Unlock the secrets of regulatory innovation as we sit down with Hdata's founder and CEO, Hudson Hollister, whose journey from SEC lawyer to tech trailblazer is transforming the energy industry's approach to data. This episode promises a wealth of knowledge as we venture into how Hudson's brainchild streamlines regulatory filing processes, applies AI to sector-wide queries, and empowers stakeholders with actionable insights. Discover how this groundbreaking platform not only reshapes energy data interaction but also eyes expansion into the vital water industry, potentially revolutionizing transparency and efficiency.
Imagine a world where utility rate making is no longer shrouded in lengthy, manual processes, but is driven by rapid and precise data analysis. Our discussion with Hudson peeks into a future where advanced tools like Hdata expedite rate case outcomes and democratize the regulatory landscape. We weigh the pros and cons, from the fear of over-transparency to the promise of a more formulaic regulatory approach and examine the role of predictive models and AI in shaping the utility sector's trajectory. This episode challenges the norm and envisions a more engaged community in utility regulation, thanks to technological advancements that could redefine industry standards.
As we chart the course of regulatory filings into a brave new world of real-time monitoring and analysis, Hudson reflects on the delicate dance between data privacy and public access. We address how AI is breaking new ground in utility management, enabling more informed decisions and benchmarking while navigating the minefield of international privacy laws. Looking forward, we consider the transformative potential of these innovations in the water sector, igniting a discussion on how technology may streamline processes and redefine transparency. Join us for a thought-provoking journey through the evolving landscape of utility regulation and the pioneering spirit of Hdata.
#water #WaterForesight #strategicforesight #foresight #futures @Aqualaurus
Unlock the secrets of regulatory innovation as we sit down with Hdata's founder and CEO, Hudson Hollister, whose journey from SEC lawyer to tech trailblazer is transforming the energy industry's approach to data. This episode promises a wealth of knowledge as we venture into how Hudson's brainchild streamlines regulatory filing processes, applies AI to sector-wide queries, and empowers stakeholders with actionable insights. Discover how this groundbreaking platform not only reshapes energy data interaction but also eyes expansion into the vital water industry, potentially revolutionizing transparency and efficiency.
Imagine a world where utility rate making is no longer shrouded in lengthy, manual processes, but is driven by rapid and precise data analysis. Our discussion with Hudson peeks into a future where advanced tools like Hdata expedite rate case outcomes and democratize the regulatory landscape. We weigh the pros and cons, from the fear of over-transparency to the promise of a more formulaic regulatory approach and examine the role of predictive models and AI in shaping the utility sector's trajectory. This episode challenges the norm and envisions a more engaged community in utility regulation, thanks to technological advancements that could redefine industry standards.
As we chart the course of regulatory filings into a brave new world of real-time monitoring and analysis, Hudson reflects on the delicate dance between data privacy and public access. We address how AI is breaking new ground in utility management, enabling more informed decisions and benchmarking while navigating the minefield of international privacy laws. Looking forward, we consider the transformative potential of these innovations in the water sector, igniting a discussion on how technology may streamline processes and redefine transparency. Join us for a thought-provoking journey through the evolving landscape of utility regulation and the pioneering spirit of Hdata.
#water #WaterForesight #strategicforesight #foresight #futures @Aqualaurus
Aqualars. This is the Waterforsight podcast powered by the Aqualars group, where we anticipate, frame and shape the future of water through strategic foresight. Today's guest is Hudson Hollister, the founder and CEO of Hdata. Hudson, welcome to the Waterforsight podcast.
Speaker 2:Matt, thank you so much for having me. I'm excited to participate in this conversation.
Speaker 1:Well, I am too, and I want to understand and get to know Hdata. Tell me, what is Hdata?
Speaker 2:Hdata is a platform for regulation. Our platform brings together all the information of regulation as structured and unstructured data and then allows people that work with regulation to get their jobs done. If you need to file forms, or if you need to compare information that comes from those forms, or if you need to query the whole sector using AI, you can do all that On the Hdata platform. We work in the energy sector today and I know that we're going to have some interesting conversations about whether the same technology could be applied to water, but in the energy sector. Our platform serves the energy companies and it also serves the energy regulatory agencies, like the commissions at every state, and it serves anybody else that cares about energy regulatory information.
Speaker 2:So I really have three kinds of customers that all use the Hdata platform.
Speaker 1:So you got the energy companies, like maybe a Duke Energy. You've got a regulator like the Federal Energy Regulatory Commission or a State Public Utility Commission. What about customers? Let's say I'm a big industrial customer. Can I be a member or subscriber to Hdata and gather information?
Speaker 2:You sure can. We have some potential customer conversations going on with a lot of large power users. Some of those are interested in Hdata because they want to use regulatory information to figure out if they can save money on what they're spending on power. But not just customers, also suppliers or those that work on transactions with assets in the industry, or consultants. We have consultants that pay for access to Hdata and we just signed a contract with a major supplier that sells to the regulated energy sector.
Speaker 1:Wow, so even if I am an energy company, I can work with you to gather even competitive information. What are my fellow energy companies doing in different states? Is there a competitive aspect to this as well?
Speaker 2:There sure is. Think about regulatory information. It's all these forms that the entities that are regulated have to file with the government, and some of those forms end up being public because of the policy decision that we've made that annual and quarterly reports by regulated utilities, for instance, ought to be published for everyone to use. Whatever is public ought to be published and ought to be really easy for you to grab information that compares your own entity to all the other ones using that corpus. So, like permits, it's reports, it's permits, and then also it's anything that goes into a docket.
Speaker 2:I know some of the audience probably spends time looking at regulatory commissions and, as they might know, those regulatory commissions publish all of their administrative records and announcements and propose the final rules on dockets, and so that's another source for us. Think about all that as one big pile. You have reports and permits and docket items all in one big mass, and some of that information has numbers in it. Some of that information is blobs of text, but all it can be used to better inform and better manage whatever it is you're doing. If you are a professional in energy regulation.
Speaker 1:Well, a lot of that is found inside a rate case where there's a lot of financial and other information, capital projects et cetera and you can get that information. Is that what I hear? That's right.
Speaker 2:Some of our customers use each data. We're getting a little ahead of ourselves because I want to talk about what our functions do, but some of our customers use each data to grab our rate case and then query it.
Speaker 1:Well, let me ask this question what inspired you to create? Each data Sounds amazing.
Speaker 2:15 years ago, I was a lawyer at the Securities and Exchange Commission. The SEC regulates public companies. The SEC was not doing a very good job of it. When I worked there that was during the financial crisis I discovered one of the reasons why the SEC failed to detect some famous frauds, like the one perpetrated by Bernie Madoff. The reason why the SEC failed to detect indicators of systemic risk. And the reason is we didn't use any technology.
Speaker 2:We at the agency were manually reviewing all those forms from public companies. We were verifying the mathematics of public company financial statements with calculators. This did not make any sense. We weren't using even the technology of that time. We weren't using the technology of that time to analyze numbers or to hunt through the text for patterns, and so ever since then, I've been trying to bring technology to regulation. I resigned from the SEC and I went to work for Congress trying to pass laws to force regulatory agencies to digitize all their forms and stuff. And then, in 2012, I started the data coalition, which is a trade association that lobbies regulatory agencies to get them to digitize. One of the agencies that we were able to persuade to adopt the right data format for all the numbers that are in the filings is the Federal Energy Regulatory Commission.
Speaker 1:Hmm.
Speaker 2:Once I began working with the FERC on digitizing all of its forms, that's when I decided to start H data and build a platform that would use all of the data of energy regulation to help the people that work in regulated energy solve problems.
Speaker 1:So we need to get rid of the typewriters, the bag phones and the slide rules. Is that fair?
Speaker 2:Yeah, it's, it's fair to say and it's it's no knock on the people that work in energy regulation, because naturally the folks that manage the whole flow of documents, the whether it's the filings going up or the dockets coming down their subject matter experts in energy, not in data, not in analytics, not in technology. And so it's quickly. It takes some time. We lag behind other kinds of industries in transforming the corpus of regulatory information from documents into data and then applying technology to it. It just takes longer.
Speaker 1:Yeah, I may be an excellent forensic accounting person, but I'm not very good at the technological aspects to how to move from analog to digital. Fair enough, two different careers yeah, interesting so broadly. What kind of or what type of future or futures did you envision when you were building H data? Was it efficiency? Was it trying to detect bad behavior? What kind of futures were you thinking about?
Speaker 2:It's efficiency and transparency. If we have all the information of energy regulation, if it's all expressed as data, then it becomes possible to automate a lot of the work that we used to do. It's also transparency. I think both the regulatory tours and the regulatory Ted and the outside observers like those suppliers and customers we talked about Everybody makes better decisions if they have access to the right data and we even have better cooperation between the parties. I can give you one really specific example. Earlier this year, the Nevada Public Utilities Commission announced that it was using H data and it had found some unsupported costs in the annual reports of Nevada Energy and Nevada Energy actually with some of that. Nevada Energy agreed and accepted that because there was some costs that had been miscategorized by accident that would never have been found in mountains of manual documents. But that became really obvious using H data for electronic review. And it wasn't acrimony, yes, it was just let's use technology to answer the questions most effectively, wow.
Speaker 1:A couple I'll call this maybe some lightning round questions, because I think you've touched on these already, but the range of solutions or aspects of H data first. You're in the energy world, you're thinking about water and we'll get to that, but it's energy. I hear you. If I hear you correctly, you can get federal data, state data and even local data. Is that fair?
Speaker 2:We've got federal and state, today we can pull local. We haven't done it yet.
Speaker 1:And then you've got regulatory data. What about statutes or even policy linked data? That's fair game for H data.
Speaker 2:It's fair game, but the way that we focus and the way that we do, the way that we've set up our platform, really has two halves to it. We have structured on one side and we have unstructured on the other, and so everything that we try to collect when we crawl through all these sources, we figure out whether it's to be structured or unstructured. Structured includes numbers. So for all the forms that get filed with the FERC because the FERC switched to a new data format instead of the PDF we are able to harvest individual numbers, each as their own data field. So every schedule, every number on every schedule of every annual and every quarterly form that gets filed with the FERC, we have individual numbers, and that means that that's all structured. Every number is its own data field, so we can use that to build charts and graphs and more complex things like whole models, and the charts and graphs and models they change automatically whenever the numbers do.
Speaker 2:That's the structured side. On the unstructured side, we have things that aren't ever going to really be fully built out as structured data. You have blobs of text, like a witness testimony in a rape case. That's always going to be a bunch of text, and so for the unstructured side, we have libraries of documents, kind of more familiar with libraries of documents, and we go through those and we can use AI to query them.
Speaker 1:I was going to ask about that. I'm glad you talked about the structure and unstructured data. What about and I think this is unstructured data? But what about utilities that are being discussed over social media? Customers are making comments about their utility on Facebook or Twitter or TikTok or even LinkedIn. Is that something that HData can eventually go get, if not today?
Speaker 2:No, our corpus is regulatory information, authoritative stuff. It's information that's either filed officially with the regulatory agency, so that's the filing that's going up, or information that is published officially by a regulatory agency that's dockets coming down. The reason why we limit ourselves to that is that our customers need to refer to the authoritative official sources. You could have Google search or you could have chatGPT make something up for you if you broadened out your work from that. What our customers need is they need the right answer and they need it to be linked all the way back to the source. They need to be able to go to the official source, the official filing that was submitted to the regulatory agency, or the document wherever it was published on a regulatory agency docket, and they need to go back to that and verify it, and they want to do that automatically. We offer them a link that goes all the way back to the official source, wherever it lives.
Speaker 1:The example would have to be if I'm a disgruntled commercial, industrial, residential customer and I file an email or letter to the consumer advocate or the commission in a rape case, that would be attached to the docket. You could find those things there, but just not random people talking about their energy or water service on Facebook. It's a different idea. Is that fair Right?
Speaker 2:We want to optimize all of the technologies we use the analytics tools that build the charts and graphs, the large language models that answer unstructured queries, the AI models and the AI technologies. We want to maximize their usefulness for this specific kind of information regulatory information.
Speaker 1:So you are focused on a lot of the energy or economic regulators. I think we talked about FERC or maybe the SEC or the Public Service Commissions. What about other agencies? Are you looking at other agencies? I don't want you to have to tip your hand, but what about EPA State environmental agencies, a lot of these energy companies? They've got environmental issues, not just the economic issues. Where do we go.
Speaker 2:I think we will go there after we do some growth. We're pretty full up with the solutions that rest on top of the energy regulatory data and with the customers that need help with energy regulatory data Interesting.
Speaker 1:Okay, well, I get the wink and nod. There I get it. It sounds like that would be quite another conversation. To talk about some of the other data that are out there that these companies could look at. What do you see in terms of a future for H data and the water sector? I know we've talked quite a bit now about the energy sector as a good foundation, but what if we took H data and all the things you've talked about and pivoted over to the water sector? What would that look?
Speaker 2:like. Well, none of the software solutions that we have built are hard-coded for energy. I think everything that we've built could work for water. We have a TurboTax-style solution that files your forms automatically and it checks them automatically. I mentioned the charts and graphs. We have solutions that pull numbers from wherever they live and make comparisons, some visual comparisons and some financial models and so on, that allow the tracking of different entities. We have regulatory AI, which hunts through millions of documents, millions of pages, and it gives you an accurate answer, summaries and recommendations, and even what will this witness say next time. All of those technologies are agnostic. We, of course, are connecting all of them to energy regulation because we built a data net, a data store integrated of energy regulation, but all of those technologies could also work for other kinds of regulation.
Speaker 1:Wow, okay. So when I'll use my foresight terms here about possible, probable and plausible futures? I hear you saying, yeah, I do see an H data for water. I mean, it is something that's out there that we could see in the future. Fair enough, yes, okay. What about the future? And I guess this would be applicable to water and energy utilities. But how would this in the future, this being your company and its tools, how would this apply to the future of rate making? I mean, as you mentioned earlier, rate cases are a big deal, would we see?
Speaker 1:We can talk about these things separately, but just alternative rate making issues, formulaic or benchmarking, future test year, the speed of rate making. What about the issue of is there too much transparency? That may make you upset. I know you want transparency, but is there such a thing as too much transparency with regulators or even customers? And then, is this a tool that can be used in a predictive manner versus just simply a reactive manner? So those are just some different questions and we could go through them, but your thoughts on any one of those?
Speaker 2:First, an alternative rate making. A lot of the complication and expensive alternative rate making is just it's not complicated work, it's just pulling the numbers from wherever they live officially and plugging them together in different configurations, and that's what Hdata excels at. If you know which numbers you need, you can use our gateway solution to identify them and make a report, or you can use our solutions to make a chart or a graph and it will just automatically update whenever the underlying numbers do you, you can see the rates or whatever the formula spits out. You can see that change in real time as forms are filed. We can save some time there.
Speaker 2:Most of the work of rate making in a rate case is creating and then submitting and then absorbing large quantities of paper or virtual paper on PDFs, and that's where regulatory AI excels. We're able to answer questions about a whole compilation of documents. What did this witness say? What are the rate formulas that are in this 200 page document? What is the commission likely to ask us about this document? Or what did the commission say here? What's it likely to say in the future? I think the substance of that work will not change, or it doesn't need to, but the whole manual part that comes before applying your brain, the whole manual part that comes from having to source numbers for structured data or having to absorb and understand documents for unstructured. That will go away. Hdata can help that go away. It'll have some of the same work but it'll go faster to be much cheaper.
Speaker 1:If I'm on staff of a commission or if I'm a consumer advocate. These PDFs or even Excel spreadsheets can be uploaded and the different things that I'm looking for can be pre-programmed so that the software or Hdata, can tell me where there are issues, instead of me trying to find it in mounds of paperwork. It can very quickly in a matter of seconds, I assume can tell me here's a big change in this form, here's a change in this number that's dramatic, or other things, and I can then spend my time not looking for things. I can spend time analyzing things. Is that fair?
Speaker 2:Yes, most of what you just talked about was reactive, not predictive. I think most of the initial work that we can assist is reactive, not predictive. I want to make sure that we cover that waterfront first, but there are a few things that are predictive, that our solution has been able to, our software platform has been able to do. For instance, you can pull up a rate case witness testimony and you can ask regulatory AI to invent questions that the commission could choose to ask of this witness.
Speaker 1:Is it even to the point where I am making certain filings and, as I go along, it will predict if you will, or tell me what my authorized return on equity might be in a year or two if I continue down this path. No, no, okay, okay, all right.
Speaker 2:I can tell you if you're working on a filing. It can tell you if the filing is going to get kicked back for violating any of the mathematical formulas that the regulator imposes. As you're working on your filing, you'll see error messages pop up that show you what you might need to correct based on what the regulator has said the formulas are. I don't want to overpromise that. We've already made a way to predict how the regulator is going to reply or going to react to that filing years from now.
Speaker 1:All right, I was joking that I would use the phrase minority rate making right Like out of minority report. We're predicting that you're going to not do well in two years, so we're going to make you a troubled utility right and put you in receivership. You're like, wait a minute, We'll save that for the movies. It seems that a lot of utilities are interested in a fully forecasted future test year as an innovative rate making methodology, and that's been received with some mixed responses by different parties. But how could H data, if we talk about predictive elements, could H data help utilities and even commissions or consumer advocates meaningfully address this type of mechanism called a future test year?
Speaker 2:If the future test year is built using data from existing, from previous ones, then we can make the modeling much easier and make the assumptions much easier to test.
Speaker 1:I wonder, in this world of alternative rate making, whether this type of a tool that you've developed will move us to a different kind of rate making environment, more formulaic, or even a benchmark type of rate making where you can put in some benchmarks and are you achieving them or not, and that drives your authorized return on equity or the other elements in a rate case. Do you think that this technology may lead us to that kind of a simplistic, formulaic type of rate making world or not?
Speaker 2:I'm not expert enough to know, but I do know that if there are any regulatory operations that we used to have to forego because they would be really expensive on the staff time, maybe requiring a lot of computation or maybe requiring a lot of quick absorption of big quantities of unstructured documents, we won't have to forego those anymore because the manual labor of crunching numbers and the manual labor of finding and absorbing documents is going away.
Speaker 1:Seems that in the future, regulated utilities will have to be very careful about the data and reports that they compile and submit to regulators, because you can't just dump a bunch of documents on them and hope for the best. It's now going to be very quickly reviewed, and it just seems that what's left is just the analysis. There's more time for analysis and response rather than flipping through pages of documents looking for a needle in a haystack. Is that kind of an outcome?
Speaker 2:It helps both sides. Both sides have to invest incredible resources, time and money in order to absorb and understand that corpus. Today, like all of the minimum information requirements and a rate case, and then all the document and information requests that are made based on those minimum information requirements and the responses to those documents and information requests, the volume is really expensive, not just for the commissions or the intervenors, but also for the utilities. That volume is probably going to stay, unfortunately, but it's going to no longer be so expensive.
Speaker 1:Well, here's one of the other questions I had, and maybe this is an easy one, but I asked about the speed of rate making. Right now, rate cases can take 300 days, maybe even a year, depending on delays With this technology. Is it simply well, we have all the data, we've analyzed it, we just need to have some testimony, and instead of nine months now it's three months or six months. Your thoughts on that?
Speaker 2:Any of the work that is delayed by the requiring people to read documents will be faster.
Speaker 1:Yeah, interesting. A lot of people complain about that takes too long. I make these investments as a company. I got to wait to get my recovery. And then the transparency issue. That's a very open-ended issue. I know that's one of the pieces of why you are sitting here. You wanted to improve transparency. But does this tool? Does it create enough transparency? Not enough, or maybe too much? Where are we going to be in 10 to 20 years? Are we going to say, wow, we've got too much transparency? I don't know if that's an oxymoron or not, but your thoughts on that?
Speaker 2:I think the most important point of modernizing regulatory filings going up and modernizing the dockets and regulatory announcements coming down is not that any new substance gets revealed. It's all the same substance. It's just easier to use that corpus and so the transparency comes from not from anything new being public, but from the existing stuff being easier to manage and understand. And, yes, there's more transparency that flows from that. It's better transparency of the existing public information. It's harder to bury something in the existing public information, but the benefit is that we get faster to a shared understanding between the regulatory tour and regulatory Ted. The regulatory tour and regulatory Ted both can get a handle on that humongous corpus faster and they can narrow down what the actual disagreements might be, and that's better for both sides.
Speaker 1:What about the customers? What about the nonprofits that you know? I'm sure you and I know lots of different nonprofits that are representing residential, commercial, industrial customers. They often intervene in rate cases and other proceedings. Do you think this tool might help them? Would they be interested in using this tool to find opportunities to better represent their concerns and issues before different regulatory bodies?
Speaker 2:We've had some really interesting demonstration projects with intervenors, some industry, some citizen representing, and we found that we can save them a lot of time. Usually they don't have the same resources as maybe the utility might, and sometimes not even the same resources as the state commission might. But most of the expense of participating in a rate case is all of that time that it takes the lawyer or consultant to absorb and understand this out of matter. First, excuse me, we shortened that time. No knock on lawyers and consultants. We elevate them, we won't get rid of them. The lawyers and consultants are going to be able to skip forward past all of the manual labor and be able to apply their expertise and their understanding earlier.
Speaker 1:I can imagine individual customers being able to someday partake of each data's resources and really setting up questions or other analytical elements that give them insights into how a company is operating within their community. That might help them as they think about testifying or in certain rate cases. That's a future that I could see, because you probably know that these rate cases can be just intractable Mountains of paperwork, complex formulas, and how does an individual customer, let alone a commercial or industrial customer, pour through this and figure out what's fair and reasonable? What's happening in my neighborhood and I could see in the future this tool could be programmed to pretty easily pop out a number or help a customer understand different things. That might give them the ability to be more participatory in regulatory endeavors in the future. Am I in the ballpark there or am I on Mars?
Speaker 2:I do not know enough to predict, but I do think that you can. You can analyze the previous periods in history. There was a time when rate cases had to be managed using literal paper, when the intervenors had to go to a physical place in order to get access to read the minimum information requirements, and that is no longer true, because now the distributions are electronic. You might have 11,000 document requests in a rate case and those document requests are now emailed to everybody, so you can sit in your home office and do your work on the rate case.
Speaker 2:Think about the ways in which intervention has changed as a result of that technological change. Probably there are more intervenors. Probably we get to a more efficient result, because if the cost of intervention goes down, we might get to a more efficient consensus between the regulator or the regulator and the intervenors faster. So I can try to analogize from that to a world in which the, the corpus of information, can be managed and understood using each data faster and better, and I can come up with conclusions that way. But I'm not a subject matter expert. It rates, and so I wouldn't venture to go further than that.
Speaker 1:Well, you can imagine, you know, 40, 50 years ago it was the companies that had these computers, right, and this idea of a personal computer boy. That was absurd. Right, a computer in every home. And now it's a computer, multiple computers, in the home. You got a computer in your hand, you got a computer on your watch and I had to think that, as you put it, you know, we've gone to. You got to go to the government file room to copy the paper to. Well, now you can sign up for the docket electronically and get PDFs emailed to you or you can go dig it out.
Speaker 1:What about the next stage, which is not just getting the documents but actually being able to create algorithms or other features that give you outcomes or insights from the materials that have been filed with those regulators? You know, I don't want to read 500 pages, I just want to know this number. Did this project get done or where is this? You know, and you know that's just a future that might be possible, if not probable, from what I hear from you. Yeah, my question. You've touched on these two words and I just wanted to ask it and let you respond. But how is Hdata helping with reactive activities or predictive activities. Is it a tool that's just reacting, or is it a tool to react to the things that are filed, or can it be used by your clients to engage in any sort of predictive activity? Now, that could be rate making. It could be and I'll have some questions on, say, business development or competitive intelligence your thoughts on these two words reactive or predictive.
Speaker 2:The whole thing is reactive just automatically, because Hdata allows our customers to hunt through over 10 years worth of filings going up and dockets coming down yeah, so there's a very rich historical record there. You can see the history of return on equity for every utility since 2011, or you can instantly access any rate case from any interstate gas pipeline at the FERC just by typing in a quick query. So it's a rich source of information that regulatory, tours, regulated companies and markets can react to. The parts of our platform that allow for predictive work include the ability to build formulas. So if you know that you want to use past information to predict future information and you already know the formula you want to use to make that prediction there's always going to be a formula. If you're working with numbers, you know the formula then you can put it into our tools and you can extrapolate forward and automatically update that forward extrapolation with whatever information is coming in. That's on the structured side. On the unstructured side, you can just ask regulatory AI to give you a prediction, and it will. Of course, you're going to have to check that prediction because it is a prediction, so that's why it's important for our regulatory AI to do citations.
Speaker 2:It's important to link all the way back to the subject matter and say, okay, here's what regulatory AI says. Why did it say that? And this is one area where we try to demystify AI. We are not interested in being like chat GPT. We do not want to wow somebody with a fully formed answer. Instead, we want you to see the components of the answer. We want you to see why the AI answered your query the way that it did, and that's why we give you citations. We'll give you a way to link back to the source material that was used to create the answer to your query in regulatory AI and then figure out which phrases even came from which sources, and so you can sort of see how the answer came. People who work in regulation need citations back to the source, because even when humans were doing it, that's what we needed.
Speaker 1:That's interesting. So you can then look at why the answer came back a certain way and then be able to Same thing with the numbers.
Speaker 2:You can link back to the original numbers there's where they are in the form Right and you can say hmm, okay that's interesting.
Speaker 1:Oh, we need to change that or that's not accurate in our. Okay, so you can actually go through and figure out where those data came from and be able to adjust because you have the source material.
Speaker 2:Everything goes back to a source, an authoritative source, and the authoritative source might be a form that was filed with the regulatory, or it might be something that was published on a regulatory stock.
Speaker 1:Interesting. What are the possibilities for Hdata being used not by the utilities but by government regulators?
Speaker 2:We've got four using it today.
Speaker 1:Tell me, if you can, what inspired them to work with you and what are they using it for?
Speaker 2:Well, I mentioned the Nevada Public Utilities Commission a few minutes ago. The Nevada Public Utilities Commission is using it for the same thing. Here's the thing the work that regulators are doing is not that dissimilar from the work that regulators are doing. Everyone's got to look at this corpus and try to analyze it.
Speaker 1:Interesting. And is it just commissions? Or what about the consumer advocates or even the environmental regulators? Have you just started to see other regulatory folks take an interest?
Speaker 2:Yes.
Speaker 1:Interesting. All right, it sounds like I shouldn't ask the next question who. But what can Hdata do to help a utility with the ever-persistent challenges of capital investments or operational efficiencies? Have you talked to some of your clients about those aspects of the tool and what can Hdata do to help with those things?
Speaker 2:Yeah, I can give you an example Plenty of utilities in their CFO office. They run analyses to show their own capital efficiency, their return on equity or their return on invested capital, their own operation maintenance expenses compared with the others. And they use and they run those analyses in order to see and benchmark themselves against their peers for efficiency across the industry. And if they see themselves lagging then they try to figure out why and they try to figure out which cost items are improving or which cost items are helping them improve or decline in their capital efficiency.
Speaker 2:All of that comes from numbers that are knowable. All of that comes from numbers that you can get from the forms. It used to be really hard to do this because you would have to hunt through the forms, find your numbers and then plug them into a spreadsheet and run the spreadsheet. Whenever new forms came out, you would have to go and find the new numbers and type them in. Or whenever you wanted to add a new company to your peer group. It would be a couple of weeks of work. All of that is instantaneous now.
Speaker 1:And of course, that cuts both ways, because if you're a utility, you can say, hey, we've scanned all the other regulated jurisdictions and our project, our numbers, our costs, they are within reasonable standards. You can back that up with data. But then commissions and even consumer advocates can use the same information and say, wait a minute, no, it isn't, let me share with you. And so it just enables different parties to use the same data and analyze it in different ways in a very efficient manner. It's not weeks of pouring through boxes. They're calling other jurisdictions and deposing people asking endless data requests. It's all right there and now, in a matter of a day or two, I can get what I need. And now I'm sifting through it and really thinking about the results of the analyses I'm getting and putting together my testimony one way or the other. Is that fair?
Speaker 2:That is fair.
Speaker 1:Okay, okay, that's interesting, all right. Okay, how does H data impact this notion of privacy and confidentiality In the world of energy and water and utilities? We'll talk about this in a minute. But we've got a lot of smart, this and that. We've got the internet of things. We've got historically confidential information that gets filed with commissions or environmental regulators. Is the filing of confidential information kind of a bit of a limitation or not? And then, how does this address other privacy issues that you may have, even if you're gathering data from international sources where there may be different privacy or confidentiality regimes? So your thoughts?
Speaker 2:Our platform only works with what's public we do. Our platform does have what I mentioned before as the TurboTax of utilities. That allows our customers, if they have to file forms, they can do it in our platform. We protect the information that is entered in there until it is filed. When it's filed, it becomes public. So there's a very big difference between information that is being entered into our platform preparing for a filing, that is protected in many different ways, and information that then, once the file button gets hit and it shows up crucial point it shows up publicly accessible on the regulators website. After that it can be used for anything at all.
Speaker 1:So if I am a utility using a consult, an engineering firm that does work around the world, if there is public information available in Australia, canada, the European Union, you could get that. Is that fair? If I am a regulator, if I am a utility, if I am a consumer, I would say, hey, I got this information from one of your consultants, where they did work in Sydney and this is what they charge. This is what they did, and we think your prices are a lot higher or what have you. Why are they doing it this way in this jurisdiction in America? Is that a possibility?
Speaker 2:It is a possibility. We are relying here on choices that we make as democratic societies. The choice that we make in the utility sector is that we are not going to have the government run power usually a lot of municipals and all that aside. We are not going to have the government run it. We are going to have private companies run it because we think that they might be better at it, and we are going to grant them monopolies in some cases on predicting your aspects of the value chain of moving electrons around, and then, in exchange for getting that monopoly, they are going to make money off of our rate payers.
Speaker 2:They are also going to need to fulfill transparency requirements. They are going to need to publish their reports, they are going to need to file their reports and let those reports be made public, and that is more transparency than a private sector organization might face. And when they want to charge more, they are going to have to go and ask for it in public and explain the reasons in public. We have already made those choices as a democratic society that publicity is going to go along with monopoly, in order to provide a restraint on the possible self-interest that a monopoly in the private sector is inevitably going to exercise, and so the technology is just going to make that work better. And if we discover that the transparency is too much, like you suggested earlier, matt, if we discover the transparency is too much, then I am sure there are countervailing forces that might try to reduce some of the transparency, and the way to do that is through the democratic process.
Speaker 1:Yeah, all right. What are the future? I'll call it reverse opportunities with customer data. We have a lot of activity on this Internet of Things discussion. We have smart devices in the home smart meters, smart toilets, what else? Smart refrigerators, smart thermostats. Is there the ability in the future for Hdata to take those data in some capacity and work them back into a particular utility's data analysis for filings with the commissions or public staff?
Speaker 2:We are going to maintain our focus on regulatory data, so we would not touch those sources unless the regulatory tour starts requiring the utility to report them.
Speaker 1:Okay.
Speaker 2:Your client you are able to get good at it, but by limiting that focus. That's how we're able to get good at it. By limiting our focus to the filings going up and the dockets coming down, we are able to develop a data store that is pretty comprehensive and we're able to build AI technology that is effective.
Speaker 1:And we achieve that effectiveness by limiting the scope, the tool is data source agnostic, where a utility could gather those data. Let's say I had a smart meter and I had 100,000 customers. I could gather those data and I could put it into the tool and have it help me run different analyses that might help me going forward with a particular regulatory filing. Is that fair? Is that a scenario that is out there?
Speaker 2:No, the structured data part of our platform is really aimed at the specific data fields that come up in the regulatory reports, and so there wouldn't be much of a head start there. On the unstructured side, our customers have a private catalog function in our regulatory AI, where our customers can upload any document they want and they can apply our regulatory AI to it. But we have not tested the regulatory AI on everything. We've only tested it, optimized it, on regulatory documents, and the reason for that is that we want to make sure to effectively serve the people that need to find summaries or insights, analysis or recommendations from this specific corpus of regulatory information.
Speaker 1:Okay, well, in 10 to 20 years, if we see your vision expand from energy into water, what does H data look like in the next 10 to 20 years when it comes to water? What are three things in your mind that you see for the future of H, data and water?
Speaker 2:If this were to happen, then the three things I would see are number one anything you have to file with a regulator or if you're regulated and you're working on a filing, information flows automatically from your source systems that you've already got to the regulator that wants them. You don't have to compile a form. The form just happens in the background, you sign off on it, but you don't have to write it. Number two if you are running efficiency comparisons or if you are trying to predict future rates assuming that the rates are formulaic then you have all those comparisons and formulas in H data. They just exist and you can go access them and you can watch how they change and you can set alerts in case they change a certain way In near real time. Well, we take 10 minutes to get there.
Speaker 1:Near real time. This isn't monthly or weekly, this is not even daily. It's you set us every 10 minutes.
Speaker 2:We're already doing that. Within 10 minutes of a form being filed, all the numbers go from that form into our data set and are reflected in our tools.
Speaker 1:It's like NASDAQ in a sense the ticker Wow.
Speaker 2:And then, third, one thing I haven't touched on this in this conversation so far, Matt that we're excited about is that I mentioned how the AI is really for the unstructured documents, and that is true. It is true for today, but within much fewer than 10 years, within one year, regulatory AI will be able to answer the structured questions as well. So you could say to regulatory AI tell me the return on equity of every utility in the American Southeast with revenue of over a billion a year, and it will draw you a table and the table will be accurate. It will calculate return on equity for you using numbers that come from the filings, and then you can click on the number and see where it came from.
Speaker 1:Wow. So if I'm a consumer advocate, I can use the function to say tell me what the problems are with this utility, or et cetera, et cetera. Where have things gone wrong? I'm kind of teasing, but it really seems to shorten and focus this whole world of utility regulation and it changes the world, accelerates, adds velocity and transparency to this thing that we've been dealing with for many decades.
Speaker 2:Other worlds have been shortened and focused already Since. Look at how the ability to invest in publicly traded companies expanded once online trading became possible. This didn't necessarily make it better or worse, just made it faster, and I do think it made it more democratic. We now don't have to hire a stockbroker, so anybody can invest in stocks. Like I mentioned, though good or bad, because we now have people investing in stocks in meme stocks that maybe shouldn't.
Speaker 1:Well, you're right. Anybody can be a movie producer now You've got YouTube. You can be an influencer on TikTok. You have apps. I don't have to go through a travel agent to reserve my seat on a plane. I can do all these things through advances in technology, and the utility world's a bit behind, but it's catching up. The energy side is probably moving faster than the water side. Those are some great ideas.
Speaker 1:Your first observation, I think, is noteworthy in that you have this near real-time regulatory system where you have the economic and perhaps in the future, the environmental regulator, knowing the challenges before you do. Almost they're seeing it real time. Uh-oh, there's an unpermitted discharge, or uh-oh, the sensor said that the drinking water's got a problem, and you don't have to wait to fill out a report or notify anybody and it will go not just to the regulator, it might go to the customer as well if those things are triggered, the reporting and things like that. Lots to think about with this tool, wow. Well, hudson, I want to thank you for being a guest today on the WaterForceSide podcast. It has been a privilege, a fascinating discussion. I probably have 10 questions right now, probably have another 10 after we're done, but you have really made us think about the future of water from a very unique perspective. How can folks get ahold of you if they want to learn a bit more about age data?
Speaker 2:You can find us at agedataus and it's pretty easy to find me on LinkedIn. There's only two Hudson Hollisters and I'm one of them.
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