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019: The power of data-driven decision making with Linda McIver

Are you wondering if you have enough, or the right data for your decision making?

As we rise up in our leadership careers, the one defining feature of all leadership is that we make more and more decisions. But how we make those decisions is crucial.

In today’s Leading Women in Tech podcast I’m interviewing Dr Linda McIver, the founder and Executive Director of the Australian Data Science Education Institute on the power of data driven decision making, her three principles for making decisions, and the danger of confirmation bias when we use data. We dig into everything from decisions in organisations to decisions about COVID, and of course, we can’t help but touch a little on decisions around building an equitable, diverse and inclusive workforce.

So let’s dive in.

Show Notes

Find out more about Linda McIver and the Australian Data Science Education Institute (ADSEI) at https://adsei.org/ or follow Linda on Twitter at: @datasciau

Catch the show notes, and more details about today’s episode here: http://tonicollis.com/episode19 

Join us in the Leading Women in Tech Facebook group: https://www.facebook.com/groups/leadingwomenintech/

Join us in the Leading Women in Tech LinkedIn group: https://www.linkedin.com/groups/12391391/

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Want to read instead of listen? Here’s the transcript:

Toni Collis:
Welcome to the first ever interview episode of the Leading Women in Tech Podcast. I’m super stoked, and hopefully you are too. This is hopefully the first of many interviews.

Toni Collis:
Today I’m joined by the simply fabulous Linda McIver. She’s the founder and director of the Australian Data Science Education Institute, also known as ADSEI, a registered charity in Australia dedicated to ensuring that all Australian students have the chance to learn data science and data literacy skills in the context of authentic projects with real impact. Gosh, that’s a mouthful. But it’s a real game changer for the way we make decisions, which is why she’s here.

Toni Collis:
Linda is also one of my clients and funnily enough, despite her expertise in data-driven decisions, I sometimes have to call her out on her assumptions, just showing how fallible we all are. That’s one of the reasons I have a coach too. I’m sure we’re going to be chatting a little bit about that today.

Toni Collis:
Linda has a PhD in computer science education and extensive experience teaching at both tertiary and secondary levels.

Toni Collis:
Through ADSEI, Linda now trains teachers and creates engaging data science projects to empower students to use tech and data science skills to make positive change in their communities. Oh, and a side note, she’s writing a book that is genuinely going to change the world.

Toni Collis:
Hey Linda, welcome to the podcast. It is delightful to have you here. I’m not sure if delightful is quite the right word, but it seems to fit wonderfully day because you are a delightful person. Just to get my audience warmed up, I’d love you to tell us a little bit more about your journey to running ADSEI, the Australia Data Science Education Institute. I know it means the world to you. Can you tell us what made you kickstart that?

Linda McIver:
Thanks Toni, it’s really awesome to be here. I started out life as a computer science academic, and I felt like I was researching computer science education, but it didn’t feel like it was translating into the classroom. It didn’t feel like I was really having an impact and making a difference.

Linda McIver:
So I went into high school teaching, and I learned so much. I worked with the school for a year and then I taught there for the next seven years after that. We did two things. One was I had projects where the kids were using real data and doing real projects for scientists. They would continue to work on the projects after the year after the the subject was ended. There was no possibility for subject credit, but they were still working on it because they could see that it was meaningful and important.

Linda McIver:
At the same time, the year 10 students that we had, we were teaching them programming, using toys. We were making robots follow lines and drawing pretty pictures in scratch, and the kids could not see the point. They were learning the same skills. We were doing the basics of programming, but they couldn’t see the point of the stuff they were doing. They couldn’t relate it to their everyday lives or to the courses they wanted to do, and these were science kids and they were not engaged with programming. So I knew we had a huge problem.

Linda McIver:
As soon as I managed to persuade the school to teach data science instead of toys and to teach programming that way, the attitude just turned around massively. The kids got really excited about it. They could see the relevance. We were doing the same programming skills, but we were also doing data literacy and basic things like which graph do you use and where’s the zero on that scale, and is this a misleading graph or is this a valid graph, that kind of stuff and asking questions like what’s wrong with this data because there’s always something wrong with it. What’s missing from this data, who’s not represented, all that kind of stuff.

Linda McIver:
Suddenly the kids were using it everywhere. They were saying, “Oh, this is so useful and I can see the point and I’m using it for my science assignments and I’m using it in math and I’m using it here and I’m using it there.” They were switched on and they were engaged. They were not all going to become data scientists of course, but they all knew that A, it was something they could do, and B, that it was something worth doing. That was a huge step up from when we’d been teaching programming using toys.

Linda McIver:
So as soon as I figured out that it was working and I saw that we got an increase in numbers in the elective computer science students, the subject that followed that subject, including a massive increase in the number of girls who were interested in doing computer science, I figured, okay, I’ve worked this out. I know how to do it now.

Linda McIver:
So I quit, because I wanted to reach more kids than just the ones in my classroom. I felt like this was a really big breakthrough and this was something that needed to be shared and to spread. It was clear that I couldn’t do that while I was teaching. It was also clear that the only reason I could do those kinds of projects as a teacher was A, because I was part time, so I used my own time to find the data sets or the scientists that we worked with and make sense of it all and pull it all together.

Linda McIver:
The other reason was that I have a PhD in computer science so I have the skills. Most teachers in schools do not have those skills. So I knew that I needed to do two things. One is to provide them with the data sets and the projects and things that they could do and the other is to train them to be able to do it.

Linda McIver:
So that’s why I started the Australian Data Science Education Institute. I wanted to make it a charity to make sure that it was available to everybody, that wealth was never going to be a barrier to access. Because the rich schools can afford all the fancy programs, the low socioeconomic status schools just can’t and I wanted to bridge that gap as well.

Toni Collis:
I love that. I know that you and I have talked a lot about working as a mission-driven person, as in working in founding mission-driven organizations. One of the things that my listeners may or may not know about me is that my work running and founding women in high-performance computing, I had exactly the same attitude. I was like, money should not be a barrier. It’s so important that we share this. It’s just too important to put barriers in the way.

Toni Collis:
I know that we have talked a lot about your, you and I, we have talked a lot about your work at ADSEI, but what I’d love to talk about today is how you think that everybody, not just the students you teach can benefit from data-driven decision making and what we all need to be doing to bring that into our day-to-day lives. In particular, with my audience, leaders in the tech industry, how the tech industry can benefit more. How do you think the world needs to change in the way it makes data-driven or rather start making data-driven decisions? I might be preempting your answer there.

Linda McIver:
That’s the really interesting part of the story, because originally I was doing this to give more kids tech skills, to bring more kids to the understanding that tech was something worth doing and something that they could do. But actually that’s turned into a side benefit. That’s a bonus because the key mission of ADSEI is really that data literacy and that idea that we are rationally skeptical of data, and that we use data to drive our decisions, to drive our policies, to drive our processes, but that we use it in, as I say, a rationally skeptical way.

Linda McIver:
One of the big issues with data science is that what we tend to do is say, “Hey, we have this data set. I just found a result. Ta Da,” and then I move on to my next result and I never actually have time because of this intense rush to data riches. Never have time to sit down and go, “Is that result correct? What is that result missing? Who is not represented in this data? What are the misleading parts of the data that we have or of the analysis that I’ve done?” Because there is no such thing as a perfect analysis, and there’s no such thing as a perfect data set.

Linda McIver:
So if I do nothing else than produce a generation of kids who come out going, “Where’s the zero on that scale? What was the sample size of the data that you’re telling me about? Where is the rest of the data? Who is not represented in this data?” If we can all start having that kind of conversation as a society, then things change significantly.

Linda McIver:
At the same time, what we have, doesn’t matter whether it’s education or business or politics, what we have is a bunch of leaders who have their favorite things or the latest shiny toy that someone’s pitched to them at a conference and they come back and they go, “This is it. This is going to change everything. We’re going to do this thing.”

Linda McIver:
They don’t necessarily look at the evidence and what they definitely don’t do is evaluate it afterwards to see if it worked. That’s a fundamental part of my project, is to go, okay, anytime you do a project and change something and you say, look, we’ve solved this problem, you actually measure it again to see if you have solved the problem. So you’ve got to measure first and you’ve got to measure afterwards and go, did we make a difference? What kind of difference did we make? What things do we still need to fix? What things worked? What things didn’t work?

Linda McIver:
We don’t do that. As a society we don’t have the habit of doing that. We have the habit of implementing the latest shiny toy idea, and then moving onto the next one without ever really stopping to see if that first one was a good idea and if it made the difference that we want that to make.

Toni Collis:
Yeah, that one’s a biggie. I have seen so many companies, and more to be fair, get stuck because os shiny objects syndrome. I talk about this a lot with people, shiny object syndrome. I know we do it too. We make something mean more than it does without proper evaluation as you said, before and after. I love that you said that because we seem to struggle as a species to realize that shiny object doesn’t make it better, the whole grass is always-

Linda McIver:
No, that’s right. We have this magpie fascination with the shiny things. It was that’s really exciting, and I do it myself as you know, having had to pull me back from it from time to time. You get stuck on something, things get a bit hard, a bit dispiriting and you see a shiny toy and you’re like, “That’s going to save me,” and you run away to the shiny toy because it’s a happier place than all of the complexity of real life.

Linda McIver:
But we see it at government level as well. Our government in Australia is currently on the shiny toy of trickle down economics, which we know does not work. It has been so thoroughly discredited that you can’t like this. We just know that if you give rich people money, what they do is they squirrel it away in their shiny dragon hoard. We also know that if you give poor people money they spend it, so it’s phenomenally good for the economy.

Linda McIver:
Now in these COVID times, when you need to stimulate the economy, we know that the most effective way to do that is to give poor people money, and what they’re doing is they’re cutting back the money they’re giving to people who are unemployed and of course there’s a lot more of them now too. They’re cutting back the money that they’re giving to everybody really, except the rich. They’re giving the rich tax cuts and we know that doesn’t work.

Linda McIver:
So if we had a government that was doing things in a data driven way, it would be building social housing, it would be implementing universal basic income. It would be stimulating the economy by looking after the most vulnerable in our community. And really when you put it like that, where’s the downside? There isn’t one.

Toni Collis:
Do you think that this is a common phenomenon? You mentioned there your government Australia, but would you say it’s common everywhere? I have my very strong opinions about that of course. You and I have talked about that privately. Do you see this happening elsewhere?

Linda McIver:
Look, I don’t think it’s everywhere, but it’s certainly really, really common. There are certainly countries that get it more right than we are at the moment, and countries that get it less right, not looking at anyone in particular. But it is a really common thing, and I think it’s the image that we hold in our heads that there are two ways to look at it. One is we mustn’t have anyone get anything they’re not entitled to, and that means you pull all your money close and you don’t give anybody anything that they haven’t worked really hard for and all that kind of stuff.

Linda McIver:
The other is we make sure everybody has what they need, and I got that, I heard Virginia Eubanks who wrote Automating Inequality. She put it that way and you get a very different society if you’re trying to make sure everyone gets what they need versus no one gets anything they’re not entitled to.

Linda McIver:
But the thing is, we have the evidence that shows that if you just give people what they need, it’s cheaper, it’s more effective and it gives you better outcomes. Again, no downside. So you house the homeless. You give money to the poor. These things work and they’re cheaper.

Linda McIver:
But we’re addicted to the idea that people are selfish and will take what they can get and won’t work and all this kind of stuff. The evidence shows that this is not true. So if we were taught to look for the evidence instead of the ideology, we’d have a different outcome.

Toni Collis:
Yeah. I feel like we could talk all day about how we can improve the conclusion by data-driven decision making. As you know that’s a topic very dear to my heart, inequality and stuff.

Toni Collis:
But we are here to talk about … The listeners are going to be like, wait, hold on. This is a leadership podcast. We all need to know about data-driven-

Linda McIver:
Where’s the leadership?

Toni Collis:
Where’s the leadership? Data-driven decision making, particularly in leadership, and I really want to take the knowledge that you’ve built up, particularly your approach. The very fact that you’ve made it accessible to teachers and to children means that this is something that I think the entire world can benefit from. So I’d love to know, are there some key principles we should all be following to make better decisions? Because at the end of the day, being a leader, I suppose the one single definition across every single sector, is being a leader means you make more and more decisions. The higher up you go, the more decisions you make. Yet we struggle with it. So yeah, are there’s some principles we should all be following?

Linda McIver:
Absolutely. I would say principle number one is don’t reinvent the wheel. In so many cases that the problems have been solved and the evidence is in for the different solutions to these problems and how they work and don’t work. So it’s really important if you’re doing something in that kind of situation where yeah, people have done this before. Go look, find out what people have done. Find out the research around it and actually find out what works and what doesn’t work rather than going with the ideology or the shiny toy or the loudest voice in your ear. Find out what works and what doesn’t work and use that in your decision making.

Linda McIver:
Of course that doesn’t work if you’re in a situation where you really have to solve a new problem. Sometimes there just isn’t data, and it’s important not to get frozen by that and trapped in, well, I can’t move until I have data, but I don’t have data so I can’t move kind of thing. That’s where you go back to the principles that I use in my projects, which is you measure where you are, you try something and you measure it again.

Linda McIver:
You think of it in terms of agile development that you don’t have to make massive changes all at once. You can do small things and measure how they work and then move on to the next thing and measure that as well so that you know the impact of the things that you’re doing rather than just flying by the seat of your pants, making a whole lot of changes, and then not knowing which ones worked, which ones didn’t cause too much change to actually know what made the difference.

Linda McIver:
If you look at COVID, for example, we don’t have the solutions ready to hand because we haven’t faced this particular virus before and there’s a whole set of novel situations and stuff. So we can’t just go, well, we’ll wait until we get the data until we start working on a vaccine, because we really need the vaccine now. But you test it every step of the way and you see if what you’re doing is working. If it’s not, you go back and try something else.

Toni Collis:
Yeah. Hell yes. I think that’s one of the things that we always struggle with, is that measure and measure again. I think some of us have lost our scientific training almost-

Linda McIver:
Yeah.

Toni Collis:
The principles of controlling your variables and stuff like that. No, not everybody listening to this podcast has science, but the principle of change one thing, measure that, the more important the thing is that you’re changing, you’re making a decision on, and one could argue that if you’re running a business, you’re responsible for a team that has output, the bigger that is, the more influence you have, then the bigger your decision is. Therefore the more important it is that you control those variables and just don’t do all the things all at once. Shiny object syndrome, again, we’re just going to run around doing things.

Linda McIver:
Nothing’s working right now so we’re going to change everything. The reality is that sometimes it’s only one tiny change that you need to make that makes everything else fall into place. But if you’ve just upended it and changed everything else, you can wind up in the same situation, where you’re not improving but you don’t know why and you don’t know what’s actually wrong. The other thing about measuring before you do stuff is that sometimes that tells you what’s going wrong.

Linda McIver:
You did, I got carried away there, you asked about principles before. I think the other principle is when you are making data-driven decisions, data can mislead you almost more easily than it can lead you right. So you do need to be really careful to do what I’ve said several times already, and it’s a key principle, which is ask yourself what’s wrong with the data.

Linda McIver:
You might have a data set that has a month of sales, but it might have been the best month of the year. What are the other months like?

Linda McIver:
We have a road near us where they were measuring traffic to figure out whether they needed to put in a bike lane or not, and we’re right near a major university. We’re right near Monash University. So during semester time, the traffic is wildly different to what it is outside semester time, and they measured outside semester time and they were like, there’s very little traffic on road. Dude, can you come back because looking at now.

Linda McIver:
So you’ve got to ask yourself what’s missing from this data. What would be different if I did it next week? What would be different if I looked at last year’s data? If you look at 2020 data, it’s going to be wildly different to 2019 data in all kinds of ways that we couldn’t possibly have seen coming. So if you just use this month’s sales you’re going to have an entirely different scenario to this month last year. So you’ve got to ask yourself what’s missing.

Toni Collis:
I love that you said that, because we see time after time stuff in the media. One news outlet will interpret the same dataset one way, one will say another way. They have different headlines. People get behind those and make decisions based on the headlines because of what’s being reported. Because you can interpret, it’s not just that your data may or may not be more or less valid based on, as you point out, when it was collected, how it was collected. I love that you said that by the way, but also how you then interpret it. I think this gets people into big black holes.

Linda McIver:
Absolutely. There’s a wonderful study and example of that. I can’t remember the field. It might’ve been physiology or something, where they had one data set and they did a matter study and found hundreds, literally hundreds of different interpretations of the same data set. So just because you’ve analyzed this data doesn’t mean you’ve analyzed it in a valid way. That’s something else that I emphasize in my projects is to get the kids to go well, okay, now that you’ve got this result, why do you believe it and how can you prove it wrong?

Linda McIver:
You’ve got to actually test it for validity and try to prove it wrong, actively try to prove it wrong. Whereas of course, confirmation bias means that mostly what we do is we try to prove our results right.

Linda McIver:
That’s a big theme in my book too, because I’m writing a book called Raising Heretics, to build that critical thinking, rational skepticism, data literacy thing and that big question is prove to me that your result is valid by doing your hardest to disprove it.

Toni Collis:
Oh, I love that you said that. I was wanting to make sure that you mentioned your book because I cannot wait for that to become available. I know it’s going to be a while off yet, but when that does land, I think we’ll be letting all of our listeners know that that’s landed because that’s going to be a rocking book.

Linda McIver:
Should be fun.

Toni Collis:
Definitely lots of fun. You mentioned there about the danger and also the different ways that we can measure and validate our data. One of the things I saw when I started out in improving inclusion was when somebody is a really good at doing the scientific approach and measuring controlling variables, but then they move into a new arena, and I started out working on improving inclusion, equity and diversity in physics, way, way back. These physicists who, I adore these people, I worked with them for years. But one of the things that struck me as bizarre was they were so good at their physics and yet when they started applying the same principles to improving equity, diversity and inclusion, they just run around and do all the things for the sake of it.

Toni Collis:
As you say, sometimes it’s just one thing that makes an improvement. Actually, when we unpicked it, when we paused and slowed down, we found out that some of the stuff that been implemented, and I think this is true across the world by the way, some of the stuff we did to improve that actually made the situation worse. But they didn’t even have the data to understand that was what was going on. Is that a common problem?

Linda McIver:
Oh, it’s immensely common. To use an example that I’m very familiar with, there’s a huge push in Australia and I think in the UK to, probably around the world, to do STEM in the classroom. Science, technology, engineering, and maths, we have to do STEM.

Linda McIver:
Nobody really knows what STEM is in schools and so what they often do is they get someone to come in and do a day’s robotics and they play with the shiny toys and then they go, hey, we did STEM. Aren’t we amazing? Or, and this drives me insane they build or makerspace and then they say, look, we’re doing STEM because we have a makerspace.

Linda McIver:
Now the problem with that is that it does often make things worse because the kids who are not into that stuff, who don’t find it fun and get frustrated when it doesn’t work and don’t know how to fix it, those kids then learn without doubt that STEM is not for them. It absolutely emphasizes to them this is this thing that everyone’s telling me is supposed to be fun. I don’t find it fun. My robot broke. I can’t do it. Therefore, I’m not a STEM person.

Linda McIver:
The truth is everyone is a STEM person because we’re born STEM people because we have that spirit of scientific inquiry of taking things apart to see how they work. Kids all do that when they’re young. But we break them. We train them that they can’t do it and we do it with these terrible programs where they do STEM and they’re supposed to find it fun and they don’t. It drives me insane.

Toni Collis:
Yeah. I can tell. That drives me insane too. It’s a good point that kids are born with that curiosity. I never thought of it that way, but you’re spot on. And we do, we talk about this a lot with women and girls and that we beat science out of them by the expectations of society, but actually you’re right. It isn’t just that we are genuinely bone with that curiosity.

Toni Collis:
I remember taking things apart as a child. I’d be like, ta da, and they were like, dude.

Toni Collis:
Let’s change track a little bit here. I want to talk about when we don’t have the data to make decisions, because I see two extremes in the amazing women I coach. One extreme is just make all the decisions irrespective of the data. It’s more important to make decisions than not. This happens fairly early on in careers where you’ve just started taking responsibility. You’re doing well because you know making decisions is important. So that’s one end, and we’ve talked about how to improve your decision making and getting the data.

Toni Collis:
The other end I see is when people are stifled and they just freeze and they can’t make decisions because they don’t think they have the data or they don’t have enough data. They just freeze, which is really damaging. Obviously scientific discovery would ask, well we just keep waiting for the data. We keep pursuing it. Reality of life is whether it’s in business or what university to send your kids to or the politics of handling COVID, we don’t have time sometimes to wait for the data. What do you do in that situation?

Linda McIver:
I think there’s two things to remember. Step one is in most situations, not all, but most situations making one mistake is not going to be fatal. So you need to take a deep breath and go, well, I don’t know what the right step is here. So I have to choose one. But then evaluate it to see if it worked and be prepared to pull back from it and go the other path. So you have to go, well, okay, there’s a fork in the road. I can go left or right. I’m going to go left. But if left turns out to not be working, which I’m going to be checking for all the time, then I can go right as well.

Linda McIver:
The other is to remember that you can create your own data by actually measuring what’s going on around you. We didn’t have the data for COVID, so we have to keep measuring it. But we move while we’re measuring just on the understanding that we might have to come back and change what we’re doing based on the new evidence that we get.

Linda McIver:
I did an interview once back in my brief career as a freelance writer. I interviewed a guy about fair trade, and he said, “The reality is we don’t always know about the companies that we’re dealing with. So you make the best decision that you can today with the evidence that you have, with the understanding that evidence you get tomorrow might show that was the wrong decision.” So you have to be prepared not to fall on your sword at that point and not to beat your breast about it, but to go, okay, I know more now turns out that was the wrong way to go. We’re going to update it. We’re going to fix it.

Linda McIver:
That requires the capacity to admit that you’ve made a mistake, and that’s an incredibly important leadership characteristic. It’s a kind of humility, which the leaders who rise to the top fastest often don’t display it, and then it gets them into really significant hot water. Because when they make mistakes, they have to dig in and double down on that mistake whereas you have to be able to go, whoops, no, that was the wrong thing.

Linda McIver:
It’s like in parenting, I stuff up sometimes. I shout at my kids and then I regret it and I go back, no, I’m sorry. That was the wrong thing to do. You have to be able to do that, to model to your kids, to model to your employees and to just be able to move on and get things right. Next time.

Toni Collis:
I think that’s one of the things that we really struggle with because we do have these role models in society of “leaders,” I’ll put that in quotes, the air quotes that nobody else can see. We have these leaders who it’s terrible for them to make a public apology or say I got that wrong. We have new data. We are seeing that now. I really love that some people are saying, Hey, we have new data in the face of this new data. Actually what we said before was incorrect. How we sort of as society pick them apart on that, and it’s not good because it means people are scared of pivoting.

Linda McIver:
Right.

Toni Collis:
But it also means that people are scared of getting it wrong.

Linda McIver:
So this is another fundamental part of my book, because it’s the way we teach science. At the moment the way we teach science is here are the facts. This is what we know about atoms. This is what we know about plants. This is what we know about cells. This is what we know about the planets and the way they move. We teach it as facts and we teach it as known processes.

Linda McIver:
Science is an education in confirmation bias, because we give the kids pracs to do where the outcome is known. If they don’t get the outcome they expected then often they fake it or they copy their neighbors results because the results, the marks that you get are for getting the right answer. So we’re teaching the kids to get the answer they expect it.

Linda McIver:
That is the definition of confirmation bias, and we’re teaching them that science is a rigid and unchanging thing. Whereas what we need to be them is that science changes science when it’s working right, changes all the time.

Linda McIver:
Somebody’s coming along and saying to me, your result is wrong because I have new evidence that shows that your theory is actually blown out of the water and now I have this new hypothesis, which explains the data that we have now. That is science working right, but it’s not the way we teach it. So we have to change the way we teach science if we want people to change the way they relate to science.

Toni Collis:
Given that all of us, all listeners listening to this, you and me, we’ve all had that education. We can’t go and then do it. What could we be doing? I don’t know if you have any ideas around this. I’m sort of just landing this one on you. Is there anything we can do to start untraining that? I feel like a lot of the work I do is untraining women in negative habits, things that they’ve learned along the way to where they are now. I think this one of that whole thing of confirmation bias being something that we’re actually seeking to validate all the time. What can we do unpick that?

Linda McIver:
That’s a really important question I think. Because yeah, as you say, the leaders of now can’t go back and re-educate themselves at school. But it requires that we train ourselves to be skeptical.

Linda McIver:
So every time you find yourself going, ah, I know how to fix this up. Ah, I’ve got this great result. You go, why am I confident that that’s a great result? And more than that, because that’s still confirmation bias, is going why, how do I know this is right? You have to go, how do I know this is not wrong? You have to prove to yourself that it’s right.

Linda McIver:
That means sometimes coming at the analysis from a different way or coming at the decision from a different angle or surrounding yourself too with people who are prepared to question you. I think that’s absolutely fundamental.

Linda McIver:
I call it people who are prepared to wield the frying pan of enlightenment. So they’re prepared to give you a good whack upside the hat if you’re on the wrong track. If you surround yourself by people who go, ah, your ideas are fabulous, regardless of what you’ve just said, then you haven’t got that as a safety check.

Linda McIver:
We know, again, the data is in, that organizations where there is a robust practice of challenging the hierarchy are much more successful and much less error prone. We know that from aircraft. I cultures where the sort of assistant captain, I don’t know the terminology, but where they can’t challenge the captain, where the first officer can challenge the captain, those planes are more likely to go down. If the first officer can challenge the captain then when the captain goes don’t know we’re on the right course, the first officer go, there’s an issue here and then they go, oh, shit. Yeah, there is too.

Linda McIver:
So you have to surround yourself with people who are prepared to challenge you and you have to make it safe for them to challenge you. You have to reward them for challenging you, which takes some getting used to it. It’s quite confronting and difficult.

Toni Collis:
Yeah. Making it safe, that’s a big one. I think it’s needed. We don’t realize how important safety of the people around us is, emotional safety, psychological safety. That’s coming to the fore with the inclusion debate.

Toni Collis:
But I don’t think people really get what that means just for the workforce, and as you say, being being able to challenge the data. The number of missteps I’ve seen because the data that the hierarchy means that you’re not allowed to raise your hand and say, hey, there’s a problem with that data, going back to those principles you talked about earlier is a biggie there, I think.

Toni Collis:
Okay. I’d love to pivot a little bit now. For those listening, Linda and I have been working together. Linda is one of the amazing women in tech that I coach. I’d love to know a little bit about your journey to coaching. What made you realize you wanted a coach? What was the trigger maybe for actually getting on board with a coach other than the fact that we just love to have a good old chat?

Linda McIver:
Well actually Toni, you were the trigger. I had never thought of myself as someone who wanted or needed a coach.

Linda McIver:
When we met, I had been running, I’d say for nearly two years. There were a lot of parts of it where I felt out of my depth, the financial parts, the admin parts, getting grants and all that kind of stuff. There were a lot of parts that I didn’t know what I was doing and I was learning as I went.

Linda McIver:
I was terrified a lot of the time, and oh, I did some silly things at first, like not tackling things because they scared me. So letting them build up and it turned out they took two minutes when I did tackle them. Stuff like that, where there was a certain amount of self sabotage going on.

Linda McIver:
But I’m not very good at being told what to do. In my head, I thought coaching is someone telling me what to do. I don’t want to piss that. No, part of the reason I started my own organization was so I could do what I wanted and not be told what to do.

Linda McIver:
But then when we started talking, it was just last year, and you were talking about what you do. I was like, oh, actually that sounds really useful. Because it’s not telling me what to do, it’s supporting me and giving me the guidance and occasionally wielding the frying pan of enlightenment and going you’re being dumb. You never put it quite that harshly.

Toni Collis:
I lovingly call you out.

Linda McIver:
Exactly. Sometimes I need someone to just kind of threaten me upside the head and go, can you stop? Can you actually listen? Look what you achieved. Yeah. It’s that support and kind of structure and guidance.

Linda McIver:
When I first signed on, I was like, oh, it’s more money than I would usually spend on a thing for a not for profit and I hope I’m doing the right thing here. But I love Toni so it’s going to be fine.

Linda McIver:
The first couple of sessions it was like, oh my God, this is amazing. It’s made me so much more productive and so much more constructive and so much less self sabotaging. It’s been amazing.

Toni Collis:
Oh, thank you. That means a lot to me. It’s actually funny you say about those first couple sessions. I remember a note you wrote me in base camp. For those who don’t know, I use base camp to support clients between calls [crosstalk 00:36:22] middle of the night, that call was amazing. You’ve paid for yourself already. You can take the next six months off. It was like, oh wow.

Toni Collis:
It’s so funny actually, because one of the reasons I do what I do, obviously there’s lots of reasons. I’m on a mission to get more women into the C-suite because I think when we have more women at the table being listened to me, like better data shifts and decisions, and funnily enough, full circle there, better innovations for everybody in society.

Toni Collis:
But it’s also calling us out and pointing out the fact that we’re using our biases to make decisions and I think a lot of what I do with my senior leaders, you included, and I think we all do this, I have my coach for the same reason. The number of times my coach calls me out on something I’ve been calling other people out on all week is just hilarious. I’m like, wow, really?

Toni Collis:
I needed to hear somebody tell me that, and a lot of the time it is we have these blinkers on with our own datasets, with our life datasets.

Linda McIver:
Absolutely.

Toni Collis:
Because data is all around us. We’re constantly getting fed stuff. I think a lot of the time it’s just properly exhausting and knowing when to pause and say, hey, by the way, there’s lots of data that contradicts what you were saying and a lot of the time for us as women, that’s about our confidence and our self worth and all that stuff.

Toni Collis:
So I just love that this all comes together and neatly tied up in a bow. Because leadership data, the whole thing, it’s all just built on this. We have a really bad natural understanding of making rational decisions. We all think we’re rational, but we’re really not as a species.

Linda McIver:
Yes. Yeah, yeah. I recommend if you really think that we’re rational, go read Predictably Irrational by Dan Ariely and you’ll be horrified. It’s a great read.

Toni Collis:
Great recommendation.

Linda McIver:
It makes it so clear. It’s just like, we think we’re in charge and we construct all of these post rationalizations of why we made the decisions we did. But actually we are not in charge. We are so easily manipulated and so easily distracted and confused. Yeah, rational decision making is actually quite … You have to work really hard to do it in a thoughtful fashion and to overcome your natural biases and stuff.

Toni Collis:
Thank you Linda for coming on the show. It has been an absolute pleasure having you here. You are simply a rock star and I love what you’re doing to change the world and I cannot wait for when your book comes out next year. Is there somewhere that my listeners can go to find out more about you, find out more about the work, about ADSEI? Is there somewhere I should be pointing them to?

Linda McIver:
Good question. So you can follow @datasciau on Twitter, and you can go to the ADSEI website, which is adsei.org. So adsei.org.

Toni Collis:
Awesome. Thank you. I will put that in the show notes for you lovely listeners. I appreciate you being here so much. I appreciate you being here to inspire me, inspire my audience. Thank you so incredibly much.

Linda McIver:
Oh, thanks so much for having me. It’s been a lot of fun. It’s always lovely hanging out with you.

 

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