In this episode, we sit down with Reema, the passionate founder of People of Data,  With infectious enthusiasm, she shares how storytelling and communication are at the heart of effective data analytics. From her journey as a mathematics graduate to founding a company that’s reimagining how organisations collect and understand community data. We discuss the importance of diversity in tech, the potential of AI in data analysis, and her ambitious goals for the census, whilst providing  valuable perspectives on making data more human and demonstrates how clear communication can bridge the gap between numbers and narratives.

The Comms Takeaways

Reema: data is not scary that we can actually do something about it. And that we should, because it is literally impacts all of us. Whether you go the bus and you’re paid with cash, or you’ve gone to the supermarket and you, use your loyalty card or just done whatever, we’re creating data

literally everywhere.

Maaria: Welcome to Pros and Cons. In this podcast, I talk to people about their professional and personal stories, uncovering the different ways and common themes of resonating with an audience. After all, communication is essentially storytelling. I’m Maria Ginai and today I’ll be talking to Rima Vadoliya, passionate data storyteller, mentor, and founder of People of Data.

We’ll be discussing how she got into data analytics, her aspirations for people of data, and how she’s on a mission to change the census.

Maaria: we tried to meet up in person, but we didn’t make it at the Fringe, but it’s nice to see you now and catch up with you.

Reema: absolutely. Thanks so much for having me. Edinburgh Fringe Time is a busy old time, but maybe next year. I’m sure next year will happen in person.

Maaria: Absolutely. Oh I’m up at the Fringe every year, so we’ve got plenty of time. So we’re going to delve into a bit about You, your career, what you’ve learned about communication. From the chats we’ve had and what I know about you, I would describe you as passionate about using data to tell stories and to bring people together.

So that’s what I would say about you. Have I got that right? Do you want to tell me a bit about yourself and your career?

Reema: Yeah, absolutely. Am I allowed to swear?

Maaria: Yeah, go on.

Reema: so I had a conversation some with someone today and they used the same word was passionate passion and data are words that probably don’t often go together. But like I fucking love data I think it’s such a cool area and domain to work in because there’s so much in it that’s super creative Really so much about storytelling and I think you know, of course communication and data and They really do go hand in hand, but I think often people think about data and they think about spreadsheets and they think about numbers and that they’re not good at maps. And so I’m here to bring all my passion and energy and excitement and love, honestly, for data so that people can start to see it in a slightly different way. So I did a maths degree. That’s where my career in data started. I didn’t really enjoy it because it was so theory based, but where I got to eventually was applying to a job as a data analyst intern and just falling in love so hard. And again, I’m sure people don’t make rom coms about falling in love with your job in data, but maybe mine can be the first one.

Let’s see.

Maaria: Let’s, they should though. They should.

Reema: let’s make that happen or maybe even a fringe show, right? Let’s put it on at the fringe next year.

Maaria: Yes.

Reema: So yeah, I, I just found, I just loved it so much and so hard because of that way that you are looking through and solving these logical problems, whilst also applying it to a business context and really seeing some of the levers changed and pulled as a result of the exploration that you’ve done. it turned into a big adventure for me looking through all of this data and Finding where the stories were and finding the people to tell that hey, did you know this thing is happening within your like masses? masses of data yeah, I’m gonna go quite fast through my career history, but I’ve been a data analyst a data Engineer a data scientist on data consultancy.

So had lots of experience manipulating data presenting it telling people, advising people how to build systems so that they can get the most out of their data. And my absolute favourite part of it is telling those stories. So yeah, last year I quit my job to start my company, People of Data, which I’m sure we’ll talk a little bit more about. But really it just all came down to the fact that I feel like there’s more that we can do with data relating to the human experiences that we have. And how do we bring those two things together?

Maaria: Amazingly summed up and your passion for data and your like enthusiasm is so infectious. You just absolutely, you just absolutely love it. And it might seem as you said, like a bit, maybe a bit like try or people wouldn’t really maybe understand what. working with data being in a data analytics job is, but increasingly now we’re seeing those types of job be really important in a lot of companies.

So what does a typical data analytics role look like?

Reema: I, for me, I think it does come back to storytelling and I think where you get the really great data professionals is where they remember that and they know that I think it can be really easy to get involved and bogged down in the technical solutions and, and I’m sure some technical people would want to come to speak to me and talk about how the storytelling is someone else’s role.

But I think if we can always keep track of what is the context of the data. that we’re trying to process, that we’re trying to analyse, that we’re trying to, know, maybe do some predictions with, then we can get the most out of it because sometimes you can be just churning away at building something, doing something with data.

But if you don’t have that context, then you don’t know for sure that you’re actually doing the right manipulation or, and, or storytelling with it. So a typical kind of data role, I guess could be somewhere, something where you’re taking, different bits of information from multiple places in some instances, moving it through and trying to manipulate it so that you can get something that’s more maybe summarizable so that you can tell other people, Hey, look, this is what I found within here. So that’s fairly vague and a broad high level, but I think it obviously depends on which sector you’re in. If you’re doing something like e commerce, you’re wanting to understand what sales have you had, what stock have you got who’s coming to your website, where are they coming from, and then what is actually being sold and what’s going on.

You might then also look at seasonality and all these different things, but for me, the way that I see that is just different forms of storytelling.

Maaria:  the first time I started, you speaking about data, it was on a marketing meetup webinar, which was really good and you were talking about dashboards and using, like having the right type of dashboards to tell your story so obviously data can be quite overwhelming at times, especially when you have a lot of it and you’re trying to tell the story.

The story using data. So what are some really, important things you can do to not get super overwhelmed in all the numbers and, and try and create a really concise and effective story using data?

Reema: Yeah, so I think having story or the context that you’re trying to tell just somewhere nearby sometimes that just means having it on a post it or whatever kind of digital workspace you’re working in, but then to use that as your anchor, and then I would do is say, okay, we’re trying to tell this story on how many sales have we had on this e commerce website. But knowing then that actually there’s so much data available to us and maybe writing all of that stuff down again, digital or otherwise, whichever way your brain works best, so that you can just see it all and then start to say, okay what, are the components that I actually need here? And this will work differently for different learning types and different approaches and different kinds of communication styles that different people have, which is brilliant because that’s where it’s really useful to work in the team because you can get some complimentary skills too. But it’s about taking that anchor, understanding what’s available to you, reducing it down then to something slightly smaller and then saying, okay, let’s come back up to our anchor here and say, okay, how do we bring that story in? And. can work as a methodology, whether you’ve got something that you need to send out by the end of the day, and it’s worth just spending 15 minutes and just throwing everything down and seeing what’s happening. can work also when you’ve got a project that’s six months long and you’re building like a much bigger, comprehensive kind of suite of dashboards for a massive company that might have tens of maybe hundreds of systems or a much smaller company over here that just has three or four systems like that. The approach is still the same It’s just the time frames that are slightly different

Maaria: And storytelling with data is something you are an expert in and you show that through people of data, which you mentioned previously. So let’s get into that. What prompted you to create people of data?

Reema: Yeah, so I’ve been working in data and analytics for the last seven years and as aforementioned, I love it a lot there’s lots of exciting stuff happening within that space and I can see how a lot of where we get to as professional data professionals is taking data that has already been created somewhere else and then trying to do the best that we can with it.

And there’s been multiple times where I’ve thought, Oh if we only collected it in this slightly different way, actually, that would have really helped us with the analysis that we’re doing now. And so bringing all of that in to my own lived experience of being woman of colour and, and some other things too, but like the protected characteristics forms that we get, whether we’re doing a HR signup process or we’re going to an event, or, there’s just someone that wants to understand you better in big air quotes. I think that approach there isn’t, Isn’t really conducive to understanding someone’s experience because there’s so much nuance in those protected characteristics and even more importantly There’s so much intersectionality so all of that kind of came to head and just thinking about who are the people behind the data and so people of data exists to help in its current format people of data exists to help organizations who are community based and or run events to understand who is turning up to their space.

What benefits are they getting from it? And how does that align to your goals? So that you can tell your funders, your sponsors, your partners, Hey, Give us money here to run this event because this is why we’re doing it. This is the benefit that we get Not only that you’ve got for your internal teams They’re really purpose driven then to say we know why we’re working and grafting really hard on putting this event on because You know the 50 people that are going to come to our space are going to get this out of it or the 1500 or whichever scale it might be and then also it’s really useful to tell future participants and audience members Hey, like this is why you should come to our space.

We really care about you. We really care about meeting this goal or need that we have. And therefore then we’re going to ask you better questions. We really care about your experience and not just asking you a generic form that says, how was this event? Would you recommend it to your friends and family? Do you? What protected characteristics do you fulfil? And yeah, really just stepping away from that sort of mindless state of collection to being super intentional so that we can actually do something good and great with it as well.

Maaria: Amazing. And how have you found creating a business just on that sort of level of founding your own business based on something that you’ve been so passionate about and that you’ve built your career on?

Reema: Yeah. To be very, very vulnerable and honest about it. I think there has been a big process recently where it’s understanding that The, the success of this business that I’m building is not directly linked to the, like the validity and the validity of the experiences that I’ve had that have led me to build this.

So not just being a woman of colour in tech and data and like being a minority there, but also, Being a person who fills out these forms, who feels frustrated every time, like all of those things are still true. success of this company does not, not invalidate those experiences that I’ve had.

So that’s the super vulnerable answer. the more kind of broad answer is how have I found it? I just, I absolutely love it. Like it’s scary. This is not necessarily clear what’s going to happen next all the time. And things can change so fast, but for me, And I guess my learning style again, it really works for me to be able to go with and how they turn up.

And yeah, I’m, I’m learning so much all the time, which I love learning is one of my absolute other passions as well. So really exciting. I’m so excited to see in a year’s time where everything is that So excited and so proud of all of the stuff that I’ve done in the last 12 months too.

And yeah, just really excited to build out this thing and to really support and help so many people be seen because I think at the moment data has a bad rap. And I understand the reason why, because a lot of companies and organizations are not using it well, but. If I can bring some of this passion and excitement that I have to people’s approaches and thoughts around data, then I think we can do something really cool.

Maaria: Absolutely. Absolutely. Absolutely. So if, say you’re an organization and you want to start harnessing your data effectively, do you have any key tips that you can give people generally about, using your data and making your data work well for you?

Reema: Yeah, I think it’s really understanding the why, and that maybe feels annoyingly simple, but it’s really understanding why do you have that data? What is it that you’re trying to do? Why do you exist as an organization? And coming back to that, and that’s not a data question, actually, that’s a, that’s a purpose, that’s a vision, that’s a goal.

That sort of question and understanding who do you exist for? And, and those sorts of things can be really useful to give you some parameters to the data exploration that you’re going to do, because otherwise you can end up feeling to your earlier point and question, how do you not feel overwhelmed with the amount of data?

And, and I think this is how it was by asking some really simple questions and being as simple as you can with it. And. And whatever people’s opinions are about AI is like it can be useful to ask chap GPT Like how would you summarize this business? And what do you think is important there? Because then that can just help you to just yeah just to narrow it down a bit because otherwise having a massive scope is not useful But use that basically to start to think about what tools do you have what data exists where? And start to just play a bit of You mapping of, okay, this thing goes over here and this thing goes over here. And if that does mean that you’re a visual learner and you want to get some little pieces of paper and kind of move things around that I find that really useful. Whiteboards also useful also digital mirror boards and all these different things. That feels to me like it’s a lot more dynamic and that there’s a lot more choice and control in it, rather than having just a big load of systems everywhere that everyone’s frustrated with because they don’t get the data out that they need. It’s just about thinking about them as movable object objects.

Maaria: You brought up AI just now, so let’s talk a bit about that. And that is going to be a massive topic. But can you give us an overview of how AI is changing the landscape for data analytics.

Reema: Yeah, I think it’s saving time. But I think sometimes the time needs to be redirected again into I’m going to sound like a broken record, I think, but into asking better questions. I think that’s always the most useful thing is not just Doing all of the data wrangling and crunching and processing, but asking the better question so that all of that time, whether it’s time spent by an AI, whether it’s time spent by a software, whether it’s time spent by your teams is more effective, most effective so that you can actually get the answer that helps you. Because I’ve heard many times over and still hear, unfortunately, that what data do you need? I need all of it. And it’s I don’t think you do because you’re not going to have time to. To, to go through all of that information and an unrelated to data quote is it takes more time to write less is the same for data as well. Like it takes more time to crunch these numbers down into something that’s really digestible or it can do anyway. Because there’s so much around data that is not collected properly, and it can feel, it can feel as though, oh, it should be really easy to just say how many people turned up to this event, but it’s not, especially when we think about physical spaces, you might have 40 people that have signed up on your eventbrite, or something like that, but then you’ve also got 15 people who are just, walking past or didn’t sign up, but they knew that when it was coming.

And so it’s just okay, now we’ve got some online and offline data. So suddenly that thing of how many people have turned up to my space? Much difficult. How does that relate back to AI? I think that’s the thing is we need to think about how AI can save time and how we can use that saved time to spend it somewhere else and spend it and invest it in a place that’s really. useful to, to have that extra time to explore because yeah, I think I’m rambling a little, but the key thing that I think is that AI can definitely save us time, but we need to not just take it and trust it verbatim. We need to just do some critical thinking around how we’re using it as well.

Maaria: Yeah, I think that’s the conversation that a lot of industries are having is AI can save you time and it’s going to be so powerful and it’s learning all the time, but how can you use it effectively and not just rely on it and then sometimes it might get things wrong and, and how do you harness it properly and then evolve it?

With the proper sort of safety aspects in mind and that sort of thing. So that sounds like it’s, those are the conversations that’s having across industries, including in sort of the world of data. So let’s go back to you and your other activities apart from creating people of data. You’re also keen on mentoring. I believe. So what do you think makes an effective mentor?

Reema: Communication again, just thinking about what

Maaria: Excellent. That’s

Reema: Yeah,

Maaria: the point of the podcast.

Reema: Yeah, but I think it’s really about understanding why you’re both showing up or whoever is showing up into the space. I’ve had a really, really successful kind of mentoring experience. Partnership agreement type, I don’t know relationship I suppose Over the last couple of months where it’s been super super intentional just to understand why why do you want to spend time with me?

Like you I understand you want to learn more about data, but what what about data? What does that mean in your context in your industry? And then at the beginning of every session just understanding how are you turning up today and what is it? is how the session is going to go. I’m going to draw stuff and we’re going to talk about data systems. How does that feel for you today? And so just giving some flexibility and time to go into here’s what it is that we’re going to learn today or explore today. in the context of the fact that we’re going to have six sessions and here’s like our rough outline over the next six months while we have a once a month session. And most importantly, really, is to set some ground rules. So if you can’t make it, that’s fine, but just let me know. And like those that sort of things that really allow for like respect and I think just understanding that we’re all human beings. So again, that can sound wishy washy to some people, but I think that’s the stuff that’s really important because if you’re in a learning, any learning environment and or mentoring environment that you’re in, it’s about feeling safe enough to ask questions that are like, that doesn’t make sense.

To me, and it’s not a given that that is the case in every single environment. So yeah, communication nicely, handily, cause this is the podcast we’re on. but also just that understanding of the purpose again, just making sure that that drives all the conversations that are had.

Maaria: Have you been on the other end of that relationship? Have you had any amazing mentors who have Guided you through your career.

Reema: Yeah. So many, so hard to name just one or two, I think, and that’s absolutely the reason why I do it now is because know how valuable, to have someone invest that time in with you and to just want to see you grow and thrive. And so for me to be able to do that for anyone else is really useful.

I, I’ve heard mentors say this to me, but I for sure feel this too as a mentor and as a mentee. It’s it’s a two way thing. There’s definitely something for both individuals to learn. It doesn’t almost matter that it’s a mentor and a mentee. It, there’s always something to learn. Certainly in those successful environments where there’s like real good respect there, but yeah throughout my career multiple different mentors some where it feels like it’s a very official and and like clear that that’s the relationship that is being had. And other times where you’re just having a conversation with a random person where you’re just like, that’s a big nugget of wisdom that I’m going to be taking away and carrying that forward.

Maaria: Absolutely. And seeing as you are now someone who is, helping mould the journeys of, of other people, what would you say to people who are maybe interested in getting into the world of data and data analytics or someone who is just new to the field, what sort of tips and things would you give them?

Say to them

Reema: Yeah. That’s a great question. So I think it’s just understand how do you feel to, Actually play around with and manipulate data. So some people will tell you excel is really terrible, but I disagree I think excel is a really good tool to just start getting in and just start playing and understanding how data can move and how things can be manipulated But also, of course, there’s certain programming languages or data query languages like SQL or Python where or R where it’s looking at manipulation of data, but also computation with data also looking at how can you really apply that in a way where you’re doing more advanced statistical things with the data, too so just understanding how does some of that feel for you to actually be hands on doing the logical problem solving In relation to the data, but also understanding how do you enjoy asking questions and more importantly answering them? because that’s my favorite thing about working in data is having a question then leaving the situation with 10 more questions and just going and trying to find some answers to get some sort of wisdom and value out of that. And also being humble enough to know that you’re not going to find the answers all the time.

And I definitely am not humble all the time, I think there’s Oh I can definitely just find this answer in 10 minutes and an hour later, I’m like hasn’t, happened the way that I thought it would. But yeah, the key thing really is to get comfortable with asking questions, get comfortable with manipulating information. And manipulating data then in particular you don’t have to be a great mathematician to be a data analyst. I don’t believe that and I’ve seen that it almost annoys me that I did a maths degree because people will be like, yeah, but you did a maths degree. So you have this as your background and yes, that is true.

But I’ve seen some really brilliant analysts who have an entirely different background, but the key thing that they have is good problem solving and good intuitive question asking skills, and that’s where you get a really great analyst. So if that sounds like you, then this feels like a really great thing to explore, and I always want to help more people get into data, because it’s Obviously, as we’ve discussed, I love it so much.

So if other people can come in with even just a little bit of love for it, I think we can start to really change the narrative of how data works. So yeah, if people want to reach out to me on LinkedIn, then I would be more than happy to have a chat with them too.

Maaria: Brilliant and you mentioned having different viewpoints and some of the best analysts you’ve come across have different backgrounds which have brought them into data. So how important is that diversity and those different perspectives How important is that in the world of data and in the world of tech more broadly

Reema: It’s so incredibly important because again, nicely, as this is a podcast, it all comes down to that communication of how are we solving the problems that we have. And I think having a more diverse set of individuals who can look at those problems that we’re trying to solve means that we’re coming at it with a lot more empathy.

And again, we’re talking about tech and data and I’m talking about empathy, but that’s the way that I approach these things. I think that’s probably part of the reason why I’ve done it. I’ve come to love it so much is because that’s what I see. Like we’re solving problems in the world, problems in the world relate to people in some way or another.

Even if you’re looking at sustainability and like ecological things, still relates to us because we, we live in the world that’s, that’s changing. And being impacted by all those things. So people with a more diverse set of experiences and lived experiences means then that we can start to bring some of that into in the problem solving.

And I know for sure that’s come up a lot for me is can be sat in quite like a homogenous room, like in terms of what you see in terms of the characteristics of a room but bringing in an outsider’s opinion. even if it’s, and I use outsiders, that feels like not the right word, outsider in the sense that there’s a different set of lived experiences, is really important because then what we can do is say, we need to make these other considerations. Sometimes that can feel like things are slowed down, but I think ultimately the end result is much, much better. more useful, much more productive much more impactful to just generally to society, which is quite a big statement to make. I appreciate, but I think that’s what we need to do when we think about data is the fact that we can really have like huge world impacts. when we work in data because it is exists literally everywhere, whether we like it or not.

Maaria: and there’s nothing wrong with aiming high as well

Reema: Yeah, why not? Yeah, it’s a Friday afternoon. Let’s

Maaria: So let’s come back to people of data. You’ve told us a bit about what you do as a company and you being you and loving data, you’ve probably got lots of different ideas about what people data is going to achieve and lots of different forecasts. So what is your ultimate hope for people of data as a business?

Reema: Yeah, there’s it’s trying to understand how much ambition I should should or could share but the thing for me, I think is just to go as high as I can in terms of I’d love to change the census thinking about how that exists and how we understand society because those questions, we do a census once every 10 years, those questions have got some incredible minds on it who are doing. bits of analysis and research to understand what questions we should ask, but there’s something there about how the rate of change in society and in terms of the demographics happens quite fast, especially in the age of the internet, right? Because especially with people going on Tik Tok and Instagram and all these different types of things that I probably am to age out of too is these like everyone has an ability to access so much information and that means they have the access to their own identities and be different people.

And so think there’s something there about measuring the. types of lived experiences in society in an entirely different way, so that when we do design that’s based off of the UK, population looks like this, then we can start to build it in a way where people can build solutions to problems that are actually existing rather than building them based on these characteristics that have existed for decades.

the past 20, 30 years as a measurement framework, but actually are no longer applicable because we live in such an intersectional society, which is brilliant. That’s the best thing as to your last question, like, why is diversity important? Because everyone has a different perspective and that’s what’s really great about it. But I think there’s something there around understanding what Certainly to begin with the UK population looks and then maybe in the future something else too. So that people can feel like yes, i’m answering the census data but I can see that that’s going to have this impact over here and it’s going to actually impact my real world life very shortly too.

So not 2031. Hopefully 2041. I’ll be changing the census

Maaria: Amazing. Changing the census, changing the world.

Reema: Yeah, a

Maaria: One question at a time. That’s it.

Reema: Yeah

Maaria: what is your favourite thing that you’ve either done or that you’ve discovered through data throughout your career, let’s say?

Reema: that’s a big question. There’s, I mean, there’s so, there’s so many, honestly, there’s one of the biggest things for me because of the way that I’ve moved through my career is understanding that data problems are actually the same in so many different contexts. It’s all about, you’ve got some data, whether you’ve gathered it yourself or system has created it or someone has given it to you. And then you’re trying to store that somewhere. You’re trying to process that somewhere so that you can actually. bring it back out and tell a story with it and then make some decisions off of it. Of course, then we have AI and machine learning and ultimately it’s just statistical manipulation within that processing bucket too.

But maybe it’s too reductive. Maybe some data people will be like, no, I think you’ve oversimplified it. But from my perspective, that’s how I see data works across and in so many different ways. I worked at a consultancy before where you’re working across multiple different industries and. Yeah, that’s what it’s come down to for me is that this is the same, it’s the same problem everywhere.

It’s just trying to understand how do we get people to understand that that’s the process flow of data and that there is control along that place that plane of the movement of data. Not only just that, then also that we shouldn’t be scared of it and that it impacts everyone. So yeah, it comes down to that is data problems. It can seem and look so complicated, and sometimes they are, but if we just reduce it down to the simplest form, then we can at least start to get a handle on it and say, okay, here’s where we should start. Here’s a starting point. Here’s another starting point. Here’s how we get one team working in this place and another team over here.

Maaria: Yeah, brilliant. I think that’s, that’s one of the things, again, across lots of, disciplines and industries is how can you make it simple? And I think i’ve probably answered the next question I have for you a little bit, but I don’t want to put words in your mouth so tell me what you think are the most important things for effective communication

Reema: Yeah, it probably, I’m curious what you think my answer would be, but I will go with mine first.

Maaria: Okay.

Reema: is just about simplicity. I think it is just reducing it to a simple form so that you can explain it to your most technical person and the least technical person in the room also. And also sometimes that also might mean explain it to someone who has no idea of your context at all.

If you can, that whole adage of, if you can explain it to a five year old, then you can, you’ve done a good job. I think is really important. I flip it a little bit kindly with my, with love to my mom. If I can explain it to my mom then also, then I think that’s a good thing. Cause she doesn’t really know what I do. And yeah, it’s really interesting to see when I can, watch her processing and understanding what, I’ve said that feels to me like that I’ve communicated well because I’ve not used unnecessary language or, and or jargon to explain something that could otherwise be seen as complicated. Yeah, I think simplicity is my answer to that question.

How, how does that feel for you?

Maaria: Yeah, I mean, that’s one of the things which I try and live by when I’m looking at just communicating in everyday life, really. And that’s quite funny that you said the story about your mom, because I think I’ve said on the podcast before, but there was an instance I was doing my PhD where I was Running a presentation by her, which I was going to do like a seminar in my department.

And so I explained something to her and then she went, I don’t get it. And so instead of trying to reword it, I just said it slower. And she was like, you can say it as slow as you want. I’m still not going to get it.

Reema: Nice.

Maaria: I need to work on this. It’s definitely that thing of boiling something down, something complex to its very core, to then be able to build it up again and, and create the message for whoever you’re talking to.

And then again, as you’ve said previously, trying to tell a story and having that as your base to how you present something. And again, going back to my PhD days, A lot of our lecturers used to say, when you’re going about your thesis, think of the story first before you then go and look at your data and then go and explore what that data is saying and go that way.

And I had that in my mind, but it was never really, I was like, Oh no, I need to do these experiments and see what the data says. And it’s all very. Data driven, but then you get overwhelmed. And so it was looking back on it and I was like, no, having that idea of where you want to go and if the, if the data says that’s, if the data correlates as good, if it’s not, you, you go with it and you explore those avenues of where it’s taking you, but always trying to have a sort of like.

basic outline in mind of like, why are you doing this? What do you want to show? And

Reema: Yeah.

Maaria: that sort of thing.

Reema: Which goes

Maaria: yeah,

Reema: sticky note earlier that we were talking about, like what’s the point? And sometimes if you, if you end up hitting exactly the point, great. And if you didn’t, that’s still useful information. I think it can sometimes feel hard when you’re looking for a pattern somewhere and it doesn’t show up, but that is still information that. Something that we thought was true actually isn’t and that’s where you get to hypothesis testing and that sort of thing and That’s just as valuable. Although I have felt very deflated when I have spent days looking at something and realizing it didn’t say what I thought it was going to I’m sure you probably had

Maaria: yeah.

Reema: your PhD as well.

Maaria: Oh, definitely. I guess that’s an interesting point actually, because, how do you, prevent, going, like having that story in your mind, how do you prevent discarding data that, disproves it, but actually is, another cause to go down? What do you think people can do if they’re working with data to not just discard something because it doesn’t fit with their hypothesis or narrative?

Reema: Yeah. On a really practical level, I always try to keep a bit of a log of what it is that I’m doing. Here’s what I’m just exploring now. Here’s why I’m discarding that so that when someone else can understand you’ve discarded this real big assumption right at the beginning, so everything else is, a bit challenged. something that we can believe because of that, I think that can really, really help but it’s not, it’s not easy, and I’ve been in conversations before where someone said can you not make this number look like this? And I, I can, but I don’t feel confident or comfortable doing that. Because that sort of, you can get data to say whatever you want, you can ask it, You can ask of it, to, to tell a story that is how many sales did we have yesterday, but then ask a slightly different question that is what sales did we make yesterday? And like this literally one word change, but that changes entirely your output.

You’ve gone from having a number to saying we sold products X, Y, and Z. the key thing really is just about keeping track of what it is that you’re doing when you’re exploring data so that if someone’s just what about this?

And you can say yeah, I did think about that three days ago at this point. And that again, feels like one of those things that can, that can help. add a lot of time to your analysis and your exploration but It’s certainly going to save it later on where someone asks you a question like here show your workings here they are. yeah

Maaria: Yeah. And I guess that sort of brings in the diversity point of having a different perspective on something is someone might come and look at the data and either give you a perspective, which you haven’t thought about or challenge you. And then you can be really robust in saying, no, this is, this is the process and this is the data, and this is why it was the route to go down.

Reema: No, it’s an absolutely brilliant word and something that i’m going to start to bring in a bit more. I think robust is a really impressing word because when I think about the data that we collect and we support organizations to collect the people of data is I’ve spoken to people about this diversity and inclusion form.

And then literally say every time that they see one, what they’ll do is just make someone new up because they don’t understand why they’re being asked this information because it’s not communicated to them. And that’s their response to that. So when you’ve got that data, fine, we’ve collected someone’s data, but how robust is that?

How, what’s the integrity of that data We could get a hundred responses that are all of that type of flavor, but then what, how can we make any robust kind of conclusions out of that? Not really. And how would you even know that that’s happened? And so I really love the word robust. I think that makes a lot of sense.

I

Maaria: Drilling in on that a little bit actually, cause I’m quite interested how do you define robust? In the sense of the data that you collect, how would you define robust data? Cause I think coming from a scientific background, the robust data we have is, how reproducible it is and how accurate the data is to, people.

Going through different statistical analyses and, having a really good data set which is indicative of your hypothesis or something that you’re trying to prove. So in terms of the data that you collect, what does robust Data look like in that sense?

Reema: think it’s something that’s well defined. So the

Maaria: Okay.

Reema: just described there in terms of repeatability, I’ve got a definition over here and you’ve got again, that nuance slightly different one, then the repeatability is much harder because we haven’t well defined it. And that governance, which again, can feel like data governance and or any governance really can feel like a really boring and or arduous thing, but it’s a save invest time now to save time later type of thing. Which can be hard to tell someone who’s doing your budget management that we need to spend X amount of money on stuff, but it comes back to communication, doesn’t it? It’s if I leave a company, if I am working just with you for just a couple of months, like it’s really important for you to know I’ve worked and what I’ve done so that you can do the same thing later on. So yeah, I think that’s what I think about as being robust is well defined and being very intentional as well. So being intentional in terms of what is actually being collected and how you, intentionally communicating to someone who you’re getting that data from that this is why I’m doing it. And obviously then that kind of just talks to primary data collection and you might have some data collection from existing places. think that’s where data starts to get complex is because we do have to mix data from multiple different places and that’s when robustness I think starts to become a lot more challenging to create a robust from data that’s from a place like A, B, and C,

Maaria: yeah. So I guess on that point where you said, looking at, trying to convince someone who has the budget to invest that time into sort of the data collection, data management early on to see payoff later, if someone was in an organization, they want to try and go about making their data collection or data practices in general more efficient and robust.

How do you think they could communicate that to a budget holder or, a manager or, or someone higher above them to get them on board with, you

Reema: Yeah, I think the first thing is to understand what is important to them. And so sometimes when you’re speaking to a board member, for example, it could just be, we just want to understand that we’re making money. Okay, fine. Then you’ve got your team leader and they are responsible for making sure that money gets to you made, but because they’ve got email campaigns, for example, to send out. So then it’s about saying how do we get the most information out of that email communication slash management system to make sure that, yes, the emails that we are sending are being open links are being clicked within there. So you’ve gone from this board level person who just wants to know we’re making money to coming down to saying the reason, the way that we do that is by getting people to see our content and not only see our content, to engage with our content. Not only that, then is to purchase something off the back of that. So having a good data system there that can help you to record and see all of that information critical to saying we need this across the organization so that we can ensure that we are making money and doing it in the most efficient and optimized way. So it’s just linking some of that into saying, here’s why data is valuable in the context of our organization. Here’s what we are losing out on as an opportunity by not investing in this and the opportunity often comes as time I’ve worked in multiple data teams where you’re spending half a day if not a whole day of one person’s time to produce a report that we then sit in the meeting for and then People are really tired and bored in that meeting because it’s the same thing and it’s very droney and no one’s really taking any action. Whereas what can we do to invest in either a new system or a new approach or a new process that allows us to actually get to action out of that information, because I think that’s the challenge that we have is if we invest in this data system, we can take these types of actions with more confidence and be more data driven. That’s the narrative that I think really helps to win, win budget and investment in this sort of space.

Maaria: So I’ve got one last question for you, obviously with your passion around data. We’ve said passion quite a lot during this, but you are just so enthusiastic and you absolutely love data. So what one misconception about data do you want to bust right now?

Reema: The data is not scary that we can actually do something about it. And that we should, because it is literally impacts all of us. Whether you go the bus and you’re paid with cash, or you’ve gone to the supermarket and you, use your loyalty card or just done whatever, like going to the dentist, we’re creating data literally everywhere.

And all of this information is incredibly valuable. But also is something that. If we’re not careful can be used against us in a bad way So I think just empowering ourselves to understand just a teeny tiny teeny bit of more about data you don’t have to love it as much as I do, but just knowing that It’s not something that we should just be so afraid of and it comes with a lot of privilege for me to be able to say that because I have time to spend and look at data.

Obviously, it’s become my career but I think the thing is that it literally is impacting us all and if we can start to get to the point where we understand that like we need To have a bit more control and informed consent around sharing data Then we can yeah get to a place where it’s not so daunting and doesn’t have such a negative impact in multiple areas of life. That was a long one thing to say, but hopefully that’s okay.

Maaria: I did say it was going to be my last question, but you’ve actually brought something else up. So I’m just going to dive in on that. And that was about We’re generating data every day and we need to be informed and aware about how we share it. What are your thoughts on the way that the tech industry and just, just the world in general is using data?

And how, how aware do people need to be about what that data is? is being used for. Excellent. We’ll put it on the list and then we’ll

Reema: but I’m also aware that I don’t know how to build a time machine. And that’s one of the biggest challenges is time. It takes time to understand how these things work. So how aware do people need to be? I think it’s aware to a level that they can try to have some consent in that.

 I think there’s an amount that they need to understand that data is existing everywhere and it is, and how it works with AI. So that when people see fake news or, AI driven content and or AI driven recommendations, they understand that it’s not always with the end users best intentions. As the key driver, like it’s a lot to be, to be driven by an organization’s want to sell you something or manipulate. information in a certain way. And I think that’s the bit that people should on. I would love for people to understand. I’d love eventually to do a PhD or something in understanding how people, the psychology of interactions with data. So I think that will have to come before the PA before the census change, but I think it would be a really cool one to just, be able to understand well, what extent can you get people to care about data? And it’s the key question that I’m, I’m curious to answer. So once I’ve done the PhD, then let’s talk about it again.

This

Maaria: can talk about it then. And just on that last, just on that question so obviously there’s a lot of responsibility that tech companies need to, should, whatever your stance is They should have some responsibility to, tell people how their data is being harnessed and processed and collected.

But what do you think people can do in the absence of that happening anytime soon to improve their data literacy?

Reema: is a really good question. It’s a hard question, because I think some of these things do just take time and time is, is a big challenge. Yeah, I think just try to find way in which you learn best and try to find content around that. I think this is why, like I’m curious to get into the space of TikTok create content so that it it doesn’t feel like you’re learning about data because you’re having fun, but you are actually then aware that there is something around yeah, how data is is actually informing your life.

So I guess my, my, I would love my answer to be follow people of data, but I, I don’t know if I think of any resources that can be quite useful, then I’ll definitely share them across. Yeah.

Maaria: Amazing. I promise that was going, that is going to be the last question that I ask you because I, I was going to wrap up a few times and then we just, we just followed the conversation like we follow data. And now here we are. But thank you so much for coming and talking to me today. It’s, it’s been fascinating and someone who, has used data in their career, but also, does know the feeling of being overwhelmed by data.

It’s just so refreshing to, to talk to you. You’re just, as I said, so enthusiastic and passionate and it really is infectious. I do just want to go and look at some data now and see what I can do in, in my own business. So so much for talking to me. I’m sure there will be, I’m sure there will be. So I.

Reema: data, hopefully a little bit more after this conversation and hopefully the audiences are too.

Maaria: I asked a bit earlier about what the one misconception about data you want to bust is. So what one thing do you want to leave our listeners with?

Reema: The data is fun and cool and exciting. Yeah.

Maaria: You’ll be hard pushed to find someone who’s as passionate and enthusiastic about data as Rima is, and that enthusiasm is infectious. She emphasized how communication drives effective data collection and analysis, and there were a few key tips that really stood out. Simplicity is key. Breaking down complex concepts into their simplest forms makes it more likely that your message will not only reach a wider audience, but make sure they’re engaging with what you’re delivering.

Knowing your why is a great way to anchor your thoughts, but don’t be disheartened if the data isn’t telling you what you expected. Remember, that’s still valuable information that you can learn from. These are great tips to remember in life science, in whatever niche you operate in. When trying to explain your concept to research, being able to break it down into simpler terms means that you can reach a wider audience outside of the life science community and build up the complexity when needed.

Remember, if the data proves your hypothesis wrong, that’s not a failed experiment, it’s still informative and something you can learn from and report. You can find more information about this episode on the Mabu website. Find the link in the show notes below. If you want more pros and cons, why not subscribe on your favorite podcasting platform?

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