0:01

The power of data is undeniable

0:03

and unharnessed, it's nothing but

0:05

chaos. The amount of data,

0:07

it was crazy. Can I trust it? You

0:10

will waste money, help the other way duct tape

0:12

to defy you. This season we're solving

0:14

problems in real time to reveal the

0:16

art of the possible. Making data your

0:18

ally, using it to lead with confidence

0:21

and clarity. Helping communities

0:23

and people thrive. This

0:25

is Data Driven Leadership, a show by

0:28

resultant. Hey

0:31

guys, welcome back. It's Jess Carter with Data

0:33

Driven Leadership and on today's episode, we're going

0:35

to be talking to Josh Richardson, who's the

0:37

Chief of Staff of the Department of Workforce

0:40

Development in Indiana. Side

0:42

note, if you ever listen to the

0:44

news and the beginning of each month, you might hear

0:46

them talk about the wage report, the wage data coming

0:48

out. That is federally

0:50

mandated as a collection of data from

0:52

the states to the feds. That

0:55

is kind of usually managed by the departments

0:57

of workforce development in your state. Josh

1:00

works in the department and he has spent

1:02

over a decade thinking deeply about the

1:05

data that they have and the unique data

1:07

that they hold and how we could leverage

1:09

that to better serve citizens. I

1:12

think this is an opportunity for anyone in leadership

1:15

to think about what data they

1:18

have in their organization that

1:20

is an asset that's unique to their own

1:22

organization and think about how

1:24

they can better leverage that creatively to

1:26

serve a purpose or to help meet

1:28

a need either in the organization or

1:31

with your constituents or clients, customers

1:33

even. As we get into this,

1:36

you'll kind of hear more about how Josh unpacks his

1:39

idea and you'll get to hear

1:41

it from idea and

1:43

vision to fulfill it.

1:46

I think the other thing that's neat

1:48

too that Josh highlights is there are a

1:50

lot of people around him that helped

1:53

bring this idea through fruition.

1:55

It's also looking at the

1:58

greater picture of what you're trying to accomplish. and recognizing

2:00

who's around you that you need to be for you

2:03

and for this vision and how do you pull them in

2:05

and help them get excited about it. I hope you enjoy

2:07

this episode. Let's get into it. Welcome

2:13

back to Data Driven Leadership. I'm your host, Jess

2:15

Carter. And today we have Josh Richardson, the Chief

2:17

of Staff at the Indiana Department of

2:20

Workforce Development. Let's get into it. Welcome, Josh.

2:22

I'm so happy to be here. Thank you, Jess. Let's

2:24

get into it. So Josh is maybe

2:26

the first person on the show that

2:29

I have had the pleasure of knowing

2:31

for nearly 10 years. Right.

2:35

It's been a minute. I remember you walking into

2:37

a room with the Commissioner, with Scott Sanders at

2:39

the time, to talk to the PMO about the

2:41

unemployment insurance system that was going live. And that

2:44

was the first time I got to meet you.

2:46

I remember the exact moment when I got to

2:48

meet you. I'm sure I was as memorable as you.

2:50

No, I do remember it. I would have had children

2:52

remembering the rest of it. But so here you are

2:54

promoted all the way up to podcast host. And I'm

2:56

still doing the same thing. It's

2:59

great to be with you, Jess. And yeah, lots of good stuff over

3:01

there. Sure, Chief of Staff.

3:03

You've also been promoted to something that's a little

3:05

more important. So OK,

3:08

this is also difficult. Let's go. So

3:11

when I first joined DWD,

3:13

or was helping as a consultant, I

3:15

had never heard of the Department of Workforce Development. So

3:18

when you pitch that to someone you meet or at

3:20

a kid's sports game or something, what do you explain

3:22

about, give me the elevator pitch for what is DWD?

3:25

So it's a good question. And the

3:27

thing is that that explanation almost switches

3:29

a little bit, depending on the economy,

3:31

because we have two real main things

3:33

that we do from an overarching perspective.

3:35

One is that we administer the state's

3:37

unemployment insurance system. So that's more than

3:40

half of the employees at the agency.

3:43

But so really, that's everything from collecting

3:45

the employer taxes that go in to

3:47

collect the trust fund all the way

3:49

to paying out those benefits to ensuring

3:51

fraud. We hold appeals within the agency.

3:53

But the other side of the agency

3:55

is really workforce training. And I think

3:57

largely that is how you would do

3:59

that. connect individuals that were

4:01

unemployed to work, but it includes things

4:03

like upskilling, existing workforce, but a huge

4:05

part of it is data, right? We

4:08

collect so much information about the workforce

4:10

through both sides of that system, information

4:12

about what's available, what

4:14

skills are in demand. And so it's

4:17

kind of that nexus that joins the agents, the

4:19

two sides of the agency. Okay, and

4:21

to your point earlier about the economy, whether

4:23

it's really, really great or really, really not

4:26

great, one of those sides of your agency

4:28

is sort of ebbs and flows. It's

4:30

an interesting piece where, when we've

4:33

gone through periods like the Great Recession or certainly

4:35

during the

4:37

pandemic, really everything that we

4:39

do at the executive level, so much

4:41

of the focus is on unemployment insurance,

4:43

but now the pendulum is almost entirely

4:47

in the other direction, where so much of the focus

4:49

is on workforce, because really across our

4:51

economy, from frontline work all the way

4:53

up through the highest skilled jobs that

4:55

we have, everybody needs skilled workers in

4:57

order to run their business. And so

4:59

lots of focus there. We're still cleaning

5:02

up some of those issues, but yeah,

5:04

it is really interesting how the

5:06

conversations change at that executive level.

5:09

So few people

5:11

in my career so far have been

5:13

as passionate and innovative in their space

5:15

as I would argue that I've observed

5:18

you. I realize that you're not gonna

5:20

suit your own horn, but I will for you, that there

5:22

is a passion that just comes out of

5:24

the work. Was that always

5:26

there? How did you become so passionate about

5:28

data beauty? So I don't know because we

5:32

probably won't get into it on this podcast, but

5:34

it was kind of an accident that I'm here.

5:36

I will say that I've always really been in

5:38

love with public policy, the idea of having

5:41

big puzzles to solve that

5:43

actually lead to real outcomes for real people.

5:46

So I think that's always been there, but

5:48

it has been interesting that the

5:50

Department of Workforce Development has been a really

5:52

good mix, because so many of the things

5:54

that are my interest from economics to data

5:57

to law to politics,

6:00

All of it has ended up being able to, you

6:02

know, there are things that I get to do every

6:04

day in many of those different areas and I think

6:06

it's helped drive the passion. So

6:08

yeah, I hope so. You're right. I don't think

6:10

maybe I won't talk too much more about that.

6:12

But that's right. It's been great for me and

6:14

it's easy to find the passion to do this

6:17

work. Okay. Well, so that's fine.

6:19

We will get into sort of the interesting thing to

6:21

talk about here that I'm really excited about is, and

6:23

I want to talk about how it kind

6:25

of came to be because I have this memory, another

6:27

memory that's a really particular image of

6:30

me sitting across a lunch

6:32

table from you in

6:34

2014, 2015 and on

6:36

a napkin, you drew out

6:38

the concept for this project

6:40

that, and I'm not even sure, what are we calling

6:42

it? Yeah. Well, so

6:44

I think the workforce recommendation engine is sort

6:47

of like all encompassing tool that covers all

6:49

of it. Yeah. And then

6:51

pivot is really the name of the tool that thus

6:53

far we've launched. Okay. So that's

6:55

the interface that people are interacting with. But I

6:57

do think just as this broad concept, the workforce

6:59

recommendation engine probably covers everything.

7:02

Okay. And so you drew

7:04

this out for me in a really high level.

7:06

Can you kind of, again, elevator a

7:08

speech of what that high level was? What was the

7:10

idea? Well, so I mean,

7:13

there are a couple of different things that

7:15

all come together at once. One is that,

7:17

at the Department of Workforce Development, we are

7:19

privy to a significant amount that we mentioned

7:21

this earlier, like a significant amount of data

7:23

about the workforce. One of the biggest pieces

7:26

of that are unemployment

7:28

insurance wage records. And so just a

7:30

quick version, every quarter, every

7:32

employer in Indiana is required to

7:34

report at that individual level, the

7:36

amount of wages that they paid

7:39

to an individual that they employ. And,

7:42

you know, okay. So over the years, researchers

7:45

often ask for that data. So they

7:47

come through the Bureau of

7:49

Labor Statistics and they come to us with

7:52

a request and do all sorts of really

7:54

interesting research topics using these wage records. And

7:57

so that's one. is

8:00

on the workforce side of the

8:02

house, there's often

8:05

so many acronyms, so many

8:07

programs, so many different baskets

8:09

of funding that it gets

8:11

really difficult, even for our staff,

8:13

let alone the people that we serve to figure out

8:15

how to connect those. And so

8:18

I think as I started

8:20

to learn a little bit more about the

8:23

data we had access to and also as

8:25

technology changes and more focus on data-driven type

8:27

decision making, I think we're really looking at

8:29

this saying, I wonder if we can use

8:32

wage records, if we can use this data that

8:34

we have access to that the private sector really

8:36

can't get. Again, a lot of this is confidential

8:38

by federal law and for good reason, but

8:41

what if we could use it to help figure

8:43

out how to match up the services we have

8:45

with the people that would be most likely to

8:47

benefit from them? And it really

8:50

kind of grew from there and technology has changed

8:52

a little bit too. I

8:54

love the idea that I grew this

8:56

out on the napkin, but I think, and

8:58

I think largely, yes, that's true, the vision

9:00

has stayed the same, but what's happened is

9:02

that I think as the technology has advanced,

9:05

it both made it sort of more tangible,

9:08

but it also has allowed us to do it in

9:11

really a way that I think probably exceeded even

9:14

what I was hoping for. And so

9:16

for right now, just to get really pragmatic and make sure I

9:18

understand, because I've been on a

9:20

lot of projects adjacent to this one,

9:23

but not on this project. So if

9:25

I understand at brass tacks here, if

9:28

I'm on unemployment in Indiana, I have

9:30

the opportunity to leverage this workforce engine

9:32

to say, hey, what are the,

9:36

I hope I don't get this wrong here, the

9:38

most effective things I could be doing, skill

9:41

sets, education to gain to skill up,

9:43

essentially get re-employed. Right. So

9:45

where we're at right now is really

9:47

focused on occupational change, right? So we're

9:50

going to add more and more elements

9:52

to this as we move forward. And

9:54

obviously this is still a work in

9:56

progress. So, you know, again, think about

9:58

it this way. So. About

10:00

3.2 million Hoosiers in Indiana's

10:03

workforce. And

10:05

every quarter, those individuals are

10:07

having those wage records reported.

10:09

And so what we can see is sort

10:11

of career transitions. We can watch people as

10:14

they move through and as their jobs change

10:16

and as their industry changes, the place where

10:18

they work changes, their wages change. And

10:21

so by using that, when an individual files an

10:23

unemployment claim, so you should

10:25

also say this, there

10:27

are something like at a higher level, we

10:30

can kind of classify jobs into 830 different

10:32

occupations. So

10:34

obviously it gets more specific than that when

10:37

you drill down. But I always

10:39

think, for a lot of people,

10:41

if you gave them a pencil and paper and

10:43

said, write down as many occupations as you could

10:45

think of, I don't think they'd get that. No.

10:48

And I think we have, is you have a person that's

10:50

filing an unemployment claim that can be a really stressful, it

10:52

can be a scary time. And

10:54

their first question is, how do I successfully

10:57

make a transition back to work? And

11:00

I like the job that I had and I'm really disappointed. It's gone,

11:02

but I didn't like the job that I had and I'm ready for

11:04

something new. But trying to be in

11:06

that spot where they've got to figure out what

11:08

to do next is tough. Well, so the idea

11:10

is that what we can maybe do is leverage

11:12

these wage records, leverage this information that we have

11:15

to figure out where we've seen people

11:17

make a successful transition that were in

11:19

that same position before. So, I'm a

11:22

factory worker in Richmond, Indiana, who lost

11:24

a manufacturing job after 20 years with

11:26

a high school education. We

11:29

can look at many of those same factors

11:31

and see, okay, of the people that were

11:34

in that similar spot that made a career

11:36

change, which ones were most effective? Putting

11:38

that as an option in front of that person. Great.

11:41

So it gives, in that moment when there might be

11:43

some high emotions, because we are unemployed and we don't

11:45

want to be, it gives

11:47

somebody some sense

11:49

of a shortened, more data-based

11:51

menu of where other people

11:54

have gone in case they haven't thought of that

11:56

or consider those as options that are easily attainable

11:58

potentially, right? Is that fair? Right. Okay.

12:00

And this is, is this the only place

12:02

this is happening? I mean, I know that

12:05

workforce agencies are federally mandated. They're in each

12:07

state. Is this, are we doing this in

12:09

other states? I mean, as far as we know,

12:11

this is the only one like it. Now you'll see,

12:13

you know, as we're adding AI

12:15

to essentially everything that exists, there are tools

12:17

out there that try to use artificial intelligence

12:20

to match people with jobs. A lot of

12:22

times those are skills based and those are

12:24

exciting too. It'll be interesting to see how

12:26

they develop, where they'll do things like you

12:28

tell us about your prior jobs. Right. We'll

12:30

try to identify skills. Then we're going to

12:32

look at job postings. We're going to try

12:34

to identify the skills and match them. So

12:37

it's an interesting component. But

12:39

what's really different about ours is

12:41

that we don't actually have to ask

12:43

this person for any additional inputs. And

12:46

we're using these administrative wage records. And so

12:48

instead of someone having to sit down and

12:50

try to match these skill pieces and try

12:52

to figure out what's relevant, we can just

12:55

see what's worked for other people. And so

12:57

nobody that we're aware of has figured out

12:59

how to use these state data sources as

13:02

the driver of this kind of

13:04

information. That is so cool. This is a

13:06

tool, this is data as an asset to

13:08

the agency that has not been utilized in

13:10

an effective way like this before. And we

13:12

can start from here. That's right. And I,

13:14

like I said, I think that the other

13:16

tools are really exciting too. And with all

13:18

this AI, really excited to see where it

13:21

goes. But I think, you know, they require

13:23

a decent amount of effort from that individual.

13:25

You usually have to try to drive them

13:27

to a standalone website or something and encourage

13:29

them to participate. And sometimes it's hard for

13:31

them to sort. You know, there's so many

13:33

different tools out there. We talked about this

13:35

before. How do I know which one to

13:37

use? And so we really love the idea

13:39

that this is just

13:41

a part of the unemployment claims filing process.

13:44

I mean, essentially, they sort of can't avoid

13:46

it. They're going to see

13:48

these options. And so it's low effort from that standpoint.

13:50

But obviously, you know, they're going to

13:52

get much out of it. We want them to interact. And,

13:55

you know, big part of this is the autonomy of that

13:57

individual. But yeah,

13:59

so I I'm excited about the skills-based

14:01

stuff, but in our case, we almost skipped

14:03

that step in the line, right, instead of

14:05

having to say, like, look, we know that

14:07

there's some skills mapping. When we

14:10

see people move from one occupation, we see

14:12

these successful careers into another. There absolutely are

14:14

going to be some skills there. But with

14:16

this tool, we don't really have to define

14:18

them. We just know that we've seen them

14:20

be successful. And I think that's really cool

14:22

and I think it will be really helpful.

14:26

You mentioned this, or alluded to it earlier, the,

14:30

call it customer experience of UI,

14:32

can be tricky because it is

14:34

a bunch of different federal or state programs, depending on

14:36

where you are, and then they get enrolled in one

14:38

or the other. And it can be a little confusing

14:40

to navigate. And the fact that you're

14:43

giving them an additional service

14:46

through this unemployment experience, right?

14:48

Yeah, so the tool

14:50

itself is absolutely the additional service. I

14:53

think one of the interesting things about

14:55

these problems are, you know, almost

14:58

in every case, we can

15:00

help, but the work is going to be on

15:02

that individual at the end of the day. They're going to be the

15:04

ones who have to take this leap

15:06

towards a new occupation or, you know, in

15:08

many cases enroll in the kind of training

15:10

that will better their outcomes. So like, we

15:12

can't do that for them, right? This is

15:14

going to take their work. It always will.

15:16

But I think that the key point is

15:18

trying to reduce the level of effort that

15:20

it takes to figure out what that path

15:22

looks like and also increase their confidence because

15:24

this tool helps them better understand the outcomes

15:26

they can expect. Yeah. You know,

15:28

again, based on people like them

15:30

who have made that transition. I love

15:32

that concept of you're almost immediately helping them

15:34

envision where they might go. That

15:37

can be a real gift to people that are

15:39

in the middle of this process. It's not exactly

15:41

a confidence inspiring moment in general. And the fact

15:43

that they can immediately start to see what might

15:45

work is a real kind of gift.

15:48

Yeah. Well, and I mean, you slowed me

15:50

down a couple of times already to talk through this just because

15:52

of how many different things that are happening. I think it's really

15:54

easy to take for granted on our side. Like,

15:56

you know, I've now spent 15, 16 years in

15:59

this world. So kind of know where

16:01

these programs are at, but for someone who's just

16:03

been working, this is an unexpected job loss. They

16:05

haven't done any of this background work. And

16:07

so really being able to cut

16:09

down on the amount of effort that it

16:11

takes in this stressful time to see what

16:14

might work is, I

16:16

think, the best thing that we're doing with the

16:18

tool. Yeah. Okay.

16:21

So question for you in

16:24

this whole process from ideation to

16:26

it's live, right? Right. Yeah.

16:29

So what surprised you about the process?

16:34

You know, I think that there

16:36

are so many different surprises as we move

16:38

through this. I think, you

16:40

know, you asked the question earlier about

16:43

whether other states were doing this. I

16:45

think one of the things that's become clear is that Indiana

16:47

has really done some things to set

16:49

the stage to make this possible. And

16:51

I think that it's, you know, there have been some

16:53

times through this where I realized like, oh, wow, you

16:56

know, like we're actually in really good shape. The conditions

16:58

were right to do this here. And

17:00

I think that the truth is that I, again,

17:02

I'm not an expert at truly what every single

17:04

other state has, but between our management and performance

17:06

hub and between these data sources

17:09

and sort of the maturity of these data

17:11

sources that we have, the relationships with other state

17:13

agencies, you know, we've been able to

17:15

do some, when we look at other states,

17:17

they're just starting different components of this process.

17:19

And so that's been exciting for me to

17:22

see Indiana's real leadership in this data driven

17:24

world. And so

17:26

that's been interesting and exciting. Yeah,

17:29

I think maybe that's the biggest surprise. Okay.

17:31

Well, and when you say that, so

17:33

I'm in a non public sector translate, some of what you

17:35

just said, and you tell me if I'm right. So the,

17:37

some of the themes of what I just heard is we

17:40

did go through this unemployment insurance modernization. So the

17:42

system, we went through that in 2014. It

17:47

was a long period, 14, kind of the launch, but yes, many

17:49

years ago. So kind of

17:51

new system with new data access that we have.

17:54

And then we have the management performance hub.

17:56

And so when data sharing data, privacy protection

17:58

that as we pulled data from different

18:01

agencies, there's protection, but there's collaboration

18:03

that's possible with inter-agencies. And so

18:05

when you talk about workforce,

18:07

so the wage data,

18:10

like my data would be just

18:12

Carter resultant, blah, blah, blah, blah, every quarter.

18:15

But then you have the unemployment data, so

18:17

you know if I'm unemployed too. But you

18:19

don't have the education data. That comes from

18:21

IDOE, right? Yeah, or

18:24

the Commission for Higher Ed or the other state

18:26

partner. Yeah, so this is, it's

18:28

necessitating that kind of a collaboration across all of those

18:30

data sets, is that fair? That is correct. Okay,

18:33

and that's where MPH plays a critical

18:35

role. Absolutely. Okay, and so

18:37

it's sort of like you have like

18:39

these modern systems and this interoperability of

18:41

data that's flowing between these entities that's

18:43

protected, that is timely as much as

18:45

it can be knowing that you know

18:47

the quarterly data, etc. Some of that

18:49

seems really important to me. When I

18:51

say IDOE, I mean, Indiana Department of

18:54

Education, I have to remember to get acronyms. And

18:57

MPH Management Performance Hub, which we already talked about. Okay,

19:00

so to your point you're saying we didn't

19:02

just build this. It was

19:04

built on a foundation of the commitment

19:06

to data and technology advances we've already

19:08

been making. That's so true. So I mean, you know,

19:11

just going back to the earlier question, sketching this out

19:13

on a cocktail napkin, I think, you know,

19:15

we could say, hey, wouldn't it be fun if

19:18

we could take all of this data and use

19:20

it in a way to help positively inform people

19:22

about outcomes. But the reason

19:24

why that's possible is because of a

19:26

history that Indiana has with the

19:28

State Longitudinal Education Database, SLDS

19:31

is the acronym, and I

19:33

don't even know Jess exactly what the acronym is for.

19:36

But it's essentially where we're able to link

19:38

up a lot of these workforce and education

19:40

related records together. And then MPH allows us

19:42

to do these data matches where, you know,

19:45

you've talked a lot on this show and

19:47

your listeners here, you talk about data silos

19:49

and everyone is familiar with this. But essentially

19:51

to allow this agency to allow

19:53

the data that these different agencies possess a lot

19:56

of a very sensitive a lot of it is

19:59

confidential. it needs to be protected. But

20:02

MPH has really allowed a secure

20:04

place to do this record linkage

20:06

that allows us to make smarter decisions

20:08

based on this data in a way

20:11

that protects people's privacy and security. And

20:14

so, you know, the from,

20:16

you know, the back of a napkin to

20:19

launch in November couldn't happen without those things

20:21

being in place. Yeah. So you kind

20:23

of already explained the difference, the experience

20:25

for somebody who's on unemployment or going

20:27

through the process compared to before, that

20:30

they wouldn't have had these kinds of

20:32

recommendations or suggestions. I want to emphasize

20:34

or ask you to emphasize the importance

20:36

of agency here. We're not telling them,

20:39

these are your only options, or these are

20:41

your options. We're saying this is what the

20:43

data, we're just presenting information to them. I

20:46

think this is such a critical part about

20:48

all of this. And so I definitely want to

20:50

answer that. But I will say like before

20:52

we started around this way, our agency has

20:54

had a long history of looking

20:56

at things like what are the most in-demand

20:58

jobs in an area. So you'll look at

21:01

things like, you know, what occupations are growing,

21:03

what are the wages. And so if you

21:05

think about a predecessor to what we're doing

21:07

here, they were posters and they would

21:09

be a poster that we would hang in a local office, maybe

21:12

send to a school, they would talk about,

21:14

you know, the top 50 jobs in their

21:16

area. But though if you think about those,

21:19

they were tough in a couple of areas.

21:21

There's one there very one size fits all.

21:23

And so, you know, it wasn't surprising to

21:25

see a lot of those require things like

21:27

higher education or master's degrees. And again, those

21:31

are absolutely still going to remain hot jobs

21:33

for the future. But for a lot of

21:35

individuals, if I again, if I've just lost

21:38

this manufacturing job, the only job that I've

21:40

known for 20 years, seeing a

21:42

list that includes a lot of things that feel inaccessible

21:44

to me isn't that helpful. The other part about this

21:46

is that people of course don't want to be told,

21:49

here's what you're supposed to do, here's your only answer.

21:51

And so I think we're really trying to do with

21:53

this tool is essentially have the approach, here's where others

21:55

have been successful, we can see this. And so the

21:57

way I like to think about it is that the

21:59

algorithm that, you know, this artificial

22:02

intelligence algorithm that we've built off of

22:04

all of these wage records, we're

22:06

really confident that it can show people

22:09

good jobs, often better jobs

22:11

than where they came from, but really they're the

22:13

only one who can figure out what the best

22:15

job is. And I think trying to have that

22:17

humbleness here to

22:19

know that we're not going to nail it,

22:23

we hope to facilitate their being able to

22:25

nail it. Right. Can you expand

22:27

on, so it's, when did it go live? We

22:29

went live on November 2nd. Okay.

22:32

So we're still in kind of

22:34

quarter one, if you will, of three

22:37

months or so of collecting the data. Do

22:39

you, are you getting feedback from users of how

22:42

it's been? What are you hearing?

22:44

So, I mean, so there's a handful of

22:46

things that we're getting. So the tool itself

22:48

has a feedback mechanism. Okay. Right. So

22:51

we're asking people when we show them an occupation for them to go

22:53

ahead and let us know. No, you

22:55

know, this isn't, this doesn't work for me yet. Maybe

22:58

like you're close. There's more that I'd like to know or yes. And

23:01

we're seeing people use that tool. So they don't have

23:03

to write. That's an optional part of this process. But

23:06

we've seen thousands of people respond already and

23:09

give us yes answers. The

23:11

tool then allows them to go and explore that

23:13

job further where they can look for training providers

23:15

that provide it. So we're seeing that. But the

23:17

other thing that's really interesting is, you know, when

23:19

we've done a lot of these campaigns before, if

23:21

you do things like, you know, an email campaign,

23:23

it's really hard to get people to open those

23:25

emails. It's even harder to get them to click

23:27

on it. But because this is really built into

23:29

the process, we're seeing really high rates of people

23:32

interact with this tool, you know, at some point

23:34

during their claim. We're still building that out a

23:36

little bit more. We're going to add a lot more clarity to

23:38

what we're seeing. But

23:40

we're seeing a significant percentage on a weekly

23:43

basis of, you know, initial of

23:45

the initial claims numbers turn into

23:47

activity within this tool.

23:50

Okay. And then I have

23:52

technical questions or context I

23:54

will try to provide. So Uplink is the name

23:56

of our unemployment system in Indiana. So

23:59

is it important? embedded within Uplink?

24:01

Absolutely. So yeah, so Indiana, again, we

24:03

talk about Indiana almost, for some reason,

24:05

the condition's just being right here. But

24:08

Indiana is essentially 100%

24:10

online claims filing. And

24:12

so other states still will use

24:14

some paper mechanisms or maybe some phone

24:16

center processes. But we're essentially

24:19

all online. And so when

24:21

that individual files that unemployment claim, they

24:23

use the Uplink system. But

24:26

so what we do is as they

24:29

register for the system, as they file an

24:31

unemployment claim, we're taking the information that we

24:33

needed for their unemployment claim. And that's the

24:35

information that we're using to generate the recommendation.

24:37

So again, you could think about building this

24:39

in a different way, where we gave it

24:42

its own URL, its

24:44

own website. And we'd have to drive people

24:46

there and encourage them to register and ask

24:48

these questions. And again, a lot of these

24:50

job matching tools do that. But we really

24:52

like the idea here that we didn't need

24:54

anything further from them to generate the recommendations.

24:56

We need something further. Obviously, like we talked

24:59

about this earlier, this is actually

25:01

going to turn into a good outcome. The individual says, yes,

25:03

they enroll in the training, they get the job. The work

25:05

is on them. But we're just trying

25:07

again to make it a lot simpler to find

25:09

this. So yes. All right. So in Uplink, I

25:12

filed my claim. Every week

25:14

they're coming back. If

25:16

they stay unemployed, they come back to

25:18

make that next week's claim for unemployment

25:20

benefits. And they would continue

25:22

to see this recommendation have

25:25

the option to enter this pivot

25:27

tool and interact with these

25:29

recommendations on a weekly basis. OK. And

25:31

then I am going to share an opinion

25:34

when I ask this question. I don't assume you will

25:36

share your own personal opinion. There

25:39

is usually in some of these federal programs around

25:41

employment, there is a requirement for a work search

25:44

that can be pretty goofy, in

25:46

my opinion, where in the past, how

25:48

you make sure an unemployment is

25:51

looking for work has not been

25:56

super effective. I don't know the right word I'd

25:58

use. and

26:00

how you measure that is difficult, is if they

26:02

click through and they say, yeah, I was in

26:04

this kind of manufacturing, I wanna be in this

26:06

one, is there any automation to work search requirements?

26:09

Okay, so we'll

26:11

talk through this here for a second. First,

26:13

I agree with you, work search is tough.

26:16

I think it's a really important concept that

26:18

someone is on unemployment benefits, that the idea

26:20

is that it's temporary and the transition to

26:23

their next job. And by the way, overwhelmingly,

26:25

there's very few people exhaust 26-week unemployment, most

26:28

claim less than that. So

26:30

we have this work search requirement every week, and

26:33

this has been with the unemployment insurance system since

26:35

it started in the 1940s. But

26:38

to a large extent, we're not really, I mean,

26:40

we're saying, hey, you have to go look for work, we're

26:42

not really telling them how to do it or how to

26:44

be effective as we do this. And so

26:46

that requirement remains, we want to see

26:48

this tool be useful. And so we're

26:50

waiting for this feedback, but I absolutely

26:53

can see the world where these things

26:55

sort of merge, where we're helping this

26:57

person connect and say, look, the use

26:59

of this tool would substitute for a

27:02

work search activity in this tool would

27:04

satisfy these work search requirements. But I think what we

27:06

want to make sure, again, is

27:08

we want to be able to look at

27:11

the data and see that we're actually achieving

27:13

better outcomes when we do that than the

27:15

traditional method. Do you have a hypothesis

27:17

about if, not

27:20

just if the algorithm

27:22

is helpful or not as we sort of

27:24

watch it play out, but is it possible

27:26

that it helps reduce exhaustion of

27:28

claims? Do you have a hypothesis about whether that would

27:30

be the case as well? Again, I hope so.

27:32

You know, we talk about this a lot, so we're

27:34

in these early days, and so our measures of success

27:37

are, hey, are people using the tool? Are they leaving

27:39

us feedback? How much time are they spending it? And

27:41

are they entering it another time? Ultimate

27:44

only measure is, is does it

27:46

make people's lives better? Can

27:49

we drive action that leads to

27:51

a better spot at the end

27:53

of it? The answer to that

27:55

is I very much hope yes, but I

27:57

think that there are also a lot of reasons to believe that

27:59

it could. I mean, I

28:01

just still think that if someone sat down and said,

28:03

look, I know I'm ready for a change in occupation,

28:06

but I've already got bills that are piling

28:08

up. Unemployment insurance, I mean, it doesn't replace

28:11

anywhere near their working wages. And in fact,

28:13

it's really set to always replace less than

28:15

half of their working wages. And

28:17

so the pressure is really on. I mean, if your

28:20

family depends on that income. So even if you wanted

28:22

to sit down and say, look, I really need to

28:24

go through a process to figure

28:26

out what I want my next job to

28:28

be, it's just really exhaustive. And so here,

28:31

I think that by speeding up that process, by

28:33

connecting that person with that next role, my hope

28:35

is that it really does put

28:37

them on a better path. You know, we do

28:39

different things within the tool. So they

28:42

can filter their recommendations by the amount of

28:44

time or training in training that it would

28:46

take. So we show them that information. So

28:48

they could say, look, I'm interested in training,

28:50

but only training that I can accomplish in

28:52

six months or less. They can look at

28:55

just that training. But they can also say,

28:57

look, no, I don't want any training, but

28:59

I'm just interested in an occupational change. And

29:01

again, like I said earlier, I think if

29:03

you ask people to sit down and make

29:05

a list of all the different occupations that

29:07

they could do with no additional training,

29:09

people just don't get very far enough. I wouldn't

29:11

get very far on it either. And so I

29:14

think this really helps that. And hopefully by doing

29:16

so will reduce the amount of time that they're

29:18

on employment. Even more, I

29:20

hope it makes it less likely that they ever

29:22

need unemployment again, or at least in the near

29:24

future. I mean, to me, that might even be

29:26

the better metric. I've sort of said this before.

29:29

If they needed to spend an extra

29:31

couple of weeks on unemployment this round,

29:33

but it resulted in them obtaining the

29:36

kinds of skills they needed so that

29:38

they were less likely to be laid

29:40

off in the future, it'd be a

29:42

positive return to the unemployment fund itself.

29:45

Certainly better for them and better for an

29:47

employer out there who really desires that skill

29:49

set. Yeah, I love it. I

29:52

can imagine that there's a world

29:54

where not just citizens on unemployment

29:56

want this. Is there

29:58

a world where that's possible? I really hope so. So

30:01

we're in the process of doing this

30:03

and we're scoping this out. And the

30:05

only things, there will be some slight

30:07

differences, right? Because the question is, is

30:09

we're going to have to obtain that

30:11

information from them upfront. So

30:13

in the unemployment insurance system, we're able to do this as a matter of

30:16

course. But yeah, we

30:18

want to make this tool available to everyone.

30:20

I think that it would be really great

30:22

if we could use the data that we

30:24

sit on so that someone could ask this

30:26

question. Look, if I wanted to make $5

30:28

more an hour than I did today, what would the

30:31

path look like to get there? And

30:33

so not only to be able to answer that question,

30:35

but to be able to show them the paths that

30:37

people like them have made that successful transition to the

30:39

$5 more an hour. Again,

30:41

I think that's something that almost only

30:44

the state could do because others couldn't get access

30:46

to the data. So yeah, I mean, we're working

30:49

on that now, a different

30:51

interface, sort of a different front door to it

30:54

as we continue to try to improve the tool

30:56

that's already available. Awesome. Because then

30:58

I'm like, it's not just a solution for

31:00

unemployment insurance, it's a solution for wages

31:02

in Indiana, which is good for everyone,

31:04

right? Absolutely. I

31:07

think what you really want

31:09

is for people to be able to meet their

31:11

needs and their goals when it comes to their

31:13

career. And the more that we

31:15

can reduce the effort that it

31:17

takes to figure out which path that would be, the

31:19

more that I think it allows that. So absolutely excited

31:22

to do that. And then so many other potential use

31:24

cases of this, the way that we might be able

31:26

to help employers identify where

31:28

future talent pools come from, those sorts of

31:30

things that are really exciting uses of. Dude,

31:32

the same data set, it's just going to

31:34

be different, sort

31:36

of different approaches and probably

31:38

different interfaces. That

31:41

makes your earlier point land for me even

31:43

further. If

31:46

it can allow employers to select the

31:48

most right employees upfront, would

31:50

they stay longer? Would there be better retention?

31:52

Would there be less unemployment in the first place? Yeah, I

31:54

think just hopefully just information to help

31:57

them understand where. There

32:00

were week we might be able to look

32:02

at where their potential pool comes from and

32:04

also were those workers might be going instead

32:06

just providing employers at El Bulli better information.

32:08

The opposite attract the kind a talented. They

32:10

need to be successful often. Okay some

32:13

money as he this when you look

32:15

ahead the next so lucky the faq

32:17

and look ahead or start the he

32:19

had still have the next five years

32:22

and you can ahead of posts on

32:24

unemployment and underemployment for he said while

32:26

to fifteen. Years. The Oh yeah, bolivia.

32:28

Fifteen Plus Fifteen. Plus okay race.

32:32

To the I know. What?

32:34

what do you anticipate is gonna be next?

32:36

Big challenge. Oh. And

32:39

you know I've I wish that we were better

32:41

at anticipating as early. Nobody saw the challenge that

32:43

we were that we were facing when we did.

32:45

I, you know, look, I'm not one of these

32:47

dues. There's about a I, in fact I'm really

32:49

excited about. but I do think that there are

32:52

a lot of folks suggesting that what will see

32:54

his, you know, sort of just. More.

32:58

Need for individuals to

33:00

continuously upscale throughout their

33:02

career to stay. Here

33:05

to do to stay employed to gainfully employed

33:07

to job progress in their wages. and so

33:09

I think that's you know one thing that

33:11

will look at where that plays out I'm

33:13

not quite sure but what we know, his

33:15

dad skills and education are really in demand.

33:17

As we're moving to the stays were more

33:19

and more can be automated and so I

33:21

know that that ah be a job for

33:23

the system. but I'm not sure that I

33:25

can quite predict exactly what the effects will

33:27

be. But the guys

33:29

are you eating Will will definitely keep an

33:31

eye on that to see if the types

33:33

of you the types. of occupation start to

33:35

change that we see from through the

33:38

unemployment insurance system. Ok, I'm

33:40

looking back as the

33:42

leader. it has had the point

33:44

of half her name an app that i

33:46

got his race you seen this through a

33:49

whole bunch of see that say that i

33:51

think a lot of leaders come in for

33:53

a moment and and are critical to one

33:55

one caught one moment any projects life cycle

33:57

not they don't get to see it through

33:59

for essence yeah Do you need

34:01

advice for a leader that is embarking on

34:03

a journey or something that you thought it

34:05

would be important to look back on and

34:08

say, you know, this is how

34:10

I, the staying power I had, like some

34:12

days was that hard, I imagine. So

34:15

you know, absolutely. It's

34:17

hard not, I mean, so I like the question

34:19

at the same time, it's hard not to almost

34:21

fall into cliche type of, you know, answers, you

34:23

know, we really tried to stay the course, I

34:26

think, you know,

34:28

so much of it when you depend on others

34:30

to buy in, to gain the support for something,

34:32

which isn't, you know, which isn't unique to government,

34:34

I think there are maybe some components of that

34:36

to our, but in any organization, and I think,

34:39

you know, it's to continue to listen to the

34:41

concerns and the objections of others and to try

34:43

to make sure that you're reflecting on

34:46

that as you make changes going forward.

34:48

But you know, it really has just

34:50

been to continue to share the message

34:52

and try to generate excitement around the

34:54

potential of the tool. That's

34:58

really been, I think, what ultimately turned us from

35:00

an idea that just kind of sat for a

35:02

while into an actual tool. Well, you

35:05

know, I know that there are a ton

35:07

of people that were part of it. And you,

35:09

you constantly mentioned that it's not just Josh Richardson,

35:11

that you had a great IT team, you had

35:13

great team around you, you had buy in you

35:16

were, it sounds like there was some, to

35:18

your point, how to galvanize how to get that

35:20

buy in and help people see the vision. And

35:23

then we can go figure out how to execute it. But sometimes I think the

35:25

hardest thing is just to get that buy in. Well, absolutely.

35:27

And I mean, again, you, you

35:30

need skeptical

35:32

people throughout the process. You need people to

35:34

challenge these things. Certainly even the people who,

35:37

you know, may have not been sure that

35:39

this could work or that it could

35:41

launch, they've been really important themselves in making it better.

35:43

So yeah, I think I'm

35:45

really excited about where it's at. And I think

35:47

there are a lot of people who did really

35:50

sort of positive things to contribute to it. But

35:53

I think even a lot of those cases, people

35:55

with the biggest concerns, you know, it's just about

35:57

sort of adding another like, you know. Part

36:00

of the problem is that we have a lot

36:02

of different tools and resources and we're saying it's

36:04

hard for people to navigate them And so your

36:06

solution to that is another tool, right? That was

36:08

a really important point that helped clarify some things

36:10

about how What

36:13

we didn't want this to look like right? So that even

36:15

those sorts of things have been really helpful Okay,

36:19

wrapping up on my questions, but one of them I have to ask were

36:22

you ever nervous? Uh,

36:24

yeah, I mean Sort

36:28

of maybe every moment. Do you mean like maybe every

36:30

moment that it wouldn't launch? You

36:33

know, I so what I do feel is

36:35

like some of the big things I use

36:37

a data show and again We're dealing with

36:40

really confidential data one thing I didn't Necessarily

36:42

have to spend a lot of time sort

36:44

of you know I'm really nervous about was

36:46

sort of our privacy and again, it's because

36:48

of the structure in Indiana The

36:51

advice that we have here and those different pieces ain't

36:53

that made it really helpful You know

36:55

those nerves to see like well people use it or

36:57

not, right? We've been I've been telling people Yeah years

36:59

that hey, there's this idea that could really be helpful

37:02

So I think there may be some nerves there early

37:04

on Okay But I think and you know now they

37:06

remain because like I said like really fun to have

37:08

a tool and I think that a Lot of us

37:10

we can sit and we're really happy with sort of

37:12

the data science work and all these different components that

37:14

go into it And so now we're sort of at

37:17

this point where I guess we're kind of we can

37:19

pat ourselves on the back and say hey Look, we

37:21

really feel like we know what people ought to be

37:23

doing But it really won't

37:25

matter unless they ultimately do it again It's

37:27

one of these things about it is that

37:29

it's going to take significant effort on that

37:31

workers part to achieve

37:33

success and so our

37:35

hope is just that we can reduce that effort by

37:37

that littlest bit or Give

37:39

them greater confidence that it's worth persisting

37:42

through that training Because they

37:44

know the outcomes better. That's awesome. I I really

37:46

appreciate The way that

37:48

from that first day on a napkin You

37:51

saw a gap in something and were able to

37:53

try and come up with some ideas and you

37:55

had some solutions on how to sell it And

37:58

I think I also I really respect how

38:00

you hold it loosely. It's not like,

38:03

well, this has to work now because it's been my

38:05

idea, I'm gonna shove it through. It's like, well, do

38:07

people use it? Is there a meaningful outcome? If not,

38:09

okay. Like I would think, but

38:11

okay. And so I really respect the way that,

38:13

I feel like that's tied back to sort of

38:15

your missional alignment with the agency of like, it's

38:17

gotta make a difference. That's why we're here. I

38:20

think that that's neat. Absolutely. Yeah, I

38:22

mean, yeah, you can launch tools, but I

38:24

think the real critical thing here is gonna be, does

38:26

it, does it make it? Is this the only way

38:28

that it will stay around, right? Is obviously if it

38:31

makes a difference, but it's

38:33

obviously the only thing that will validate that it was

38:35

ultimately worth doing. Yep. What

38:37

have we not talked about that we should? No,

38:40

I think it's been pretty thorough. We covered

38:42

most of it. I

38:44

hope that it's clear. It's

38:47

been sort

38:49

of a really fun process, like I said, to

38:51

move through it. And so it was just really

38:53

good to talk to you about it. Awesome.

38:56

Thank you for listening. I'm your host Jess

38:58

Carter. And don't forget to follow the Data

39:00

Driven Leadership wherever you get your podcasts. Rate

39:02

and review letting us know how these data

39:04

topics are transforming your business. We can't wait for

39:06

you to join us on the next episode.