In an era where technology seamlessly blends with daily life, artificial intelligence (AI) and machine learning are not just buzzwords but pivotal tools reshaping industries. The water sector, the critical component of our environmental ecosystem you know so well, is no exception.
At the forefront of this technological revolution is True Elements, a company that’s introducing the concept of “water intelligence” by leveraging the power of AI. So today, let’s dive into how AI and machine learning are not just modernizing but revolutionizing water management strategies, providing deeper insights and smarter solutions for a sustainable future. As Kimberly Nelson tells in today’s release of the podcast (you’ll find just here ⬇️): “We are just at the beginning of people starting to appreciate and understand the importance of this kind of information and intelligence.”
Let’s dive deeper…
with 🎙️ Kimberly Nelson – COO @ True Elements
🔗 Have a look at True Element’s website
🔗 Get to understand the Water Intelligence concept
🔗 Start you water stewardship journey with this interactive tool
🔗 Come say hi to Kimberly on Linkedin
is on Linkedin ➡️
Table of contents
- Full Video:
- The Transformative Power of AI in Water Management
- The Future of AI in the Water Industry
- What will you learn in today’s release of the “(don’t) Waste Water” podcast?
- Full Transcript:
- Rapid fire questions:
- Other Episodes:
The Transformative Power of AI in Water Management
The integration of AI in water management marks a paradigm shift in how we approach this essential resource. In the world of water, AI is much more than a technological upgrade; it’s a necessary evolution to meet the complex demands of our times. AI’s capability to process and analyze vast amounts of data from various sources, including IoT devices and sensors, translates into enhanced operational efficiency and decision-making. This ranges from real-time monitoring of water systems to predictive maintenance, significantly reducing wastage and optimizing resource utilization.
Moreover, AI’s role in water quality management cannot be understated. By employing advanced algorithms, AI assists in detecting pollutants in water bodies, ensuring the availability of clean water. This is crucial in a world where water scarcity and pollution pose significant threats to both human health and environmental sustainability. True Elements harnesses this transformative power of AI, offering innovative solutions that not only address current challenges but also pave the way for a more resilient future in water management.
As we delve deeper into the specifics of True Elements’ contributions and the broader impacts of AI in this sector, it becomes clear that we’re witnessing a significant leap towards smarter and more sustainable water strategies.
True Element’s Unique Solutions and TrueQI Score
True Elements stands out in the realm of water intelligence with its cutting-edge approach to environmental data aggregation and analysis. Their strategy is a significant leap in how we manage and understand water resources. Central to their offerings is the innovative TrueQI score, a quality number assigned to key aspects like drinking water and surface water. This proprietary scoring system exemplifies the company’s commitment to providing actionable insights into water quality, empowering decision-makers with reliable and comprehensive data.
The TrueQI score is more than just a metric; it represents a nuanced understanding of water intelligence. By integrating diverse data sources, True Elements offers a holistic view of water resources, crucial for effective management and environmental protection. The company’s technology and methodologies reflect a deep commitment to innovation in the water sector, aligning perfectly with Kimberly Nelson’s vision: “For those people who say we don’t need new innovation, I say then get out of the way because we live in a world where there will be innovation.”
Case Studies and Real-World Applications
The practical application of AI in water management is vividly demonstrated through various case studies and real-world examples. One such example is the collaboration between True Elements and the Waterkeeper Alliance, showcasing a model for strategic environmental protection. This partnership exemplifies how localized knowledge, combined with advanced AI technologies, can lead to significant improvements in water resource management.
AI’s capabilities extend beyond collaboration; they’re evident in projects focusing on digital flood simulations, drought monitoring, and water quality improvement. Organizations globally are utilizing AI to create more resilient water management systems, capable of handling both current needs and future challenges. These real-world applications underline the critical role AI plays in not just managing but safeguarding our most precious resource: water.
The Future of AI in the Water Industry
As we gaze into the future of water management, the potential of AI and machine learning is limitless. AI is set to play a crucial role in evolving water strategies, ensuring sustainability and addressing the pressing challenges posed by climate change. True Elements, through its innovative approach, is at the vanguard of this evolution. The company’s commitment to leveraging state-of-the-art technology for water intelligence is a beacon for the industry, providing insights crucial for protecting and preserving water resources globally.
Kimberly Nelson’s vision for True Elements and its partners echoes this sentiment, aiming to be indispensable in their journey towards smarter water strategies. As AI continues to advance, its integration into water management will not only improve operational efficiencies but also foster a deeper understanding of water as a vital resource, driving the industry towards a more intelligent, efficient, and sustainable future.
What will you learn in today’s release of the “(don’t) Waste Water” podcast?
In the rapidly evolving landscape of water management, AI stands as a game-changer. This exploration through the lens of True Elements and insights from the water industry underscores the transformative impact AI and Machine Learning have on crafting smarter water strategies. As we’ve seen, these technologies are not just about efficiency and sustainability; they represent a new frontier in understanding and managing our most vital resource. The journey of AI in water management, as highlighted by True Elements’ innovative approaches, demonstrates a promising path towards a more resilient and sustainable future.
In embracing AI, we’re not just adapting to new challenges but also shaping a world where water is managed with the wisdom it deserves.
(by the way, if you want to dig further, have a look here once you’re done with today’s release!)
These are computer-generated, so expect some typos 🙂
Antoine Walter: Hi Kim, welcome to the show.
Kimberly Nelson: Good morning Antoine, it’s a pleasure to be here.
Antoine Walter: Maybe we have to give a bit of background here. We have a very scenic backdrop behind us.
Can you tell me what’s the link between your company and Waterkeepers, which is very, very gentle to lend us their headquarters for that interview?
Kimberly Nelson: Yes, we are very fortunate to be in this lovely location. Waterkeeper Alliance is a global organization with over 300 chapters around the world, on all continents, and, uh, their goal is to, um, bring together volunteers to help, uh, protect and preserve the local water bodies in their community.
Could be a river, could be a bay, could be a lake you’ll find around the world. Local waterkeeper chapters. Sometimes they’re called bay keepers, river keepers, but uh, yeah, millions of volunteers around the world. What’s the link to True Elements? So the link to True Elements is we are in a strategic partnership with Waterkeeper Alliance.
We announced that in the spring, actually about a week or two before the UN Water Week that was here in New York City. And through that global partnership with Waterkeeper Alliance, we are working with one of their local chapters to use our data to better inform. The local volunteers, and then simultaneously as they collect data, they do sampling, they’ll add their data to our, our system.
Antoine Walter: We’ll get a bit more in the details of what True Elements actually does, but I’d like to understand your path first. When I looked it up, I don’t want to be the bad behaved European here, but you have quite a career behind you, and you’ve been at the intersection of IT and environment, and I would say even long before all of that was even a thing.
Yeah. What’s the big learning you had along that journey and what is the red thread along that journey? Yeah.
Kimberly Nelson: So, um, I have been very, very fortunate in my career. I started out, uh, in state government in Pennsylvania, just south of where we are here and spent 14 years in the Pennsylvania department of environmental protection.
And in the early part of that career, I had the good fortune of working with some managers who wanted. Better information for managing the agency. How did we know if we were making an impact on the environment? We’re spending billions of dollars in taxpayer dollars. Were we making a difference? That was way back, actually, before my first daughter was born, and she’s 33.
Started working on how do we collect the right information to understand whether we’re making the impact we want. Did that for many years at Pennsylvania Department of Environmental Protection because of some of the groundbreaking work we actually did it at Pennsylvania DEP. I was recruited to go to EPA and to take an assistant administrator position at EPA for the Office of Environmental Information.
Again, that common thread, right, environmental information, and spent five years there trying to make good information available to the public in terms of what we were doing at the federal level. Protecting the environment from there. I went to Microsoft for 16 years was a great job fabulous job It was sort of a once in a lifetime opportunity and I worked with government leaders there on digital transformation but I never had the opportunity to really drill deep on environmental issues there and I knew three of the co founders for True Elements.
I’ve been following their career and some of the other work they did. And a couple of years ago, as they launched this company, True Elements, which is a water intelligence company, they shared with me what they were doing. And it just so resonated with me, given my early environmental experience. A year and a half ago, I decided to make the leap and leave a great company like Microsoft to become chief operating officer for True Elements and to help get this company off the ground and introduce the concept of water intelligence.
Antoine Walter: Actually, you say take the leap, but it really is because you’ve been working on the administrative part of things. You’ve been working on a very big group like Microsoft, which is one of the largest in the world. And now you’re in a startup. How different is it? And how much of a clash of cultures is it for you?
Kimberly Nelson: Oh, you know, it’s almost like night and day at times. So real. Leaving Microsoft with 160, 000 people and all the resources at your disposal, the big name of Microsoft, and going to a brand new company. A company a year and a half ago almost no one ever heard of. And, you know, about a dozen people, so you can just imagine.
But I will tell you the thing that is fascinating is this issue of water has become so important. And getting water Data and information and intelligence and insights into the hands of right people. Even this little tiny company, we are able to get in and have conversations with some of the biggest companies in the world.
Antoine Walter: You tease now the term of water intelligence a couple of times. I think it’s time we define it. What is water intelligence?
Kimberly Nelson: Yeah, that’s a really, really good question because if you looked it up, you probably would not find a definition for water intelligence anywhere. And I really think we’re the ones making that possible.
So water intelligence is really a unique combination. of using state of the art technology, scientific analysis, artificial intelligence, and machine learning, so state of the art tools, combining those with lots of data. And by lots of data, I mean we aggregate data from many, many, many different data sources.
We normalize that data. Because when you bring data together from different sources, you have to normalize it. We process that data, and then we make it visually appealing to people so that you can see the information in a geospatial format, so that you can see the information and it’s easier to understand.
And by that, I mean, scores like 70 to 100, red, yellow, green. And by doing this, by taking vast amounts of data, using state of the art technology, visualizing it, making it easier to understand, that means we’re giving decision makers, whether in business, in government, in non profits. Insights they need to make the best possible decisions to protect and preserve water resources around the world.
Antoine Walter: What is the status quo? What is that replacing?
Kimberly Nelson: We live in a world of what I would call, it’s a, it’s a tale of two data worlds. On one hand, you have a situation where there are dozens and dozens and dozens and dozens of different data sets available. From the federal government alone here in the United States.
There are about 30. federal agencies that have water data scattered among the federal government. Within those agencies, there are close to 60 different information systems that have that data, and over 550 different data types, okay? So, can you imagine being a single individual and you had a job to do?
How would you ever go and find all that data? Bring it all together and then try to make sense out of it because it’s really apples and oranges and peaches and pears, right? I mean, there are no data standards. So it’s almost impossible in that sense. That’s the status quo today. If people want good water information, they have to go to many, many different places to get it.
That’s hard. The other thing they might do is maybe they’ll just go to one place and say, Oh, there’s an organization out there and they have, they have a system and we’re going to, we’ll go there and use that, that, that data. Well, it’s a little bit about like touching just one part of an elephant, if you will, you know, sometimes just getting one perspective is not giving you the full picture of what’s happening out there.
So the status quo today is people are either overwhelmed, there’s way, way, way, way too much data. Or they may go to one system and get one perspective, but that perspective may not always be the most accurate perspective.
Antoine Walter: So you’re trying to consolidate that data. And I’d like to go into the depth of that in a minute.
But before that, I’d like to define your playground. Where do you act? Is it the watershed? Is it a community? Is it a building? What is it?
Kimberly Nelson: Excellent, excellent question. We can drill down from a very high level and get to a very discreet level. Within the United States, for instance, we can provide information at what we call the hydrologic unit code 12, HUC 12 level, right?
That’s about a 4 to 5 Ks perimeter or square area, right? And all the information we would provide at the watershed level, we can then build up and build up at a higher level, at a basin level. But people generally need information at a much more discreet level. To be really useful, you have to get more and more granular.
For instance, one of the things we’re doing right now is Yes, we have information at the HUC 12 level, watershed level, but now we’re starting to do information at the stream reach level, getting much more discreet. And the difference is there are 83, 000 HUC 12s in the United States today. We’re starting to do analysis at the stream reach level, and there’s 2.
7 million stream reaches in the United States that we’re doing analysis for. So that gives you a sense. of how discreet we’re starting to get, to look at water stress and to understand what’s happening in much more specific geographic locations.
Antoine Walter: You mentioned the dozens of different sets of data out there which are describing that nature.
Is that your main source of information you consolidate or do you have additional ones?
Kimberly Nelson: That is generally the main source of information that we, we use. The federal data sets, the government data sets, the data sets from non profits that are made publicly available. But what we can also do, now keep in mind, it’s more than just making data available, right?
What we do then is we have our unique algorithms that we put on top of all this data. To do things like create scores and to do forecasting and predictions. It’s more than just putting all the data sets in one place. But yes, they, those data sets are our primary base for, for the work that we do. However, as we start to work with organizations, for instance, here working with Waterkeeper Alliance, we can take data sets from individual customers, companies, organizations that they may have and incorporate that as well.
And we can blend that in with our data. So that we can still apply our algorithms to it and our forecasting to it with the data they have.
Antoine Walter: Still on this data set, and then I’ll leave you alone with that. I have two questions. The first is how exhaustive is that data? Because last year on that microphone, I discussed with Nick Shufro.
Kimberly Nelson: From the Frima, so federal emergency management agency. Yes outstanding
Antoine Walter: and he was explaining how
Kimberly Nelson: if you think about the united states And all of our territories and alaska and hawaii. We have about three million miles of waterways coastal river Lakefront, we are going to map all of those coastal and riverine areas.
We currently map about a million miles, and those are really focused on densely populated areas. And what we’re trying to do is trying to give people an idea of where there’s risk.
Antoine Walter: How do you deal with fragmented information and non exhaustive pieces of data?
Kimberly Nelson: We live in a tale of two data worlds. Right.
One is too much data. The other side of that is we don’t have nearly enough data for just as one of the reasons you described. Many of the data sets that are available out there are fragmented or incomplete. The other side of it is we don’t have nearly enough real time data. We bring data in from 1.5 million sensors in the United States.
Some belong to Noah. Some belong to USGS, but that’s not nearly enough. Because when you look at the data sets we have, even if they’re complete, they may only get updated once a year. They may only get updated every six months. Maybe they get updated six months after an event. So we really need to get to a point where we have more real time data.
from sensors that are out there and as sensors get cheaper, much, much less expensive, the technology evolves and the ability to connect directly to the internet through IOT like devices. That’s the world we want to be in.
Antoine Walter: My second question on the data set. And then I promise you I’ll stop with that. How private is that information?
Are you able as a private company to Play around with it, or are there some rules and some walls you cannot
Kimberly Nelson: The data sets that come, for instance, from the federal government are all publicly available data sets. They’ve been anonymized and we can do anything we want with those. As we start blending data from our customers, we always ask them that question, that this is your data and you’re in charge of that data.
If you want to make that data publicly available, for instance, we have the capability to provide a direct link into EPA’s water quality exchange, so we can provide the data to EPA, and they can use it, and they can make it public as well, or we can make it public, but we always give our strategic partners the choice whether, as they’re collecting data, they want to make that public or not.
Antoine Walter: So now you have the best possible set of data, and I guess that’s where True Elements Magic comes in. So what’s the first step? What do you do on that data and how do you extrapolate based on that data?
Kimberly Nelson: I don’t think we’ll ever be at the point where we have the best possible set of data. That would, that would be a dream.
I’m not sure we’re ever going to be there. The point is we have to use what we have and it might not be the best, um, today and a year from now it might not be the best or two years from now, but the fact of the matter is it’s all we have. So that’s what we have to do. So to your point is one of the things we do is we collect all the data and then we identify where there might be gaps or holes in that data.
So that the cleaning. Yeah, through all the cleaning process. And so this is a little bit where some of the magic comes in, where things like artificial intelligence come in, where you can sort of impute certain values. If you know certain things and there might be a data gap, you can say, Hmm, based on everything we know, we might put a value in there.
That’s equal to this. We’ll do things like that with our sensors and with some of our scoring. For instance, we don’t have sensors every place we want to have sensors. So we can create something we call. Those sensors, artificial sensors, and what we do with those artificial sensors is say, hmm, it’d be nice if there were a sensor here, there’s not, so why don’t we look at all the data that we know that currently exists in that particular location and compute what we think the score would be based on everything we know.
Even though there’s not a real sensor there that’s taking measures. We’ll do things like that. And that’s, that’s where some of the magic comes in. That’s where some of our unique capabilities come in. Our intellectual property. That’s one thing we do.
Antoine Walter: Are you using modeling as well? And if yes, does it come before or after the AI step?
Kimberly Nelson: We have our own algorithms and models that we use. to create, uh, the information that we’re presenting, the scores and the predictions. If we look at a watershed, we’ll provide all the information somebody we have about that particular watershed. What kind of industrial facilities are there, what kind of wastewater facilities, what’s happening from an agriculture perspective in terms of crops that are planted and things like that, stormwater, any real life sensors that are there.
So, we’ll provide additional information about what do we know about. of their facilities in the area. Like, are there super fun sites? Are there sites that have, uh, indicated there’s some PFAS contamination there? So lots of information about the watershed, but then some of the modeling and forecasting comes in when, for instance, if we want to look to what the future might hold for that watershed right now.
We might put a score of water quality on, on that particular watershed of maybe an 89, but as we look to the future and we bring weather data in and we start doing some predictions and models about what might happen in that watershed, we might put a different score on it because maybe an extreme storm weather events going to happen that would result in a lot of runoff, stormwater runoff, agriculture runoff, more phosphorus, more nitrogen that might degrade the quality of the watershed.
So yes, we do that kind of unique modeling.
Antoine Walter: You mentioned the 1. 3, 1. 5 million sensors, how much was it?
Kimberly Nelson: There are 1. 5 million sensors that we use today, yes.
Antoine Walter: With your software sensors, artificial sensors, or whatever we want to call them, what’s your lever effect? How many can you create? How many nodes can you add in the system?
Kimberly Nelson: The 1. 5 million sensors that are out there today that we’re using, the data we’re collecting from those sensors, they’re not our sensors, they belong to the federal government. Geologic Survey or NOAA. We’ll collect the data from as many sensors that are out there. Some private companies put their own sensors out there for their own property and so when we work with them we’ll bring their data in as well.
But I think the sky’s the limit when it comes to the potential number of sensors out there that could be placed out there.
Antoine Walter: Let’s use an example. We have the East river, if my geography of New York is right, just behind us, probably it has some turbidity sensors, COD sensors along the way, which are placed by, by federal agencies.
You could say, Oh, just under the Harlem bridge. I want to know what’s the TDS level. And then you would put a soft sensor here. And then based on the real sensor placed left and right, plus meteorological conditions, plus what you would know about maybe another wastewater stream sensor of the network of New York, you could then calculate that.
So that’s what we’re discussing here, right? Yes, that is exactly correct. Yes. Coming back to, to, to what True Elements does. So you have data, you clean the data, you bring that AI modeling layer on it. Now you have a full, even expanded data set, but you need to bring it in a shape so that people understand it.
Yes. How do you do that?
Kimberly Nelson: That’s part of the magic is first putting it all in a geospatial visualization tool so that people can look and zero in on a zip code or an address or a. A watershed and be able to see something that they recognize the place they live or something like that. And then after that, it’s, uh, making that information easier to understand.
So we put a score on it. We might do red, yellow, green, but we always then allow the person to drill down. Somebody might just be, you know, talking about generally. They’re just really interested in a score between 70 and 100, and if they know that’s in the 85 range, that’s good enough for them. But other people will want to drill down and say, Well, tell me how you got to a score of 85.
What were all the factors that went in to creating that score? And then we can give them the ability to drill down and say, these are the things that resulted in a score being degraded. If we’re talking about drinking water quality, for instance, one of the reasons why some areas might have a lower score for drinking water is because of the disinfection byproducts that are used.
And that might hit the upper limits that are allowed. They’re maybe within the legal limits that are set, because there are maximum contaminant level limits. So they may be within the legal limits, but they’re bumping up the top of the limit. And disinfection byproducts can be very harmful to the health.
So, this drinking water might have a score of 85 for that reason, but we tell people, you can look at it, it’s 85, but if you want to know why it’s 85, you can drill down and see exactly where we deducted points to get to that score.
Antoine Walter: And to which of these GIS systems do you integrate? Is it like anyone at home could then check your data because it’s integrated in what they do?
Or is it more for the utility and their central SCADA or whatever it is?
Kimberly Nelson: Right now, our capabilities are not consumer facing. Although we have made data available like drinking water data, we have made available to a strategic partner called the Republic group. They have posted our information on their governing website so that at the state and local level, anybody could see that information.
Primarily, our audiences are more enterprise customers. We’re not really a consumer facing organization at this point in time. Everything we have is built on Mapbox, but Mapbox can easily integrate with other tools. So if another customer, for instance, happens to use the Esri software, ArcGIS or something like that, we can integrate with them.
Antoine Walter: So the ambition is. To start with a hardware layer, which is not yours, but which you understand and complete. Correct. Build all the incredible, what you defined as magic, in between, which is this brain gathering the data. And then on the top of that vertical, again, integrate into whatever is existing, because you don’t want to compete with S3, I guess.
Kimberly Nelson: One of the things I learned very early on in my career, one of the things that I worked hard at when I was in the Department of Environmental Protection was you don’t want to create new silos of data for people. Makes sense. Right? I mean, what we’re about is eliminating the silos, bringing all the information together in one place.
So we don’t want to then create. for one of our customers, a new silo of data for them. So if we can integrate what we do into an existing system that they already have, they already have some kind of operational system, some kind of dashboard, we can certainly work with them to build what we have into their dashboard.
Antoine Walter: That is a field where I think everybody would agree that there is a huge need that it brings a tremendous value. The other face of that same coin is that. Many companies are now exploring that field. Do you see that competition as a very good sign because it validates your thesis that there’s an interest?
Or is it a threat because, yeah, it’s a race?
Kimberly Nelson: I think your question is, as we look to this market around digital water and water intelligence, yeah, what does that look like? The good news is, and somebody made this comment yesterday, at the conference you and I both attended, the Science Water event. This is water’s moment.
For far too long, people have been focused on greenhouse gas emissions and carbon tracking, but 90 percent of the impacts of climate change are water. And that means in the form of drought, in the form of sea level rise, riverine flooding, extreme weather events of which we’ve seen over a billion dollars worth of extreme weather events in the last year here in the United States and just this year alone in 2023, this is water’s moment and there is It’s an emerging market for water data.
There will be competitors out there because this is an emerging field. There are people who do parts of what we do, but what we’re doing at True Elements is pretty expansive across drinking water, surface water, groundwater, current, and future. That part of it’s unique. But clearly, there’s a market out there for water data and providing people with the information to make the best decisions.
Antoine Walter: Talking what’s unique, if you have to define just one special source for True Elements, what is it?
Kimberly Nelson: We’re a software as a service company, and I’ve been told in the last Last month, three people have told me, who are very knowledgeable in this space, what True Elements does is so unique because you’re dealing with all varieties of water.
You’re dealing with drinking water and surface water. Most of the other companies out there, you’ll see they might be dealing with other issues around extreme weather events, risk, surface water. Uh, but, but, uh, I’m not aware of anybody that’s going across the board and dealing with a full spectrum of
Antoine Walter: So the full spectrum, that is really the killer element.
Kimberly Nelson: When you think about it again, all water is connected. Our drinking water comes from places like this beautiful river. To not provide the full perspective on water, all kinds of water, drinking water, surface water, groundwater, and all of it is a little bit limiting when we want to be a water intelligence company.
Antoine Walter: You mentioned software as a service, which gives me a smooth transition to your business model. So What is your business model?
Kimberly Nelson: Our business model is, we are a software as a service company. It’s a very easy subscription service for any one of our capabilities. And we have different capabilities, you know, a person wouldn’t have to acquire everything that we have.
But, um, just as importantly. Our business model is we want to be strategic partners. This is not just a consumable kind of SaaS offering. This issue is far too important in terms of the future of the world and water, and we like to work in a strategic partnership with our, instead of customers, right, with our partners so that we’re really addressing the most compelling issues that they’re trying to face.
That might involve a little bit of custom development for them to ensure they get what they want on top of our, our SAS subscription. But we want to make sure we’re working with them to deliver exactly what they need to address the most compelling problems out there.
Antoine Walter: I feel there’s an importance of semantics here.
You’re not doing water digitization. You’re doing water intelligence. You don’t have customers, you have partners. So I guess there’s, there’s some nuances here. Can you share an example of a rollout so that we get how you work with these partners?
Kimberly Nelson: Typically, like most things in life, we start out on a smaller scale.
We will look at when we’re working with one of our partners that this is a journey for them. They’re going to, no pun intended, to They’re toe in the water a little bit to learn a little bit about water intelligence and how they can use it. We’ll start out, um, with an early implementation. I don’t like to call them pilots.
I think pilots have a bad connotation, but we’ll start out with some early implementation for them. Here with Waterkeeper Alliance, for instance, we’re starting with one of their most important areas in Milwaukee, but with other customers, we may be starting off. with just doing a high level risk assessment for them, looking at all of their facilities across their portfolio and saying, here’s what we determined to be a risk assessment for you.
And that risk assessment will cover things like what’s the drinking water like in your location? What’s the surface water like? Do you have contaminated facilities near you? Those kinds of things. And there’s at least a dozen different indicators we can use. But once they have that information, then what if they need to do something with it, right?
Now they have this kind of insight. So we might drill deeper with them and we may take them further on that journey of saying, well, let’s look deeper into some of these, right? Maybe if you gave us 100 facilities, let’s look at 10 of those and let’s go deeper. Let’s take a look at that watershed and understand really what’s happening in that watershed.
What do you want to do in terms of that watershed? Do you want to embark on a public private partnership, a collaborative effort with people in the community to start cleaning up the watersheds, maybe start protecting and preserving it more in some way? I mean, it could be any number of things. And, and to a large degree, that’s their decision, but we’ll help them create the baselines for the measurement and understanding whether they’re getting a return on the investment.
And then we may go forward. further with them, you know, the next part of the journey may be, we’re really looking strategically as a company here or as an organization, what is the future going to hold? As we look 10, 20, 30, 40 years down the road out to 2100, are we a company that can continue to operate in this part of the world?
If we want to deliver high quality products, we look at it as a journey.
Antoine Walter: Some weeks ago, I had a conversation on the microphone with Jennifer Muller Guland, an absolute powerhouse when it comes to water security, water risk assessment. She believes that there is a full evangelization still to do in that market.
Because lots of companies are still not so much aware. I mean, they’ve heard of water. They don’t realize yet how much of a risk it is. And so they are not at the stage where they spontaneously assess it. So if you give them the tools to assess how future proof they are, because building microchips in Colorado might be at risk on the long run.
When you come in, are they fully aware and then you just have to give them the tool or do you have to start by, Hey, you know, that is water, that is the water risk. Maybe you shall look into it and do you as well, this kind of evangelization.
Kimberly Nelson: I think like everything else in the world, you’ll speak with leaders and companies or organizations or government entities out there.
Some of whom are further down the road in terms of understanding these issues and others you have to bring along. I don’t like to pigeonhole people in any one area. But I will say I do think we are just at the beginning of people starting to appreciate and understand the importance of this kind of information and intelligence to create the insights for them.
I think there’s a lot of room to go in the future in terms of people embracing. These capabilities, some of them, I’m not even sure that these capabilities are real today, right? Because they haven’t been exposed to them. It’s a little bit of a wondrous thing if once they see it, and we’ve had that happen.
We’ve had customers look and say, wow, you you can do that at that level. That’s That’s pretty amazing. They hadn’t seen that before.
Antoine Walter: Who are your people? I mean, who are your not customer partners? I get it. Yeah. Is it like the super, super, super well aware, which are looking for something or is it more like this second category with this other type of companies which are, yeah, they know they might have to do something, but they have no clue where to start.
Kimberly Nelson: Yeah, certainly now. The conversations we’re having are among the leaders in their industry. There are the ones that are setting the trends. There are the ones that are paving the way in terms of recognizing as an organization, it’s important to their future to, to start addressing this issue. And it might be in government, it might be in business, but they’re, they’re the leaders.
These are the people, as we say, on the tip of the sphere who are, are really. paving the way for others.
Antoine Walter: Coming back to your product, how is it called TrueQI, TrueIQ?
Kimberly Nelson: True Elements Water Intelligence. We use the term TrueQI. TrueQI refers to our scores. It’s a quality, uh, number that we assign. So we use our TrueQI score and that’s where we put a score on things like drinking water and surface water.
We look at surface water from industrial area, from industrial waste water, storm water, and agriculture. So anytime you see a or like an assessment risk, we call it true QI.
Antoine Walter: I was wondering because QI in French is IQ in English. And I was thinking, you know, water intelligence IQ makes about sense, but no, it’s QI.
Kimberly Nelson: Okay. And it’s, it’s, it’s a little, it’s a little spin on water intelligence, but it stands for, yeah, you picked up on it. Very good.
Antoine Walter: You explain how you’re. Partners, I’m trying to use your terminology, can do some customer development and then build upon your pieces of software. Does that mean what you deliver is fully standard and then they do the customization or would you do the customization as well?
Kimberly Nelson: So we would work with our partners to identify where we might have some gaps in their needs, and then getting their feedback. We would. fill those gaps, and we would do the development work to fill those gaps. What that does is that enhances our platform in terms of capabilities. If we deliver something for that particular partner, but we also incorporate that into our platform going forward.
So, you know, if you just think of some of the software tools you use today, like if you use a Microsoft Office suite, the capabilities that are in Word today, probably 80 percent of them didn’t exist 10 years ago. or 20 years ago. So there will always be an evolution in our product as we add new features.
Antoine Walter: Yet the difference here is that Microsoft obviously has a huge customer base, which means that they can really kind of do some stats and look what is really needed. Whereas you’re super dependent that you’re working with the right partners right now, that you don’t get trapped to develop stuff for a full market of three companies.
Yeah. When maybe There were other stuff which were needed by others. So how do we ensure that
Kimberly Nelson: Well, I think that that’s very fair, you know. We ask ourselves these questions. We might get some requirements from a customer and say, Does it make sense for us to invest in that? Does it make sense for us to do product development in that space?
Is that where we want to go with our roadmap? That’s exactly the kind of decisions that have to be made internally.
Antoine Walter: Talking of these resources I guess at that stage of the development of a company, you’re probably not profitable, right?
Kimberly Nelson: That’s fair to say, yes. We are a startup, yes.
Antoine Walter: So does that mean you need to get through fundraising?
Do you have active plans for that? At what stage of the company are you and how do you envision the future?
Kimberly Nelson: Yeah, we are in a series A round right now. We have a network of investors in Naples, Florida that have been very generous in terms of getting the company to where we are. But we are in a series A round now.
Antoine Walter: The reason why I’m asking is from the profile of your investors, you will have to adapt the path of your company. If you are backed by venture capital, you will have to go through hyper growth. If you are backed by different types of money and pockets of money, then you might have a bit more time to build it up.
So what is your intended path? Do you think you can take like the market by storm and go hyper growth and, and bring that to the free US?
Kimberly Nelson: This is a really important.are in terms of understanding climate change and understanding water and what’s happening? And it may be water’s moment, but some people will tell you, you know, we’ve been looking for water’s moment for a long time. I think it’s fair to say our most important goal is to be aligned with somebody who understands this market. That we want to be mission aligned with somebody who understands the impacts of climate change and understands the seriousness of the conditions the world is facing.
As we look to investors, we want investors who are mission aligned. That’s very important to us.
Antoine Walter: You mentioned very rightfully how Water’s moment has been announced several times and we all true heartily believe that’s the moment, but we might still be wrong, but that’s not my question. My question is the ones which have been riding what they thought would be a wave 10 years ago now start to come back a bit to the new companies and say, you know, the challenges.
really need so much new stuff. Technology exists and could solve everything. We don’t need artificial intelligence because we have hydraulic modeling since the eighties. We don’t need a fully agnostic and bringing all the data together because We have GIS systems. So it might be about time to adopt tech, which is around for 15 years and not push new stuff out there.
Not giving you my personal opinion here. I’m just stating what I’ve heard and read over the past weeks, which kind of surprised me also to be, to be fair, what would be your take on that?
Kimberly Nelson: I would simply say we live in a world where there’s constant innovation. And for those people who say, we don’t need new innovation, I say, then get out of the way, because we live in a world where there will be innovation.
All we have to do is look back in history. And for people who say it’s good enough, I’d say, no, there, there are opportunities to do better.
Antoine Walter: If we look in the future, what is the horizon? at which you’re projecting true elements. Is it five years, 10 years, 20 years, 50 years? What is like the vision of the company?
If you, if you,
Kimberly Nelson: I think that’s a little bit hard to say. I, I envisioned a company that’ll be around 50 years from now. I’m sure it will. I won’t be working there.
Antoine Walter: My question is. Uh, when do you want to have an impact? Because it’s not, I mean, if you’re just starting out, it’s not realistic to think that you will have a worldwide impact in six months.
But your ambition is probably to be at a certain type of impact in a certain type of time. And what is that?
Kimberly Nelson: Well, I guess it depends on how you measure impact. I do believe we can. How do you measure impact? I, I, well, I believe we can have an impact in the next year to, to 24 months in terms of, it might be a smaller impact, it might be individual, uh, locations, but, uh, hey, we’re, we’re in this to have an impact.
That’s why our founders, I, we have three founders who are very passionate about this. And we’re here to make a difference and we’re going to make a difference sooner rather than later.
Antoine Walter: But if you have just one metric for impact, what is it?
Kimberly Nelson: I would like our partners to be able to say after we worked with them, we could not have done what we did without you.
Antoine Walter: That’s a good one. If you look down the line, let’s say 10 years, you look back, okay, over the past 10 years we’ve achieved what?
Kimberly Nelson: We have a global. network of partners around the world who are using water intelligence to make the best possible decisions to protect and preserve our resources.
Antoine Walter: That is a super interesting one around the world.
Yes. Because bringing together the administrative mess of a country is already something. Yes. But if you start adding to that, that you need to have new layer of policies, understanding of the rules of Who does what in different countries. You really want to take the word
Kimberly Nelson: I know from working at Microsoft that, uh, going global carries with it a great deal of complexity because of the different standards and requirements in every country.
But if we really wanna make a difference, yes. True elements will be a global company in the future. There are
Antoine Walter: 40 un bodies with a mandate for water. Mm-Hmm. . And that’s just the un Yes. And then you have. Each country with, I mean, I can give you a data example. In France, 10 years ago, they took a regulation about micropollutants and the measurements of micropollutants.
Micropollutants needed to be measured in the outlets of wastewater treatment plants. They did that for three years. And after three years, they just removed their regulation simply because they were analyzing stuff. taking the data, putting it in Excel table. That was it. They could not cope with it. It was too much data and they had no clue what to do with it.
So you come in and you’re like, Oh, now I have a data set from 2012 up to 2015 of micropollutants and I need to integrate that into my TrueElements dashboard. That’s a crazy Sisyphus work. How do you do that?
Kimberly Nelson: Well, we’re not doing that. Historically, and I’ve been involved in environmental information for many, many, many, many years, it is not unusual for people to collect vast amounts of data and never analyze that data.
One of the biggest examples was years ago when I did some smart city work, was every building that gets built today has an incredible system of sensors in it, right? And a tremendous amount of data. And it was always shocking to me how much data were collected about buildings and building performance that no one ever looked at.
So, the situation described is not at all unusual. The trick is, that was my point in the beginning when I talked about the tale of two data worlds. Sometimes there’s just too much data out there. It’s overwhelming for people. And we, that’s our mission, is to make it not overwhelming. To find the data that’s useful out there, to put it in one place, but more specifically, it’s not about the data.
It’s about creating the water intelligence that allows them to have insights for decision making. So it’s the forecasting, it’s the analysis, it’s the scoring. That’s what’s critical. If those, if somebody had figured out how to do that. Back then, in the situation you were talking about, those microplastics, they’d still be collecting that data today.
And it would be useful probably, and maybe there’d be a reduction in microplastics. Very true.
Antoine Walter: Yeah. There are like dozens of segues that could open from there, but I need to be cautious of your time. So I propose to switch to the rapid fire questions to round it off.
Rapid fire questions:
Antoine Walter: What is the most exciting project you’ve been working on and why?
Kimberly Nelson: We’re sitting right here in Waterkeeper Alliance and I’m just going to say, I think that is the most exciting one because going back to the discussion we had earlier, they have almost 350 chapters around the world and the ability to work with volunteers, people who are volunteering out in their community, their local community and have this desire to make a difference.
That to me is very exciting.
Antoine Walter: What’s the thing that you learned the hard way?
Kimberly Nelson: You have to connect the dots for people. We could have the greatest platform out there, but if we don’t start connecting the dots for people in terms of this is how you actually use it and started creating some products. If you go to our website today, you’ll see we have sort of product categories and we had to do that because not enough people knew how to connect the dots between all this.
Even though we made it easy, we still had to make it even easier. You have to make it as easy as possible and connect the dots to a Very specific application or use.
Antoine Walter: Is there something you’re doing in your job today that you won’t be doing in 10 years?
Kimberly Nelson: I won’t be doing this job in 10 years.
Antoine Walter: Fair one.
There we go. Yeah. What is the trends to watch out for in the water industry?
Kimberly Nelson: You know, we just spent a whole day talking about this yesterday, but I think, I think the biggest trend has to to be. This is an industry that is getting so complex. If you look back on the history of water, people drilled a well and they took water out of the ground and they used it.
They used it to drink and cook and bathe and that was it. We’re dealing with such complex issues today in terms of drinking water quality and surface water quality and the impacts it has on human health. Availability of it, even if it’s available, is it? quality water. The trend is going to be this is an industry that will just get more and more sophisticated, even though it hasn’t been sophisticated in the past, in terms of the use of technology.
Antoine Walter: I agree, hopefully. If I instantly became your assistant, so you have the opportunity to delegate whatever you want to me, what is the first thing you would delegate, knowing that I never said I would do it?
Kimberly Nelson: I would love to have one conversation on my calendar every day. for an hour with somebody who is an expert in their field.
So I would ask as an assistant to find me who are the most influential people in their spaces and get me an hour conversation so that every day I could have a one hour conversation with somebody and pick their brain and learn.
Antoine Walter: That’s a very good one. I would have to to to follow up on that one. The first is you should start a podcast That was my trick to speak one hour with people which is super interesting see what we’re doing And and the second is you should listen to my podcast because then you cannot ask the question yourself But you can pick the answers so I will do that It is on my list now.
It was a shameless plug. Last question, we have somebody, someone that you would recommend me to invite on that microphone as soon as possible.
Kimberly Nelson: Yes, I would recommend Mark Yagi, who is the CEO for Waterkeeper Alliance. He’s been involved in this space for many decades and has, uh, fabulous perspective that I think your listeners would enjoy
Antoine Walter: Kim.
It’s been a pleasure to explore true elements and your path in that packed tower. I will leave you to your next endeavors in New York’s buoyant climate week. Yes. And thank you very much.
Kimberly Nelson: Thank you, Antoine. It’s been a pleasure. I love chatting.