Sara Menker discusses why data alone doesn't drive change. Trust and communication matter as much as the numbers when it comes to climate, food security, and economic systems.
When we started class last week, one of the top movies on Netflix was Don't Look Up. And I found that super interesting. I had watched it over the holidays as well. And you know, the whole entire sort of point of the movie, for those of you who have watched, I won't. No spoilers, I promise is, you know, it's kind of a parody on society right now. And big picture, these scientists come and they're like, there's a meteor coming. And you know, here are all the numbers and we've done like the analysis backwards and forwards and 25 other scientists have done their analysis as well. This meteor is going to hit Earth. Yet the story, the whole don't look up is there's like an entire backlash. A whole entire people were like, there's no meteor coming. Earth is going to be fine. And so I guess maybe like coming back to the topic at hand, you know, we, we have information, we have data, but people still make bad decisions or people still are unwilling to sort of look at the science or look at the information rationally. So, you know, back to you, sa, I know that you're able to really bring some incredible intelligence to your customers. The fact that you're able to show a country like China a year or two ahead of what policy makers can publish right now, what their numbers look like is incredible. But beyond the data itself.
What other.
Things need to be in place for action to result from information?
Yeah, I watched the movie and I was like, I don't know whether to laugh or cry here. It's a great film, but, you know, it's extremely erotic. Yeah, it's, you know, because it was so, I mean, just replace that with climate change and the conversations on climate change or every topic, food security. Right. And you know, if you go back to the start of COVID and all the supply chain shocks that we had, and you know, I, we went from a world where I remember, you know, we talked to journalists, is like every publication had like one dedicated agriculture journalist, if that or some. And then all of a sudden every journalist became a food journalist because they were, you know, it's like, oh, food and agriculture is so linked to like our macroeconomic systems. It drives inflationary pressures. It does, you know, all these things. And when the breaks happen for a company like ours, it wasn't surprising because we fundamentally knew that some of the structures that are broken are sort of around things like lack of trust. Right. I mean, knowing that in the, you know, if you take the fresh produce space, contracts are negotiated day to day. Week to week, if you're lucky, forget, you know, years in advance to create stability and sort of a surety of supply and the price at which you're going to get it at. And so that was sort of the ultimate, you know, sort of signal that like hey guys, the system is broken and it continues to show us it's broken. Right. I mean global food inflation is at all time highs and it's only getting worse. It's not getting better right now. Right. And then on the other side you have climate change and there's lots of yelling and screaming about that and it's like, hey, I hope we're not all underwater by the time we decide it's time to take action. And, and I think that goes to something which is really around 2, 2, I think key aspects to driving change. Right. And across the system. Right. Because I think you can drive behaviors of organizations and individuals and organizations very differently than in inspiring change across the system, which is really what we're talking about here. Right. So for us it's easy to go motivate a sale in a corporate because you do an ROI analysis and say here's, here's how much more money you make or here's trader how, how, how much better your trading strategy can get as a result of what we do. But you take that back and you go back to sort of the mission as it's two things. One is trust. There's no trust across these systems and trust is broken. And two is communication, which is scientists produce great science, but scientists are maybe not the best communicators. How do we actually, and we spend a lot of time doing this at grow, which is taking deeply scientific knowledge and making it humanly accessible because the more people understand, the more questions get asked. But also if people trust you as a source, then it becomes like a basis for starting a conversation. Maybe it's not the thing that drives the ultimate change, but there's a reference point by which people start when the reference point is completely disparate and different and nobody even knows how much, you know, stockpile of a particular crop there is in the world to start the conversation around. How do I fund that? You know, how do, how do we exchange it across the system? I think trust is not there. And one of the things that's been so important to us as a company is actually building that trust. And it's been sort of the harder road to building a company because of that. Because we say, you know, we started with understanding very individual components very well. Meaning can we predict the supply of corn in the United States every day and, or, you know, wheat in Russia every day, to starting to connect the dots between, or the demand of, you know, pork in China. And then how do you connect the dots between all those things? So it's like forecasting individual components to connecting a real world system and being very transparent about it. Because ultimately all of those players across the ecosystem need to trust you. And so we adopted a few key principles around the way we model things, which is if you work with us, all our models are available to you, so you can get the output, but you can get the methodology too, because you know what, that builds one layer of trust. Two, we chose some set of our models and we made them free and publicly accessible to everyone, regardless of what type of thing you have. Because again, you have to build trust. And so we started with the markets that everybody understood, like the United States, and have now gone to markets where our advanced lead time is 24 months in advance. You're not going to wait 24 months to validate if what we told you is, is good enough. So you're going to say, do I trust the rigor, the integrity of the data, the integrity of the process and sort of their models. And so it's about sort of honesty. So can we provide you the honest answer, which is very different than just the correct answer? Because the honest answer sometimes is actually the confidence interval is very wide and it will only get better over time. And so expand. Explaining that to non statisticians and business leaders or policymakers is a challenge we confront every single day. But I think it's doable. But I think it comes down to trust and transparency and to sort of communication and communicating that correctly.
O processo de reconhecer limites, mesmo quando a vida parece exigir sempre mais.
Depois de tantas histórias, aprendizados e pausas necessárias, o @interior_iza está de volta
