This was a fascinating conversation with a remarkable communicator and activist, Jas Chambers. One of the themes that keeps recurring in this podcast is how richly varied the backgrounds and educations are of many of the interviewees. Our education system tends to push the “what you study in year 10 will define your life” line, but it’s so untrue.
I” love data. I love noodling with it and nerding with it. What I’m excited about in terms of the data community is this thinking about ethics in data. That data for its own sake is numbers on a page, but back to what we were talking about. Am I bringing bias into this? What is this going to be used for? Who is it for? Who is it looking at? The ethics piece around that, I think drives us towards greater inclusivity.”
“I’m excited about that aspect, and as people become more data literate, what you start to understand is just how connected everything is. The planet that we live on is a closed system. Outside of asteroids and meteorites dropping in here, we generally sit in this bubble. It means there is no “away”. There’s nowhere that stuff that we make goes. We don’t go anywhere. We stay on this planet, and everything gets reused. Understanding that, being more inclusive in our thinking about data, and really questioning the ethics of it, I think really leads people to understand that connectivity.”
“I remember my first day at university, I was one of those overly friendly people who introduced myself to everyone in a lecture theatre of a thousand and never saw those people again. I remember sitting there in that first term, one of the lecturers did say ‘just be aware. we are another species, we’re an animal, but the data would suggest that we’re not going to be as successful as the dinosaurs.'”
Transcript (with thanks to Sujatha Gunja for correcting the AI generated transcript)
Linda: Welcome to Make Me Data Literate, the podcast that makes data intelligible from the Australian Data Science Education Institute. We’re on a mission to make the whole world data literate. I’m your host Dr Linda McIver, and every week I’ll interview someone with a wild and wonderful relationship to data. Let’s see what we can find out.
Linda: Welcome back to another episode of Make Me Data Literate. I’m very excited this time we’ve gone from elections in the last episode, and now we’re looking at the oceans and I just love the variation in guests that we’ve had and fields that we’ve covered. That just goes to show that data is absolutely everywhere. So welcome Jas Chambers.
Jas: Thank you for having me Linda. It’s great to be here.
Linda: I’m very excited about this one. It’s going to be great. So, can you tell us who are you and what do you do?
Jas: Thanks. So yeah, Jas Chambers is my name. I have a portfolio career, which means that I work with a lot of different organisations, either as a board director or a consultant or sometimes a coach, to people in those organisations. I’m also the founder of an organisation that advocates for the ocean and that primarily means getting lots of different people from different sectors and industries together to talk about how we use the ocean neighbourhood and how we can use it together without using it up, essentially. So that’s what I spend most of my time on.
Linda: That’s awesome and so important. I love that idea of not using it up because we do have a bit of a kind of spend and throw attitude to our environment.
Jas: Yeah. That’s right.
Linda: That’s not sustainable.
Jas: Ultimately, if you think about it, it all ends up in the ocean in the end. The things that we do on land do have this huge impact on what is a vastly unknown space, which we like to think of as a neighbourhood, because there is actually only one ocean. We’ve got lots of different names for what we say are different oceans, but actually there’s only one, and it connects all of us up on our different pieces of land.
Linda: We tend to forget too that there’s a lot more ocean than there is land, isn’t there?
Jas: Yeah.
Linda: It’s the fundamental ..
Jas: Absolutely.
Linda: ..part of our ecosystem.
Jas: Yeah, 71% of the planet, in fact, is covered by ocean.
Linda: That’s a lot.
Jas: So, we like to think that the planet is possibly improperly named as Earth. Perhaps it should have been called ocean instead.
Linda: I like that. “Rename the planet” – that can be a campaign. I like it.
Jas: Totally.
Linda: Yeah, so you do so much and so many different things. What did you have to learn to do all of this work and what was missing from your formal education?
Jas: Yeah, so such a good question, that missing piece. So, my background is in marine science. So, I did a science degree in that area and absolutely loved it. I didn’t stay in science as a scientist though. I went into communications. I studied communications after my science degree. And that was probably at the beginning of what people were starting to call science communication. As a profession, it wasn’t a thing at that time. There were some very well-known people with science backgrounds who were good communicators. But actually, sort of taking that on as a career was not a known thing, I guess. And so those two, that formal training in both scientific thinking, so using data, understanding it, knowing how important it is, and then communicating what that data tells us about ourselves, the world and so forth. The combination of those two things were really compelling for me and that’s what I studied.
In terms of what I did for work, I moved quickly into a science communications marketing role, and I guess, you know, in order to learn, “what did you have to learn to do your work” as a question, I think I had to learn how to communicate with people from very vastly different backgrounds. You know, mathematicians, physicists and biologists are not the same type of scientists. Just putting scientists into a grouping like that is -they’re very different. And then the outcomes that they’re achieving in their research or that they’re trying to teach people and educate people about, that’s for people who are not necessarily science literate. So, really, understanding people – how people learn, how people can communicate best with one another – that was a lot of on the ground, you know, just spending time talking to people. You’ve got to do the work to understand what motivates people and how they think and learn. So, I’ve been a great worker. I haven’t really stopped working in terms of just trying to understand that.
In terms of missing from my formal education to do what I do now, I did study some economics, and I have subsequently studied some finance. But in those early years, I think when your brain is kind of, you know, still forming how to think, I think that that would have been really helpful for me to understand finance. It’s sort of been bamboozling as a, which is, you know, this kind of funny thing that you find its still data, financial data. But the interpretation of it is, you know, perhaps we think of it as being quite different to the way in which we interpret scientific data. And so there are these, you know, different types of information that actually don’t speak very well to one another right now – information about nature and scientific data and how that sort of the nexus of that with financial and economic data and how the two are related. And interestingly, that’s actually becoming something worldwide that’s starting to link up, which is, it’s extremely exciting for people like me who they work in, you know, thinking about systems and planetary systems in particular.
Linda: Tell me a bit more about that. The match between economics and the environment and the federal government at the moment has got this idea of having a marketplace – a green marketplace – and that that will solve environmental problems. Do you feel like the answer to getting us to value the environment is actually putting a dollar figure on it? Is that what we need to do?
Jas: Yeah, you know, it’s such a, it actually is a really tricky, complex world. And that’s why we need more people participating in it from both sides – both the scientific side and the financial side as well as businesses who are ultimately the users of this data – to make decisions about what it is that they make and do. So, I think you’re right. I mean, the government has got a number of programs on the go looking at how you embed nature into economics. And I think there’s many good examples of why being nature positive or thinking about the natural environment, and how it underpins our economy is important. It’s been pretty much invisible for hundreds of years. We’ve sort of, in accounting terms, we’ve treated nature as an externality. These are the sorts of things I’ve learned as I’ve hung around with finance people more recently, externalities.
And so, when you actually think about a chair and what it might sell for and what have you, did you account for the chair before it was a chair? So, it was a tree, and it provided these services like shade or food and the carbon cycle, obviously, is part of that. If you actually were to go and figure out what the value of those things are, like breathing clean air, and that’s the kind of question that’s being asked. If these things underpin actually our survival, how do you value that? And that, and you actually have to take into account lots of different perspectives on that value. Because we’re not all the same. We don’t all come to that question in the same way. So, it is extremely complex. There’s many, many good lessons to be learned from First Nations and Indigenous culture and practice around this. And I heard someone say recently, if you take care of the rivers and the air and the land and the ocean, they will take care of you. That is absolutely correct. And yet we seem to lose that when we walk into boardrooms and that knowledge. So, when it comes to what’s the solution around that, I think because the economy works the way that it does and the financial sector thinks in terms of dollars and that is the way in which all of our systems have been programmed for hundreds of years then yes we need to actually take that view of it.
But a cautious approach to that is extremely important because you could end up in an interesting place where people suddenly own parts of nature and that would also not be correct. When we talk about this idea of the commons – the air is owned by no one, the ocean is owned by no one – we actually are able to own things in theory at least on land but that’s the complexity of this. There’s this commons issue that we have to grapple with – and that’s why dealing with the ocean is different to talking about, you know, agriculture. We talk about fishing in the ocean as you know the farming of the ocean..
Linda: Yeah.
Jas: But people who do that don’t own the space in which they fish. And also, that the stock can move, your neighbour doesn’t come and bring the fish back to you if it moves over into their place, so it’s different.
Linda: I think one of the things we forget, you know, it came up in my, when I interviewed Richard Denniss we were talking about gross domestic product and sort of the measure of a nation’s activities and it’s all made up. We could choose different measures, we could choose different priorities and we could build our economy on different ideals, I suppose, you know, there’s nothing, nothing inviolable about the way we do things now, we could change them.
Jas: Absolutely right. That’s, yes, but willingness to change is, you know, you obviously need a lot of data, often, to get people to look at. And even if you have the data, it’s not always guaranteed.
Linda: Yeah, well, we know, don’t we? And this is the interesting part about trying to communicate these things. We know that data isn’t actually all that persuasive on its own and that people are swayed by storytelling and emotions. And it’s being able to convert the data into a compelling story or to maybe not – convert is the wrong word – but to communicate the data using a compelling story is a phenomenal skill and really important in trying to change minds in this.
Jas: That’s right yeah and I think that’s part of the tension really. Between the lots of scientific data that’s complex to understand – you need a, you know, PhD in something to actually understand the impact of it. That is, as you say that is the critical skill in helping people understand simply what that information means to them. And it is as humans, we do have, we are very self-interested. So, understanding what this means to me, day to day, is incredibly important. I do see there being an opportunity though, to start thinking further beyond our own lifetimes. And people are certainly, that’s part of the, you’re hearing that in conversation now, that we need to be thinking generations ahead of where we are, as opposed to just us and our children and our grandchildren. So, I do think that’s exciting.
Linda: And that’s not a new idea in First Nations people.
Jas: No.
Linda: So, they’ve always had that kind of lens on things that we’re not here just for our lifetime. That we are almost stewards of the environment for holding it in trust for future generations.
Jas: That’s correct.
Linda: It’s not a new idea, but it’s one we’ve struggled to adopt. I think it’s very easy to be in the moment, especially when profit in the next quarter is your kind of measuring stick.
Jas: Correct.
Linda: What do you wish everyone knew about data?
Jas: So, first of all, just how powerful it is, obviously. You just mentioned data on its own is not particularly compelling in terms of getting people to change their minds. And so, I think people discount just how powerful it is. I think there’s two things I’d say here that are picking up on what we were just talking about there.
First of all, you need to actively participate in your own data governance. I guess, understand what you give away, when you give it away, the consequences of that, as long as you understand that that’s what you’re doing, and the downstream effects of that. People give away loads of data all the time. I know I do. But go into that with your eyes wide open. But to that conversation that we were just having about that custodianship piece. Understanding that the data that we have now is telling us things about the past and projecting things for the future. And if we only think about the next quarter or the next financial year or what have you, then we will keep having this conversation that thankfully has extended beyond our children. And now we’re starting to talk about our grandchildren. But I’m really interested in my grandchildren one day and hope that they have a good life. But we really need to start thinking about using that information to project longer term for people that we will never meet. So, I think there is sort of data custodianship about what that can tell us. And that we plan appropriately for that.
I thought it was really interesting yesterday; I think António Guterres published a letter to his great-great-granddaughter or grandchild. And that is the way we need to think. We do tend to think about the generation, the two generations before us and the two generations after us and sort of stay in that mindset. I think that we can use data to think far more long-term and learn from the past. That’s probably what I wish people knew about data, the power of it.
Linda: That’s really powerful, isn’t it? And the idea too of learning from the past, I was on a panel recently talking about AI and the topic came up of the tech industry not paying attention to what’s gone before and not paying attention to the body of knowledge and research that we already have and kind of sitting in this little bubble. Reinventing phrenology every couple of years, that sort of thing. Hang on a minute: if you actually looked more broadly. And that’s, you know, I think that’s the interesting thing about your educational background that you’ve got that sort of range from science to communications and building in some finance because you realized you needed that – that the more we know from, you know, wider fields, I think the more powerfully we can work in our own field.
Jas: Definitely. Yeah. But again, it’s just taking those different perspectives, isn’t it?
Linda: Yeah, that’s right.
Linda: Have you ever seen data deliberately misused? And how do we spot things like that?
Jas: Look, I think that what I have seen is data interpreted for the purposes of making decisions that suit, you know, the needs and wants of either management or, you know, the scene at the time. And this is the thing about data, it’s open to interpretation. Deliberate misuse, I probably haven’t seen something as pointy as that. I think how we spot things like that, though, or the chance for that to occur, is to actually understand and interrogate the data. So you’ve got to ask for it, you know, where’s the information that tells us that? Can I see the raw data so that I can extrapolate for myself on these sorts of, particularly when decisions involve, you know, lots of people and their livelihoods. So, I think that’s the big thing is educating yourself and asking for the information. We need to be data literate in order to, you know, make sure that doesn’t work, that doesn’t happen.
Linda: Right, that’s, I mean, that’s the, big thing, isn’t it? We need to have enough data literacy to be able to spot those things, to be able to kind of critique the data that’s presented to us and to be able to ask the key questions, like what was the sample size and how did you collect that data and what are the flaws in it, because there’s always flaws.
Jas: Correct. Yeah. Yeah.
Linda: That’s fundamental, isn’t it? That literacy.
Jas: Absolutely right. Literacy. Yeah.
Linda: What are the worst data mistakes you’ve seen?
Jas: Hmm. So, I think one of the worst things that I’ve seen is actually people thinking that we don’t have enough data. That is, I think that that can really arrest movement and momentum when people believe that we need more in the system. We’re seeing that a little bit in this conversation about the environment and finance. And that really is a result of those two groups of people. So, people who have got a lot of data about nature and people in the finance world, not knowing each other and not being aware of a lot of the information that already exists. If you talk to most scientists and most data people I know, they generally will say, we’ve got too much data. We don’t know how we interpret this is there’s just too much of it. So, I think that it really is it’s down to – and that there is the opportunity for perverse outcomes if we don’t actually have people who understand where the data came from. Back to that piece around methodology and how we collected that and the veracity of the data, alongside people who are interpreting that for businesses to make decisions or indeed governments to make policy decisions around next steps. That’s an area I think that is ripe for more marriages, more collaboration, more partnership to occur. And in terms of that, there is something underway, which is a financial sustainability taxonomy that some amazing people are working on in the finance system. It would actually be great for the podcast, these people.
Linda: Yeah!
Jas: But that marriage requires, like all marriages, a common language to really work. And so that has become a really important thing. Are we all talking about the same thing when we say sustainability, when we say ethical, when we say nature-positive, do we all understand that? So, I do think that data can help us get there as well, to avoid the mistakes.
Linda: Yeah, that’s super interesting. I like the idea that we’re building this sort of common language, common platform to base progress on, really.
Jas: Absolutely, that’s right. Without it – and that’s, you know, the story of my career – is are we back to, you know, the way that scientists have information to give to the community, and then the people who need to ingest that information, you know, are we even on the same page here? So, I do think that that’s going to be essential.
Linda: Yeah, it all comes back to communication.
Jas: It really does.
Linda: It’s the key to everything.
Linda: When you see graphs in the media, is there a sort of – what’s your go-to? What do you ask about the graphs? What’s your – how do you engage your critical thinking?
Jas: Yeah, I thought about this, I opened up a new site and the first thing I do is actually just go straight to the source. Before I even look at the graph, I look underneath the graph to understand where this came from. Is it a credible source? Are these people who know how to crunch data and give us interpretations of it that I’m going to trust? So, I think trust is a huge part of it. So where did it come from? First of all, who are the people who crunched it? Can I link to that and understand that methodology? And is it trustworthy? Because when you see a graph in the media, that’s the pivot point on which the whole story around it is written. So, if that’s compromised, then you know, I just won’t bother reading the story. So, I think that’s critical. Good sources.
Linda: That’s – yeah – that keeps coming up when I ask that question. And it really is fundamental to be able to go back and look at the sample size or where the data was collected and it’s so easy to collect the data that you want sometimes. Even not necessarily deliberately not trying to skew the data but just – we all have our own biases and our own perceptions of what’s accurate and sometimes we just collect the data that, you know, that suits our purpose and that, you know, fits our narrative.
Jas: Yeah.
Linda: I think that it’s important to be able to question that.
Jas: I think that being aware of our biases is just absolutely critical. It’s one of the things that I’ve really loved about the conversation about data is actually becoming, you know, more human about it. And being aware that humans are imperfect, and we will bring our opinions, our life experience, to the way in which we might ask questions to receive data and actually really managing for that in the process. That’s super critical. Its why scientists are trained the way that they are to really try and take that bias out of the question so that we can get to the true information. Yeah, that is exciting actually.
Linda: Yeah, it’s an interesting angle too because people often take things like survey results just on face value, but then when you look back at questions, there was a question, oh, there was a question some time ago, a survey that looked at people’s attitude, Muslims’ attitude to jihad. And the media story was, you know, some terribly high percentage of Muslims support Jihad. But then when we actually go back and look at the definition of Jihad, it’s peaceful struggle. And so, of course, they were like, “Yes, peaceful struggle. Peaceful struggle is good.” And the media is going, “You know, they do want to kill us.” And to come back to, you know, well, hang on, what you’re saying this percentage of people…
Jas: The language. Yeah.
Linda: Yeah. The language of the survey really matters. And just simple things like, “How awesome was my workshop” gets a different result to: “Tell me what you thought of the workshop, you know, on a scale of Terrible to Awesome.” You know, people will tend to give the result to you, give the answer that they think that you want. And so, the phrasing of the questions is really important. People forget that, I think.
Jas: Absolutely right.
Linda: What is it that excites you about data?
Jas: So I think what excites me in general, I love data. I love noodling with it and nerding with it. And, you know, like most people who have come, you know, from looking at data, sort of getting lost in it. What I’m excited about in terms of the data community, and certainly what I hear and what I read and what I see is this thinking about ethics in data. Data for its own sake is, you know, okay, it’s numbers on a page. But back to what we were talking about, you know, am I bringing bias into this? What is this going to be used for? Who is it for? Who is it looking at? The ethics piece around that, I think, drives us towards greater inclusivity. Because when you pause and ask, you know, where’s the – why are we doing this? You start to think about the humans, that it might impact the animals, the ecosystems and the planet. I’m excited about that aspect.
And as people become more data literate, I think what you start to understand is just how connected everything is. The planet that we live on is a closed system outside of – I mean it’s not perfect – but outside of asteroids and meteorites dropping in here, we generally sit in this bubble. It means there is no way, there’s nowhere that stuff that we make goes, we don’t go anywhere, we stay on this planet and then everything gets reused. So, understanding that, being more inclusive in our thinking about data and really questioning the ethics of it, I think actually leads people to understanding that connectivity. And it’s back to what we talked about at the beginning, you know. We take care of the air, the ocean, the land, the waterways, they will take care of us. And I think that is exciting to me, that we’re really turning our minds to the question of ethics.
Linda: Yeah. That’s super important and I love when you said inclusivity, I went to, my thoughts were all people-based because I talk about that a lot in my work about inclusivity and diversity and talking about neurological and physical and, you know, cultural and linguistic diversity and all those kinds of things, but then to talk about inclusivity as this much broader thing of the environment that we live in and, you know, the animals and the ecosystems – that is really a perspective that we need to make the default.
Jas: I’m glad to hear it, we do. It’s easy to forget, it’s easy to forget.
Linda: Yeah.
Jas: Yeah, it’s really weird because we are part of nature. We are not separate from it. Damon Gameau, who is just an absolute legend who created a film called “Regenerating Australia” – he says this great thing, which is “we are the nature”. And when you start to think about yourself as being part of nature, you cannot remain disconnected from it. What we do impacts those animals. What they do impacts us. And we’re all trying to share space together inside this closed system. We need data to do that well.
Linda: We do, you know, to understand our impact and understand how we can shift our behaviour all comes down to that data. And we still have – there are still sections of society who struggle with the idea that we are animals. It’s like “there are animals and there are us “and “there’s nature and there’s us” – like, it’s – we’re not separate from it you still need to breathe..
Jas: No.
Linda: you’ll need to eat you still need to, you know, be out in the world and we like to think that we have control over our environments and we just don’t. We’ve seen so much of that you know, the last few years with the catastrophic bushfires and floods and things where you might think you’re separate from nature but it’s coming for you.
Jas: Absolutely Linda and one of the – I remember my first day at university. I was one of those overly friendly people who introduced myself to everyone in a lecture theatre of a thousand and then never saw those people again. You know that person!
Linda: I relate to that very well!
Jas: Yeah but I remember sitting there and it was in that first term – I can’t remember – if it was the first day but one of the lecturers did say “You know, so just be aware, we are just another species, we’re an animal, but the data would suggest that we’re not going to be as successful as the dinosaurs” and I have always kept that in mind.
Linda: Wow.
Jas: I mean the dinosaurs were around for millions and millions of years. Can I imagine that for humans, if you know, I’m not sure and the data is telling us something different. So, look, will we be as successful as the dinosaurs? It’s a good question to ask, the data will maybe help us.
Linda: That’s something to aim for, isn’t it? That’s our goal, beat the dinosaurs!
Jas: Beat the dinosaurs!
Linda: Outlive the dinosaurs, I think that’s a good goal. Yeah, wow, that was really powerful. Thank you so much.
Jas: That’s alright.
Linda: Thanks for listening to Make Me Data Literate. You can support the work of the Australian Data Science Education Institute at givenow.com.au/adsei. Tune in next time for more conversations with amazing data experts.
