Prodity: Product by Design

Get ready for an informative and exciting episode of Product by Design, as host Kyle introduces his guest, Gopal, the CTO and head of Sust Global, a climate and data startup. In this episode, Kyle and Gopal discuss the increasing use of climate-related data and analytics in corporate reporting and risk management. They also dive into how Sust Global has developed an API and dashboarding products to help companies understand and prepare for climate risk. You'll learn about the importance of driving a shared understanding through data and how machine learning and AI can help with climate analytics. Gopal also shares valuable advice for those starting out or looking to level up in data analytics. Don't miss this insightful episode filled with expert tips on how to effectively address the challenges posed by climate change.

Gopal Erinjippurath:
Gopal serves as CTO and Head of Product at Sust Global, a venture focused on geospatial analytics for climate adaptation.

Most recently, he led the Analytics Engineering team at Planet Labs (NYSE:PL), an integrated aerospace and data analytics company that operates history’s largest commercial fleet of earth observation satellites. Planet Analytics serves a range of customers from city planning teams in governments and the World Bank to defense and intelligence functions across the world. He is known for agile engineering execution from concept to scalable high quality products. He has been an invited speaker at global industry conferences like Google Cloud Next and leading technical conferences in the machine learning space such as ICML, CVPR and NeurIPS.

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[00:00:12] Remote sensing and climate modeling for predictive analytics
[00:04:42] Sports and Climate Risk for Businesses
[00:09:05] Creating API and Dashboard for Specific Market Use Cases
[00:13:40] Sustainability and Climate Change Risks
[00:18:08] Machine learning & AI for climate change adaptation
[00:22:31] Driving Shared Understanding through Data
[00:26:51] The Three Dimensions of Successful Businesses
[00:31:21] Challenges of Working with Earth Observation Satellites
[00:35:33] Bridging gap between early adopters and mainstream users
[00:40:03] Tips for Advancing in Data Analytics

What is Prodity: Product by Design?

Fascinating conversations with founders, leaders, and experts about product management, artificial intelligence (AI), user experience design, technology, and how we can create the best product experiences for users and our businesses.

PbD Ep73_mixdown.mp3
Kyle 00:20
A podcast about product management user experience design, technology, and more.
This is product by design.
Kyle 00:35
Welcome back to another episode of Product by Design.
I am Kyle and today we are joined by another Excellent guest.
At Gopel, welcome to the show.
Gopal 00:38
Hi, Kyle.
Delighted to be here.
Kyle 01:05
Well, we are excited to have you.
Let me introduce you really quickly, Gopal.
And then we'll have you kind of tell us a little bit more about yourself.
But Gopal is CTO and head of Sus Global, a climate and data startup, enabling corporations, financial services, and NGOs to make climate resilient decisions and sustainable investments, which I'm excited to talk about because I think that's a really, really important topic.
Kyle 01:23
And previously, he led analytics engineer, and the analytics engineering team at Planet Labs, an aerospace company that operates history's largest commercial fleet, of earth observation satellites, which sounds really, really intriguing as well.
But why don't you tell us a little bit more about yourself?
Gopal 01:36
To all of you listening, very delighted to be here.
Thanks for tuning in.
So I'm a do you need a scientist who started his career in electrical engineering.
So I spent the early years of my career in multimedia.
Gopal 02:10
And around 10 years ago, got medium testing in the application of deep learning and AI bound techniques in the space of computer vision.
And on which geo spatial datasets.
And initially started exploring public datasets in the from the European space agency and NASA.
And through a scientific sequence of events, ended up leading the analytics engineering team at Planet Labs.
So Planet operates the largest constellation.
Gopal 02:53
Of earth observation satellites and for for medium resolution imagery.
And through their constellation, they have products which are almost like weekly and monthly mosaics of the world.
Think of it as Google Earth every month.
And that just created a fair bit of rich imagery.
So 1 of the things my team helped stand up over there were rich value added insights that provide time series as well as summarized analytics and insights to audiences where those who need to geo spatial data, transformation expertise.
Gopal 03:48
So the ability to get insights from earth observation data without being an earth observation expert was what we focused on.
And along the way in the last few years, I got really interested in the changing climate and how that's impacting human activity, urban activity, and nature based capital.
And remote sensing seems to be I've always maintained this will be that remote sensing is 1 dimension which is more on the historic side telling me what happened in the past.
And climate modeling is where the expertise around what happened in the future resides.
So bringing those 2 worlds together, frontier climate modeling for forward looking predictive analytics and remote sensing derived data and insights for historic and retrospective analysis.
Gopal 04:02
To a common shared set of privileges on the compute side is what we stood up as a product, and we are selling on the market today.
So that's that's been inspiration so far.
Kyle 04:23
I'm excited to talk more about those because I think that this it's a really, really interesting topic and probably a lot to dive into.
But before we do that, I need to tell us a little bit more about what you like to do outside of the office when you're not looking at data modeling or any of these things that we're kind of going to talk a little bit more about.
Gopal 04:40
Out outside of work, you know, we live in like a very rich environment here in the Bay Area.
In the San Francisco Bay Area in California.
I mean, I'd state.
So being all in nature is something I definitely enjoy.
Gopal 05:02
I feel like my best ID is there.
And over the course of the last few years, we've been taking a more of like motor, sport, related activity, and sports like tennis rackets sports like tennis and squash.
Those are things that I play.
So those are some of my non work related pastimes.
Kyle 05:15
Have you gotten into pickleball?
I know that that's a growing popular sport across.
I know across our area, but I I don't know if that's gotten popular in in the Bay Area yet.
Gopal 05:24
It's getting more popular here.
So I'm seeing more and more venues where folks are playing pickleball.
I personally haven't picked it that much.
Kyle 05:44
Well, I I I've even seen like I've seen like a whole bunch of news articles and and the growing popularity of it.
So I'm I'm interested in in just how popular it is across.
But I am also a tennis player.
That's that's probably of all the racket sports, my absolute favorite, and I'm definitely the most passionate about it.
So That's a super great 1.
Kyle 06:37
My son and I, when it's warm out, like to play tennis, that's probably our favorite 1.
So that's that's a great 1II want to dive in a little bit more to some of the things that you mentioned about your background and what you're working on now some of the things you have been working on in the past.
But maybe you can start by telling us a little bit more about SUS Global and how you're using data to help companies understand climate risk and incorporate those into their modeling and opportunity analysis because that sounds really interesting.
You know how you're incorporating both, you know, past information and future modeling in order to help companies and organizations make decisions in order to both help prepare them and and maybe identify opportunities.
So maybe you can tell us a little bit more about that.
Gopal 07:19
So You know, today more than any time in the past is a tremendous awareness in terms of what the climate is doing to the physical systems and to human activity.
And that broader understanding has led to the desire for broader venues has led to a desire for greater understanding of what how climate impacts businesses.
1 of the big drivers of the last of increasing use of climate related data and analytics in the corporate landscape has been regulatory or voluntary reporting amongst organizations.
Gopal 08:08
Largely driven by the TCFT or the task force for climate related disclosures.
And recent directives from a few world governments in terms of how physical risk has reported to the public when it comes to aspects of the business.
So before I get into specifics, I feel like there are 3 dominant use cases that we love to enable team's fit.
The first 1 is reporting.
So if you wanna report on the risk to your business, and your tangible assets on the ground to the changing climate and channel that to your stakeholders and your investors these regulatory or semi regulatory frameworks can be on that.
Gopal 09:01
So we enable that capability The second 1 is second use cases that are risk management.
So once you know the risk, how do you manage that?
And how do these steps you take towards managing the risk actually manifesting to improvements in terms of errors profile and reduction of your risk exposure.
And the third use case is around that new product.
So across different aspects of database capital, real estate, as well as financial financial products, we have seen the desire towards creating climate informed products and climate in their business decision making So those new products and new instruments can use clean validated data into how they model workflows.
Gopal 09:43
So that's the third use case or third outcome.
So we stood up an API and a dashboarding product that serves into all these 3 use cases and across different markets.
So We've gone and we're focused on a few specific verticals, being able to estate, financial data, and nature based capital solutions.
And in all of them, there is a desire on these 3 dimensions, which is report on this, manage potential financial loss exposure and create new capabilities that previously didn't exist.
Kyle 10:39
Got it.
So I'm curious.
You mentioned kind of focusing in on some specific verticals or specific market areas, how do you go about identifying those verticals or those market areas that maybe you want to focus on first because I assume that each probably has specific needs or specific things that they're focused on.
Obviously, when you're looking broadly at the whole market and the way that everybody is is working on risk analysis and everything.
As a company, as data analytics, you kind of have to you'll focus on specific areas first and probably get those right and then kind of move to, you know, to new ones.
Kyle 11:06
How how do you go about that?
You know, how do you think about focusing and then building for different verticals.
And and what's kind of the prioritization that you use to think of through?
Here's how we're going to focus on on this vertical or, you know, this market segment.
And then here's how we're going to leverage what we know and and what we've done in order to now build out for new market segments or new market verticals?
Gopal 11:33
Greekas and Kals.
I feel like that fundamentally how we think about product management in this space.
The benefit we have had trying to build this product and we set of capabilities in the market today is that there's an existing suite of products that that that are sold into the market.
Albeit with limitations, albeit with some legacy which is limiting how they are used.
Gopal 12:05
But there's been some market definition on branding.
And there are increasing number of stakeholders who are paying attention to physical climate risk data into these venues.
And that's helped us identify.
Here certain kinds of teams and certain user personas that can benefit from having data with these differentiated features and can benefit from a very specific set of product features that don't exist today in the market.
Gopal 12:42
So you largely gone after an understanding of what stakeholders are doing.
Or their designables are.
And this is largely been through customer discovery, deep understanding of their workflows, how they are looking at the problem and connecting our outputs into those outcomes that we are seeking.
So when it comes to some of these these specific verticals, we've done a lot of discovery in we've we've been delivered in terms of not going after 2 verticals simultaneously.
Even though when you talk about foundational climate data, you can go to many verticals.
Gopal 12:57
And picked a few which where there is a fair bit of early adopters, and there is enough maturity to understand the proposition.
And to be able to discriminate solutions based on the proposition that you need to serve.
Kyle 12:59
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Kyle 13:58
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Kyle 15:03
You you've kind of talked about, you know, what you see as some of the most important things are the biggest risks that companies are looking at.
And you talked about some of it really being around the reporting and the identifying of risks.
Are those some of the main risks that that you're seeing, you know, companies looking for right now.
And are there other climate risks that companies are either thinking a lot about right now or that you you think that companies should be thinking about in addition to, you know, some of the reporting risks and the regulatory risks that you're either prepping for or that you're helping companies really think about using a lot of the data and the analytics that that you're helping put together.
Gopal 16:10
I I feel like the number 1 thing I often tell stakeholders and our partners is You know, the risk when you think about climate related impacts, you just start thinking about it more holistically across across ecosystems and across supply chain because you might not be directly impacted in terms of your tangible assets, but your operations might be.
So if you're an industrial site, in the site might not have direct risk, but the site workers and the materials going into an industrial facility, but often be along routes or in locations who's impacted by climate.
So having a more holistic view into climate related risk at a global scale over time horizons, which are more than the next few months.
It's kind of what is needed if you want to plan for the future and make better decisions.
That enables sustainability in a climate and their passion.
Kyle 16:48
That that makes a lot of sense where it's not just 1, not just reporting or regulatory, but it a much more holistic view of, you know, how does all of your operations as a company work together?
And and what are all the potential impacts You know, when we talk about climate, I feel like it it almost could become, you know, this very hot touch point, you know, because it it it's become almost this probably over politicized thing.
Do you run into that I assume you probably do run into it.
You know, how how do you deal with that?
Kyle 17:14
You know, when it comes to either companies or individuals where it may become either a difficult conversation or something where you have maybe opinions that have been formed or you have to kind of overcome maybe some preconceived biases when it comes to climate risks or those types of things.
Like, are you running into that and how are you dealing with it?
Gopal 17:37
There are biases.
And I think when it comes to I think this is the case which is often misunderstood.
When it comes to an understanding of the changing climate.
Most people have been affected either directly or directly by it already where they have an understanding and there is an acknowledgement today compared to 10 years ago.
Gopal 18:07
I think the challenge we face is how to incentivize and how to assess economic activity in the context of climate change.
I feel like that's the big difference.
And there there's a lot of politicizing, so fossil fuels a good thing.
Or do our electric vehicles a good thing.
So there's a live debate around that.
Gopal 18:34
Now, but we've been to some extent insulated by it because we are largely serving physical climate testing and analytics to a range of different verticals where they've seen it impact their bottom line.
So it's no longer a debate on whether the problem is real.
It's often a debate around how to solve it and how to account for it and how to think about time frames when it comes to risk adaptation and risk mitigation.
Kyle 19:24
Now we're more and more well, I think we're very, very deep into the age of, you know, machine learning and AI, and it's something that, obviously, we've been talking about for many years and is definitely very recently coming again into the news with a lot of advancements in AI technology and and and things that are are becoming much more common use.
How are you both viewing machine learning and AI?
And how are you incorporating those technologies into, you know, what you're using for for climate analytics for, you know, for your company.
And and how do you view that as both helping and impacting, you know, what you're doing going into the future.
Gopal 20:20
There is a lot of debate around how machine learning related technologies are used in human in environments with significant human impact.
The benefit from the fact that to inherently climate data, climate is a data intensive problem.
It requires a lot of compute power, a fabulous storage And in environments like that, it's just not humanly feasible to pass through the entire data through a bunch of panelists.
So machine learning can help and machine learning power analytics can help.
The big view of had success characterizing our machinery outputs is along with predicted predictive capabilities also surfacing the tolerance or the uncertainty in the predictions that help a stakeholder make a very fair assessment of what they can do with the predictive analytics.
Gopal 21:00
And in climate in particular, I think that's very important because there's a lot of uncertainty and unless you take longer time horizons, it's hard to make very accurate predictions in very localized context.
And that's what that's how we have approached it because we've come into climate analytics with the standpoint that having regional or country level assessments are very limited.
What if this is scarce about is what's the risk to my group of assets.
My sites and my offices and my facilities.
And those are points in the global context.
Gopal 21:24
So when you make assessments at a highly local setting of time and relatedness, you need to in order to preserve the accuracy of the system, you need to go to longer time horizons.
In the sense you're trading of us space for time, and that's how you get to higher position machine learning derived capabilities.
Kyle 21:43
I want to touch on that.
Well, touch on a few things that we've talked about a little bit more too.
You know, building off of both that and what we're talking about a little bit earlier.
In what do you see as the the ideal output, bringing all those things together.
Kyle 22:31
So, you know, you have some of the predictive things that you're you're building as far as the analytics.
And then all of the the output that you're you're helping to provide to stakeholders and and to to companies.
You know, what do you really see as the ideal outcomes that you're hoping to drive from all this information.
So as you're you're hoping to create a lot of these the analytics and the information and hopefully helping to kind of paint this holistic view, what is it that ultimately you're hoping to help drive as far as some of the outcomes for some of these companies that you're helping to provide this information to.
And as well as the individuals that you're working with Yeah.
Gopal 23:01
So I would say, you know, primarily, it's driving a shared understanding through data.
Into impacts, into exposure, into financial impacts, and into potential resilience based measures.
The eventual output is, you know, we wanna enable every business decision to be planned and informed over the next 10 years.
And towards the end, we are playing with the we're working with the early adopters in the space.
Gopal 23:48
That financial decision making is happening was when decision impact the bottom line.
That's the easiest 1 to influence if you have the right data and the right motivations.
With clear actions.
So the ideal state would be where business decisions have the necessary tools and the data, and the analytics towards making informed decisions, and the standing is common.
It gets distilled down to the new knowledge gets distilled down to a recent knowledge where everyone understands, okay, this is kind of what the metrics mean This is what exposure means to a property and means to a business and means to a community at large.
Kyle 24:27
Really driving some of those, like, bigger outcomes as far as both a shared understanding and then you what we can do with some of that shared understanding and the change that it can that can really be.
So I want to to kind of talk about some of your broader experience as well because you've you've worked at a number of different companies.
You mentioned in in the intro, Planet Labs, which is an aerospace company, which is super fascinating, And then obviously you've been at a number of other companies as well.
I'm interested in some of your broader experience.
Kyle 24:57
What has been some of the over your career, some of the key things that you have learned, both building up companies, building up teams, and some of the I guess the key takeaways that you've you've learned across the space of your career that have both helped you in in what you're doing now and helped you both build your current company as well as help form some of the teams that you've been on?
Gopal 25:26
I would say my experience has always been coming in from products and from engineering into the business aspect.
And that's a fairly direct connection to make because products are like the the engine that drives business, businesses forward, and help them grow.
And all other functions connect really aggressively into that.
We had marketing with sales.
Gopal 26:01
Many of the times marketing is the product.
And the product enables some marketing function to be highly effective.
So over the course of the last couple of decades, even my learnings have been All these different teams within an organization, we they work here marketing, we they work here operations, we're working on growth, commercial growth or on product and engineering.
You'll need to have a shared understanding and shared context around the business.
And in teams where that exists, you can almost see direct correlation with how successful they are.
Gopal 26:35
That's 1 big piece of learning which is this creating cohesive deep context and understanding of what the business does across the functions It's all things we better collaborate with 1 another.
The second thing I will share is many of the times when it comes to engineering, we often tend to get very focused on value creation.
Hey, this feature will be very interested.
This product must must be there in the market.
This capability doesn't exist.
Gopal 27:09
Let's build it.
So I think the value creation aspect is very native to us.
But what we think a little less about is you created value for the customer, but in order for you to be able to sustain that you need to be able to distribute that value in a sense in a way.
And that's normally a function of partnerships, growth, marketing, the kind of deals you originally worked with, the first few people who who drive value from your product and you offering.
And the third dimension is that of value capture.
Gopal 27:44
So you might be able to create a lot of value.
But if you don't capture a faction of that, you can't sustain as a business.
So successful business is powered by creative and skilled engineering and powered by groundbreaking products normally have all 3 of them.
They not is value creators, their great value distributors, and also great value capturing functions.
And I feel like that's the context I would share with the product teams to think about all those 3 dimensions.
Kyle 28:54
You you've touched on a number of really, really good things that I I think are really critical for successful teams really at any organization, being able to kind of like you you were mentioning, have that shared understanding be able to create value for for customers, be able to then capture that value.
As you've gone about creating teams, working with teams, how have you both seen really good teams operate?
And then how have you helped teams at various companies kind of develop a lot of those those qualities from being able to, you know, develop a lot of that shared understanding, being able to deliver the right value, and then being able to kind of capture that value as well as you're really creating a lot of value for users so that you're having the right balance.
What are some of the things that you've seen be successful in helping to build up these things both on a a product team level as well as a company level.
Gopal 29:43
Everything from a product team level, it's just enabling product and engineering functions to be very close to each other and working as 1 team.
And being the glue for other aspects of the the company because a big part of their role is enablement like, no 1 understands the signs or the tech as well as the engineers and the scientists do.
But if other functions don't understand it, like the marketing team doesn't understand they can't write copy.
They can't find the right kind of venues under which to channel the message they can't be genuine in how they present the voice of the customer and the the salient features of the product.
So I would say that's building close relationships is 1 thing.
Gopal 30:25
That's something is trying to get right within organizations.
And then I would say that connects it touches into both product as well as like company level functions.
Secondly, getting the voice of the customer directly channeled into Teams can be very inspiring.
And can also be very, very idea generating because sometimes the product teams don't have the best ideas.
But if they facilitate the flow of information and the flow of signal, from the customers downstream to the engineering team.
Gopal 30:39
It can lead to a lot of aha moments that lead to new features that lead to greater value creation and to that distribution and to that capture.
Which enables the company to succeed.
Kyle 31:21
I I've I have also found that bringing in the the voice of the customer and and how however you're able to do that being 1 of the the most important and 1 of the most successful things, whether that's partnering with customers, whether that's, you know, going to the customer, bringing the customer in however you're able to do that, but making sure that you're hearing them and listening to them as frequently as possible.
Because ultimately that is where you're solving the problems and where you're actually getting the best and most pertinent information from.
So absolutely agree with that 100 percent.
Kyle 32:12
I wanted to ask too about the your experience that at Planet Labs because the largest commercial fleet of of earth observation satellites, that to me is just absolutely fascinating Because you're talking about what sounds like a massive, massive hardware operation and just a lot of probably really, really interesting dynamics and really interesting problems to solve.
Can you tell us a little bit more about, you know, your your time there, some of the the challenges that you faced, and both how that is similar to kind of what you're doing now.
And maybe some of the different challenges that you face both working at an aerospace company and probably I'm presuming working with a lot of of hardware and software together.
Gopal 32:41
I I would say planet had a unique blend of like the spacecraft expertise, the software platform expertise, working with those earth observation data.
And the third bit is like analytics expertise.
So bringing all those 3 things together is what makes that company unique.
And very thankful for the opportunity to be able to work there and build from teams as much as teams who are doing them for me.
Gopal 33:16
I would say the most exciting challenges there were when you're building something like that, and enabling a new data capability into markets which have not had that before.
The ability to see new use cases emerge on a near monthly basis was very exciting.
Secondly, just enabling a longer cycle over which those learnings can happen.
And over which those use cases can surface.
But 2 things that I felt were incredibly valuable for me to see first hand.
Gopal 33:45
And secondly, I also learned from what our customers were doing because the early adopters of the observation data in their workflows.
The things that even the data creators will not understand.
And through that this, like a synergistic or symbiotic relationship between the data providers as well as take the data consumers.
And we kind of doing the same.
We kind of go into a similar cycle here at Sus global.
Gopal 34:18
Which is, you know, we won't enable the application of climate datasets in many markets, but there are only a couple of markets we understand really well.
So if we were to provide it to an academic who's doing research in a specific area of new expertise and how they use it.
That's new for us.
So we are we are kind of planting the seeds for that sort of a symbiotic relationship ourselves.
And we've seen some very exciting germination as we speak.
Gopal 34:26
So Those are some of the learnings I was able to pick up from Planet and apply into how we do things at SaaS Global.
Kyle 34:29
That's that's super fascinating.
Kyle 35:20
always think that obviously, you know, taking any of the the challenges from some from previous places and then applying them to new ones is a very, very useful exercise.
And then especially when it involves so many different dynamic areas of expertise and then kind of bringing them into some of these new areas.
It is both really, really useful and can be a really helpful exercise.
You've mentioned kind of a couple times, and I'm I'm really kind of fascinated on this to get to get your thinking on this idea of of the early adopters because I think that in a lot of our technologies and a lot of our products and and companies and everything here.
We have a lot of those early adopters and, you know, those very early to their technology and very comfortable with with using it.
Kyle 35:44
And then as we kind of bridge into a lot of those more mainstream users who are not as maybe as tech savvy or as early, obviously as our early adopters.
There is that gap in in you know, we're familiar with, you know, that, you know, kind of that technology gap or the or that even that chasm as we call it sometimes.
Gopal 35:44
But -- Yeah.
Kyle 35:44
Kyle 36:16
how do you how do you view that as you're moving from your your your early adopter group, you know those very early users into some of the more mainstream.
You know, what what are some of the challenges that you see and how have you overcome them or how are you overcoming them?
In order to make the use case much more mainstream for both for the analytics for adopting climate risk assessment for all of the things that you're kind of working on?
Gopal 37:15
I will say there is no the quick answers the early adopters provide patterns of usage that can inform the early majority.
And getting those patterns of usage, documented, enabling case studies, enabling user stories which are very clear.
And that couple with regulatory or voluntary or stakeholder led tailwinds can enable businesses to move from the majority into early adopters and like learn from each other.
So that's how that's the methodology we have followed, which is just make share enough of our learnings across customer basis so that we are enabling the new set of customers and prospects to see the patterns of usage.
Kyle 38:08
Well, that makes a lot of sense so that as they're well, as each of them are kind of working, that chasm or that gap becomes a lot more narrow.
And it is much less of a bridge to go across and they're able to you're able to both take a lot of the learnings from both of those groups, and they're also able to help take a lot of the learnings from each other.
III think that makes a lot of sense.
What things are you most excited for or what things do you see coming down the pike as far as technology or even, you know, things to do with climate and climate change or climate risk assessment or even more broader technologies, that you're excited for or that you see coming over the near or mid term?
Gopal 38:43
See, I'm very excited about general modeling.
Through AI.
You know, it's changed the nature of the game in aspects like search and knowledge.
Gathering with capabilities like Chat EBT, it's changed the nature of generated images using solutions like like Dali or using other capabilities that exist today in the market.
That include groups that are doing very interesting things with generator modeling.
Gopal 39:03
I feel like there's a version of that that can be applied to climate related data and into environmental data as you think about future scenarios, that play out based on human activity today.
It requires a lot of finessing and a lot of development, but that's 1 area we're particularly excited about here at SUS Global.
Kyle 39:50
That's a super exciting 1 as well.
I think probably most of us in the technology space right now are super, super excited about a lot of the possibilities there.
I know I've been both using and exploring a lot of the potential in a lot of those areas just in my own personal space and professional space and it feels like we've made a very interesting and intriguing leap and we're just at the very, very forefront of what is possible right now.
And I think we're all just trying to figure out what is possible right now and and what what comes next.
And it's it feels like a very very exciting time for what is coming next in so many different areas.
Kyle 40:17
And And I don't think any of us know exactly, but it definitely feels like we're at the cusp of a lot of new things right now.
So Yeah.
That that definitely agree.
What advice would you have for somebody just starting out or early on in in their career or looking to kind of level up in data analytics or in any of these types of fields
Gopal 40:56
I would say understand users and customers, and I would say keep the earning because the space is so dynamic.
This is so much changing.
So make learning fundamental to how you operate how much despite the expertise that you build up over time and keep talking to people in the real world as you think about your modeling because models are deadlocked in the virtual world but deployed in the real world.
So if you need to bridge that gap and oftentimes it's the data scientist or the data and the machine learning engineer or the model who's in in the thick of that.
Kyle 41:10
Global, it has been an amazing conversation and and really, really appreciate all of your insight and the time that you spent with us.
So thank you again for everything that you have shared with us.
It has been a pleasure talking to you.
Gopal 41:29
Thank you, Kat.
Delighted to be here.
Thank you all for like tuning in.
LinkedIn is the best place to reach me.
We're always seeking like the best talent to work with us on working on these frontier problems at the intersection of earth observation, climate modeling, as well as financial services.
Gopal 41:40
So if you're interested in any of the open roles or our products and services, feel free to reach out on LinkedIn.
Or reach us through our website.
Kyle 41:43
And we'll put all those links in the show notes.
Gopal 41:45
Thank you so much, God.
Have a good day.
Kyle 41:50
And thank you everybody for listening.
We'll talk again next time.
Kyle 42:11
Thanks again for listening.
If you like the show, we to follow or subscribe on your favorite podcast app.
You can follow the show on Twitter at prod by design.
That's prod underscore by underscore design.
You can follow me at kylery evans on Twitter as well.
Kyle 42:11
If you want more product conversation, check out my news that are product thinking at product thinking dot c c.
You can follow me on medium at kya larry evans as well.
Or check out my medium publication, a product by design.
Thanks again.