0:03

Aqualars . This

0:06

is the Waterforsight podcast powered

0:08

by the Aqualars group , where we anticipate

0:10

, frame and shape the future of water

0:13

through strategic foresight . Today's guest

0:15

is Hudson Hollister , the founder and

0:17

CEO of Hdata . Hudson

0:20

, welcome to the Waterforsight podcast

0:22

.

0:23

Matt , thank you so much for having me . I'm excited

0:25

to participate in this conversation .

0:27

Well , I am too , and I

0:29

want to understand and get to know Hdata

0:32

. Tell me , what is Hdata ?

0:35

Hdata is a platform for regulation

0:37

. Our platform brings together all the

0:39

information of regulation as structured

0:41

and unstructured data and then allows people

0:44

that work with regulation to get their jobs done

0:46

. If you need to file forms

0:48

, or if you need to compare

0:50

information that comes from those forms , or

0:53

if you need to query the whole sector

0:55

using AI , you can do all that On the

0:57

Hdata platform . We work in

0:59

the energy sector today and I know

1:01

that we're going to have some interesting conversations

1:04

about whether the same technology could

1:06

be applied to water , but in the energy

1:08

sector . Our platform serves

1:10

the energy companies and it also

1:12

serves the energy regulatory agencies

1:15

, like the commissions at every state , and

1:17

it serves anybody else that cares

1:20

about energy regulatory information . So

1:22

I really have three kinds of customers that

1:24

all use the Hdata platform .

1:26

So you got the energy companies , like maybe a Duke

1:28

Energy . You've got a regulator

1:31

like the Federal Energy

1:33

Regulatory Commission or a State Public

1:36

Utility Commission . What

1:39

about customers ? Let's say I'm a big industrial

1:42

customer . Can I be a member or

1:44

subscriber to Hdata and gather

1:46

information ?

1:47

You sure can . We have some potential

1:50

customer conversations going on with a

1:53

lot of large power users . Some

1:55

of those are interested in Hdata because they want to

1:57

use regulatory information to figure out if

1:59

they can save money on what they're spending on

2:01

power . But not just customers , also

2:03

suppliers or those

2:05

that work on transactions with assets

2:08

in the industry , or consultants

2:10

. We have consultants

2:12

that pay for access to Hdata and

2:14

we just signed a contract with a major

2:16

supplier that sells

2:18

to the regulated energy

2:21

sector .

2:22

Wow , so even

2:25

if I am an energy company

2:27

, I can work with you to gather

2:30

even competitive information . What

2:32

are my fellow energy

2:34

companies doing in different states ? Is

2:37

there a competitive aspect to this as well

2:39

?

2:39

There sure is . Think about regulatory

2:41

information . It's all these forms that the

2:44

entities that are regulated have to file

2:46

with the government , and some of those forms end

2:48

up being public because of the

2:50

policy decision that we've made that annual

2:53

and quarterly reports by regulated

2:55

utilities , for instance , ought to be

2:57

published for everyone to use . Whatever

2:59

is public ought to be published

3:02

and ought to be really easy for you to grab

3:04

information that compares your own

3:06

entity to all the other ones using that

3:08

corpus . So , like permits

3:10

, it's reports

3:13

, it's permits , and then also it's anything

3:15

that goes into a docket . I know some of

3:17

the audience probably spends

3:19

time looking at regulatory commissions

3:21

and , as they might know , those regulatory

3:23

commissions publish all of their administrative

3:25

records and announcements and propose the final rules

3:28

on dockets , and so that's another source

3:30

for us . Think about all that as one

3:32

big pile . You have reports

3:35

and permits and docket items

3:38

all in one big mass , and

3:40

some of that information has numbers in it . Some

3:43

of that information is blobs of text

3:45

, but all it can be used

3:47

to better inform and better manage

3:50

whatever it is you're doing . If you

3:52

are a professional in energy regulation

3:54

.

3:55

Well , a lot of that is found

3:57

inside a rate case where

3:59

there's a lot of financial and other information

4:01

, capital projects et cetera and

4:03

you can get that information

4:06

. Is that what I hear ? That's right .

4:08

Some of our customers use each data . We're

4:10

getting a little ahead of ourselves because I want to talk about what our

4:12

functions do , but some of

4:14

our customers use each data to

4:17

grab our rate case and then query

4:19

it .

4:21

Well , let me ask this question what

4:23

inspired you to create

4:25

? Each data Sounds amazing .

4:27

15 years ago , I was a lawyer at the Securities

4:29

and Exchange Commission . The SEC regulates

4:32

public companies . The SEC was

4:34

not doing a very good job of it . When I worked there

4:36

that was during the financial crisis

4:38

I discovered one of the reasons why

4:41

the SEC failed to detect

4:43

some famous frauds , like the one perpetrated

4:45

by Bernie Madoff . The reason why the SEC

4:48

failed to detect indicators

4:50

of systemic risk . And the reason is we didn't

4:52

use any technology . We at

4:54

the agency were manually reviewing

4:57

all those forms from public companies . We

4:59

were verifying the mathematics of

5:01

public company financial statements with calculators

5:04

. This did not make any sense . We

5:06

weren't using even the technology

5:08

of that time . We weren't using the technology of that

5:10

time to analyze numbers

5:13

or to hunt through the text

5:15

for patterns , and so ever since

5:17

then , I've been trying to bring technology to regulation

5:20

. I resigned from the SEC and I went

5:22

to work for Congress trying to pass laws to

5:24

force regulatory agencies to digitize

5:27

all their forms and stuff . And then , in 2012

5:29

, I started the data coalition , which is a trade

5:31

association that lobbies regulatory

5:34

agencies to get them to digitize . One

5:36

of the agencies that we were able to persuade

5:39

to adopt the right

5:41

data format for all the numbers that are

5:43

in the filings is the Federal Energy

5:45

Regulatory Commission .

5:46

Hmm .

5:48

Once I began working with the FERC on digitizing

5:50

all of its forms , that's when I decided to start

5:52

H data and build a platform that

5:54

would use all of the data of energy

5:56

regulation to help the people that work

5:59

in regulated energy solve

6:01

problems .

6:02

So we need to get rid of the

6:04

typewriters , the bag phones and the slide

6:06

rules . Is that fair ?

6:09

Yeah , it's , it's fair to say and it's

6:11

it's no knock on the people that work

6:13

in energy regulation , because naturally the

6:15

folks that manage the whole flow

6:18

of documents , the whether it's the filings

6:20

going up or the dockets coming down their

6:22

subject matter experts in energy , not

6:25

in data , not in analytics , not in technology

6:27

. And so it's quickly . It takes some time

6:30

. We lag behind

6:32

other kinds of industries in

6:34

transforming the corpus

6:36

of regulatory information

6:38

from documents into data

6:40

and then applying technology to it . It just takes longer .

6:43

Yeah , I may be an excellent forensic

6:45

accounting person

6:47

, but I'm not very good at

6:49

the technological aspects

6:52

to how to move from analog to digital

6:54

. Fair enough , two different careers yeah

6:57

, interesting so

6:59

broadly . What kind of

7:01

or what type of future or

7:03

futures did you envision when

7:05

you were building H data ? Was

7:08

it efficiency ? Was it trying to detect

7:10

bad behavior ? What kind of futures

7:12

were you thinking about ?

7:14

It's efficiency and transparency . If we

7:16

have all the information of energy

7:18

regulation , if it's all expressed as data , then

7:21

it becomes possible to automate a lot of the work

7:23

that we used to do . It's

7:25

also transparency . I think both the

7:27

regulatory tours and the regulatory Ted and

7:30

the outside observers like those suppliers

7:32

and customers we talked about Everybody makes

7:34

better decisions if they have access to the right data

7:36

and we even have better cooperation

7:39

between the parties . I can give you one really specific

7:41

example . Earlier this year

7:43

, the Nevada Public Utilities Commission announced

7:45

that it was using H data and it

7:48

had found some unsupported

7:50

costs in

7:53

the annual reports of Nevada Energy

7:55

and Nevada Energy actually

7:57

with some of that . Nevada Energy agreed and

8:00

accepted

8:03

that because there was some costs

8:05

that had been miscategorized by accident that

8:08

would never have been found in mountains of

8:10

manual documents . But

8:12

that became really obvious using H data

8:15

for electronic review . And it wasn't

8:17

acrimony , yes , it was just let's

8:19

use technology to answer

8:22

the questions most effectively , wow

8:24

.

8:26

A couple I'll call this maybe some lightning

8:28

round questions , because I think you've touched on these already

8:30

, but the range of solutions

8:32

or aspects of H

8:34

data first . You're in the energy

8:37

world , you're thinking about water and we'll get to that , but

8:39

it's energy . I hear you . If

8:42

I hear you correctly , you

8:44

can get federal data

8:46

, state data and even local data

8:48

. Is that fair ?

8:51

We've got federal and state , today we

8:54

can pull local . We haven't done it yet .

8:58

And then you've got regulatory

9:00

data . What about statutes or

9:04

even policy linked data

9:06

? That's

9:08

fair game for H data .

9:10

It's fair game , but the way that we focus and

9:13

the way that we do , the way that we've

9:15

set up our platform , really

9:17

has two halves to it . We

9:20

have structured on one side and

9:22

we have unstructured on the other , and

9:24

so everything that we try to collect when

9:26

we crawl through all these sources , we

9:29

figure out whether it's to be structured or unstructured

9:32

. Structured

9:34

includes numbers . So

9:37

for all the forms that get filed with the FERC because

9:40

the FERC switched to a new data format instead

9:42

of the PDF we are able to harvest individual

9:44

numbers , each as their own data field . So

9:47

every schedule , every number on every schedule

9:49

of every annual and every quarterly

9:51

form that gets filed with the FERC , we have individual

9:54

numbers , and that means that that's

9:56

all structured . Every number is

9:58

its own data field , so we can use that to

10:00

build charts and graphs and

10:02

more complex things like whole models

10:04

, and the charts and graphs and models they

10:07

change automatically whenever the numbers do

10:09

. That's the structured side . On

10:11

the unstructured side , we have things that

10:13

aren't ever going to really be fully

10:16

built out as structured

10:18

data . You have blobs

10:20

of text , like a witness testimony in a rape

10:22

case . That's always going to be a bunch of text , and

10:25

so for the unstructured side

10:27

, we have libraries of documents , kind

10:30

of more familiar with libraries of documents

10:32

, and we go through those and we can use

10:34

AI to query them .

10:37

I was going to ask about that . I'm glad you talked about the

10:39

structure and unstructured data . What

10:42

about and I think this is unstructured

10:44

data ? But what about utilities

10:46

that are being discussed

10:49

over social media ? Customers

10:52

are making comments about their utility

10:55

on Facebook or Twitter

10:57

or TikTok or even LinkedIn . Is

11:00

that something that HData can eventually

11:02

go get , if not today ?

11:04

No , our corpus is regulatory

11:06

information , authoritative

11:09

stuff . It's information that's

11:11

either filed officially with the regulatory

11:13

agency , so that's the filing that's going up

11:15

, or information that is

11:17

published officially by a regulatory

11:19

agency that's dockets coming down . The

11:22

reason why we limit ourselves to that is

11:24

that our customers need to refer

11:27

to the authoritative

11:31

official sources . You could have Google

11:33

search or you could have chatGPT make something up

11:35

for you if you broadened

11:37

out your work from that . What

11:40

our customers need is they need the right answer and

11:42

they need it to be linked all the way back to the source . They

11:45

need to be able to go to the official source , the

11:47

official filing that was submitted

11:50

to the regulatory agency , or the

11:52

document wherever it

11:54

was published on a regulatory agency docket

11:56

, and they need to go back to that and verify

11:58

it , and they want to do that automatically . We

12:01

offer them a link that goes all the way back

12:03

to the official source , wherever it lives .

12:08

The example would have to be if I'm

12:10

a disgruntled commercial

12:13

, industrial , residential customer and

12:16

I file an email or letter to

12:19

the consumer advocate or the commission in

12:21

a rape case , that would be attached

12:23

to the docket . You could find those things there

12:25

, but just not random people talking

12:27

about their energy or water service

12:30

on Facebook . It's

12:33

a different idea . Is that fair

12:35

Right ?

12:36

We want to optimize all of the technologies

12:39

we use the analytics tools that build

12:41

the charts and graphs , the large language

12:43

models that answer unstructured queries

12:45

, the AI models and the AI technologies

12:47

. We

12:50

want to maximize their usefulness for this specific

12:53

kind of information regulatory

12:55

information .

12:56

So you are focused

12:58

on a lot of the energy

13:00

or economic regulators . I think we talked

13:02

about FERC or maybe the

13:05

SEC or the Public Service Commissions

13:07

. What about other agencies

13:09

? Are you looking at other agencies ? I don't want

13:12

you to have to tip your hand , but what about EPA

13:14

State

13:16

environmental agencies , a lot of these

13:18

energy companies ? They've got environmental

13:20

issues , not just the economic issues

13:22

. Where do we

13:25

go .

13:26

I think we will go there after we do

13:28

some growth . We're

13:31

pretty full up with the

13:34

solutions that rest

13:36

on top of the energy regulatory data

13:39

and with the customers that

13:41

need help with energy

13:43

regulatory data Interesting .

13:45

Okay , well , I get the

13:47

wink and nod . There I get it . It

13:49

sounds like that would be quite another

13:51

conversation . To talk about

13:54

some of the other data that

13:56

are out there that these companies

13:58

could look at . What

14:02

do you see in terms

14:04

of a future for H data and the water

14:06

sector ? I know we've talked quite a bit now about the

14:09

energy sector as a good foundation

14:11

, but what if we took

14:13

H data and all the things you've talked about and

14:16

pivoted over to the water sector

14:18

? What would that look ?

14:20

like . Well

14:23

, none of the software solutions that we

14:25

have built are hard-coded

14:28

for energy . I

14:30

think everything that we've built could work for water

14:32

. We have a TurboTax-style

14:35

solution that files your forms automatically and

14:37

it checks them automatically . I

14:40

mentioned the charts and graphs . We have solutions

14:43

that pull numbers from wherever they live and

14:46

make comparisons , some visual

14:48

comparisons and some financial models and

14:50

so on , that allow the tracking

14:53

of different entities . We have regulatory

14:55

AI , which hunts through

14:57

millions of documents , millions of pages

14:59

, and it gives you an accurate answer , summaries

15:02

and recommendations , and even what will this witness

15:04

say next time . All of those technologies

15:07

are agnostic . We

15:10

, of course , are connecting all of them to energy

15:12

regulation because we built a data net , a

15:14

data store integrated of energy

15:16

regulation , but all of those technologies

15:19

could also work for other kinds of regulation

15:21

.

15:22

Wow , okay . So

15:24

when I'll use my foresight terms

15:26

here about possible , probable and

15:29

plausible futures ? I hear

15:31

you saying , yeah , I do see an

15:33

H data for water . I mean , it is something

15:36

that's out there that we could see in

15:38

the future . Fair enough , yes , okay

15:41

. What

15:45

about the future ? And I guess this

15:47

would be applicable to water and

15:49

energy utilities . But how

15:51

would this in the future , this

15:53

being your company

15:56

and its tools , how

15:58

would this apply to the

16:00

future of rate making ? I mean , as you mentioned

16:02

earlier , rate cases are a big deal , would

16:07

we see ? We can talk about

16:09

these things separately , but just alternative

16:11

rate making issues , formulaic or

16:13

benchmarking , future test year

16:15

, the speed of rate making . What

16:19

about the issue of

16:22

is there too much transparency

16:24

? That may make you upset . I know

16:26

you want transparency , but is there such a thing as

16:28

too much transparency with regulators

16:30

or even customers ? And then , is this

16:32

a tool

16:35

that can be used in a predictive manner

16:37

versus just simply a reactive

16:39

manner ? So those are just some different

16:41

questions and we could go through them , but your

16:43

thoughts on any one of those ?

16:47

First , an alternative rate making . A

16:49

lot of the complication

16:53

and expensive alternative rate making is just it's

16:56

not complicated work , it's just

16:58

pulling the numbers from wherever they live officially

17:00

and plugging them together in different

17:03

configurations

17:05

, and that's what Hdata excels at . If

17:07

you know which numbers you need , you

17:10

can use our gateway solution to identify

17:13

them and make a

17:15

report , or you can

17:17

use our solutions to make a chart or a graph and

17:19

it will just automatically update whenever the underlying

17:21

numbers do you , you can see the

17:23

rates or whatever the formula

17:26

spits out . You can see that change in real time

17:28

as forms are filed . We

17:30

can save some time there . Most

17:33

of the work of rate making in a

17:35

rate case is creating

17:38

and then submitting and then absorbing

17:40

large quantities

17:42

of paper or virtual

17:45

paper on PDFs , and

17:47

that's where regulatory AI

17:49

excels . We're able to answer

17:52

questions about a whole compilation

17:54

of documents . What did this witness

17:56

say ? What

17:58

are the rate formulas that are

18:00

in this 200 page document ? What

18:03

is the commission likely to ask us

18:06

about this document ? Or what did

18:08

the commission say here ? What's it

18:10

likely to say in the future ? I

18:13

think the substance of that work will not change

18:15

, or it doesn't need to , but

18:17

the whole manual part that

18:20

comes before applying your brain , the

18:22

whole manual part that comes from having

18:25

to source numbers for structured data

18:27

or having to absorb and understand

18:30

documents for unstructured . That will go

18:32

away . Hdata can help that go away

18:34

. It'll have some

18:36

of the same work but it'll go faster to be much cheaper

18:38

.

18:40

If I'm on staff of a commission or

18:42

if I'm a consumer advocate . These

18:45

PDFs or even Excel

18:47

spreadsheets can be uploaded and

18:50

the different things that I'm

18:52

looking for can be pre-programmed so that

18:54

the software or Hdata

18:57

, can tell me where there are issues

18:59

, instead of me trying to find it

19:01

in mounds of paperwork . It

19:04

can very quickly in a matter of seconds

19:06

, I assume can tell me

19:09

here's a big change in this form , here's a change

19:11

in this number that's dramatic , or other

19:14

things , and I can then spend my time not

19:16

looking for things . I can spend time analyzing

19:19

things . Is that fair ?

19:21

Yes , most of what you just talked about was

19:23

reactive , not predictive . I think

19:25

most of the initial work that

19:27

we can assist is reactive

19:29

, not predictive . I want

19:32

to make sure that we cover that waterfront first

19:34

, but there are a few things that are predictive

19:37

, that our solution has been able to , our software

19:39

platform has been able to do . For

19:41

instance , you can pull up a rate

19:43

case witness testimony and you

19:45

can ask

19:48

regulatory AI to

19:50

invent questions that the commission

19:52

could choose to ask of this witness .

19:56

Is it even to the point where I

20:00

am making certain filings and

20:02

, as I go along , it will predict

20:05

if you will , or tell

20:07

me what my authorized

20:09

return on equity might be in a year or two if

20:12

I continue down this path . No

20:14

, no , okay , okay

20:16

, all right .

20:19

I can tell you if you're working

20:21

on a filing . It can tell you if

20:23

the filing is going to get

20:26

kicked back for

20:28

violating any of the mathematical formulas that

20:30

the regulator imposes . As

20:33

you're working on your filing , you'll see error

20:35

messages pop up that show you what you might

20:37

need to correct based on what

20:39

the regulator has said the formulas are . I

20:44

don't want to overpromise that

20:46

. We've already made a way to

20:48

predict how the regulator is

20:50

going to reply or going to react

20:52

to that filing years from now .

20:55

All right , I was joking that I would use the

20:57

phrase minority rate making

20:59

right Like out of minority report . We're

21:02

predicting that you're going to not

21:04

do well in two years , so we're going to make you a troubled

21:06

utility right and put you in receivership

21:09

. You're like , wait a minute , We'll

21:12

save that for the movies . It

21:15

seems that a lot of utilities

21:17

are interested in a

21:20

fully forecasted future test year as

21:22

an innovative rate making methodology

21:25

, and that's been received with some mixed

21:27

responses by different

21:29

parties . But how could

21:31

H data , if we talk about predictive elements

21:34

, could H data help utilities

21:36

and even commissions or consumer advocates

21:39

meaningfully address

21:41

this type of mechanism called

21:43

a future test year ?

21:46

If the future test year is built using

21:48

data from existing , from previous

21:50

ones , then we can

21:54

make the modeling much

21:56

easier and make the

21:58

assumptions much easier to test .

22:02

I wonder , in this world of alternative

22:04

rate making , whether this

22:07

type of a tool that you've developed will

22:09

move us to a different kind

22:11

of rate making environment

22:14

, more formulaic , or

22:18

even a benchmark type

22:20

of rate making where you can

22:22

put in some benchmarks and are

22:24

you achieving them or not , and that drives your

22:27

authorized return on equity

22:29

or the other elements in

22:31

a rate case . Do you think that this technology

22:33

may lead us to that kind

22:35

of a simplistic , formulaic

22:38

type of rate making world

22:40

or not ?

22:42

I'm not expert enough to know , but I do know that

22:44

if there are any regulatory

22:46

operations that we used to have

22:49

to forego because they

22:51

would be really expensive on the staff time

22:53

, maybe requiring a lot of computation

22:56

or maybe requiring a lot of quick

22:58

absorption of big quantities

23:00

of unstructured documents , we

23:02

won't have to forego those anymore because

23:04

the manual labor of

23:07

crunching numbers and the manual labor

23:09

of finding and absorbing

23:11

documents is going away .

23:14

Seems that in the future , regulated utilities

23:16

will have to be very careful about the data

23:19

and reports that they compile

23:21

and submit to regulators , because you

23:24

can't just dump a bunch of documents on them

23:26

and hope for the best . It's now going to be

23:29

very quickly reviewed , and

23:31

it just seems that what's left is

23:33

just the analysis . There's more time for analysis

23:36

and response rather than flipping

23:38

through pages of documents looking for a needle

23:40

in a haystack . Is that kind

23:42

of an outcome ?

23:43

It helps both sides . Both sides have

23:45

to invest incredible resources

23:49

, time and money in order to absorb

23:52

and understand that corpus . Today

23:55

, like all of the minimum information

23:57

requirements and a rate case , and then all the document

24:00

and information requests that are made

24:02

based on those minimum information requirements and the responses

24:05

to those documents and information requests , the

24:08

volume is really expensive

24:10

, not just for the commissions

24:12

or the intervenors , but also for the utilities

24:15

. That volume is probably going to

24:17

stay , unfortunately , but

24:19

it's going to no longer be so expensive .

24:24

Well , here's one of the other questions I had , and maybe

24:27

this is an easy one , but I asked about the speed of

24:29

rate making . Right now , rate cases

24:31

can take 300 days , maybe

24:33

even a year , depending on delays With

24:36

this technology . Is it simply

24:38

well , we have all the data , we've analyzed

24:41

it , we just need to have some

24:43

testimony , and instead of nine

24:45

months now it's three months

24:47

or six months . Your thoughts on that

24:50

?

24:51

Any of the work that is delayed by

24:53

the requiring people to read documents

24:55

will be faster .

24:58

Yeah , interesting . A

25:01

lot of people complain about that takes too long

25:03

. I make these investments

25:05

as a company . I got to wait to

25:07

get my recovery . And

25:10

then the transparency issue

25:12

. That's a very open-ended

25:15

issue . I know that's one of

25:17

the pieces of why you are sitting here

25:19

. You wanted to improve transparency . But does

25:23

this tool ? Does

25:26

it create enough transparency ? Not

25:28

enough , or maybe too much ? Where

25:30

are we going to be in 10 to 20 years ? Are

25:33

we going to say , wow , we've got too

25:35

much transparency ? I

25:38

don't know if that's an oxymoron or not , but your

25:40

thoughts on that ?

25:43

I think the most important point of modernizing

25:46

regulatory filings

25:48

going up and modernizing the

25:50

dockets and regulatory announcements coming down

25:53

is not that any new substance gets

25:55

revealed . It's all the same substance . It's just

25:57

easier to use that

25:59

corpus and so the transparency comes

26:01

from not from anything new being public

26:03

, but from the existing

26:06

stuff being easier to manage and understand

26:08

. And , yes

26:10

, there's more transparency that flows

26:12

from that . It's better transparency

26:15

of the existing public information

26:17

. It's harder to bury

26:19

something in the existing public information , but

26:22

the benefit is that we get faster

26:25

to a shared understanding

26:27

between the regulatory tour and regulatory Ted . The

26:30

regulatory tour and regulatory Ted both can get

26:32

a handle on that humongous corpus

26:34

faster and they can narrow

26:36

down what the actual disagreements might be , and

26:39

that's better for both sides .

26:41

What about the customers ? What about the nonprofits

26:44

that you know ? I'm sure you and I know

26:46

lots of different nonprofits that are representing

26:49

residential , commercial , industrial customers

26:51

. They often intervene in rate cases

26:53

and other proceedings . Do you think this tool

26:55

might help them ? Would they be interested in using

26:58

this tool to find opportunities

27:00

to better represent their concerns

27:03

and issues before different regulatory

27:05

bodies ?

27:06

We've had some really interesting demonstration

27:09

projects with intervenors

27:12

, some industry , some citizen

27:14

representing , and

27:16

we found that we can save them a lot of time . Usually they don't

27:18

have the same resources as

27:21

maybe the utility might

27:23

, and sometimes not even the same resources as the state

27:25

commission might . But

27:28

most of the expense of participating

27:30

in a rate case is all

27:33

of that time that it takes the

27:36

lawyer or consultant to absorb and understand

27:38

this out of matter . First , excuse

27:44

me , we shortened that time . No knock

27:46

on lawyers and consultants . We elevate

27:48

them , we won't get rid of them . The lawyers and consultants

27:50

are going to be able to skip forward past

27:53

all of the manual labor

27:55

and be able to apply their expertise

27:58

and their understanding earlier .

28:01

I can imagine individual customers being

28:03

able to someday partake

28:05

of each data's resources

28:09

and really setting up questions

28:11

or other analytical elements

28:14

that give them insights

28:17

into how a company is operating within their

28:19

community . That might help them as

28:21

they think about testifying or

28:24

in certain rate cases . That's

28:28

a future that I could see , because

28:30

you probably know that these rate

28:32

cases can be just intractable

28:34

Mountains of paperwork , complex

28:38

formulas , and how

28:40

does an individual customer , let alone a

28:42

commercial or industrial customer

28:44

, pour through this and figure out

28:46

what's fair and reasonable ? What's happening

28:48

in my neighborhood and

28:50

I could see in the future this tool

28:52

could be programmed to pretty easily pop

28:55

out a number or help

28:57

a customer understand different

28:59

things . That might give them the ability

29:02

to be more participatory

29:05

in regulatory

29:08

endeavors in the future . Am I

29:10

in the ballpark there or am I

29:12

on Mars ?

29:15

I do not know enough to predict , but I

29:17

do think that you

29:20

can . You can analyze the previous periods in history

29:22

. There was

29:24

a time when rate cases had to be managed

29:26

using literal paper , when

29:29

the intervenors had to go

29:32

to a physical place in order to get access

29:35

to read the minimum information requirements

29:37

, and that

29:39

is no longer true , because now the distributions

29:41

are electronic . You might have 11,000

29:44

document requests

29:46

in a rate case and those document

29:48

requests are now emailed to everybody , so you

29:50

can sit in your home office and do your work on the rate

29:53

case . Think about the

29:55

ways in which intervention

29:57

has changed as a result of that technological

30:00

change . Probably there are more intervenors

30:03

. Probably we

30:05

get to a more efficient result , because

30:07

if the cost of intervention goes down

30:10

, we might get

30:13

to a more efficient

30:16

consensus between

30:18

the regulator or the regulator and the intervenors

30:20

faster . So I

30:23

can try to analogize from that

30:25

to a world

30:27

in which the , the corpus

30:29

of information , can be managed and understood using each

30:31

data faster and better , and

30:34

I can come up with conclusions that way . But

30:37

I'm not a subject matter expert

30:39

. It rates , and so

30:41

I wouldn't venture to go further

30:43

than that .

30:44

Well , you can imagine , you know , 40

30:47

, 50 years ago it was the companies that had

30:49

these computers , right , and

30:52

this idea of a personal computer boy . That

30:54

was absurd . Right , a computer in every home . And

30:56

now it's a computer , multiple computers

30:59

, in the home . You got a computer in your hand

31:01

, you got a computer on your watch and

31:03

I had to think that , as you put it , you know , we've gone

31:06

to . You got to go to the government

31:08

file room to copy the paper to

31:10

. Well , now you can sign

31:12

up for the docket electronically and get PDFs

31:14

emailed to you or you can go dig

31:16

it out . What about

31:18

the next stage , which is not just getting the

31:21

documents but actually being able to

31:23

create algorithms

31:26

or other features

31:30

that give you outcomes or

31:32

insights from the materials

31:35

that have been filed with those regulators

31:37

? You know , I don't want to read 500

31:39

pages , I just want to know this number . Did

31:42

this project get done or where is this ? You know , and

31:45

you know that's

31:47

just a future that might be possible , if

31:50

not probable , from what

31:52

I hear from you . Yeah , my

31:55

question . You've touched on these two words

31:57

and I just wanted to ask it

31:59

and let you respond . But how

32:02

is Hdata helping with reactive

32:04

activities

32:06

or predictive activities . Is

32:11

it a tool that's just reacting , or

32:13

is it a tool to react to the things that

32:15

are filed , or can it be used by your

32:18

clients to

32:21

engage in any sort of predictive activity

32:24

? Now , that could be rate making . It could be and

32:27

I'll have some questions on , say , business development

32:29

or competitive intelligence your

32:32

thoughts on these two words reactive or predictive

32:34

.

32:36

The whole thing is reactive just

32:39

automatically , because Hdata

32:41

allows our customers to hunt

32:44

through over

32:46

10 years worth of filings going up

32:48

and dockets coming down yeah

32:50

, so there's a very rich

32:52

historical record

32:55

there . You can see

32:57

the history of return on equity

32:59

for every utility since 2011

33:02

, or you can instantly

33:05

access any rate case from

33:07

any interstate gas pipeline at

33:10

the FERC just by

33:12

typing in a quick query . So

33:16

it's a rich source of information

33:18

that regulatory

33:20

, tours , regulated companies and markets

33:23

can react to . The

33:26

parts of our platform that

33:28

allow for predictive work include

33:32

the ability to build formulas

33:35

. So if you know that you

33:37

want to use past information

33:39

to predict future information and you already know

33:42

the formula you want to use to make that prediction there's

33:44

always going to be a formula . If you're working with numbers , you

33:46

know the formula then you can put it into

33:48

our tools and you can extrapolate

33:51

forward and automatically

33:53

update that forward extrapolation

33:56

with whatever information is coming in

33:58

. That's on the structured side

34:00

. On the unstructured side , you can just

34:02

ask regulatory AI to give you

34:04

a prediction , and it will . Of course

34:06

, you're going to have to check that prediction

34:08

because it is a prediction , so

34:11

that's why it's important for our

34:14

regulatory AI to do citations . It's

34:16

important to link all the way back to the subject matter

34:18

and say , okay , here's what regulatory AI

34:20

says . Why did it say that ? And

34:22

this is one area where we try to

34:26

demystify AI . We

34:28

are not interested in being like chat GPT

34:30

. We do not want to wow somebody

34:32

with a fully formed answer . Instead , we want

34:34

you to see the components of the answer . We want

34:36

you to see why the AI

34:38

answered your query the way that it did

34:40

, and that's why we give you citations . We'll give you a

34:42

way to link back to the source material

34:45

that was used to create

34:47

the answer to your query in regulatory

34:49

AI and then figure out

34:51

which phrases

34:54

even came from which sources

34:56

, and so you can sort of see how the

34:58

answer came . People who work

35:00

in regulation need

35:02

citations back to the source , because

35:05

even when humans were doing it , that's

35:07

what we needed .

35:10

That's interesting . So you can then

35:13

look at why the answer

35:15

came back a certain way and

35:17

then be able to Same thing with the numbers .

35:18

You can link back to the original numbers there's

35:21

where they are in the form Right and you can say

35:23

hmm , okay that's interesting

35:25

.

35:26

Oh , we need to change that or that's not accurate

35:28

in our . Okay , so you can actually

35:30

go through and figure out where those

35:33

data came from and be able to adjust

35:36

because you have the source material .

35:38

Everything goes back to a source , an authoritative

35:41

source , and the authoritative source might be

35:43

a form that was filed with the regulatory

35:45

, or it might be something

35:47

that was published on a regulatory stock

35:50

.

35:52

Interesting . What are the possibilities

35:55

for Hdata being used

35:57

not by the utilities but by government

35:59

regulators ?

36:01

We've got four using it today .

36:05

Tell me , if you can , what inspired them to

36:07

work with you and what are they using

36:09

it for ?

36:11

Well , I mentioned the Nevada Public Utilities

36:13

Commission a few minutes ago

36:15

. The Nevada Public Utilities Commission is using

36:18

it for the same thing . Here's the thing the work

36:20

that regulators are doing is not that dissimilar

36:23

from the work that regulators are doing . Everyone's got to

36:25

look at this corpus and try to analyze

36:27

it .

36:30

Interesting . And is it just commissions

36:32

? Or what about the consumer advocates

36:35

or even the environmental regulators

36:37

? Have you just started to

36:39

see other regulatory

36:42

folks take an interest ?

36:44

Yes .

36:46

Interesting . All right , it

36:48

sounds like I shouldn't ask the next question

36:50

who . But what can Hdata

36:53

do

36:56

to help a utility with

36:59

the ever-persistent

37:02

challenges of capital investments

37:04

or operational efficiencies ? Have

37:06

you talked to some of your clients about those

37:08

aspects of the tool and what

37:12

can Hdata do to help with those things ?

37:14

Yeah , I can give you an example Plenty

37:17

of utilities in their CFO office

37:19

. They run analyses to

37:22

show their own capital efficiency

37:24

, their return

37:27

on equity or their return on invested capital

37:29

, their own operation maintenance expenses compared

37:31

with the others . And they use

37:33

and they run those analyses in

37:35

order to see and benchmark

37:38

themselves against their peers for

37:40

efficiency across the industry . And if they

37:42

see themselves lagging then they try to figure out

37:44

why and they try to figure out which cost items

37:46

are improving or

37:49

which cost items are helping them improve or

37:51

decline in their capital efficiency . All of

37:53

that comes from numbers that are knowable . All

37:56

of that comes from numbers that you can get from the forms

37:58

. It used to be really hard to

38:00

do this because you would have to hunt through the forms , find

38:03

your numbers and then plug

38:06

them into a spreadsheet and run the spreadsheet . Whenever

38:08

new forms came out , you would have to go and find

38:11

the new numbers and type them in . Or

38:13

whenever you wanted to add a

38:15

new company to your peer group . It

38:18

would be a couple of weeks of work . All

38:21

of that is instantaneous now .

38:23

And of course , that cuts both ways , because

38:26

if you're a utility , you can say , hey

38:28

, we've scanned all

38:30

the other regulated jurisdictions and our

38:33

project , our numbers , our costs

38:35

, they are within reasonable

38:38

standards . You can back that up with

38:41

data . But then commissions and even consumer

38:43

advocates can use the same information

38:45

and say , wait a minute , no , it isn't , let

38:47

me share with you . And

38:49

so it just enables

38:52

different parties to

38:54

use the same data and analyze

38:56

it in different ways in a very efficient

38:59

manner . It's not weeks

39:01

of pouring through boxes . They're calling other jurisdictions

39:04

and deposing people asking

39:06

endless data requests

39:08

. It's all right there and

39:11

now , in a matter of a day

39:14

or two , I can get what I need . And

39:16

now I'm sifting

39:18

through it and really thinking about the

39:20

results of the analyses I'm getting and

39:22

putting together my testimony one way or the other

39:25

. Is that fair ?

39:26

That is fair .

39:27

Okay , okay , that's interesting , all right . Okay

39:30

, how

39:33

does H data

39:35

impact this

39:37

notion of privacy and confidentiality

39:40

In the world of energy and

39:42

water and utilities ? We'll

39:44

talk about this in a minute . But we've got

39:46

a lot of smart , this and that

39:48

. We've got the internet of things . We've got

39:52

historically confidential information

39:54

that gets filed with commissions or environmental

39:56

regulators . Is

39:59

the filing of confidential information

40:01

kind of a bit of a limitation or not

40:04

? And then , how

40:06

does this address other

40:09

privacy issues that you may have , even

40:11

if you're gathering data from international

40:13

sources where there may be different privacy

40:16

or confidentiality regimes ? So

40:19

your thoughts ?

40:21

Our platform only works with what's public we

40:26

do . Our platform does have what

40:28

I mentioned before as the TurboTax of

40:30

utilities . That allows our

40:32

customers , if they have to file forms

40:35

, they can do it in our platform . We protect

40:37

the information that is

40:39

entered in there until

40:41

it is filed . When it's filed , it

40:43

becomes public . So there's a very big difference

40:45

between information that is being

40:48

entered into our platform preparing

40:50

for a filing , that is protected

40:53

in many different ways , and information

40:55

that then , once

40:57

the file button gets hit and it shows up crucial

40:59

point it shows up publicly

41:02

accessible on the regulators website . After

41:04

that it can be used for anything at all .

41:07

So if I am a utility

41:09

using a consult , an engineering

41:11

firm that does work around the

41:13

world , if there is public

41:16

information available in Australia

41:18

, canada , the European Union , you

41:20

could get that . Is that

41:22

fair ? If I am a regulator , if I am a utility

41:25

, if I am a consumer , I would say , hey , I got this

41:27

information from one of your consultants , where

41:29

they did work in Sydney

41:31

and this is what they charge . This is

41:33

what they did , and we think

41:35

your prices are a lot higher or what

41:37

have you . Why

41:39

are they doing it this way in this

41:41

jurisdiction in America ? Is that a possibility

41:44

?

41:45

It is a possibility . We are relying here on choices

41:47

that we make as democratic societies

41:49

. The choice that we make in the utility

41:52

sector is that we

41:54

are not going to have the government run power usually

41:57

a lot of municipals and all that

41:59

aside . We are not going to have the government run it . We are going to have

42:01

private companies run it because we think that they might be better

42:03

at it , and we are going to grant them monopolies

42:05

in some cases on predicting your aspects

42:07

of the value chain of moving electrons around

42:09

, and then , in exchange for getting that monopoly

42:12

, they are going to make money off of our rate payers . They

42:15

are also going to need to fulfill transparency

42:17

requirements . They are going to need to publish their

42:20

reports , they are going to need to file their reports and let those

42:22

reports be made public , and that is more transparency

42:24

than a private sector organization might face

42:26

. And when they want to charge more , they are going to have to

42:28

go and ask for it in public and explain the reasons

42:30

in public . We have already made those choices

42:33

as a democratic society that

42:35

publicity is

42:38

going to go along with monopoly , in

42:41

order to provide a restraint on

42:44

the possible self-interest that a monopoly

42:46

in the private sector is inevitably going to exercise

42:48

, and so

42:51

the technology is just going to make that work better . And

42:54

if we discover that the transparency

42:56

is too much , like you suggested earlier , matt

42:58

, if we discover the transparency is too

43:00

much , then I

43:02

am sure there are countervailing forces

43:05

that might try to reduce some of

43:07

the transparency , and the way to do

43:09

that is through the democratic process .

43:11

Yeah , all

43:14

right . What are the future

43:16

? I'll call it reverse opportunities with

43:18

customer data . We have a lot of activity

43:21

on this Internet of Things discussion

43:24

. We have smart

43:27

devices in the home smart meters , smart

43:29

toilets , what else

43:31

? Smart refrigerators , smart

43:34

thermostats . Is there

43:36

the ability in the future

43:38

for Hdata to take

43:42

those data in

43:44

some capacity and work them back into

43:47

a particular utility's

43:50

data analysis

43:52

for filings

43:55

with the commissions or public staff ?

43:58

We are going to maintain our focus on regulatory

44:00

data , so we would not touch those sources unless

44:03

the regulatory tour starts requiring the utility

44:05

to report them .

44:07

Okay .

44:08

Your client you are able to get good

44:10

at it , but by limiting that focus

44:12

. That's how we're able to get good at it . By

44:14

limiting our focus to the filings

44:17

going up and the dockets coming down , we

44:19

are able to develop a

44:21

data store that is pretty comprehensive and we're

44:23

able to build AI technology

44:26

that is effective .

44:28

And we achieve that effectiveness by limiting

44:31

the scope , the tool

44:33

is data source

44:35

agnostic , where a utility could

44:37

gather those data . Let's

44:40

say I had a smart meter and I had 100,000 customers

44:42

. I could gather those data and

44:45

I could put it into the tool and

44:48

have it help me run different analyses

44:50

that might help me going forward

44:52

with a particular regulatory filing . Is

44:54

that fair ? Is that a scenario that is

44:56

out there ?

44:58

No , the structured data part of our platform

45:00

is really aimed at the specific

45:02

data fields that come

45:05

up in the regulatory reports , and

45:07

so there wouldn't be much of a head start there . On

45:09

the unstructured side , our

45:12

customers have a private

45:15

catalog function in our regulatory AI , where

45:17

our customers can upload any document they want

45:19

and they can apply our regulatory

45:21

AI to it . But we have not

45:23

tested the regulatory AI on everything

45:25

. We've only tested it , optimized

45:28

it , on regulatory documents , and

45:30

the reason for that is that we

45:32

want to make sure to effectively

45:35

serve the people that need to find

45:37

summaries or insights

45:40

, analysis or recommendations from

45:42

this specific corpus of regulatory

45:44

information .

45:47

Okay , well , in

45:49

10 to 20 years , if we

45:51

see your

45:53

vision expand

45:56

from energy into water , what does

45:58

H data look like in

46:00

the next 10 to 20 years when it comes to water

46:02

? What are three things in your mind that

46:04

you see for the future of H

46:06

, data and water ?

46:08

If this were to happen , then the

46:11

three things I would see are number one anything

46:14

you have to file with

46:16

a regulator or if you're

46:18

regulated and you're working on a filing , information

46:21

flows automatically from your source systems

46:23

that you've already got to the regulator that

46:25

wants them . You don't have to compile

46:27

a form . The form just happens

46:30

in the background , you sign off on it , but

46:32

you don't have to write it . Number

46:35

two if you are running

46:37

efficiency comparisons or

46:40

if you are trying to predict future

46:42

rates assuming that the rates are formulaic then

46:45

you have all those comparisons and formulas

46:47

in H data . They just exist

46:50

and you can go access them and you can

46:52

watch how they change and you can set alerts

46:54

in case they change a certain way In

46:56

near real time . Well

46:59

, we take 10 minutes to get there .

47:01

Near real time . This

47:03

isn't monthly or weekly

47:06

, this is not even daily . It's you set

47:08

us every 10 minutes .

47:10

We're already doing that . Within

47:13

10 minutes of a form being filed , all the

47:15

numbers go from that form into our data

47:17

set and are reflected in our tools .

47:19

It's like NASDAQ in a sense the ticker

47:21

Wow .

47:24

And then , third , one

47:26

thing I haven't touched on this in this conversation so far

47:28

, Matt that we're excited about is

47:30

that I mentioned how the AI is

47:32

really for the unstructured documents , and

47:36

that is true . It is true for today , but

47:39

within much fewer

47:41

than 10 years , within one year , regulatory

47:43

AI will be able to answer the structured questions as well

47:45

. So you could say to regulatory AI tell

47:48

me the return on equity of every utility in

47:50

the American Southeast with revenue of over

47:52

a billion a year , and it will draw

47:54

you a table and the table will

47:56

be accurate . It will calculate return

47:59

on equity for you using numbers that come from the filings

48:01

, and then you can click on the number and see where

48:04

it came from .

48:05

Wow . So if I'm a consumer

48:07

advocate , I can use the

48:09

function to say tell me what the

48:12

problems are with this utility , or

48:14

et cetera , et cetera . Where

48:17

have things gone wrong ? I'm

48:19

kind of teasing , but

48:22

it really seems to

48:25

shorten and focus

48:27

this whole world of

48:30

utility regulation and

48:33

it changes the world , accelerates

48:35

, adds velocity and transparency

48:38

to this thing that we've

48:40

been dealing with for many decades .

48:45

Other worlds have been shortened and focused already

48:47

Since . Look at how the

48:50

ability to invest in publicly traded companies

48:52

expanded once online

48:54

trading became possible . This

48:57

didn't necessarily make it better

48:59

or worse , just made it faster , and

49:01

I do think it made it more democratic

49:03

. We now don't have to hire a

49:05

stockbroker , so anybody can invest in stocks

49:07

. Like I mentioned , though good or

49:09

bad , because we now have people investing in stocks

49:12

in meme stocks that maybe shouldn't .

49:16

Well , you're right . Anybody

49:18

can be a movie producer now You've

49:21

got YouTube . You can be an influencer

49:24

on TikTok . You

49:27

have apps . I don't have to go through a travel agent

49:29

to reserve

49:31

my seat on a plane . I can do all these

49:33

things through advances in technology , and the

49:36

utility world's a bit behind , but it's catching up . The

49:39

energy side is probably moving faster than the water

49:41

side . Those are

49:43

some great ideas . Your

49:46

first observation , I think , is noteworthy

49:50

in that you have this near

49:53

real-time regulatory system

49:55

where you

49:57

have the economic and perhaps in the

49:59

future , the environmental regulator , knowing

50:02

the challenges before

50:05

you do . Almost they're seeing it real time

50:07

. Uh-oh , there's an unpermitted discharge

50:09

, or uh-oh , the sensor said

50:11

that the drinking water's got a problem

50:13

, and you don't have to wait

50:16

to fill out a report or notify anybody and

50:18

it will go not just to the regulator , it might go to

50:20

the customer as well if those

50:22

things are triggered , the

50:25

reporting and things like that . Lots

50:27

to think about with this

50:29

tool , wow . Well

50:31

, hudson , I want to thank you for being a guest today

50:34

on the WaterForceSide podcast . It has

50:36

been a privilege , a fascinating

50:38

discussion . I probably have 10

50:40

questions right now , probably have another 10

50:42

after we're done , but you

50:45

have really made

50:47

us think about the future of

50:49

water from a very unique perspective

50:52

. How can folks get ahold of you if they want

50:54

to learn a bit more about age data

50:56

?

50:56

You can find us at agedataus and

50:59

it's pretty easy to find me on LinkedIn . There's only

51:01

two Hudson Hollisters and I'm one of them .

51:04

Thank you for listening to the WaterForceSide podcast

51:07

powered by the Aqualaurus Group . For more

51:09

information , please visit us at Aqualauruscom

51:12

or follow us on LinkedIn

51:14

and Twitter .