Water In Sight: Better Data to Make Better Decisions About Water

WELCOME

Katherine Radeka:
Welcome to Accelerate Net Zero, the podcast dedicated to the acceleration of the technologies we need to address climate change. My name is Katherine Radeka, and I've been accelerating innovation for a long time in a number of different industries. And nowhere is that acceleration more important today than in the renewable energy, materials, food production, transportation, and other programs to limit carbon emissions so that we can stabilize our climate.

So if you are working on these programs as a technologist, as a manager, an investor, a public policymaker, an activist, or just a concerned citizen, then you have come to the right place to learn how we can eliminate obstacles and put more momentum into these programs so that we can begin delivering impact on carbon emissions sooner. And we can reach Net Zero faster.

Katherine Radeka:
My guest this week is Louise Croneborg-Jones. She's the founder of Water Insights, a project that uses simple mobile phone technologies to resolve hidden yet massive digitization problems in the collection of data on water in low-income countries. In the United States and much of the developed world, all of this is automated. We have sensors in a variety of places, measuring things like the level of water in a reservoir or the amount of water flowing through a river. And we use that data to make decisions about water usage, about hydropower and to predict downstream flooding. This is a significant problem because of the fact that climate change will cause some places to become a lot wetter and many other places to become a lot drier. Understanding the patterns in the water data helps provide useful insights into the effects of climate change. And that's especially important in the low-income countries that are going to bear the brunt of much of the effects. But for many reasons today, the collection of this data is manual. Louise is going to explain why the technology we use here in the United States is not a very good fit for the places that she seeks to serve.

Louise has been working on these problems since 2009. Her career spans positions at the World Bank where she worked on long-term financing for water in Africa, working for Oxfam in Pakistan, and recently the global environmental consulting firm Sweco. She holds an Executive MBA from the Stockholm School of Economics, a Master's in Science in Development Studies and a Bachelor's in Science, in Geography.

Louise is going to share the challenges she faces in trying to build a demonstration project in Malawi during the time of COVID when most of her team is based in Europe. And she's going to talk a little bit about the Rapid Learning Cycles framework and how it is helping her team do that.

Katherine Radeka:
So tell me a little bit about your project.

Louise Croneborg-Jones:
The project is called Water Insight and it's trying to address the hidden challenge within water management, especially in low-income countries and poor countries in the world. And that is that there is a huged earth of data on water. And when I say water I'm actually covering the whole spectrum of anything from how much rainfall there is to how much water there is in the humidity of the air to how much water there is in lakes and rivers. And so what we're trying to do with this project is we're trying to see how we could improve the collection of data on water by strengthening the human interface that exists, meaning that there's people out there collecting data, especially for governments and using their access to mobile phones as a way to help that transmission of data. So it's like a traditional digitization project going from an archaic paper-based system to more of a mobile phone-based and digital system.

Katherine Radeka:
So what kinds of data are you collecting?

Louise Croneborg-Jones:
We built our first prototype two years ago. And we've started with the most simple parameters, just simply to make the testing and the mobile solution adapted to not such a complex parameter. So the data is the level of rivers. What is the depth of rivers and lakes? And the second one is rainfall, how much rainfall there is that's coming down. So those are traditionally measured in millimeters or centimeters in the metric system. And so again, just quite simple, basic parameters that are also essential in any type of modeling that's done to try and understand the water system.

Katherine Radeka:
And why is collecting that data important? What do people get out of that?

Louise Croneborg-Jones:
As traditional people talk about it in the water data world? They talk about crap in / crap out. So if the data itself is not correct, then iyou won't be able to make use of it. And if all these different uses of the data require very different types of records, like they talk about time series, like having long time series, like historical data sets is really important for understanding the water balance, like how much water is there in a, in a particular river system, for example. But if we speak about the urgency of water data and why it is very, very important right now, many low-income countries situated in areas where you already have quite big differences in the hydrological events, meaning cyclones, hurricanes, that type of events or droughts, we're going to see an increased frequency of these events, and we're going to see greater magnitudes of these events.

Louise Croneborg-Jones:
And so one thing for example, is just understanding the pattern of these events with climate change is a massive challenge. This type of challenge inhibits things like where do you best build your bridge? Where do you best build your dams? How do you best invest in water supply? And of course, the detrimental, hugely costly events of floods or droughts, how do you warn people of floods in time or help people make wise decisions on where to grow their crops in areas that are flood prone? There is a multitude of areas where data can help people make better decisions and be able to understand the impact of climate change better. And just to give you a sense of what we're talking about, in many parts of Subsaharan Africa, the,gap of data, this dearth is rapidly grown, and it's now up to 70% of data is missing. Just to have the basic type of data that's actually needed.

Katherine Radeka:
So how does your solution solve this problem?

Louise Croneborg-Jones:
Water monitoring in our parts of the world, in Sweden or in the U S all of this is automated and has been for a long time. And that means we have equipment that's out there and it communicates automatically. And, you know, nobody goes out and steals the little pieces of battery or parts of it, but that type of equipment is often sold in low-income countries, too. However, the equipment has as a long record of not being able to fulfill the data needs that are out there. And it could be something as simple in like in Malawi where we work. It could be so simple as saying, well, the government access is a big funding grant from a donor, for example, purchases 25, highly automated pieces of equipment, but because of customs and regulations, it'll take very long time before that equipment enters the country.

Louise Croneborg-Jones:
Once it's installed, then things like maintaining batteries where staff have to drive four days maybe to go out to the location to replace those batteries. A thing like they don't have a budget for the fuel to travel. That will actually mean they might not be able to go. And then you see equipment failure, maybe you'll see solar panels being stolen, et cetera. So equipment that has been adapted to I'll say, a Western environment often gets purchased and installed in low-income countries, but without the support system. So what do we then do to, as an alternative is basically in many countries, governments have maintained a system of soliciting people from the public, and this is normally, I'm going to generalize here, but it's a subsistence farmer who has his or her daily work, but goes out to the measuring station twice a day and takes a recording on paper.

Louise Croneborg-Jones:
And that paper then gets picked up ideally maybe once a month by somebody from the government. And then that paper gets transported to the headquarters in the capital city. And then somebody sits and puts it into the software. Now, what we want to do is that our mobile phone solution enables these type of people from the public often called observers or volunteers to use their mobile phones and with a no-cost option, they can make a phone call to a number all going through the internet and they can submit their recording. So let's say the river is 2.35 meters deep. Then they just put in two, three, five for the 235 centimeters. And because we know that that phone number is tagged to a station, we then have an immediate record that we then store online and make available online.

Katherine Radeka:
And that seems like that would be a lot more robust to all kinds of things like the papers getting lost or data entry errors, or problems like that.

Louise Croneborg-Jones:
So what we're doing is basically, we're just, shortcutting all of those different steps, but you're absolutely right. Each of those steps that are open to errors.

Katherine Radeka:
Okay. So tell me a little bit about where the program is right now.

Louise Croneborg-Jones:
We secured a grant from the Swedish Innovation Agency in July of this year. And it's not massive, but it's one of those early startup grants that lets us take lots of risk and test different aspects. So we've gone from the first prototype to basically being able to mature that prototype. And we are now at the stage of preparing for a lengthy period of testing in Malawi. And so we have the partnership with the government in Malawi. We have staff in Malawi and we have the prototype almost ready to be launched. I feel like we're just about to set off on this big exploration path now and see what works and what doesn't work.

Katherine Radeka:
So in this, in this exploration path, what are some of the questions that you're looking to answer?

Louise Croneborg-Jones:
I'm going to pivot back to your Rapid Learning Cycles because of the way that that methodology helps you articulate what we're trying to explore and make a decision on, then the Key Decisions. I mean, overall, it's going to be the primarily the government as a client and whether or not this is simple and cheap enough for them to easily say, yeah, this is what we want to transition to.

Louise Croneborg-Jones:
And then on the other aspect is what I consider to be the user, which is the citizens that are solicited to collect data. And we're talking with people with no access to smartphones, limited access to electricity and markets and perhaps in some many ways limited access to literacy. How do we make the solution as again, simple and easy and preferable for them to want to be, Oh yeah, this I can use, and I can be incentivized by this. Key Decisions around that.

Louise Croneborg-Jones:
For example, one of the things we're looking at is we're going to pay the citizen for the data submission, which they normally are meant to be paid like 10 US dollars a month, but this money is actually rarely paid out in cash and there are problems, like, it costs more to travel to the place where you're meant to get paid than the payment. So we're going to see how can we use mobile payment to incentivize them and use behavioral psychology almost to say ping them with a little bit of funding for every data submission. And how does that make them feel? Like not only financially remunerated, but also, Oh, I'm actually doing something that has a value. So we're looking at those like user aspects as well.

Katherine Radeka:
So since you brought up Rapid Learning Cycles, one of the key things there is the idea of a Core Hypothesis. Can you talk a little bit about some of the hypotheses you're testing?

Louise Croneborg-Jones:
Mm, well, the overarching hypothesis that we're testing is the, I want to say assumption, but that the use of a simple mobile phone, no apps or anything complicated, but the use of a simple mobile phone to replace a paper-based system, how is that going to really improve the gap on data significantly? And my hypothesis here is like, you will given this, how scalable is it? Because we see very similar patterns of challenges in, in many low-income income.

Katherine Radeka:
Yes. And not probably not just with water, but also with other kinds of data collection around environmental conditions.

Louise Croneborg-Jones:
Exactly. Yes. We've begun with water data. We're focusing mostly on the whole climate parameters spectrum, just because this is the sort of field that we're in, but you're absolutely right. I mean, this is something that can be maybe potentially applied for a lot of data challenges.

Katherine Radeka:
All right. So I would characterize that maybe as something like your program uses a simple, mobile phone based data collection system to deliver better water data for governments and policymakers and researchers, and the society will then get better data about these key parameters that can help us figure out, what we need to do to address the impacts of climate change. So what does Water Insights get from it? What is the value for you? Is this a nonprofit model? Is it a for-profit like, what are you thinking?

Louise Croneborg-Jones:
I set it up as a for-profit model with a simple sort of corporation based here in Sweden. One of the reasons why we were using this model and exploring this is simply because there's going to be a lot of evolution to this technical solution. And there's going to be a need to try to test it in lots of different countries. From my perspective, the idea of having a profit based model is that that profit actually can be channeled back into funding, more of the development of the innovation and expansion. Obviously we're going to be looking at next year. We'd look at reaching out to investors to see if we can find investors that are interested in this type of sustainability solution, not for the profitability of the business solely, but I'm seeing like a combined investor and grant and profit model being what we would ideally have.

Louise Croneborg-Jones:
Also, interestingly, I've looked at some of our competitors and some of them, they choose a nonprofit model and that works quite well when there's a lot of established soft money out there. And what I'm experiencing is here is because we're quite specific, the soft money that's out there, there's not an abundance of it in the field of water data. One of the things I would say is that because data is like a core of an operation but people want to see results. People want to see, okay, let's say an example of people protected from floods or from cholera. They want numbers, they want results, but we're narrowing in on a part of the machinery. That's like the cogs and wheels, and that might not be so exciting. So another option for not going nonprofit is, is again the amount of funding that's out there.

Katherine Radeka:
Right? And I actually interviewed a person who's working on putting solar energy into Puerto Rico. And she ran into a similar issue, which is that this is one of those things that falls in between. It's not really, because you're providing raw data and that raw data is going to be used to drive a lot of different decisions. Right? You can talk about the fact that the second, third, fourth order effects are more protection from flooding, better ability to react to dry conditions and et cetera. But those are the longer term effects and they need your data in order to do those things. So maybe it's like the non-profits who are doing those things, become your customers or something.

Louise Croneborg-Jones:
And there's also an interesting area where when we look at other service provider in this field, people who do like use satellite information for producing maps. They can sometimes create a model, for example, they'll supply services to UN agencies who work in humanitarian crises for them, they have a very subsidized rate, but then they will also do services for the insurance business and say, helping out with understanding flood risks in floodplains, et cetera, but there they will charge the premium. So they find a way to cover their costs and be successful. But you're absolutely right. This is not the traditional NGO activity.

Katherine Radeka:
Yeah. Okay. So, one of the key issues with something like this is of course, having a good enough demonstration to prove that your idea actually works. So talk about what you're doing to provide that proof.

Louise Croneborg-Jones:
We quickly realized that we could not explore all the ideas that we had. Our team started off with thinking of a thousand features we wanted to test, but it came back to that idea of like, what is the actual challenge that we're trying to solve here and do we understand that challenge correctly? And so in that process, I think what we're trying to do is, again, we're focusing in, on very basic parameters and we're focusing in on showcasing the benefits and the fast, like the so cool, you can have this data that's visualized online immediately and then show to the client and the governments that they can just, they can access this on their phone they can download a file and share it via WhatsApp. And then, they easily can adopt that into their own forecasting or their own modeling work. So that's what we're trying to show now.

Louise Croneborg-Jones:
What also becomes very important for us to show in the next six months of this project now that in the demonstration is potential for scale. That's one of my goals is to be able to say, this is robust enough to be able to be scalable. That doesn't mean that we're arrived or anything like that. It just means we've got some of the building blocks in place and the government, the users, are positive, you know?

Katherine Radeka:
All right. So you've talked about one obstacle being the fact that you're in this, like you said, you're a gear in a much larger system and the larger system is sexy and attracts a lot of philanthropy, a lot of energy, but not necessarily for your data collection piece, even though it's essential, right. So what are some of the other obstacles that you think you'll be encountering in this next six months phase?

Louise Croneborg-Jones:
I'm seeing three areas of challenges. And I'll start with the one on the innovation process where we're at at the moment, which is basically that we're a team working primarily pro bono. And we have a couple of people hired, financed by the grants. But that set-up of people working pro bono means we have some level of unpredictability in terms of what people can actually allocate in time and effort. And I have to, in the innovation process, it means basically being very clear within the team of what we're trying to do and not sort of let the reins loose. And again, going back to Rapid Learning Cycles is actually that structure provides a tool for us to structure the work and narrow the focus of what we're trying to show. So I think that's an interesting part.

Louise Croneborg-Jones:
It's not like we're a startup that has a bunch of funding at the moment, so we can actually just run a very smooth operation. It's a flexible operation and all that has to sync up in the next six months. The second area of challenge is that we are far away from the market. And this is something that's really interesting about emerging is that you have a lot of tech development happening in developed countries. And you have an emerging area of startups in poor countries emerging, of course, but there is a tendency to think, we are far away let's, Oh, this solution works perfectly. It should work perfectly in Malawi or in Mozambique, whatever, but we are so far away from the markets and with COVID restricting our travel. There are so many nuances that we don't see or understand. We can't just pop into the office of the government and say, Hey, what do you think today?

Louise Croneborg-Jones:
You know, that interaction is missing. So way that we're dealing with it, we have two brilliant people hired in Malawi now. So without them, this would not be possible at all, even if I were able to travel there for a month or so, it wouldn't just be possible. So that's an area of challenge when working on sustainability issues in emerging markets or low-income countries, the third challenge is being able to use the demonstration project as a springboard to the next level. I'm spending a lot of time looking for that second phase financing without having the results ready to be able to secure the funding catch-22. But I think that's probably part of the whole startup.

Katherine Radeka:
Yeah. And, and especially again, it seems like it's part of this journey in particular, where if you were building the water sensor itself and that was the thing, and it was a new technology, the venture capitalist universe seems to know what to do with those things, seems to know how to manage risk for those things. But you do have the software and everything behind it. So you definitely have something that looks like a product, but it's really a method — it's really like a data collection system that's underneath this that's a little bit, very, very much buried in the infrastructure usually. Right? Yeah. So I can see why that would mean that the demonstration aspect is going to be so important to prove that collecting the data in this manner is going to actually lead to better decision making at the level in the governments and also help scientists make, gather better data for their research.

Katherine Radeka:
Yeah. It's not enough to just, put together a tablet demonstration showing how cool the data visualization is. They need to see that it's going to actually drive better decisions, I would think.

Louise Croneborg-Jones:
Yes, yes. Yeah, exactly. No, you're absolutely right. Yeah.

Katherine Radeka:
Okay. So given all of that would you be willing to share some of the some of the learning activities you are specifically doing to help you address some of these obstacles?

Louise Croneborg-Jones:
So with the first one, and going back to the learning Rapid Learning Cycles, one of the learnings there has been that we're adopting a methodology like this is, is really useful and crucial, but doing so you have to spend almost some time learning what it is to get the team to a little, almost the same level of awareness. How does the tool work? Just the basics of it, but once they've done that, they see the logic of it, and then you can dive into the process of finding Knowledge Gaps, and allocating Knowledge Gaps to be closed and so on. So I'm finding that that's been a good learning experience and that it takes a little bit of time. And again, navigating the fact that some people in the team are, they're very narrowed in, on their technical area of responsibility, but that this type of experience actually gets them to understand what are the other multitude of Knowledge Gaps that need to be closed in order for all the pieces to sync up a little bit.

Katherine Radeka:
So to get a sense of not just what they're doing, but how it fits into a bigger picture. Yes,

Louise Croneborg-Jones:
Absolutely. And part of that and how it fits into the process of, of making a decision towards your Core Hypothesis. I think the second part from working on a project or a sustainability solution, when you're very far away from the actual market to such, you can't just rely on your own experiences, although you think, okay, I've been working on this for 10 years. I feel like I know it, but just my learnings here is like how willing people in my network have been to help out. It's been fantastic. I must say that reaching out and not thinking, Oh, I have all the knowledge already. You don't, so you need people to help you find out things. And so it's, yeah. Like anything from people I know. I used to work in Malawi quite a lot and have old colleagues mobilized, and I have people who are helping out with renting cars and for testing operations and so on.

Louise Croneborg-Jones:
So that's not being afraid of that. Mobilizing your network of people that you have. And then thirdly, on that funding thing, I think again too, not isolating yourself too much and putting all your hopes on one or two horses alone. I'm now applying for these labs here in Stockholm, these business labs, or they're not really accelerators, but they help people in very early stages and through them, I'm again, reaching out to get help and finding the right funding for next stage and despite not having the results already.

Katherine Radeka:
Yeah. And looking at, kinda like what your next stage looks like and what funding you need, and then, working on multiple alternatives to try to find a person that's willing to invest in that next step.

Louise Croneborg-Jones:
Another thing I would say, we just had a grant application fail and it was really interesting because it was, it looked like the perfect grant mechanism. And we spent a lot of time trying to get the application in place. And we got interviewed, we got shortlisted and everything, but from that, there's a lot of learnings from that too. And both in terms of how do you allocate your time and trying to find funding and understand what stage you're at and how that matches up to the funding that's out there. Where do you hope to be at the end of this next phase of work? What's the end stage for the next six months, if you're successful, what does it look like? It looks like we have a prototype that's technically sound to the level that we can actually start offering to as a service and take that leap of faith and say, this is very early.

Louise Croneborg-Jones:
We can offer it as a subsidized rate, but we really want it to be used because I think in that using phase, we can just continue to learn. So that would be my ideal and also to have enough data being collected, to showcase that potential of use downstream. So in our team, we have a guy who is a flood expert. And so one of the things that he's tasked to do is to look at the data that we're having coming in and basically showcasing two actors, such as Google and NASA and showcasing what this could mean for their type of modeling. Because going back to the aspects of climate change and the unpredictability of our water and our climate systems, the actors that are out there, whether it's NASA or the European space agency, they use a lot of satellite information for example, or they use some ground, but the biggest gap they have is they don't have any ground-based data being collected in situ. So that's another aspect that we're really trying to see. That will be really cool to see what these agencies think about the data that we're able to collect.

Katherine Radeka:
Full disclosure. The Rapid Learning Cycles Institute provided the training materials and a colleague of mine, Fernanda Torre of Next Agents, a Rapid Learning Cycles Certified® Affiliate based in Sweden facilitated the kickoff event so that Louise and her team could get off to a good start with the Rapid Learning Cycles framework.

And we did that because her program is exactly the program that we are poised to accelerate by helping them make decisions at the right time with the right people and the best available knowledge. They're at a critical point. They need to build a demonstration program in Malawi, in the COVID era, in order to demonstrate the viability of their idea and to show that it generates value and that it can scale.

And along the way, they're going to need to make some very important decisions that are going to get embedded into their solution that will be difficult to unmake later. And that's exactly what the Rapid Learning Cycles framework is intended to help a team like Louise's do.

And so I'll take this opportunity to invite you, if you are working on a program that is like this to consider using the Rapid Learning Cycles framework to help you make those decisions. And if you're working in an NGO or nonprofit, or if your program is so early stage that it's still funded by research grants and philanthropists to consider reaching out to us, you can contact me through the website or through the Rapid Learning Cycles Institute or LinkedIn.

And we'll set up some times to talk, to see whether or not the Rapid Learning Cycles framework can help you and whether or not we can support you the way that we supported Louise and her team. And we're doing this because we see this as part of our mission to accelerate the development of the technologies we need to address climate change so that we can do our part to Accelerate Net Zero.

Thank you for listening to Accelerate Net Zero. If you want to help Accelerate Net Zero, please leave a rating or review with your podcast provider so that other listeners can find us and subscribe so that you don't miss an episode yourself. For links, resources, transcripts, and additional information about this project, visit our website, acceleratenetzero.com.

The Accelerate Net Zero project is sponsored by the Rapid Learning Cycles Institute. We help innovators change the world faster. To learn more about the Rapid Learning Cycles framework, and how it can help you accelerate innovation, visit rapidlearningcycles.com.