240
And we actually had a company that's much more talented than myself in the data science world

433
00:23:50,240 --> 00:23:57,240
actually sort of validate and provide a feasibility study and guidance to see could we even use our data for forecasting purposes?

434
00:23:57,240 --> 00:24:00,040
And the answer was yes, but it's tricky.

435
00:24:00,040 --> 00:24:06,440
And the one interesting thing that we never even thought of when it comes to all these forecasts is that most of these forecasts are

436
00:24:06,440 --> 00:24:09,640
built off of the idea of demand generated.

437
00:24:09,640 --> 00:24:12,840
This is what your demand will look like there or any of that sort of stuff.

438
00:24:12,840 --> 00:24:14,840
And we're really a capacity limited system.

439
00:24:14,840 --> 00:24:19,240
We don't have we can't flux our resources to match demand.

440
00:24:19,240 --> 00:24:24,040
And so that's been one interesting approach when it comes to that prick demand analytics is that a lot of the literature

441
00:24:24,040 --> 00:24:30,240
and all that's built out of demand based modeling when we're actually capacity limited.

442
00:24:30,240 --> 00:24:33,440
And so that's something that we've had to incorporate as well with our approach.

443
00:24:33,440 --> 00:24:38,440
One other question I wanted to kind of circle back to is something,

444
00:24:38,440 --> 00:24:41,640
sorry, you mentioned about seasonality and Sam,

445
00:24:41,640 --> 00:24:46,040
you mentioned that you don't necessarily see that total population flux,

446
00:24:46,040 --> 00:24:50,840
but you see their location shift and that that got me thinking about,

447
00:24:50,840 --> 00:24:53,440
you know, as we're talking about defining success,

448
00:24:53,440 --> 00:25:03,040
as we're talking about predictive indicators, how does location and geography play into these factors?

449
00:25:03,040 --> 00:25:04,640
I don't know that we've touched on it much,

450
00:25:04,640 --> 00:25:09,040
but are you mapping these folks or certain neighborhoods and locations?

451
00:25:09,040 --> 00:25:16,240
And do you find that there are certain areas that you watch more closely for one reason or another

452
00:25:16,240 --> 00:25:21,740
because it's location driven rather than other indicators that are spiking.

453
00:25:21,740 --> 00:25:23,840
So any thoughts on that right there?

454
00:25:23,840 --> 00:25:32,240
Yeah, so actually recently we launched a sort of live mapping tool for our encampment teams here in the city.

455
00:25:32,240 --> 00:25:38,240
And so this is, you know, your up to a minute map of where the encampments are located in the city,

456
00:25:38,240 --> 00:25:43,040
when they've been last supported, and any sort of notes that these teams are providing to one another.

457
00:25:43,040 --> 00:25:47,840
A really cool thing that we'd like to highlight here is it's not just one organization that's using this map.

458
00:25:47,840 --> 00:25:51,240
It's a multi-organization tool.

459
00:25:51,240 --> 00:25:57,040
And so right now it's aimed at just simply here is the map of where folks are,

460
00:25:57,040 --> 00:26:01,040
and here's when they were last served, here's those types of services, here are their needs,

461
00:26:01,040 --> 00:26:04,840
here are some potential safety pieces as well to think of.

462
00:26:04,840 --> 00:26:09,440
But then we're trying to take a step further and working with those programs on self-coordination of movement.

463
00:26:09,440 --> 00:26:17,840
So right now, if I'm an encampment team, I'm going to say I'm going to go to XYZ neighborhood today to provide services.

464
00:26:17,840 --> 00:26:21,840
But that's actually, they might not know what the other organizations have been up to.

465
00:26:21,840 --> 00:26:25,440
And so you might have these four different teams going to the same encampments,

466
00:26:25,440 --> 00:26:31,440
providing similar services or the same folks and actually be missing an entire part of the city.

467
00:26:31,440 --> 00:26:37,440
And so we're working with them to really create the platform for them to self-coordinate their services.

468
00:26:37,440 --> 00:26:41,840
Versus, and we don't want to get into a space where we're telling these different organizations,

469
00:26:41,840 --> 00:26:46,040
we provide funding, but we don't boss them around or anything.

470
00:26:46,040 --> 00:26:50,440
We want to provide them the resources for them to determine what makes the most sense for them

471
00:26:50,440 --> 00:26:54,440
from both that caseworker perspective, once again, that micro, mezzo, macro level.

472
00:26:54,440 --> 00:26:58,040
And then we're looking to expand it for all outreach teams in the city.

473
00:26:58,040 --> 00:27:01,040
And so this is a sort of our three phased approach going on right now.

474
00:27:01,040 --> 00:27:05,640
We're done phase one into phase two, and then phase three would be that all outreach teams

475
00:27:05,640 --> 00:27:11,640
and really just providing them a good tool that allows their lives easier on the front end

476
00:27:11,640 --> 00:27:17,440
and then provides us a better understanding of what's occurring on the streets of Calgary is kind of our goal.

477
00:27:17,440 --> 00:27:20,840
OK, so from a technology perspective, how does that work?

478
00:27:20,840 --> 00:27:25,440
Like, how are you live mapping up to a minute, like knowing where people are at and what's going on?

479
00:27:25,440 --> 00:27:27,640
Where's that data coming from? How are you collecting that?

480
00:27:27,640 --> 00:27:32,240
So we found, I found an open source tool called Mage.

481
00:27:32,240 --> 00:27:38,440
Interestingly enough, so, you know, when you think geographic information data, you think Esri.

482
00:27:38,440 --> 00:27:43,440
But after talking with our sales rep, there's some weird data residency requirements in Canada.

483
00:27:43,440 --> 00:27:48,640
And some of the data might have been in transit in the States and there might be some health information,

484
00:27:48,640 --> 00:27:55,440
which is a no go. And so I had to figure out an alternative solution and found this Mage open source app.

485
00:27:55,440 --> 00:28:03,840
And so we run it on our own VMs and it actually has just a phone app right there that the caseworkers can take out,

486
00:28:03,840 --> 00:28:08,240
either a company provided phone or their own phone, have their security.

487
00:28:08,240 --> 00:28:13,440
We also have, you know, API connections into our broader HMIS world.

488
00:28:13,440 --> 00:28:18,440
So that way, individuals names can be mapped and we can once again map it to that longitudinal journey.

489
00:28:18,440 --> 00:28:23,840
And then track sort of consent. We operate under consent based privacy model here.

490
00:28:23,840 --> 00:28:29,440
So then we can make sure that we have a single source of truth for the consent while still allowing those field teams that

491
00:28:29,440 --> 00:28:37,640
optimum access. And then further on, we actually integrate that with our BI tool to then sort of see what's going on

492
00:28:37,640 --> 00:28:44,640
at that macro level when folks were last served and sort of do that time decaying of service provision and all that.

493
00:28:44,640 --> 00:28:52,840
So, yeah, it's been a great tool so far. And I like the fact that it's no licensing fees for us, which is as a nonprofit key.

494
00:28:52,840 --> 00:28:59,040
Yeah. But having the I guess you have the technical skills to be able to stand up and like host your VMs and to run an open source project,

495
00:28:59,040 --> 00:29:04,040
but not every organization can muster the skills to be able to pull that off.

496
00:29:04,040 --> 00:29:13,240
Yeah. Well, it was something new to me. We have an IT team as well that is familiar with standing up VMs and all that jazz.

497
00:29:13,240 --> 00:29:19,440
And one interesting just small thing was everything was written in Node.js for this package.

498
00:29:19,440 --> 00:29:24,440
And our IT team is not going to be out supporting Linux because they don't know how it works.

499
00:29:24,440 --> 00:29:29,040
And so we learned a lot about running Node.js in a Windows environment just to stand it up.

500
00:29:29,040 --> 00:29:34,440
But, you know, you got to work with the resources you got and make it happen in that result.

501
00:29:34,440 --> 00:29:39,640
Yeah, I love that. Just think about the technical capacities your team as developers ambitious enough to tackle.

502
00:29:39,640 --> 00:29:46,040
And earlier in the conversation, you talked about a new leadership person came in and said, hey, we need to take data seriously.

503
00:29:46,040 --> 00:29:52,040
And I want to touch on that a little bit more. Like what did and I don't know how long ago that was or where that fit with your tenure there.

504
00:29:52,040 --> 00:30:02,240
But like, what did things look like before and how what were the what were the baby steps looking like to go from whatever data collection look like at the Caviar Foundation before to some of those early steps to move?

505
00:30:02,240 --> 00:30:08,640
Because a lot of organizations we talk with and work with are on the very early end of that data is not taken seriously. They don't have the infrastructure.

506
00:30:08,640 --> 00:30:12,640
You've built it into a very mature environment at your organization.

507
00:30:12,640 --> 00:30:17,040
So I'm just curious a little bit about that journey, what it looked like before and how do you take to start to take steps there?

508
00:30:17,040 --> 00:30:20,440
Yeah, so it was an interesting place when I started.

509
00:30:20,440 --> 00:30:23,640
So we knew that we wanted to invest in the data analytics world.

510
00:30:23,640 --> 00:30:26,440
We were starting a proof of concept of data warehousing.

511
00:30:26,440 --> 00:30:34,840
But before that, we were doing sort of a record keeping software had its own in-app querying tool that would be used.

512
00:30:34,840 --> 00:30:45,440
We dump it all and then we'd use these massive R scripts and run them on some person's local environment and come up with some data.

513
00:30:45,440 --> 00:30:51,040
And then obviously, you know, you're rocking a lot of Excel manipulations too, in addition to those R scripts.

514
00:30:51,040 --> 00:30:53,840
And, you know, it's always Excel.

515
00:30:53,840 --> 00:30:56,440
You can't get away from it. And it does have some value.

516
00:30:56,440 --> 00:30:59,340
But you need to make sure that your governance is in place that way.

517
00:30:59,340 --> 00:31:05,640
The data that's going to Excel is actually what you think it is, which I think is the number one problem.

518
00:31:05,640 --> 00:31:10,640
And so I think there are replication problems quickly emerged from this situation.

519
00:31:10,640 --> 00:31:15,840
And if, you know, a key individual was off or anything, the whole process would fall apart.

520
00:31:15,840 --> 00:31:23,040
And if you're running all these in-app querying tools and all that, unless you're successfully storing these in some sort of database,

521
00:31:23,040 --> 00:31:28,440
you will almost always lose that historical context and trend analysis and all that.

522
00:31:28,440 --> 00:31:34,740
So effectively, our data before was reporting to government or just providing data maybe to researchers.

523
00:31:34,740 --> 00:31:42,640
And we'd have some operational usage, but the operational usage was so capacity driven that it would simply take weeks for us to discover

524
00:31:42,640 --> 00:31:47,040
who is like what trends have been emerging on our triage list.

525
00:31:47,040 --> 00:31:52,340
We'd spend, I'd lose, I think when I joined the organization, the first two weeks of an analyst,

526
00:31:52,340 --> 00:31:57,140
the first two days of an analyst week were just spent creating our triage list for the week

527
00:31:57,140 --> 00:32:03,240
to see who would be triaged into housing. It was, yeah, it was something.

528
00:32:03,240 --> 00:32:06,640
It was something emerging coming into this world and seeing that.

529
00:32:06,640 --> 00:32:08,240
So that's where we started.

530
00:32:08,240 --> 00:32:17,340
And so personally, from like your experience, like where did you come from and what drew you to want to work in this organization and work with homelessness?

531
00:32:17,340 --> 00:32:23,040
Yeah, like why is this meaningful to you and why did you direct your career this way?

532
00:32:23,040 --> 00:32:31,240
Yeah, so before I was in a venture capital, venture builder organization here in Calgary

533
00:32:31,240 --> 00:32:36,440
and sort of played a lot in that startup world sort of throughout my career,

534
00:32:36,440 --> 00:32:42,340
and we're supporting small businesses and finances and all that sort of stuff.

535
00:32:42,340 --> 00:32:49,640
So I've always liked the idea of making an impact, even if it's a person small business, seeing it grow or any of that sort of stuff.

536
00:32:49,640 --> 00:32:53,240
But really, the main thing that drew me here was I can make an impact.

537
00:32:53,240 --> 00:32:59,940
I could do work with some really cool data without also moving into, and this is a bit of a selfish piece, government or academia.

538
00:32:59,940 --> 00:33:07,140
I never worked in those two, but I don't feel like my personality and the way I like to approach work would necessarily mesh.

539
00:33:07,140 --> 00:33:12,540
And so that was kind of the main thing was that I'd be working with numbers that, you know,

540
00:33:12,540 --> 00:33:22,840
a 1% gain when you're working for, let's say a series B startup is exciting, but it really wasn't necessarily getting me fired up

541
00:33:22,840 --> 00:33:28,140
versus a 1% gain when you work in the nonprofit world, that 1% is an individual's life.

542
00:33:28,140 --> 00:33:35,340
And so if we can figure out with the data how to keep making those 1% gains, that's a lot of lives that can add up.

543
00:33:35,340 --> 00:33:37,340
And that makes it really cool.

544
00:33:37,340 --> 00:33:45,540
And it makes it, the other selfish reason I like to say is I have to spend less energy hyping myself up to do the work.

545
00:33:45,540 --> 00:33:46,640
It's a simple reality.

546
00:33:46,640 --> 00:33:51,040
It's mentally easier for me to be like my work is going to matter today.

547
00:33:51,040 --> 00:33:53,640
And that makes it a lot easier to come in in the morning.

548
00:33:53,640 --> 00:33:54,640
That's wonderful.

549
00:33:54,640 --> 00:33:59,340
I love the stories that I hear from people in the nonprofit world because what you just said,

550
00:33:59,340 --> 00:34:07,040
like the people show up and they're there for a reason, like it matters in a different way than it does when you're going into a for-profit

551
00:34:07,040 --> 00:34:08,640
W-2 job.

552
00:34:08,640 --> 00:34:11,340
There's a different sort of mindset and different sort of impact that comes out of that.

553
00:34:11,340 --> 00:34:15,140
And that affects more than just the type of work you do or the people that are impacted,

554
00:34:15,140 --> 00:34:20,740
but affects like your psychology about showing up at the office and showing up to your work.

555
00:34:20,740 --> 00:34:27,940
I'm curious now over the next six months, what are you most excited about from a data perspective or from your organization's perspective?

556
00:34:27,940 --> 00:34:31,140
New initiatives, something on the horizon that you're looking forward to?

557
00:34:31,140 --> 00:34:39,040
Yeah, I'd say the first one is, you know, the continuing phased approach and rollout of our mapping system.

558
00:34:39,040 --> 00:34:46,840
I think that's going to be a real huge value add to our teams and level of understanding and really in meshing that level of support.

559
00:34:46,840 --> 00:34:51,040
We're revamping some of our assessment tools.

560
00:34:51,040 --> 00:34:58,940
It's going to be really exciting as well as that principles based evaluation that our evaluation team is running where we can now start to actually,

561
00:34:58,940 --> 00:35:03,240
you know, next time Troy asked me the question of which KPIs are we tracking,

562
00:35:03,240 --> 00:35:06,540
we'll be able to say here are the indicators that we're looking across the system.

563
00:35:06,540 --> 00:35:10,940
Here's what we're seeing across the programs in a meaningful driven fashion.

564
00:35:10,940 --> 00:35:12,940
So excited about that as well.

565
00:35:12,940 --> 00:35:20,040
And then we just have, you know, ongoing quality continuous improvement with some of our pieces that gets me excited.

566
00:35:20,040 --> 00:35:27,340
But, you know, that's your data governance type stuff and things like that that aren't the sexy pieces, but we'll make that long term impact.

567
00:35:27,340 --> 00:35:29,440
You mentioned a couple of tech tools already.

568
00:35:29,440 --> 00:35:34,340
I'm curious what other pieces overall make up your technology stack that you're working with?

569
00:35:34,340 --> 00:35:36,040
So you mentioned BI and databases.

570
00:35:36,040 --> 00:35:37,440
Like what does that look like for you?

571
00:35:37,440 --> 00:35:41,540
Yeah, so we're mostly an Azure based organization.

572
00:35:41,540 --> 00:35:45,540
So we're running Azure SQL.

573
00:35:45,540 --> 00:35:49,340
We've got a data factory operating as our orchestration tool.

574
00:35:49,340 --> 00:35:52,640
We're just actually just using stored procedures for all of our ETL.

575
00:35:52,640 --> 00:35:59,340
Quick, easy, everyone can interpret SQL, plug in some Python and function apps where, you know,

576
00:35:59,340 --> 00:36:07,140
where we need that additional bit more advanced than SQL or when we're working with our semi unstructured data is where we got Python playing in.

577
00:36:07,140 --> 00:36:09,740
For the predictive analytics, we're integrating Databricks.

578
00:36:09,740 --> 00:36:11,740
So that's based off of Azure.

579
00:36:11,740 --> 00:36:16,440
And then for our BI tool, we do use Qlik.

580
00:36:16,440 --> 00:36:20,340
It's been, yeah, I hadn't worked with Qlik up until this organization,

581
00:36:20,340 --> 00:36:24,040
but it's a classic data visualization tool.

582
00:36:24,040 --> 00:36:31,640
And yeah, and then we have our wrapper of sort of Azure DevOps on top for managing all these deployments and all that sort of stuff.

583
00:36:31,640 --> 00:36:44,840
Well, I do have to ask you, Sam, as a Canadian, I stumbled across this at one point in time where two analysts were trying to troubleshoot the SQL together.

584
00:36:44,840 --> 00:36:50,540
And the one analyst just couldn't figure out what the other one was referring to.

585
00:36:50,540 --> 00:36:57,440
He said, yeah, it's the the table that I aliased a why couldn't he find the table?

586
00:36:57,440 --> 00:37:04,640
Well, you know, sometimes you have to use different words up here.

587
00:37:04,640 --> 00:37:09,640
No, yeah, it's there's actually Canadian English that has some different spellings sometimes.

588
00:37:09,640 --> 00:37:16,340
So I will say I wish programmers allowed color to be the original spelling.

589
00:37:16,340 --> 00:37:20,040
But I guess we have to sometimes switch to American English sometimes.

590
00:37:20,040 --> 00:37:24,740
That was the trouble. You nailed it, Sam. Good job.

591
00:37:24,740 --> 00:37:28,740
Great. Well, Sam, this has been great. Thanks so much for your time today.

592
00:37:28,740 --> 00:37:37,040
Thanks so much for sharing about your organization, the work you're doing, the really advanced and exciting work with data that you're doing, the technologies you've been implementing.

593
00:37:37,040 --> 00:37:42,140
It's been fun to hear about it for people who want to maybe reach out to you or find out more about your organization.

594
00:37:42,140 --> 00:37:44,540
Where can people find you online?

595
00:37:44,540 --> 00:37:48,240
Yeah. So our organization is CarryOwnless.com.

596
00:37:48,240 --> 00:37:53,740
We are also, I think, active on a few of the social media platforms so you can find us there as well.

597
00:37:53,740 --> 00:37:55,440
And for myself, I'm on LinkedIn.

598
00:37:55,440 --> 00:38:01,640
So if you want to ever reach out and learn more, happy to answer any questions and connect as well.

599
00:38:01,640 --> 00:38:04,540
All right. Thank you so much, Sam, for joining us today.

600
00:38:04,540 --> 00:38:07,740
And folks, listeners, thanks so much for joining us as well.

601
00:38:07,740 --> 00:38:10,140
That's it for today on Making Data Matters.

602
00:38:10,140 --> 00:38:38,140
We'll see you again next time.