Sarah (00:09)
Welcome to Undubbed, the podcast that's unscripted, uncensored and undeniably data. I'm Sarah.
Fiona (00:15)
And I'm Fiona. And today we have a guest who tackles head on the maths or data anxiety. We're joined by Dr. Selena Fisk, whose brilliant book, I Am Not a Numbers Person, How to Make Good Decisions in a Data-Rich World, is reshaping how leaders approach data. Selena is also the host of a great podcast, Make Data Talk, which is available on YouTube, Apple, podcasts, and
Selena (00:42)
Hey, team thanks for having me.
Fiona (00:44)
Yeah, it's awesome to have you here. we're really, really looking forward to having you on the podcast.
Sarah (00:49)
We really are today. If you've ever felt overwhelmed by data, confused about what numbers actually matter or frustrated by not knowing how to act on the data at your fingertips, you're in exactly the right place.
Fiona (01:04)
But before we dive in, remember to like and subscribe to Undubbed. And most importantly, please share this episode with someone who'd find real value in improving their data decisions. ⁓ Selena it's been a while. I've been really hanging out and now I'm a little bit nervous about today's session. We've got some awesome questions that are lined up around your book because
Selena (01:19)
I'm
Fiona (01:28)
I believe that bringing literacy to something that makes people so nervous is really going to change the way that people can live their lives.
Selena (01:40)
Yeah, that's certainly the goal. There's so much fear, isn't there, around using numbers and using data? And yep, I think we're all just chipping away trying to improve that for everyone.
Sarah (01:49)
Yeah, and I think as well recently, the fear has grown and shifted as AI and agents and everything come into force as well.
Selena, your book, I'm Not a Numbers Person, directly challenges a belief many people secretly hold. Why do you think this statement resonates so deeply with individuals across roles and backgrounds?
Selena (02:12)
Yeah, so I named the book that title because people say to me all the time, they say, yes, Selina I get it, but I'm not a numbers person. So it's the thing that I get told the most, I think, in my work. I used to be a high school maths teacher and I've really seen the impact of maths anxiety, but perceptions about ability with maths and with numbers really unfolds for people.
from kind of the early years through into senior secondary. And I am a trained secondary teacher, but I used to see young people come in as 12 year olds saying, well, I'm not good at maths or I'm not good at numbers. And it's just been really interesting to kind of watch that. as a teacher, I really saw that as a bit of a challenge to try and make kids numbers people and not hate coming to my class to learn maths. I think it really stems from.
parents and how young people grow up and what they're exposed to as little people. Because by the time they're 10, they've got an identity around whether or not they're good at numbers or not. And obviously those little people grow into adults who are in influential roles with teams, in executive roles, running different companies. And that can be pretty tricky when we now know that numbers and data needs to actually be everybody's business. It's not just good enough to hire good analysts.
and to have good people we can outsource to, we've all got to be able to ask really good questions and critique the information we're being given.
Sarah (03:36)
Yeah, nice. And I read in your book as well, you talked a little bit around people that say they're not numbers. People are using Google Maps or their smartwatches and all the gamification that sits in there is all numbers based and relating it back to them at that level as well.
Selena (03:52)
Yeah, a hundred percent. And that's the thing, part of what I do in talking about data literacy is expanding the view of what data actually is. Like there's certainly a fear sometimes to some people around numbers and spreadsheets, but we're using data all the time. It's around us everywhere. It's not just numbers in the spreadsheet.
I look at screenshots and the ways that policies and things change over time, like that's all data. It's just qualitative and it's visual, it's different type. And so it's, guess, expanding people's perception about what they can do and just hopefully teaching them a few skills so that they can have a bit more self-confidence and self-belief in what they can do.
Fiona (04:32)
Yeah, I mean, life is so complex and there are so many areas that we do have access to data the smartwatches is a great example, but one of them is financial literacy as well. it's so easy to just say, that's tomorrow's problem. And then all of a sudden you wake up and you're pushing 50.
And it's like retirement is that much closer than you ever knew before. And the whole compounding thing, you you've missed a lot of time where you could have been putting a lot of effort in
Why do you think moving from unconscious or casual data use to reflective and active data use is important for everyone, especially when making impactful decisions?
Selena (05:18)
Yeah, well that example you just used about retirement I think ⁓ is a perfect one. I know my parents never spoke to me about superannuation. It wasn't something that I learned in school. It was something that now as a business owner, I've had to learn more about and I've become more active in that because I've had more control over what goes in.
And for me now, and this is for anybody in this position, I think about when I first started my career or when I first started working in retail as a teenager, I was a really unconscious participant in that superannuation retirement planning. It was being done for me and to me, but I had no real engagement in it. like, for example, I could have been salary sacrificing a bit more of my pay.
Even though I was always good at maths, I didn't have that contextual understanding of that type of financial, literacy. I didn't understand the long-term impact of potentially salary sacrificing and what that would mean 10, 20, 40 years down the track. And now that I've essentially had to kind of teach myself, and I'm sure you guys are in a similar position, once you step away from a salary and you're taking control over these things, you're forced to be reflective.
Otherwise there is a fear that you get to retirement age and it's like, gosh, what have I done? And I've not set myself up for success or for living a life that I want to live it. So I think that example is a really great one where unconscious at one end is it's being done to you when you've got no control or engagement and we can step through. And this is obviously the model in my book that you're referring to, but the very top level of that model is that reflective user.
Being able to say, well, this is what I've put in this year. Can I put in any more? Do I want to put any more in? And then what would be the long-term impact, but also measure that up with what my current impact would be if I invest more. So you're able to engage in that kind of critical reflection and discussion and decision-making. And it's really empowering then to know that you're in charge.
as opposed to it being done to you or done by somebody else.
Fiona (07:21)
Spot on, mean, I remember thinking early days coming from New Zealand into Australia. We didn't have superannuation in New Zealand at the time. And so it was just kind of like, I get a, I think it might've been an eight or eight and a half percent at the time, extra going somewhere. The company will know the right place
to put my superannuation, I'll trust the company that I'm working for, turns out no, not so much. I, laziness, sheer laziness, I left my superannuation with a company that destroyed it for a number of years. And I could have been so much further ahead if I'd taken the time to get myself the knowledge. And the great thing is, that there's some great
Selena (07:50)
No.
Fiona (08:08)
Reddit groups out there and different things where you can start to increase your financial literacy, which of course, knits really well into the data literacy piece.
Selena (08:19)
Yeah. And I guess then it also feeds into the piece around curiosity because yes, there are all of those resources available. All that information has been available for decades, but you weren't in a space or I wasn't in a space where I was curious enough to ask the questions. And so now you could leave all of those resources alone and never engage with them if you weren't genuinely curious and wanting to learn more. And I think that's true, not just to superannuation, but any of the
data literacy that we talk about, or any of the visualization dashboards that we talk about, or how to interpret different graphs and visuals. It's like, you could ignore them, but if you're inherently curious, you know, we know that there are so many resources available now online. And with generative AI, we can get so many answers really quickly and easily to very specific questions that we have. And all of that feeds into being really powerful resourcing to build data literacy. And I talked through in the book, it's like,
data literacy and then data visualization and data storytelling. And those resources that we've got available are the best we've ever had and they're only going to get better.
Sarah (09:24)
Yeah, for sure. And I love the way that you've broken it up into those three stages, because they naturally, in my mind, flow between each other, right? You've got all the data, then you got to visualize it. Now what's the story you're trying to tell with that? So I really love the way in the book that you've broken it down like that. We spoke about curiosity and the amount of data and the resources that are available. What are some of the common pitfalls
Selena (09:40)
Mm.
Sarah (09:49)
or blind spots you think that people encounter when they're trying to become more data literate.
Selena (09:57)
that's a really good question. It's not really pitfall when they start, but there's sometimes a perception that you can get to a point where you know everything. And I really love like Simon Sinek's next work on the Infinite Game one of the examples he uses is that if somebody's goal is to get to Broadway and perform on Broadway, they don't just go and perform on Broadway once and then give up and go, yes, we'd have achieved my goals. I'm out.
It's that constant what's the next benchmark? What's the next thing I'm aiming for? And so people who are inherently very good at using data and responding to changes in the types of data we have, the ways we visualise, like this is a new field, but the people who are doing this really well are constantly evolving and learning and understanding that this is an infinite game.
And there's not just some end point where you go, okay, sweet, done, literacy is done. Tick that. can move on to visualization. Like it's just, I love that you're just laughing at that, because it's just, it's so silly, but it kind of needs to be said because like clearly the three of us, we use data all the time, but when there are new tools, when we work in different fields, you know, when people change jobs, when they get promoted, like their, access to data changes and their access to different platforms change. we know tech is evolving so rapidly.
So it's going to be a constant learning process. There's not going to be an end point where we say, yeah, okay, all good. I can move on to a completely different skillset now and work on that.
Fiona (11:22)
The reason why I'm laughing is because I would say having worked in data for over 25 years now, I feel like I'm confident in it. But reading through your book, there were things that I hadn't considered. Triangulating data, for instance, and using multiple data sources to then come through and determine, yes, this is what is happening based on different sources. Can you walk?
our listeners through what triangulating data means and how they could use it to apply it for making confident decisions in their life.
Selena (11:58)
Yeah, so every data set we have has noise and bias in it. Like it's noisy data sets, they're biased in different ways. And so if we were hypothetically just to make a decision based on one data set, and that was potentially going to be a big, decision, what we might identify as a trend or an insight in that data set could be
inaccurate potentially, or might not be the most important thing that's emerging because we've just got this one data set that's not perfect. And it's kind of funny because our black and white data brains sometimes hope and expect that data sets are perfect, but they're not. so what triangulation allows us to do is to counteract that noise and bias. If we have three to five, my limit's generally five data sets.
If we can then see that there is a very clear trend or insight across those multiple data sets, then we know with more conviction that the thing we're seeing is actually a thing. Whereas if we're just relying on one, it's tricky to know for sure whether that's the most important bit. I have two data storytelling questions around what are the trends and the insights in the data. So that becomes more robust and yeah, we can hold that belief with more conviction.
But then the next bit is around, what do we do with that information now that we know it? So we can then start to act again with more conviction, knowing that we're working on the thing that actually needs to be addressed and that's most important and that we haven't missed anything. Whereas on the flip side, if we just look at one data set, we could potentially go down a rabbit hole trying to address something or act on a particular trend or insight we see. And it might still be useful, but it may not be the most effective use of our time. So we're trying to make the most
effective and efficient decisions with the data we have. And the problem with only having two data sets is that they might be conflicting. You might have slightly different messaging happening in one versus the other. So the third one allows us to trust the majority of the data. So if two out of three sets of information are telling you a particular thing, then you can trust those two sets. Whereas if it's 50-50, it's
Hard to know which one to believe.
Fiona (14:02)
Absolutely, and I'm really looking forward to bringing this into some of the education that we do with our clients. Typically, people love to come at you with metrics. I want all of these metrics. So I think that it will really help to shape the conversation around.
Are those metrics actually important? Are they fallible? Or can we rely on the metrics that are coming from these sources? Or should we introduce other sources to really verify?
Selena (14:32)
Yeah. And even like back to the financial literacy, financial position conversation. It's like, if you said to me, what's your financial position? Well, if I just told you how much money I have in my transaction account, like that's useful, but it doesn't talk about the loans I have or, any of the debt I'm in, or what I have in superannuation, whereas actually combining lots of different pieces together, we can talk about our net financial position.
by looking at all of the different pieces together, not just focusing on one.
Sarah (15:01)
So we talked about having a couple of data sets and Fi mentioned, talking to different clients about I want all the metrics. What about when you've got a particular stakeholder and I think we've all come across them where they're like, I've got this hypothesis and I'd like you to prove it. And regardless of what the data says, I'd like you to prove it. Like, what's your advice on that one?
Selena (15:23)
Yeah, and I know your listeners will be in all different roles and so I'm trying to speak very generally and broadly to cover all of the different positions that they would be in. I really love Adam Grant's work in his book Think Again around having a hypothesis and yes we can look for data that proves our hypothesis.
The problem with going in with a hypothesis like that into any inquiry with data is that we're likely to be led by confirmation bias and we're likely to jump on the data and the data points that confirm what we believe our hypothesis to be. So if somebody's coming to you asking for that, if you believe in it too, if you believe the hypothesis, you're likely to latch onto that.
The problem with confirmation bias is that it can blind us from seeing other things in the data that are more important or actually the opposite. the part that I love in Adam Grant's book is he said, we need more people in the world who think like scientists. And he says, too often people think like politicians, prosecutors or preachers. And in those three ways of thinking, our thinking is really closed down and we're not.
we're not up for taking on new data or new evidence or new information. We've got a really fixed understanding of reality. But he says, when we can think like a scientist, we're able to collect the data that proves our hypothesis. But importantly, like scientists, we have to actively look for data that disproves the hypothesis. So I guess if somebody was coming to me saying, this is my hypothesis, and I want the data to prove it.
I would always be fact checking the opposite. What data do we have that disproves that particular thing? And then I would also be trying to present that back. I know that some leaders potentially might not be open to having that conversation, but that's where as the analysts or the people who are the most confident with data, if we're able to produce and provide both sides and try to persuade, and that's where the power of telling an effective data story, really comes into an important part of our skillset.
But we're trying to persuade that leader to actually see that the opposite is true. And there's an example in my book that I talked about, about 10 KPIs. And this executive saw that four of them were lower than previous and literally said, I'm going to put a rocket up my staff tomorrow. And actually there were six metrics that were the best ever. because his hypothesis was...
things are not going well, I'm not happy with performance, I need to find something that I can really kind of start to accelerate their work and put a bit of pressure on them. He just latched onto these four things. And thankfully, an executive in his team before he shared it, she came to me and said, Selena, this is the data we've got and he's about to rip into our staff tomorrow. I'm not seeing what he's seeing, can you just confirm it? So again, it's looking for data that disproves So in that case, was,
the six data points that actually were opposite to his hypothesis, but then also bringing in additional brains really helps and also more objective brains because I'm one step removed from that organization. I'm able to look at that data a bit more objectively and not be led by confirmation bias. But yeah, it's definitely a thing. It's definitely a problem.
Sarah (18:28)
Yeah, nice,
I like that. Thinking like scientists instead of politicians.
Selena (18:33)
Yeah, politicians, prosecutors and preachers.
Fiona (18:38)
that's definitely something that we can encourage every analyst to do is look at it with a critical eye, think about themselves as a scientist, think about that null hypothesis, try and destroy it and bring the data to the table in a way that we communicate it simply and easily with clarity.
Selena (18:48)
Yeah.
Fiona (18:58)
Selena, you mentioned Adam Grant, and I'm a bit of an Adam Grant fan as well. You strike me as a true giver. You've come onto the podcast and taken your time out today to help our listeners. Very generous. How would you say that has shaped your approach to data coaching and education? And have you ever found that being a giver in a data-driven world comes with challenges?
Selena (19:05)
Yeah.
Yeah, it's a really interesting question. I think what I would, I would like to think that people see my approach as trying to bridge the gap between analysts and the rest of the organization. So often I work with analysts who are frustrated and saying, we're doing all this work and it's not being used or accessed by the team. And I work with the team or the exec who were saying our analysts are not doing what we need them to do. And often it's the
fighting of heads and bringing them together. Like I'm not a data scientist. ⁓ I have a doctorate and I have education degrees, but it's the merger of the people and the human for me. earlier on in the show, we talked about the fact that data is increasingly everyone's business and we need to promote the fact that it is.
the business of all of our employees, all of our team leaders, all of our executives to at least have an understanding of what's relevant to them in their role, what's within their sphere of influence and what they need to be tracking and measuring and what success looks like. If we're not givers in that context, it can come across as really negative, performative, data-driven, focused on numbers, not on people.
The majority of our people are people people, they're not numbers people. So giving really helps with that messaging.
Sarah (20:38)
Yeah, and there's a lot to unpack there as well, because you kind of leaned into a little bit around soft skills and having that ability in tune with data skills. And I love that. You also touched on that data is everybody's problem or solution or however we want to frame it, which kind of dovetails nicely into a big part of your book around data democracy and making data accessible and understandable.
Selena (20:53)
Hmm. Yep.
Mm.
Sarah (21:05)
across organizations to help foster a more informed and empowered culture for everyone involved. How do you go about doing that and ensuring data democratization?
Selena (21:18)
Yeah, it's really interesting when I work with different organizations, just the, level of locked down data sets that exists within silos and within teams. so an example I use in the book is the, marketing and sales teams that I worked with in one organization where the marketing team had truckloads of data and the sales team had truckloads of data and they had no access to one another's information.
The marketing team are not getting really much feedback beyond click rates and views, that type of thing on socials. They're not actually getting a lot of information back about what products are selling the best. They're not getting feedback around the demographics that they should be targeting in the marketing around the regions geographically. So it's like these two separate silos where all good people trying to do their best with what they've got. And they had great resources and great information.
But actually those two data sets are really complimentary and would have been far more beneficial if they'd been accessible to everybody. So for me, a data democracy is where the people who need access to the data have got it. People whose job is going to be enhanced by them having that information. It's readily available. It's accessible. It's in a format that they can access. And that's existing across the entire organization.
That also sits at different levels of granularity. Like obviously in the exec, I talk about that idea of being in a helicopter and looking at the summary data and summary statistics. It's at that level, but then it's also down to the micro level for individual teams and employees who are able to access the information that I guess is a measure of or determinant of success.
A lot of the work that I do is around strategy and, know, what do you care about? and you can articulate what you care about, then we can work out what data and what metrics then support the tracking of success. And so that looks very different if you're an employee in a team of 200, as opposed to being an exec. So it's the flexibility to oscillate and move between the helicopter and then down on that micro level.
So whatever's helping you do your job and be most successful in a perfect world is what a data democracy is for me.
Sarah (23:26)
Yeah, lovely. And it's still so surprising, right? Like there's some real basic in my mind, data assets that most people in the organization need. And when we go in and look at clients and just the org hierarchy as well. And obviously there's different levels of, what access people need to that. But knowing who someone's line manager is, for example, is something that stems back to just about every report that you need to run internally and having those accessible.
you would think would be common ground, but in so many organizations, they're not.
Selena (23:59)
Yep. Yeah, absolutely.
Fiona (24:02)
And I'm curious when you're going through this process of deeply caring, are you finding in organizations it's quite different Step us through what that looks like.
Selena (24:12)
Yeah. Like to be honest, I've never worked with two organizations that are exactly the same. Like there are a lot of similarities. There's no doubt about that. and teams in similar organizations have similar priorities and focus areas, but I really connect the data strategy to the organization or the team's vision and goals as well and the strategic plan. And so even like similar
organizations in the same field, for example, their aspirations or their goals or their strat plan or their annual improvement plans will be slightly different. So then the metrics and the data they're collecting need to align with those goals. in a perfect world for me, know, we're going strategic planning into annual improvement planning, into team planning, into individual planning. And there's a very clear through line of metrics that line up so that everybody's working towards the same big strategic kind of goals.
And that looks different for every organization and it looks different for every team. And as I said lots and lots of similarities, but that's always unique. And I genuinely think that's how it's gotta be. That's how we engage people when they can see the significance and the importance of what we're asking them to do with data, and they can see the clear connection back to the mission of the business or the organization and back to their role in the organization.
Sarah (25:11)
Hmm.
Selena (25:27)
⁓ If we're asking them just to do compliance, tick box stuff that people can't connect with, it's never going to light them up.
Sarah (25:35)
An organization I was in a while ago did a really awesome strategy session on that with having the North Star and then dissecting it down. And it was really honed in on what yourself and what your team could contribute to each point within that. And I loved how it was always related back and you kind of looked at your own OKRs and KPIs through that lens.
Fiona (25:36)
for sure.
Selena (25:52)
Yep.
Yeah, 100 % because regardless of the size of the organization, if you've got your strategic plan and your North Star, for example, and then you've got all of your employees setting their own priorities and working in a million different directions, it's like, you might achieve that North Star, but it's going to take you a lot longer to get there if you've got people working in all like on all different things. Whereas if everybody's aligned and heading in the same direction, you're going to get there a lot faster.
Fiona (26:25)
for sure.
Sarah (26:26)
So you talk a little bit around being data informed rather than data driven. do want to just maybe talk a little bit about the differences there?
Selena (26:35)
Yeah. So, I really struggle with the language around being data-driven. I think it really excludes people from the conversation. I know some, some people are kind of pushing back now on my little argument about that and they're saying that's not what it's about. But, for me, being data informed is that we're using the metrics, but we're using our knowledge of the context and the qualitative data as well. that sits around the quantitative, but we're also thinking about, like what
research we have or what case studies we have around what good practice could look like or what possible responses could be. We're involving a whole lot of different knowledge sources and our own professional experience and experience in that company or in previous organizations. And so we're combining all of those things together to inform the decisions that we make. Whereas data-driven can be really negative and really numbers oriented at the expense of people.
And I was in a context when I worked in the UK where My success as a middle leader was determined by one single annual metric and I either hit it or I didn't. And it didn't matter. There was so many other metrics that they could have used to determine my success or to make an on balance judgment about where I was at. And that's just not the system that I was working in. So it was just this one thing.
And luckily I hit my targets every year, but if I hadn't, would have been performance managed regardless of all of the other things that were going on in my world. So for me, that's why I push really hard on the data informed versus data driven, because I've seen it done really poorly. And I've seen the impact that that's had on individuals and the team and the whole organization. But we actually need to lean into.
professional experience and the experience of our people because we want to tap into both the human and the numbers because the humans are the ones that adding the context and the understanding of what's going on into the decision making. The numbers alone can't do that. The numbers can't tell us what to do. It's the humans that we need to kind of lean on. So it's combining all of that for me that's far more powerful than being data driven.
Fiona (28:42)
For sure, when I read this part of your book about being data informed rather than data driven, it challenged my beliefs I've been working in data this long. This is one of the lines that we use with senior leaders about how to have a great digital transformation in your business. This is how you will achieve.
Sarah (28:42)
Yeah.
Fiona (29:05)
the next steps, you will get all of those KPIs, you will get those bonuses But it struck me as being such a nice, step to the left. It's not to say that you're not action oriented around your data, but it's saying the data is one part.
of this process that is informing me of where we need to change. Sarah and I really lean into human centered design. It's a part of what we do in helping people to unlock value with data. So this piece around being data informed seemed to me to be a really great way to articulate how we should be moving forward.
Selena (29:39)
Yeah.
Fiona (29:47)
it really resonated with me. I was curious though, you mentioned that people are
perhaps arcing up or coming back at you with a little bit on this. Can you tell us what they're reasoning for about they still believe that we need to be data driven?
Selena (30:01)
Yeah. So it's interesting. I've had some people say, well, it's the whole argument about data doesn't lie. So we need to just listen to that and, be pretty ruthless I'm like, that's cool. You can think that you're just not my people. That's just, that's just you do you boo but we're not mates. But then I've also seen arguments online where people have said, well, you can be data driven and include the human aspect and still be human centered.
in a data-driven context. And I struggle with that. Like even just the language about being driven and human-centered, really struggle to see the alignment in my brain between those two things. And I know I've seen leaders try to lead change with people and I've done it as well. And I've had some success in some areas and failed in others when I was an employee, but...
Just imagine if you're a leader going into a new team and you're saying to them, right, we're going to be Data driven now. Like I've just listened to this podcast and we all want to be data driven and, use data to drive the decisions we make and tell us what to do next. And in my book, I talk about people with math anxiety, like that's a real thing. And we've got people who say they're not numbers people. And if you've got a leader standing up saying, Hey, we're going to be data driven from now on.
I think I'm recoiling in my chair even thinking about that and I love numbers. Whereas the sell to people into our teams, if we're saying, well, we're to be informed by it, we're going to lean on it more and we want to know more about what's going on from this more objective data than say the more subjective observations or qualitative data we have. That's a much nicer way of introducing it into an organizational team or upping the ante a bit.
I think it's a pretty hard sell to people with mouth anxiety if you're using the language of being data driven.
Fiona (31:46)
Yeah, absolutely. I'll come and stand on your picket line.
Selena (31:49)
Thank you. I appreciate you.
Sarah (31:51)
Selena, are there some specific examples that you'd like to share with us of organizations around the globe that capture the transformation when people truly embrace data literacy?
Selena (32:06)
It's really hard. they're obviously looking at levels of data maturity through an organization is one is one way. So doing self reflections on that is useful. I've certainly seen changes in.
small teams in whole organizations who've really embraced the use of data. I think through the strategy piece, like by starting kind of big picture and going, well, what matters here, what matters to us and what matters to individual teams and providing some of that clarity. So we've said that data is everybody's business now. We know that. But often what we haven't had the time to do is kind of step back and zoom out and go, well, actually, what does that mean for me? Like if I'm.
employing somebody into this role in this team, what are my expectations of the organization's expectations for them around what they would do with the data or what they would collect, what they would monitor and what they would do with that information. organizations that are doing this well have real clarity around what that looks like for all of the different levels in the organization. And then individual people, so whether it's exec leaders, like middle leaders and employees.
They can talk to that. So you'd work with people like this all the time where they can tell you what they do, what they track, why they track it, how they use that information, where they access it, all of that. And so I guess for me, the goal is getting. Individuals then teams, then big sections of the organization, ideally in that same kind of space. Tom Davenport in competing on analytics says that, it's a three to five year.
change process to get organizations to that point. And that's certainly what I've seen in my experience.
Sarah (33:40)
Yeah, it's really interesting. I think people take for granted the time it takes in which to change and mature in data literacy. And sometimes we don't give it enough breathing space to let it evolve and to potentially fail on the way through, right? It's not saying that what we line out in the beginning is gonna work for everyone or majority.
Selena (34:02)
Yeah. And we're trying to change habits. We're trying to change the way people think about their world and their work. And, you think about somebody who's never used data really in their role. If we're saying, well, we want to shift that, that's a massive change for that person. I often run workshops and stuff, and I'm really open with people where I just say, this, is not going to change your data culture like that, a four hour workshops, not going to change your whole organization, but it'll spark.
people to think about what they can do and it'll start a bit of a ball rolling. But this is it's a long game.
Fiona (34:30)
think the whole habit forming and building is a really important part of this. having that initial spark of the four hour workshop, great way to get create some interest, there'll be some people that are already on the bus, there'll be some people that are reluctantly there and there'll be some people that are waving you goodbye.
and quite happy to probably to see the back of your head. But speaking of making those new habits, I did do a little bit of research And I noticed that you have a shop on your website and in that you have some data cards that are for sale. Could you share a couple of examples? Because I think that these might be good ways of creating new habits.
Selena (35:17)
part of the habits and the ability to use data really lies in the ability to ask good questions. And it's really interesting when I was writing my first book, which was for schools,
I had my editor at the time said, can you write a template that people could use to guide them through a conversation? the more I've done this work, I'm like, there's actually no set template. I can provide prompt questions and I've done that in, I'm not a numbers person as a bit of a list, but, I can't tell you the right questions to be asking because I don't know your context. I don't know what you're trying to find out.
What I was identifying was that people go into conversations about data and don't necessarily know what to look for or what to ask, or they'd just be, supported by a few prompts. So yeah, that's, that's the little box. I've got these two questions with using data and data storytelling. And the first one is looking for trends and insights. And then the second one is thinking about actions. So in this, there's like 25 cards for each.
And they're they're just prompts for people, guess, who are not super confident in using data. Like what trends and insights are the most urgent and important or what concerns do you have about the data? So they're some of the first ones. And then the actions are what's going to be your first step and when are you going to do it? Or what questions do you have about potential actions or next steps? So they're just prompting questions that get people thinking about what's possible.
The idea is not that you use all 50 of them at the same time. You just select a couple of them and just even just use two or three insights questions or two or three actions questions. But I think one of the problems I see in conversations about data, whether that be like with just colleagues or with teams is that we spend a lot of time in the, what do we see and notice and what are the trends in the insights? And we don't spend enough time talking about what we do about it.
And so I guess the challenge is how do you weigh that up equally or if not even have more questions about the next steps and the actions because at the end of the day, if we're not actually acting on that information that we've got, the collection, the storage, the visualizing, all of that completely a waste of time. So the goal is to get to that decision about what we do, what we do next.
Fiona (37:21)
Do you think that will make people say, well, you're actually data driven and not data informed?
Selena (37:25)
I hope not.
Fiona (37:28)
really love those cards and I am pretty sure we're gonna buy some at the end of this podcast as well. I think it's a really lovely way to take away some of the anxiety as a developer or in having the right ⁓ questions to ask people.
Selena (37:32)
Hahaha
Fiona (37:46)
Often when you can ask a few questions, you get into the swing of things and you feel a bit more relaxed and you sort of loosen up. So I think it's a really fantastic way. And I also think it's really good for newer analysts as well who are out there. We've actually had on the past couple of podcasts, both sets of guests have talked about the importance of asking great questions. And they've talked about a book by I think it's Warren Burger,
Selena (37:49)
Mm.
Yeah.
Fiona (38:11)
the big book of questions. And so just really getting yourself into that mindset of being curious and knowing how to ask a good question,
Selena (38:19)
well, it's even and as a designer, there's also the questions around. the user experience is what the user of the dashboard that you're designing or the report you're designing actually needs. So there's a whole other series of questions that we could do about that.
Sarah (38:37)
Yeah. And I love with the cards that you just showed, it's something that you could use on yourself and just kind of pick a card and figure out how you would answer it or maybe how you think a potential stakeholder would answer it. And then also start using it in group environments as well, just to build up that confidence and maybe do it as a fun activity within the room.
Fiona (38:38)
For sure.
Selena (38:46)
Yeah.
Yep, for sure.
Sarah (39:02)
really cool. So there's a question that we've been asking a few of our podcast guests recently. And it's, can you share an example of a data disaster you've encountered or witnessed? And what would you suggest doing differently now with hindsight?
Selena (39:22)
my performances as a teacher in the UK used to get ranked from top to bottom. so against other teachers. So we'd go back to work at the beginning of the year and our value added scores, used to be ranked. that, that was a disaster. Yeah. Within the school. Yep. Yeah. So we'd be ranked. but the one I, I'm going to spend a bit more time on was one I saw, I was on a plane a couple of years ago and
Sarah (39:34)
What, that was within the skull? Was that within the school?
Selena (39:46)
I was sitting next to somebody who was like dutifully going through printout of like PowerPoint slides and they were writing notes on them. And the vibe I got was that this person was about to present this presentation to a group and they were just writing notes and going over their presentation. And like with no exaggeration, there would have probably been 50 slides. And I reckon every slide had a minimum of two, even three charts on each slide. And.
And I was looking at it and I'm never going to be the person that would say anything. I would not do that. But I was sitting there thinking, mate, you would have lost me in the first five to 10 minutes. And I'm a data person. And I think as a disaster, even though I wasn't there for the disaster, if you want people to genuinely and authentically engage with the information you're sharing and engage in the conversation about the actions and what to do next.
We as the data storytellers and the presenters of the information, we have to be so selective about what we put in front of them. And we have to understand cognitive load theory because five slides into that presentation, I've hit my capacity. I can't take in any more information as much as I would want to. So yeah, I didn't see the disaster unfold, but I did witness the prep disaster.
Sarah (41:06)
think we've all been in presentations where that has unfolded. One of my favorites is like, let's see as many words on a slide as we can and let's read off it. It's like, no, please don't. And you kind of look up to the left and you're like, there's like 90 slides in this deck and we're gonna be here for the next hour listening to someone read it. Yeah, not
Selena (41:10)
Yep.
Yeah.
and then the presenter goes, look, I know you can't read this, but cool. Why is it on your slide? If I can't read it.
Fiona (41:33)
Well, yeah,
for sure. but even Sarah and I have been guilty of that. I think actually for us coming in and building our business, when we go and present in a different area, there's not necessarily data, sales for instance.
there's a tendency to want to put everything on the slide because they might have to take the pack away and read it. And so it's really easy to forget that you need to be so careful about how much information that you're presenting to people. So that's a good reminder.
Selena (42:05)
Yes.
Sarah (42:07)
And if you need them to get into the details later, you can share an appendix or some handouts or something as well. So there's ways to solve for that.
Fiona (42:08)
All right.
Selena (42:15)
100%.
Yep.
Fiona (42:17)
Yeah,
for sure. No one wants to be burning a bin fire of PowerPoint presentations. Before we wrap up, is there anything else from your experience or research that listeners today should particularly hear about data literacy?
Selena (42:34)
the space I'm working in more and more now is, so I think about it as pre-decision data and we're getting better at the pre-decision data and working out what to use and how to use it and how to make good decisions. I'm spending more time in the post-decision data at the moment. And so while we get good and build the data literacy around the pre-decision, it's also then being able to loop back on the other end and go, well, did the thing we change and do actually work.
And what data are we collecting to be able to talk to them? What evidence we have of that and the success of that change? that's the next space I'm working in. So it's not only just the pre decision, but also the post decision to know whether or not what you did worked.
Fiona (43:15)
That's really interesting because I think that even data people struggle struggle with the post implementation review. Well, what's my ROI or how did it go? And so I really love is there going to be a book about it? get it. Ah, that'll be really exciting.
Selena (43:23)
100%.
Yeah, there's a white paper first.
Sarah (43:33)
exciting.
Selena (43:35)
Yep.
Fiona (43:36)
Yeah, for sure. I think it's certainly something that I've seen that people need help with because there's no rubric for it.
Selena (43:46)
No,
And so I have a new model. ⁓ But there's also impacts that we have that are intentional and unintentional. So even if you plan, for particular types of impacts to be positive and, there are always going to be things that emerge through the change process that you didn't expect. So you have to be able to actually have the data and the evidence to show
those unintentional things, even though you can't go back to the beginning and collect some baseline data. Anyway, that's probably a whole other podcast episode, yeah, it's exciting. I'm enjoying it.
Sarah (44:17)
We can always have you back.
Selena (44:19)
Awesome. Sounds good.
Sarah (44:20)
Okay, we're gonna switch gears a little bit now and go into a quick fire lightning round. So we're gonna ask you a couple of questions, quick fire, and if you can just come up with the first thing that comes into your mind. Fi, I'm gonna pass it over to you for the first question.
Fiona (44:36)
Righto, what's one common myth about data that needs busting?
Selena (44:42)
that it sucks.
People who hate data. People who hate it. I'm like, no, it's so good.
Fiona (44:50)
Oh
okay. I thought you meant the other way round. I'm like, no!
Selena (44:53)
No,
Sarah (44:54)
Me too!
Selena (44:54)
no.
Sarah (44:58)
Okay, what's one metric that gets way too much attention?
Selena (45:04)
oh one metric, was just going to say KPIs in general. Like it's, it's often not the KPIs that we can directly influence. It's all of the things underneath it then feed the KPIs that probably need more attention than the big headliners.
Fiona (45:19)
Hmm interesting. Okay one small signal that says this team gets it.
Selena (45:26)
⁓ when you're in a room and you might ask them a question or whatever, and they're just riffing with themselves and they're like, what about this and what about that? And you can see the energy and the curiosity and the genuine engagement. Like they're people who get it. They're my people. None of these data driven people.
Sarah (45:46)
Complete this sentence, great data work always starts with.
Selena (45:51)
Why?
Fiona (45:52)
Love it. All right, that's us for today. So what an insightful conversation. Huge thank you, Selena, for sharing these powerful strategies and insights with us and stories as well.
Selena (46:05)
Thanks so much for having me guys, it's been awesome.
Sarah (46:07)
Thank you. Remember the data journey doesn't have to be daunting. Selena's book, I'm Not a Numbers Person makes it approachable, actionable and relatable. Grab a copy and start building a data informed culture today. You can also check out her podcast, Make Data Talk. We'll put the link and the cards link below. Check it out.
Fiona (46:08)
See.
And if you've enjoyed today's episode, please like, subscribe, and most importantly, share this with your teams, those people that are not numbers people, your networks and anyone else who's ready to move from data hesitation to confident action.
Sarah (46:43)
Until next time, stay curious, keep exploring your data, and thank you for joining us here on Undubbed, Unscripted, Uncensored, and Undeniably Data.
